pax_global_header00006660000000000000000000000064141273246200014513gustar00rootroot0000000000000052 comment=b5896913a2d1e9326efc4137e61bee631d3c0c74 xrstools-0.15.0+git20210910+c147919d/000077500000000000000000000000001412732462000161525ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/.gitlab-ci.yml000066400000000000000000000027351412732462000206150ustar00rootroot00000000000000stages: - test # - deploy variables: PROJECT_NAME: "xrstools" LOCAL_REPO: "/dev/shm/gitlab_ci/xrstools" VENV_DIR: /dev/shm/gitlab_ci/xrstools/venv USE_CLEAN_VENV: "false" CLEAN_RESULTS: "true" test: stage: test only: - nocdi tags: - scisoft10 before_script: - unset HTTP_PROXY - unset HTTPS_PROXY - if [ ! -d $LOCAL_REPO ]; then mkdir -p $LOCAL_REPO; fi - if [ -d $VENV_DIR ]; then if [ "$USE_CLEAN_VENV" == "true" ]; then rm -rf $VENV_DIR; fi; fi - python3 -m venv $VENV_DIR - source $VENV_DIR/bin/activate - echo "Running $(python --version) from $(which python) on $(hostname) ($(arch))" - pip install --upgrade pip setuptools wheel - pip install --upgrade numpy silx - pip install PyQt5 - pip install -r requirements.txt - pip install . script: - pwd - cd nonregressions/xes - /usr/bin/xvfb-run --server-args="-screen 0 1024x768x24" -a python xes_analysis.py >| output - cd ../xrs_raman - /usr/bin/xvfb-run --server-args="-screen 0 1024x768x24" -a python non_reg_testing_XRS.py >| output1 - /usr/bin/xvfb-run --server-args="-screen 0 1024x768x24" -a python non_reg_testing_XRS_small.py >| output2 - /usr/bin/xvfb-run --server-args="-screen 0 1024x768x24" -a python non_reg_testing_XRS_raman_extraction.py >| output3 - cd ../volumes/test1 - /usr/bin/xvfb-run --server-args="-screen 0 1024x768x24" -a python test.py >| output - deactivate after_script: - pwd xrstools-0.15.0+git20210910+c147919d/.gitmodules000066400000000000000000000001371412732462000203300ustar00rootroot00000000000000[submodule "HighFive"] path = fitcc/HighFive url = https://github.com/BlueBrain/HighFive.git xrstools-0.15.0+git20210910+c147919d/CHANGES.txt000066400000000000000000000000471412732462000177640ustar00rootroot00000000000000v0.0.0, 07/18/2013 -- Initial release. xrstools-0.15.0+git20210910+c147919d/LICENSE.txt000066400000000000000000000000001412732462000177630ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/MANIFEST.in000066400000000000000000000001771412732462000177150ustar00rootroot00000000000000recursive-include XRStools *.h *.c *.cu *cpp *.cc *.hpp recursive-include doc * recursive-include data * include MANIFEST.in xrstools-0.15.0+git20210910+c147919d/OFFDIAG/000077500000000000000000000000001412732462000172115ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/OFFDIAG/Si111_10_10_-05.py000066400000000000000000000601501412732462000215440ustar00rootroot00000000000000###### """ On the other hand, the angle between Ki = array([4.01513195, 0. , 0. ]) Kdetector = array([3.64006633, 1.41115784, 0.93799533]) is just 24.96 deg. Using G in the lab frame, I get: K1 = Ki+G_lab = array([ 3.01513195, -1. , -1. ]) then the angle between Ki and K1 is the Bragg angle as it should be... Hi Alessandro, using my definitions as in the xrs_utilities.cixsUBgetQ_primo(tthv, tthh, psi) function: the G=[-1,-1,-1] vector in the Lab-frame is after rotation by the Bragg angle around the lab-y-axis and after rotation around the G-vecotor: U.dot(B).dot(G) = [ 1.42476636, 1.00415622, -0.98849196] K_in in the lab frame is: K_in = [4.01513195, 0. , 0. ] And the vector to the detector (in the lab frame) is KO = 2.0*np.pi/lambdao*vrot(vrot(X,Y,-tthv) ,Z, tthh) = [3.64006633, 1.41115784, 0.93799533] (if X in the lab is positive, tthv rotates negative) so the angle between G_lab and KO is: 45.13 degrees (then the asymmetry should be subtracted from this, right?) The surface normal before any crystal rotations is (in the crystal frame): n = [-0.9014985 , -1.11187038, -0.97521521] in the lab frame this becomes: U.dot(B).dot(n) = [ 1.53082821, 0.86861388, -0.95785185] after rotating this by psi: [ 1.44878323, -0.46741813, 0.8261643 ] Now I am very confused about the vector called "v" in the function: v = np.array([-np.sin(np.radians(th)), 0.0, np.cos(np.radians(th))]) If this was the G vector after rotation of the Bragg angle in the lab-frame it should be +np.sin(np.radians(th)). But I doubt this was meant there... it may again have to do with the definitions of the lab X and instrument X-directions (because K_in is positive in X!!!)??? does any of this make any sense? cheers Christoph produrre ancora grafico coefficienti per fit nominale cercando parametro slit e magnification mostrare per varie scelte coefficienti curve componenti . Confrontare normalizzazioni. Confronto con teoria. Usare curve diagonali . COntrollare normalizzazione. Produrre fuori-diagonale. Purificare. COntrollare coefficienti. Cercare di avere i buoni valori agli estremi """ import sys from XRStools import resintesi import matplotlib matplotlib.use('Qt4Agg') # from silx import sx from silx.gui.plot import Plot2D from silx.gui import qt qapp = qt.QApplication([]) from XRStools import xrs_read, theory, xrs_extraction, xrs_rois, roifinder_and_gui, xrs_utilities, math_functions, roiSelectionWidget, Bragg from pylab import * # ion() import numpy as np from scipy import interpolate, signal, integrate, constants, optimize, ndimage from XRStools import ixs_offDiagonal,xrs_utilities import h5py def saveDictH5( obj, fn): file = h5py.File(fn,"w") for k in obj.keys(): file[k] = obj[k] file.close() def loadDictH5( fn): obj = {} file = h5py.File(fn,"r") for k in file: obj[k] = file[k] [:] file.close() return obj kapmonitor = "kaprixs" kapmonitor = "kap1dio" ############################################################################## # off-focus resolution tests ############################################################################## repertorio = "/data/id20/inhouse/data/run5_15/run6_hc2251" # repertorio = "/olddisk/data/id20/inhouse/data/run5_15/run6_hc2251" ############ # off-dia terzo: G=[-1,-1,-1], q0=[1.0, 1.0, -0.5], q1=[0.0, 0.0, -1.5] ############ # # # the tthh angle was POS. 21.18 degrees, which is the # correct angle for the primo crystal!!!!!!!!!!!!!! # # BUT terzo crystal was used !!!!!!!!!!!!!!!!!!!!! # # this point may be not valid !!!!!!!!!!!!!!!!!!!! # from XRStools import ixs_offDiagonal asymm_grad = 5.0 tthv_grad = 13.51 tth_grad = 21.19 psi_grad = 25.4 # 65.23 source_distance = 60.0 magnification = +0.128 ### nominal a 0.118 poi 0.1, 0.11, 0.12, 0.13, 0.14 E0 = 7950.0 hkl = np.array( [ 1,1,1 ]) DeltaAngle =0.00000*180/np.pi ### if the two crystal of the monocromator are parallel this parameter is Zero Ew_source_ev = 4.0 Aw_rocking_rad = 2.0e-3 Hw_slit_mm = 0.4 nEpoints = 500 nApoints = 1000 nHpoints = 100 if(0): #############################################################################################################################" ## This routine perform the calculation of the averaged coefficients considering different wavelenghts ## and different incoming directions. ## Contribution from different energies are considered beaing mutually incoherent. ## Moreover ech incoming direction is considered incoherent. ## This is a strong assumption because the beam in reality has a coherence lenght. ## But here we calculate the weight of all the contributions by a sort of ray-tracing, the weight being obtained ## from dynamical theory for a plane wave of a given energy and of a given direction calculating the reflection ## of the monochromators and what happens in the sample: ## A sampling is done over the energy, considering and energy width Ew_source_ev around nominal E0. ## A sampling is done also over the wavefront height. Each height define and incoming angle ## ( given by the source position and the distance from it). ## E0 is the nominal energy, eV ## source_distance is given in meters STW_infos = Bragg.calculate_RC_factors(E0 = E0 , hkl=hkl, asymm_grad = asymm_grad, tthv_grad=tthv_grad, tth_grad=tth_grad, source_distance = source_distance, magnification=magnification , DeltaAngle = DeltaAngle, Ew_source_ev = Ew_source_ev, Aw_rocking_rad = Aw_rocking_rad, Hw_slit_mm = Hw_slit_mm, nEpoints = nEpoints, nApoints= nApoints, nHpoints = nHpoints ) np.savetxt("monochromator_response_energy.txt",monochromator_response_energy) np.savetxt("rocking_curve.txt",rocking_curve.T) raise roifinder = roifinder_and_gui.roi_finder() print(" REPERTORIO ", repertorio) offdia_ixsobj = ixs_offDiagonal.offDiagonal(repertorio ,en_column='sry', moni_column=kapmonitor) ####################################################################### ## ACTIVATE THIS ONCE TO GENERATE THE ROIS FOR BACKGROUND EXTRACTION ## if 0: image4roi = offdia_ixsobj.SumDirect( [2934] ) w4r = roiSelectionWidget.launch4MatPlotLib(im4roi=image4roi, layout = "1X5-1" ) if w4r.isOK : roiobj = w4r.getRoiObj() roiobj.writeH5('back_ROI_Si111_q0_10_10_-05.h5') sys.exit(0) ####################################################################### ## ACTIVATE THIS ONCE TO GENERATE THE ROIS FOR SIGNAL EXTRACTION ## roiName = "ROI_Si111_q0_10_10_-05.h5" if 0: image4roi = offdia_ixsobj.SumDirect( [2934] ) w4r = roiSelectionWidget.launch4MatPlotLib(im4roi=image4roi, layout = "1X5-1" ) if w4r.isOK : roiobj = w4r.getRoiObj() roiobj.writeH5( roiName ) sys.exit(0) ###################################################### ### ROI IS SET HERE FOR OFFDIAGONAL DATA ### print(" LOADING ROI for offdiagonal and diagonal") roifinder.roi_obj.loadH5(roiName) offdia_ixsobj.set_roiObj(roifinder.roi_obj) ## LONG ROCKING CURVE FIT ## And generation of oefficients ## Once you have generated on or more STW files you can disactivate this section. STW files can be choosed and reused, while skipping this section. if(1): longrockingcurve_ixsobj = ixs_offDiagonal.offDiagonal(repertorio ,en_column='sry', moni_column=kapmonitor) longrockingcurve_ixsobj.set_roiObj(roifinder.roi_obj) ################################################################# ##### ACTIVATE THIS ONCE TO LOAD THE LONG ROCKING CURVE if(0): longrockingcurve_ixsobj.loadRockingCurve(list(range(2936,3457,4)),direct=True, storeInsets = True) longrockingcurve_ixsobj.save_state_hdf5( "longrockingcurve_ixsobj.h5", "after_loading", comment="" , overwriteFile = True, overwriteGroup=True) sys.exit(0) if(1): # this reloads the rocking curve longrockingcurve_ixsobj.load_state_hdf5( "longrockingcurve_ixsobj.h5", "after_loading") ## alignes using the R(ocking)C(urve) monitor alirixs longrockingcurve_ixsobj.stitchRockingCurves(I0moni=kapmonitor,RCmoni='alirixs') ## save it for future inspection longrockingcurve_ixsobj.save_state_hdf5( "longrockingcurve_ixsobj.h5", "after_stichting_aligning", overwriteFile = False) #####################################################################" #### You find alot of documentations in the source of this routine STW_infos = Bragg.calculate_RC_factors(E0 = E0 , hkl=hkl, hkl_mono=hkl, asymm_grad = asymm_grad, tthv_grad=tthv_grad, tth_grad=tth_grad,psi_grad = psi_grad, source_distance = source_distance, magnification=magnification , DeltaAngle = DeltaAngle, Ew_source_ev = Ew_source_ev, Aw_rocking_rad = Aw_rocking_rad, Hw_slit_mm = Hw_slit_mm, nEpoints = nEpoints, nApoints= nApoints, nHpoints = nHpoints ) saveDictH5( STW_infos,"STW_infos111_nominal_%f.h5"%magnification) np.savetxt ("rocking_curve111_nominal_%f.txt"%magnification,STW_infos["RC_curve"].T) np.savetxt ("A_curve111_nominal_%f.txt"%magnification,STW_infos["A_curve"].T) np.savetxt ("B_curve111_nominal_%f.txt"%magnification,STW_infos["B_curve"].T) np.savetxt ("C_curve111_nominal_%f.txt"%magnification,STW_infos["C_curve"].T) STW_infos_nowidth = Bragg.calculate_RC_factors(E0 = E0 , hkl=hkl, hkl_mono=hkl, asymm_grad = asymm_grad, tthv_grad=tthv_grad, tth_grad=tth_grad,psi_grad = psi_grad, source_distance = source_distance, magnification=magnification , DeltaAngle = DeltaAngle, Ew_source_ev = Ew_source_ev, Aw_rocking_rad = Aw_rocking_rad, Hw_slit_mm = Hw_slit_mm*0.00001, nEpoints = nEpoints, nApoints= nApoints, nHpoints = nHpoints ) saveDictH5( STW_infos_nowidth,"STW_infos111_all_parallel.h5") np.savetxt ("rocking_curve111_allparallel.txt",STW_infos_nowidth["RC_curve"].T) np.savetxt ("A_curve111_allparallel.txt",STW_infos_nowidth["A_curve"].T) np.savetxt ("B_curve111_allparallel.txt",STW_infos_nowidth["B_curve"].T) np.savetxt ("C_curve111_allparallel.txt",STW_infos_nowidth["C_curve"].T) stw_2use_for_resynt = STW_infos_nowidth longrockingcurve_ixsobj.offDiaDataSets[0].alignRCmonitor( stw_2use_for_resynt["RC_curve"] ) synth_coeffs= resintesi.resintetizzaTV( longrockingcurve_ixsobj.offDiaDataSets[0].masterRCmotor * np.pi/180.0, longrockingcurve_ixsobj.offDiaDataSets[0].alignedRCmonitor.mean(axis=0), stw_2use_for_resynt["RC_curve"] , 0.001 ) ## STW_infos_nowidth["RC_curve"] , 0.001 ) np.savetxt("synt_coeffs111.txt", np.array( [ longrockingcurve_ixsobj.offDiaDataSets[0].masterRCmotor * np.pi/180.0 , synth_coeffs]).T) np.savetxt("xy111_long.txt", np.array( [ longrockingcurve_ixsobj.offDiaDataSets[0].masterRCmotor * np.pi/180.0 , longrockingcurve_ixsobj.offDiaDataSets[0].alignedRCmonitor.mean(axis=0)]).T ) xrads, synth_coeffs_vals = longrockingcurve_ixsobj.offDiaDataSets[0].masterRCmotor * np.pi/180.0 , synth_coeffs ## commentare qui sotto se si vuole tenere ultimo con magnification STW_infos = resintesi.get_resynth(stw_2use_for_resynt , synth_coeffs_vals, xrads ) saveDictH5( STW_infos,"STW_infos111_resynt.h5") np.savetxt( "rocking_curve111_long_resynt.txt",STW_infos["RC_curve"].T) np.savetxt("A_curve111_long_resynt.txt",STW_infos["A_curve"].T) np.savetxt("B_curve111_long_resynt.txt",STW_infos["B_curve"].T) np.savetxt("C_curve111_long_resynt.txt",STW_infos["C_curve"].T) sys.exit(0) ######################################################################### # CHOICE OF THE COEFFICIENTS # # STW_infos = loadDictH5( "STW_infos111_nominal_%f.h5"%magnification) STW_infos = loadDictH5( "STW_infos111_resynt.h5" ) ######################################################################### ## OFFDIAGONAL if(0): print(" LOADING OFFDIAGONAL FROM RAW DATA") offdia_ixsobj.loadRockingCurve(list(range(2937,3458,4)),direct=True, storeInsets = True) offdia_ixsobj.save_state_hdf5 ( "offdia_ixsobj_bis.h5", "after_loading", comment="" , overwriteFile = True, overwriteGroup=True) sys.exit(0) else: if(0): print(" RE-LOADING OFFDIAGONAL FROM ALREADY EXTRACTED DATA AND REALIGNEMENT. STW_infos[\"RC_curve\"] IS USED FOR ALIGNEMENT OF THE EXP RC ") offdia_ixsobj.load_state_hdf5( "offdia_ixsobj_bis.h5", "after_loading",) offdia_ixsobj.stitchRockingCurves(I0moni=kapmonitor,RCmoni='alirixs') for k in range(3): (pos, M) = roifinder.roi_obj.red_rois["ROI0%d"%(k+1)] offdia_ixsobj.offDiaDataSets[k].signalMatrix *= 1.0/ (np.less(0,M).sum()) ################################################################################### ############################# THE POINT 17,7 HAS A PROBLEM for k in range(3): sm = offdia_ixsobj.offDiaDataSets[k].signalMatrix sm[17,7] = 0.5*( sm[17,6]+sm[17,8]) ############################# #################################################"" for k in range(3): offdia_ixsobj.offDiaDataSets[k].normalizeRC() for k in range(3): offdia_ixsobj.offDiaDataSets[k].alignRCmonitor( STW_infos["RC_curve"] ) offdia_ixsobj.save_state_hdf5( "offdia_ixsobj_aligned.h5", "after_stichting_aligning", overwriteFile = False, overwriteGroup=True) np.savetxt("xy111_short_mean.txt", np.array( [ offdia_ixsobj.offDiaDataSets[0].masterRCmotor * np.pi/180.0 , offdia_ixsobj.offDiaDataSets[0].alignedRCmonitor.mean(axis=0)]).T ) sys.exit(0) else: print( " RELOADING OFFDIAG " ) offdia_ixsobj.load_state_hdf5( "offdia_ixsobj_aligned.h5", "after_stichting_aligning") # if 1: # # problem with data point 32: # for ii in list(range(21)): # offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[32,ii] = np.mean( (offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[31,ii],offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[33,ii]) ) # # problem with data point 80: # for ii in list(range(21)): # offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[80,ii] = np.mean( (offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[79,ii],offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[81,ii]) ) ############## # diagonal ############## dia_ixsobj = ixs_offDiagonal.offDiagonal(repertorio ,en_column='sry', moni_column=kapmonitor) dia_ixsobj.set_roiObj(roifinder.roi_obj) # plasmon and core part if 0: print("LOADING DIAGONAL") dia_ixsobj.loadRockingCurve(list(range(2939,3460,4)), direct=True) print("STITCHING DIAGONAL") dia_ixsobj.stitchRockingCurves(I0moni=kapmonitor,RCmoni='alirixs') for k in range(3): (pos, M) = roifinder.roi_obj.red_rois["ROI0%d"%(k+1)] dia_ixsobj.offDiaDataSets[k].signalMatrix *= 1.0/ (np.less(0,M).sum()) dia_ixsobj.save_state_hdf5( "dia_ixsobj_aligned.h5", "after_stichting") for k in range(3): dia_ixsobj.offDiaDataSets[k].normalizeRC() dia_ixsobj.offDiaDataSets[k].alignRCmonitor(None) dia_ixsobj.save_state_hdf5( "dia_ixsobj_aligned.h5", "after_stichting_aligning") sys.exit(0) else: print( " RE-LOADING DIAG " ) dia_ixsobj.load_state_hdf5( "dia_ixsobj_aligned.h5", "after_stichting_aligning") # if 1: # # problem with data point 32: # for ii in list(range( dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix.shape[1] )): # dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[32,ii] = np.mean( (dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[31,ii],dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[33,ii]) ) # # problem with data point 80: # for ii in list(range( dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix.shape[1] )): # dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[80,ii] = np.mean( (dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[79,ii],dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[81,ii]) ) ############################# # background for off-diagonal ############################# back_ixsobj = ixs_offDiagonal.offDiagonal(repertorio ,en_column='sry', moni_column=kapmonitor) backroifinder = roifinder_and_gui.roi_finder() backroifinder.roi_obj.loadH5('back_ROI_Si111_q0_10_10_-05.h5') back_ixsobj.set_roiObj(backroifinder.roi_obj) # plasmon and core part if 0 : if 0: print(" Loading background") back_ixsobj.loadRockingCurve(list(range(2937,3458,4)), direct=True) back_ixsobj.save_state_hdf5( "back_ixsobj.h5", "after_loading", comment="" , overwriteFile = True, overwriteGroup=True) sys.exit(0) else: print(" RE-Loading background") back_ixsobj.load_state_hdf5( "back_ixsobj.h5", "after_loading",) back_ixsobj.stitchRockingCurves(I0moni=kapmonitor,RCmoni='alirixs') ################################################################################### ############################# THE POINT 17,7 HAS A PROBLEM for k in range(3): sm = back_ixsobj.offDiaDataSets[k].signalMatrix sm[17,7] = 0.5*( sm[17,6]+sm[17,8]) ############################# #################################################"" for k in range(3): (pos, M) = backroifinder.roi_obj.red_rois["ROI0%d"%(k+1)] back_ixsobj.offDiaDataSets[k].signalMatrix *= 1.0/ (np.less(0,M).sum()) for k in range(3): back_ixsobj.offDiaDataSets[k].normalizeRC() back_ixsobj.offDiaDataSets[k].alignRCmonitor(STW_infos["RC_curve"]) back_ixsobj.save_state_hdf5( "back_ixsobj_aligned.h5", "after_stichting_aligning") # replace background signals by constant fit through signals for k in range(3): back_ixsobj.offDiaDataSets[k].replaceSignalByConstant([7.95,10.0]) back_ixsobj.save_state_hdf5( "back_ixsobj_aligned_rep.h5", "after_stichting_aligning_replacing") sys.exit(0) else: print( " RELOADING BACK " ) back_ixsobj.load_state_hdf5( "back_ixsobj_aligned_rep.h5", "after_stichting_aligning_replacing") if(0): print( " checking out what is going on with the background subtraction") f=h5py.File("confronti.h5","w") g0=f.require_group("D0" ) g0["off"] = offdia_ixsobj.offDiaDataSets[0].alignedSignalMatrix g0["back"] = back_ixsobj.offDiaDataSets[0].alignedSignalMatrix g0["diff"] = offdia_ixsobj.offDiaDataSets[0].alignedSignalMatrix - 1.0*back_ixsobj.offDiaDataSets[0].alignedSignalMatrix g1=f.require_group("D1" ) g1["off"] = offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix g1["back"] = back_ixsobj.offDiaDataSets[1].alignedSignalMatrix g1["diff"] = offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix - 1.0*back_ixsobj.offDiaDataSets[1].alignedSignalMatrix g2=f.require_group("D2" ) g2["off"] = offdia_ixsobj.offDiaDataSets[2].alignedSignalMatrix g2["back"] = back_ixsobj.offDiaDataSets[2].alignedSignalMatrix g2["diff"] = offdia_ixsobj.offDiaDataSets[2].alignedSignalMatrix - 1.0*back_ixsobj.offDiaDataSets[2].alignedSignalMatrix f.close() sys.exit(0) print(" LAST PART ") ####################################### # subtract background from off-diagonal ####################################### scale = 1.0 offdia_ixsobj.offDiaDataSets[0].alignedSignalMatrix -= back_ixsobj.offDiaDataSets[0].alignedSignalMatrix*scale scale = 1.0 offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix -= back_ixsobj.offDiaDataSets[1].alignedSignalMatrix*scale scale = 1.0 offdia_ixsobj.offDiaDataSets[2].alignedSignalMatrix -= back_ixsobj.offDiaDataSets[2].alignedSignalMatrix*scale ####################################### # window ####################################### f=h5py.File("confronti_L23.h5","w") goff = f.require_group("off" ) g0=goff.require_group("prima_sub" ) g0["signal_prima"] = offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix subtract = np.zeros_like( offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix) ## #### SUBTRACT L23 PRE-EDGE TO OFF ## # try subtraction the SiL23-pre-edge region to zero OFF-dia for ii in range(len(offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[0,:])): inds = np.where(np.logical_and(np.array(offdia_ixsobj.offDiaDataSets[1].energy) >= 8.015,np.array(offdia_ixsobj.offDiaDataSets[1].energy) <= 8.022))[0] back = np.polyval(np.polyfit(np.array(offdia_ixsobj.offDiaDataSets[1].energy)[inds],offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[inds,ii],0),np.array(offdia_ixsobj.offDiaDataSets[1].energy)) subtract[inds,ii] = back[inds] offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[:,ii] -=back g0["subtract"] = subtract g0["signal_dopo"] = offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix g0["energy"] = offdia_ixsobj.offDiaDataSets[1].energy ## #### SUBTRACT L23 PRE-EDGE TO DIA ## gdia = f.require_group("dia" ) g0=gdia.require_group("prima_sub" ) g0["signal_prima"] = dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix subtract = np.zeros_like( offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix) # try subtraction the SiL23-pre-edge region to zero DIA for ii in range(len(dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[0,:])): inds = np.where(np.logical_and(np.array(dia_ixsobj.offDiaDataSets[1].energy) >= 8.015,np.array(dia_ixsobj.offDiaDataSets[1].energy) <= 8.022))[0] back = np.polyval(np.polyfit(np.array(dia_ixsobj.offDiaDataSets[1].energy)[inds],dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[inds,ii],0),np.array(dia_ixsobj.offDiaDataSets[1].energy)) subtract[inds,ii] = back[inds] dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[:,ii] -=back g0["subtract"] = subtract g0["signal_dopo"] = dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix g0["energy"] = dia_ixsobj.offDiaDataSets[1].energy saveDictH5( {"energy" :offdia_ixsobj.offDiaDataSets[1].energy , "angles_rad":offdia_ixsobj.offDiaDataSets[0].masterRCmotor * np.pi/180.0, "signals":offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix } , "Signals111.h5" ) saveDictH5( {"energy" :dia_ixsobj.offDiaDataSets[1].energy , "angles_rad":dia_ixsobj.offDiaDataSets[0].masterRCmotor * np.pi/180.0, "signals":dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix } , "Signals111_dia.h5" ) f.close() sys.exit(0) xrstools-0.15.0+git20210910+c147919d/OFFDIAG/Si220_10_10_-05.py000066400000000000000000000572661412732462000215630ustar00rootroot00000000000000from XRStools import resintesi import matplotlib matplotlib.use('Qt4Agg') # from silx import sx from silx.gui.plot import Plot2D from silx.gui import qt qapp = qt.QApplication([]) from XRStools import xrs_read, theory, xrs_extraction, xrs_rois, roifinder_and_gui, xrs_utilities, math_functions, roiSelectionWidget, Bragg from pylab import * # ion() import numpy as np from scipy import interpolate, signal, integrate, constants, optimize, ndimage from XRStools import ixs_offDiagonal,xrs_utilities import h5py ############################################################################## # off-focus resolution tests ############################################################################## repertorio = "/data/id20/inhouse/data/run5_15/run6_hc2251" repertorio = "/olddisk/data/id20/inhouse/data/run5_15/run6_hc2251" ############ # off-dia terzo: G=[-1,-1,-1], q0=[1.0, 1.0, -0.5], q1=[0.0, 0.0, -1.5] ############ # # # the tthh angle was POS. 21.18 degrees, which is the # correct angle for the primo crystal!!!!!!!!!!!!!! # # BUT terzo crystal was used !!!!!!!!!!!!!!!!!!!!! # # this point may be not valid !!!!!!!!!!!!!!!!!!!! # from XRStools import ixs_offDiagonal offdia_ixsobj = ixs_offDiagonal.offDiagonal(repertorio ,en_column='sry', moni_column='kaprixs', SPECfname='fourc_220', EDFname='fourc_220_' ) print(" OK ") if 0: image4roi = offdia_ixsobj.SumDirect( [1] ) w4r = roiSelectionWidget.launch4MatPlotLib(im4roi=image4roi, layout = "1X5-1" ) raise roifinder = roifinder_and_gui.roi_finder() # roifinder.get_zoom_rois(image4roi) #roifinder.roi_obj.writeH5('/data/id20/inhouse/data/off_dia_jan18/rois/ROI_Si111_q0_10_10_-05.h5') # roifinder.roi_obj.loadH5('/data/id20/inhouse/data/off_dia_jan18/rois/ROI_Si111_q0_10_10_-05.h5') asymm_grad = 5.0 tthv_grad = 23.9 tth_grad = 7.5 psi_grad = -40.3 source_distance = 60.0 magnification = 0.11 E0 = 7950.0 hkl_mono = np.array( [ 1,1,1 ]) hkl = np.array( [ 2,2,0 ]) Hw = 0.5 # millimetri DeltaAngle =0.00000*180/np.pi # STW_infos = Bragg.calculate_RC_factors(E0 = E0 , hkl=hkl, Hw = Hw, asymm_grad = asymm_grad, # tthv_grad=tthv_grad, tth_grad=tth_grad, psi_grad = psi_grad, # source_distance = source_distance, magnification=magnification , DeltaAngle = DeltaAngle) #np.savetxt("monochromator_response_energy_111.txt",STW_infos["RC_curve"].T) # np.savetxt("rocking_curve.txt",rocking_curve.T) roifinder.roi_obj.loadH5("ROI_Si220_q0_10_10_-05.h5:/roi_from_selector") offdia_ixsobj.set_roiObj(roifinder.roi_obj) # plasmon and core part if(0): offdia_ixsobj.loadRockingCurve(list(range(4,525,4)),direct=True, storeInsets = True) offdia_ixsobj.save_state_hdf5( "offdia_ixsobj_220.h5", "after_loading", comment="" , overwriteFile = True, overwriteGroup=True) raise " OK " else: if(1): print(" RELOADING " ) offdia_ixsobj.load_state_hdf5( "offdia_ixsobj_220.h5", "after_loading",) print(" RELOADING OK" ) print(" stitchRockingCurves ") offdia_ixsobj.stitchRockingCurves(I0moni='kaprixs',RCmoni='alirixs') print(" stitchRockingCurves OK ") # ROI1 offdia_ixsobj.offDiaDataSets[0].filterDetErrors(threshold=0.95*np.amax(offdia_ixsobj.offDiaDataSets[0].signalMatrix)) offdia_ixsobj.offDiaDataSets[0].normalizeSignals() #offdia_ixsobj.offDiaDataSets[0].alignRCmonitorCC(repeat=2) # ROI2 offdia_ixsobj.offDiaDataSets[1].filterDetErrors(threshold=0.95*np.amax(offdia_ixsobj.offDiaDataSets[1].signalMatrix)) offdia_ixsobj.offDiaDataSets[1].normalizeSignals() ## tutti i segnali all'interno di una roi #offdia_ixsobj.offDiaDataSets[1].deglitchSignalMatrix(110,1140,10000) #offdia_ixsobj.offDiaDataSets[1].alignRCmonitorCC(repeat=2) # ROI3 offdia_ixsobj.offDiaDataSets[2].filterDetErrors(threshold=0.95*np.amax(offdia_ixsobj.offDiaDataSets[2].signalMatrix)) offdia_ixsobj.offDiaDataSets[2].normalizeSignals() ####################################################################################" STW_infos = Bragg.calculate_RC_factors(E0 = E0 , hkl=hkl , hkl_mono = hkl_mono , Hw = Hw*0.00001, asymm_grad = asymm_grad, tthv_grad=tthv_grad, tth_grad=tth_grad, psi_grad = psi_grad, source_distance = source_distance, magnification=magnification , DeltaAngle = DeltaAngle) STW_infos_nowidth = Bragg.calculate_RC_factors(E0 = E0 , hkl=hkl , hkl_mono = hkl_mono , Hw = Hw*0.00001, asymm_grad = asymm_grad, tthv_grad=tthv_grad, tth_grad=tth_grad, psi_grad = psi_grad, source_distance = source_distance, magnification=magnification , DeltaAngle = DeltaAngle) offdia_ixsobj.offDiaDataSets[0].alignRCmonitor( STW_infos["RC_curve"] ) synth_coeffs= resintesi.resintetizza( offdia_ixsobj.offDiaDataSets[0].masterRCmotor * np.pi/180.0, offdia_ixsobj.offDiaDataSets[0].alignedRCmonitor.mean(axis=0), STW_infos_nowidth["RC_curve"] ) STW_infos = resintesi.get_resynth_coeffs( STW_infos_nowidth, synth_coeffs, offdia_ixsobj.offDiaDataSets[0].masterRCmotor * np.pi/180.0 ) np.savetxt("xy220.txt", np.array( [ offdia_ixsobj.offDiaDataSets[0].masterRCmotor * np.pi/180.0 , offdia_ixsobj.offDiaDataSets[0].alignedRCmonitor.mean(axis=0)]).T ) np.savetxt("synt_coeffs220.txt", np.array( [ offdia_ixsobj.offDiaDataSets[0].masterRCmotor * np.pi/180.0 , synth_coeffs]).T ) np.savetxt("rocking_curve_nowidth220.txt",STW_infos_nowidth["RC_curve"].T) np.savetxt("rocking_curve220.txt",STW_infos["RC_curve"].T) raise offdia_ixsobj.offDiaDataSets[1].alignRCmonitor( STW_infos["RC_curve"] ) offdia_ixsobj.offDiaDataSets[2].alignRCmonitor( STW_infos["RC_curve"] ) np.savetxt("rocking_curve220.txt",STW_infos["RC_curve"].T) #offdia_ixsobj.offDiaDataSets[2].alignRCmonitorCC(repeat=2) offdia_ixsobj.save_state_hdf5( "offdia_ixsobj_aligned_bis_220.h5", "after_stichting_aligning") raise "OK" else: print( " RELOADING OFFDIAG " ) offdia_ixsobj.load_state_hdf5( "offdia_ixsobj_aligned.h5", "after_stichting_aligning") if(0): #ion() plot = Plot2D() # Create the plot widget plot.addImage(offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix, legend='image') plot.show() qapp.exec_() imshow(np.log(offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix)) show() qapp.exec_() if 1: # problem with data point 32: for ii in list(range(21)): offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[32,ii] = np.mean( (offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[31,ii],offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[33,ii]) ) # problem with data point 80: for ii in list(range(21)): offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[80,ii] = np.mean( (offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[79,ii],offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[81,ii]) ) ############## # diagonal ############## dia_ixsobj = ixs_offDiagonal.offDiagonal(repertorio ,en_column='sry', moni_column='kaprixs') dia_ixsobj.set_roiObj(roifinder.roi_obj) # plasmon and core part if 0 : print(" loadRockCurve Diag ") dia_ixsobj.loadRockingCurve(list(range(2939,3460,4)), direct=True) print(" Stitch Diag ") dia_ixsobj.stitchRockingCurves(I0moni='kaprixs',RCmoni='alirixs') dia_ixsobj.save_state_hdf5( "dia_ixsobj_aligned.h5", "after_stichting") # ROI1 dia_ixsobj.offDiaDataSets[0].filterDetErrors(threshold=0.99*np.amax(offdia_ixsobj.offDiaDataSets[0].signalMatrix)) dia_ixsobj.offDiaDataSets[0].normalizeSignals() dia_ixsobj.offDiaDataSets[0].alignRCmonitor() # ROI2 dia_ixsobj.offDiaDataSets[1].filterDetErrors(threshold=0.99*np.amax(offdia_ixsobj.offDiaDataSets[0].signalMatrix)) dia_ixsobj.offDiaDataSets[1].normalizeSignals() dia_ixsobj.offDiaDataSets[1].alignRCmonitor() # ROI3 dia_ixsobj.offDiaDataSets[2].filterDetErrors(threshold=0.99*np.amax(offdia_ixsobj.offDiaDataSets[0].signalMatrix)) dia_ixsobj.offDiaDataSets[2].normalizeSignals() dia_ixsobj.offDiaDataSets[2].alignRCmonitor() dia_ixsobj.save_state_hdf5( "dia_ixsobj_aligned.h5", "after_stichting_aligning") else: print( " RELOADING DIAG " ) dia_ixsobj.load_state_hdf5( "dia_ixsobj_aligned.h5", "after_stichting_aligning") if 1: # problem with data point 32: for ii in list(range( dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix.shape[1] )): dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[32,ii] = np.mean( (dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[31,ii],dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[33,ii]) ) # problem with data point 80: for ii in list(range( dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix.shape[1] )): dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[80,ii] = np.mean( (dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[79,ii],dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[81,ii]) ) ############################# # background for off-diagonal ############################# back_ixsobj = ixs_offDiagonal.offDiagonal(repertorio ,en_column='sry', moni_column='kaprixs') backroifinder = roifinder_and_gui.roi_finder() #backroifinder.get_zoom_rois(image4roi) #backroifinder.roi_obj.writeH5('/data/id20/inhouse/data/off_dia_jan18/rois/back_ROI_Si111_q0_10_10_-05.h5') backroifinder.roi_obj.loadH5('back_ROI_Si111_q0_10_10_-05.h5') back_ixsobj.set_roiObj(backroifinder.roi_obj) # plasmon and core part if 0 : back_ixsobj.loadRockingCurve(list(range(2937,3458,4)), direct=True) back_ixsobj.stitchRockingCurves(I0moni='kaprixs',RCmoni='alirixs') # ROI1 back_ixsobj.offDiaDataSets[0].filterDetErrors(threshold=0.99*np.amax(back_ixsobj.offDiaDataSets[0].signalMatrix)) back_ixsobj.offDiaDataSets[0].normalizeSignals() back_ixsobj.offDiaDataSets[0].alignRCmonitor() # ROI2 back_ixsobj.offDiaDataSets[1].filterDetErrors(threshold=0.99*np.amax(back_ixsobj.offDiaDataSets[0].signalMatrix)) back_ixsobj.offDiaDataSets[1].normalizeSignals() back_ixsobj.offDiaDataSets[1].alignRCmonitor() # ROI3 back_ixsobj.offDiaDataSets[2].filterDetErrors(threshold=0.99*np.amax(back_ixsobj.offDiaDataSets[0].signalMatrix)) back_ixsobj.offDiaDataSets[2].normalizeSignals() back_ixsobj.offDiaDataSets[2].alignRCmonitor() back_ixsobj.save_state_hdf5( "back_ixsobj_aligned.h5", "after_stichting_aligning") # replace background signals by constant fit through signals back_ixsobj.offDiaDataSets[0].replaceSignalByConstant([7.95,10.0]) back_ixsobj.offDiaDataSets[1].replaceSignalByConstant([7.95,10.0]) back_ixsobj.offDiaDataSets[2].replaceSignalByConstant([7.95,10.0]) back_ixsobj.save_state_hdf5( "back_ixsobj_aligned_rep.h5", "after_stichting_aligning_replacing") else: print( " RELOADING BACK " ) back_ixsobj.load_state_hdf5( "back_ixsobj_aligned_rep.h5", "after_stichting_aligning_replacing") f=h5py.File("confronti.h5","w") g0=f.require_group("D0" ) g0["off"] = offdia_ixsobj.offDiaDataSets[0].alignedSignalMatrix g0["back"] = back_ixsobj.offDiaDataSets[0].alignedSignalMatrix g0["diff"] = offdia_ixsobj.offDiaDataSets[0].alignedSignalMatrix - 1.3*back_ixsobj.offDiaDataSets[0].alignedSignalMatrix g1=f.require_group("D1" ) g1["off"] = offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix g1["back"] = back_ixsobj.offDiaDataSets[1].alignedSignalMatrix g1["diff"] = offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix - 1.15*back_ixsobj.offDiaDataSets[1].alignedSignalMatrix g2=f.require_group("D2" ) g2["off"] = offdia_ixsobj.offDiaDataSets[2].alignedSignalMatrix g2["back"] = back_ixsobj.offDiaDataSets[2].alignedSignalMatrix g2["diff"] = offdia_ixsobj.offDiaDataSets[2].alignedSignalMatrix - 1.55*back_ixsobj.offDiaDataSets[2].alignedSignalMatrix f.close() # ion() # imshow(np.log(back_ixsobj.offDiaDataSets[1].alignedSignalMatrix)) ####################################### # subtract background from off-diagonal ####################################### # ion() # imshow(np.log(offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix-back_ixsobj.offDiaDataSets[1].alignedSignalMatrix*1.15)) scale = 1.3 offdia_ixsobj.offDiaDataSets[0].alignedSignalMatrix -= back_ixsobj.offDiaDataSets[0].alignedSignalMatrix*scale scale = 1.15 offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix -= back_ixsobj.offDiaDataSets[1].alignedSignalMatrix*scale scale = 1.55 offdia_ixsobj.offDiaDataSets[2].alignedSignalMatrix -= back_ixsobj.offDiaDataSets[2].alignedSignalMatrix*scale ####################################### # window ####################################### # ii=9 # plot(np.array(offdia_ixsobj.offDiaDataSets[1].energy),offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[:,ii]) # ROI1 f=h5py.File("confronti_L23.h5","w") goff = f.require_group("off" ) g0=goff.require_group("prima_sub" ) g0["signal_prima"] = offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix subtract = np.zeros_like( offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix) # try subtraction the SiL23-pre-edge region to zero OFF-dia for ii in range(len(offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[0,:])): inds = np.where(np.logical_and(np.array(offdia_ixsobj.offDiaDataSets[1].energy) >= 8.015,np.array(offdia_ixsobj.offDiaDataSets[1].energy) <= 8.022))[0] back = np.polyval(np.polyfit(np.array(offdia_ixsobj.offDiaDataSets[1].energy)[inds],offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[inds,ii],0),np.array(offdia_ixsobj.offDiaDataSets[1].energy)) subtract[inds,ii] = back[inds] offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[:,ii] -=back g0["subtract"] = subtract g0["signal_dopo"] = offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix g0["energy"] = offdia_ixsobj.offDiaDataSets[1].energy gdia = f.require_group("dia" ) g0=gdia.require_group("prima_sub" ) g0["signal_prima"] = dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix subtract = np.zeros_like( offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix) # try subtraction the SiL23-pre-edge region to zero DIA for ii in range(len(dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[0,:])): inds = np.where(np.logical_and(np.array(dia_ixsobj.offDiaDataSets[1].energy) >= 8.015,np.array(dia_ixsobj.offDiaDataSets[1].energy) <= 8.022))[0] back = np.polyval(np.polyfit(np.array(dia_ixsobj.offDiaDataSets[1].energy)[inds],dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[inds,ii],0),np.array(dia_ixsobj.offDiaDataSets[1].energy)) subtract[inds,ii] = back[inds] dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[:,ii] -=back g0["subtract"] = subtract g0["signal_dopo"] = dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix g0["energy"] = dia_ixsobj.offDiaDataSets[1].energy f.close() E0 = 7.92515 inds = np.where(np.logical_and(np.array(offdia_ixsobj.offDiaDataSets[1].energy)>=7.927, np.array(offdia_ixsobj.offDiaDataSets[1].energy)<=8.020))[0] SVDMatrix1 = offdia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[inds,:] diaMatrix1 = dia_ixsobj.offDiaDataSets[1].alignedSignalMatrix[inds,:] energy1 = (np.array(offdia_ixsobj.offDiaDataSets[1].energy)[inds] - E0)*1e3 SVDMatrix1 = np.insert(SVDMatrix1,0,diaMatrix1[:,0],axis=1) SVDMatrix1 = np.insert(SVDMatrix1,-0,diaMatrix1[:,1],axis=1) # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # inds = np.where(np.logical_and(np.array(offdia_ixsobj.offDiaDataSets[0].energy)>=7.929, np.array(offdia_ixsobj.offDiaDataSets[0].energy)<=8.020))[0] # SVDMatrix0 = offdia_ixsobj.offDiaDataSets[0].alignedSignalMatrix[inds,:] # diaMatrix0 = dia_ixsobj.offDiaDataSets[0].alignedSignalMatrix[inds,:] # energy0 = (np.array(dia_ixsobj.offDiaDataSets[0].energy)[inds] - E0)*1e3 # SVDMatrix0 = np.insert(SVDMatrix0,0,diaMatrix0[:,0],axis=1) # SVDMatrix0 = np.insert(SVDMatrix0,-0,diaMatrix0[:,1],axis=1) # ion() # imshow(SVDMatrix0) # # ROI 2 # E0 = 7.92525 # # try subtraction the SiL23-pre-edge region to zero OFF-dia # for ii in range(len(offdia_ixsobj.offDiaDataSets[2].alignedSignalMatrix[0,:])): # inds = np.where(np.logical_and(np.array(offdia_ixsobj.offDiaDataSets[2].energy) >= 8.015,np.array(offdia_ixsobj.offDiaDataSets[2].energy) <= 8.022))[0] # back = np.polyval(np.polyfit(np.array(offdia_ixsobj.offDiaDataSets[2].energy)[inds],offdia_ixsobj.offDiaDataSets[2].alignedSignalMatrix[inds,ii],0),np.array(offdia_ixsobj.offDiaDataSets[2].energy)) # offdia_ixsobj.offDiaDataSets[2].alignedSignalMatrix[:,ii] -=back # # try subtraction the SiL23-pre-edge region to zero DIA # for ii in range(len(dia_ixsobj.offDiaDataSets[2].alignedSignalMatrix[0,:])): # inds = np.where(np.logical_and(np.array(dia_ixsobj.offDiaDataSets[2].energy) >= 8.015,np.array(dia_ixsobj.offDiaDataSets[2].energy) <= 8.022))[0] # back = np.polyval(np.polyfit(np.array(dia_ixsobj.offDiaDataSets[2].energy)[inds],dia_ixsobj.offDiaDataSets[2].alignedSignalMatrix[inds,ii],0),np.array(dia_ixsobj.offDiaDataSets[2].energy)) # dia_ixsobj.offDiaDataSets[2].alignedSignalMatrix[:,ii] -=back # inds = np.where(np.logical_and(np.array(offdia_ixsobj.offDiaDataSets[2].energy)>=7.928, np.array(offdia_ixsobj.offDiaDataSets[2].energy)<=8.020))[0] # SVDMatrix2 = offdia_ixsobj.offDiaDataSets[2].alignedSignalMatrix[inds,:] # diaMatrix2 = dia_ixsobj.offDiaDataSets[2].alignedSignalMatrix[inds,:] # energy2 = (np.array(dia_ixsobj.offDiaDataSets[2].energy)[inds] - E0)*1e3 # SVDMatrix2 = np.insert(SVDMatrix2,0,diaMatrix2[:,0],axis=1) # SVDMatrix2 = np.insert(SVDMatrix2,-0,diaMatrix2[:,1],axis=1) # ion() # imshow(SVDMatrix2) ####################################### # decompose using SVD after metere ####################################### #U0,S0,V0 = np.linalg.svd(SVDMatrix0) U1,S1,V1 = np.linalg.svd(SVDMatrix1) #U2,S2,V2 = np.linalg.svd(SVDMatrix2) # tthv, tthh, psi = xrs_utilities.cixsUBgetAngles_primo([1.,1.,0.5]) # Q, K0, K2 = xrs_utilities.cixsUBgetQ_primo(tthv, tthh, psi) # G = [-1.,-1.,-1.] # Kh = G+K0 # e0 = K0/np.linalg.norm(K0) # eh = Kh/np.linalg.norm(Kh) # e2 = K2/np.linalg.norm(K2) # pre_factors = np.array([ (e0.dot(e2))**2, (eh.dot(e2))**2, 2.0*(e0.dot(e2))*(eh.dot(e2)), 2.0*(e0.dot(e2))*(eh.dot(e2)) ]) # forx = np.loadtxt('/media/christoph/Seagate/data/off_dia_jan18/wfc/Si111_alpha12.0_z0.01.dat') # cenofx = xrs_utilities.fwhm(forx[:,0], forx[:,5])[1] # forx[:,0] -= cenofx # forx_i = np.zeros((len(offdia_ixsobj.offDiaDataSets[1].masterRCmotor),len(offdia_ixsobj.offDiaDataSets[1].masterRCmotor) )) # forx_i[:,0] = offdia_ixsobj.offDiaDataSets[1].masterRCmotor # for ii in range(4): # forx_i[:,ii+1] = pre_factors[ii]*np.interp(forx_i[:,0], forx[:,0], forx[:,ii+1]) # V1h = V1.T # T = forx_i.dot(V1h) # S = np.zeros((U1.shape[0], V1.shape[0])) # for ii in range(2):#range(V1.shape[0]): # S[ii,ii] = S1[ii] # dsf = U1.dot(S).dot(np.linalg.inv(T)) # cla() # plot(energy1[2::], dsf[:,0]) # plot(energy1[2::], -dsf[:,1]*1.8) # plot(energy1[2::], -dsf[:,2]*1.3) # plot(energy1[2::], dsf[:,3]*24) # plot(energy1[2::], -dsf[:,4]*0.85) # plot(energy1[2::], dsf[:,5]*0.49) # plot(energy1[2::], dsf[:,6]*2.2) # plot(energy1[2::], -dsf[:,7]*0.45) # plot(energy1[2::], dsf[:,8]*1.05) # plot(energy1[2::], dsf[:,9]*1.75) # plot(energy1[2::], dsf[:,10]*6) # plot(energy1[2::], -dsf[:,11]*1.5) ####################################### # decompose using SVD ####################################### U1,S1,D1 = np.linalg.svd(SVDMatrix1) f=h5py.File("result.h5","w") f["energy"] = energy1 f["C1"] = -U1[:,0]*S1[0] f["C2"] = -U1[:,1]*S1[1] f["C3"] = -U1[:,2]*S1[2] f.close() if(0): plot(energy1,-U1[:,0]*S1[0], energy1,-U1[:,1]*S1[1], energy1,-U1[:,2]*S1[2]) # load theory theo = np.loadtxt('/data/id20/inhouse/data/off_dia/theory_final/Si111_1.0_1.0_-0.5.dat') # load diagonals dia0 = np.array(zip(energy1,SVDMatrix1[:,0])) thedata = np.loadtxt('/data/id20/inhouse/data/run2_18/run6_comm/Si_diagonals/Si_0.0_0.0_1.5_a.dat') dia1 = np.zeros_like(dia0) dia1[:,0] = energy1 dia1[:,1] = np.interp(energy1, thedata[:,0], thedata[:,1]) # # subract rest of elastic and background from dia1 # from scipy.optimize import curve_fit # inds = np.where(np.logical_or(dia1[:,0]<=5.0 , dia1[:,0]>=85.0 ))[0] # popt, pcov = curve_fit(math_functions.pearson7_linear_scaling_forcurvefit, dia1[inds,0], dia1[inds,1], p0=[0.0, 1.5, 1.0, 1000, -0.1, 0.0, 1.0]) # back = math_functions.pearson7_linear_scaling_forcurvefit(dia1[:,0], popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6]) # plot(dia1[:,0], dia1[:,1]) # plot(dia1[:,0], back) # plot(dia1[:,0], dia1[:,1]-back) # dia1[:,1] -= back # # ROI 1 # # q-point 0.96354415, 1.02352979, 0.49399393 # thedata = np.zeros((len(energy1),4)) # thedata[:,0] = energy1 # thedata[:,1] = U1[:,0]*S1[0] # thedata[:,2] = U1[:,1]*S1[1] # thedata[:,3] = U1[:,2]*S1[2] #np.savetxt('/media/christoph/Seagate Expansion Drive/data/off_dia/offdia_results/svd_res/Si111_10_10_05_a.txt',thedata) ####################################### # decompose using constrained power method ####################################### #dia = np.loadtxt('/data/id20/inhouse/data/off_dia/offdia_results/Si100_00_00_05_dia.txt') #dia /= np.linalg.norm(dia) numC = 3 W_ini = np.random.random((SVDMatrix1.shape[0],numC)) W_ini[:,0] = SVDMatrix1[:,0] W_ini[:,2] = dia1[:,1] W_up = np.zeros_like(W_ini) W_up[:,1] += 1.0 W_up[:,2] += 0.0 coeff_ini = np.random.random((SVDMatrix1.shape[1],numC)) coeff_ini /= np.linalg.norm(coeff_ini) coeff_up = np.ones((SVDMatrix1.shape[1],numC)) W, coeff, err = xrs_utilities.constrained_mf(SVDMatrix1,W_ini, W_up, coeff_ini, coeff_up, maxIter=1000, tol=1.0e-8) thedata = np.zeros((len(energy1),4)) thedata[:,0] = energy1 thedata[:,1] = W[:,0] thedata[:,2] = W[:,1] thedata[:,3] = W[:,2] np.savetxt('/data/id20/inhouse/data/off_dia_jan18/results/Si111_terzo_5deg_asym_q0_1.0_1.0_-0.5_b.dat', thedata) ####################################### # decompose using un-constrained power method (updating off-dia and dia2) ####################################### numC = 3 W_ini = np.random.random((SVDMatrix.shape[0],numC)) W_ini[:,0] = SVDMatrix[:,0] W_ini[:,2] = dia W_up = np.zeros_like(W_ini) W_up[:,1] += 1.0 W_up[:,2] += 1.0 coeff_ini = np.random.random((SVDMatrix.shape[1],numC)) coeff_up = np.ones((SVDMatrix.shape[1],numC)) W, coeff, err = xrs_utilities.constrained_mf(SVDMatrix,W_ini, W_up, coeff_ini, coeff_up, maxIter=1000, tol=1.0e-8) thedata = np.zeros((len(energy),4)) thedata[:,0] = energy thedata[:,1] = W[:,0] thedata[:,2] = W[:,1] thedata[:,3] = W[:,2] np.savetxt('/home/christoph/data/off_dia/offdia_results/pm_res/Si111_10_10_05_a.txt',thedata) xrstools-0.15.0+git20210910+c147919d/OFFDIAG/components_MF.py000066400000000000000000000135771412732462000223470ustar00rootroot00000000000000from XRStools import xrs_utilities import h5py def saveDictH5( obj, fn): file = h5py.File(fn,"w") for k in obj.keys(): file[k] = obj[k] file.close() def loadDictH5( fn): obj = {} file = h5py.File(fn,"r") for k in file: obj[k] = file[k] [:] file.close() return obj import numpy as np # W, coeff = constrained_mf(signals,W_fixed, W_free, coeff_ini, maxIter=100, tol=1.0e-14) def constrained_mf(signals,W_fixed, W_free, coeff_ini, maxIter=100, tol=1.0e-14): if W_free is not None: W = np.zeros( [W_fixed.shape[0] , (W_fixed.shape[1]+W_free.shape[1]) ] ) else: W = np.zeros( [W_fixed.shape[0] , (W_fixed.shape[1]) ] ) W [:, 0: W_fixed.shape[1] ] = W_fixed if W_free is not None: W [:, W_fixed.shape[1]: ] = W_free for i in range(10): #### W *coeffs = signals coeffs = (np.linalg.lstsq( W, signals ))[0] if W_free is None: return W, coeffs diff = signals - np.dot( W , coeffs) error = np.linalg.norm(diff) print( "ERROR MF ", error*error) reduced_signals = signals - np.dot( W[:,: W_fixed.shape[1] ] , coeffs[0:W_fixed.shape[1], : ] ) W_free = (np.linalg.lstsq( (coeffs[W_fixed.shape[1]:,: ] ).T , reduced_signals.T ))[0].T W [:, W_fixed.shape[1]: ] = W_free for i in range( W.shape[-1] ) : W[:,i] = W[:,i]/np.linalg.norm(W[:,i]) return W, coeffs signals_dict = loadDictH5( "Signals111.h5") # coeffs_dict = loadDictH5( "STW_infos111_allparallel.h5") # coeffs_dict = loadDictH5( "STW_infos111_nominal_0.130000.h5") # coeffs_dict = loadDictH5( "STW_infos111_nominal_0.118000.h5") # coeffs_dict = loadDictH5( "STW_infos111_all_parallel.h5") #coeffs_dict = loadDictH5( "STW_infos111_nominal_-1.000000.h5") coeffs_dict = loadDictH5( "STW_infos111_resynt.h5") print( list(signals_dict.keys()) ) print( list(coeffs_dict.keys()) ) for key in ['angles_rad', 'energy', 'signals']: exec("%s=signals_dict['%s']"%(key,key)) coeffs_matrix = [] for key in ['A_curve', 'B_curve', 'C_curve']: xrad, coeffs = coeffs_dict[key] mycoeffs = np.interp( angles_rad,xrad,coeffs ) coeffs_matrix.append(mycoeffs) # coeffs_matrix[2] = coeffs_matrix[2] coeffs_matrix = np.array( coeffs_matrix ) # 21x131 = 21 x 3 3 x 131 # signals.T = np.dot(coeffs_matrix.T , scatterings ) E0 = 7.92515 inds = np.where(np.logical_and(np.array(energy)>=7.927, np.array(energy)<=8.020))[0] signals = signals[inds] energy = energy[inds] E0 = 7.9255 dia_1_1_05_exp = np.array(h5py.File("dia_ixsobj_aligned.h5","r") ["after_stichting_aligning"]["offDiaDataSets"]["DS0001"]["alignedSignalMatrix"][:]) dia_1_1_05_exp_energy = np.array(h5py.File("dia_ixsobj_aligned.h5","r") ["after_stichting_aligning"]["offDiaDataSets"]["DS0001"]["energy"][:] ) dia_1_1_05_exp = dia_1_1_05_exp.sum(axis=-1) dia_1_1_05_exp = np.interp( energy, dia_1_1_05_exp_energy , dia_1_1_05_exp ) sum_res = np.trapz( (energy-E0)*dia_1_1_05_exp , x=(energy-E0) ) dia_1_1_05_exp = 2.25/sum_res * dia_1_1_05_exp tmp = np.loadtxt("Christoph_Documents/Diagonals/Re_partie_diagonale/dia_results/Si100_15_00_00_a.txt") dia_0_0_15_exp = tmp[:,1] dia_0_0_15_exp_energy = tmp[:,0] dia_0_0_15_exp = np.interp( (energy-E0)*1.0e3 , dia_0_0_15_exp_energy , dia_0_0_15_exp ) sum_res = np.trapz( (energy-E0)*dia_0_0_15_exp , x=(energy-E0) ) dia_0_0_15_exp = 2.25/sum_res * dia_0_0_15_exp #18, 66 signals[18] = 0.5*(signals[17]+signals[19]) signals[66] = 0.5*(signals[65]+signals[67]) ## signals = np.array(signals.T) ####################################### # decompose using constrained power method ####################################### #dia = np.loadtxt('/data/id20/inhouse/data/off_dia/offdia_results/Si100_00_00_05_dia.txt') #dia /= np.linalg.norm(dia) numC = 2 W_fixed = np.random.random((signals.shape[0],1)) W_fixed[:,0] = dia_1_1_05_exp W_free = np.random.random((signals.shape[0],1)) coeff_ini = np.random.random((numC, signals.shape[1])) coeff_ini /= np.linalg.norm(coeff_ini) W, coeff = constrained_mf(signals,W_fixed, W_free, coeff_ini) W_fixed = W[:, :W_fixed.shape[-1] ] W_free = W[:, W_fixed.shape[-1]: ] thedata = np.zeros((len(energy),1+numC)) thedata[:,0] = (energy-E0)*1.0e3 thedata[:,1] = W_fixed[:,0] thedata[:,2] = W_free[:,0] # thedata[:,3] = W[:,2] np.savetxt('RESFACT_w.txt', thedata) thecoeffs = np.zeros((len(angles_rad),1+numC)) thecoeffs[:,0] = angles_rad thecoeffs[:,1:1+numC] = coeff.T[:,0:numC] np.savetxt('RESFACT_c.txt', thecoeffs) for i in range (10): print (i) Wtest = np.array(W) Wtest[:,1] = W[:,1]-0.1*i*W[:,0] Wtest, coeff = constrained_mf(signals,Wtest , None, coeff_ini) thedata = np.zeros((len(energy),1+numC)) thedata[:,0] = (energy-E0)*1.0e3 thedata[:,1:] = Wtest np.savetxt('RESFACT_w_%d.txt'%i, thedata) thecoeffs = np.zeros((len(angles_rad),1+numC)) thecoeffs[:,0] = angles_rad thecoeffs[:,1:1+numC] = coeff.T[:,0:numC] np.savetxt('RESFACT_c_%d.txt'%i, thecoeffs) raise W_up[:] = 0 W, coeff, err = constrained_mf(signals,W_ini, W_up, coeff_ini, coeff_up, maxIter=1000, tol=1.0e-8) thecoeffs[:,1:1+numC] = coeff[:,0:numC] np.savetxt('RESFACT_c_again.txt', thecoeffs) print(" SHAPE SIGNALS ", signals.shape) newsig = [] for l in signals.T: f = 1/l.sum() l = l*f newsig.append(l) signals = np.array(newsig).T h5py.File("newsig.h5","w")["signals_renorm"] = signals np.savetxt("signals_renorm.txt", signals) sm = signals.mean(axis=-1) signals = signals-sm[:,None] newsig = [] for l in signals.T: f = 1/(l*l).sum() l = l*np.sqrt(f) newsig.append(l) signals = np.array(newsig).T h5py.File("newsig2.h5","w")["signals_renorm2"] = signals np.savetxt("signals_renorm2.txt", signals) xrstools-0.15.0+git20210910+c147919d/OFFDIAG/plotcommands000066400000000000000000000065101412732462000216360ustar00rootroot00000000000000plot "xy111_long.txt" u 1:($2/338124*0.323) w l,"rocking_curve111_long_resynt.txt" w lp plot "xy111.txt" u 1:($2/1.968e+7*0.323) w lp,"rocking_curve111_long_resynt.txt" w l,"A_curve111_long_resynt.txt" u 1:2 w l,"B_curve111_long_resynt.txt" u 1:2 w l, "C_curve111_long_resynt.txt" u 1:2 w l plot "xy111.txt" u 1:($2/1.968e+7*0.323) w lp,"rocking_curve111_allparallel.txt" w l,"A_curve111_allparallel.txt" u 1:2 w l,"B_curve111_allparallel.txt" u 1:2 w l, "C_curve111_allparallel.txt" u 1:2 w l, plot "xy111.txt" u 1:($2/1.968e+7*0.323) w squares,"rocking_curve111_long_resynt.txt" w l,"A_curve111_long_resynt.txt" u 1:2 w p,"B_curve111_long_resynt.txt" u 1:2 w p, "C_curve111_long_resynt.txt" u 1:2 w p,"A_curve111_allparallel.txt" u 1:2 w l,"B_curve111_allparallel.txt" u 1:2 w l, "C_curve111_allparallel.txt" u 1:2 w l plot [-0.0004:0.0004]"xy111.txt" u 1:(0.3+$2/1.968e+7*0.323) w p,"rocking_curve111_long_resynt.txt" u 1:(0.3+$2) w l,"A_curve111_long_resynt.txt" u 1:2 w p,"B_curve111_long_resynt.txt" u 1:2 w p, "C_curve111_long_resynt.txt" u 1:2 w p,"A_curve111_allparallel.txt" u 1:2 w l,"B_curve111_allparallel.txt" u 1:2 w l, "C_curve111_allparallel.txt" u 1:2 w l ############# plot [-0.0004:0.0004]"xy111.txt" u 1:(0.3+$2/1.968e+7*0.432) w p,"rocking_curve111_nominal_0.118000.txt" u 1:(0.3+$2*0.83) w l,\ "A_curve111_nominal_0.118000.txt" u 1:2 w p,\ "B_curve111_nominal_0.118000.txt" u 1:2 w p,\ "C_curve111_nominal_0.118000.txt" u 1:2 w p,\ "A_curve111_allparallel.txt" u 1:2 w l,\ "B_curve111_allparallel.txt" u 1:2 w l,\ "C_curve111_allparallel.txt" u 1:2 w l,\ "A_curve111_long_resynt.txt" u 1:2 w l,\ "B_curve111_long_resynt.txt" u 1:2 w l,\ "C_curve111_long_resynt.txt" u 1:2 w l,"rocking_curve111_long_resynt.txt" u 1:(0.3+$2*1.1) w l _ plot [-0.0004:0.0004]"xy111.txt" u 1:(0.3+$2/1.968e+7*0.37) w p,"rocking_curve111_nominal_0.100000.txt" u 1:(0.3+$2) w l,\ "A_curve111_nominal_0.100000.txt" u 1:2 w p,\ "B_curve111_nominal_0.100000.txt" u 1:2 w p,\ "C_curve111_nominal_0.100000.txt" u 1:2 w p,\ "A_curve111_allparallel.txt" u 1:2 w l,\ "B_curve111_allparallel.txt" u 1:2 w l,\ "C_curve111_allparallel.txt" u 1:2 w l,\ "A_curve111_long_resynt.txt" u 1:2 w l,\ "B_curve111_long_resynt.txt" u 1:2 w l,\ "C_curve111_long_resynt.txt" u 1:2 w l plot [-0.0004:0.0004]"xy111.txt" u 1:(0.3+$2/1.968e+7*0.50) w p,"rocking_curve111_nominal_0.140000.txt" u 1:(0.3+$2) w l,\ "A_curve111_nominal_0.140000.txt" u 1:2 w p,\ "B_curve111_nominal_0.140000.txt" u 1:2 w p,\ "C_curve111_nominal_0.140000.txt" u 1:2 w p,\ "A_curve111_allparallel.txt" u 1:2 w l,\ "B_curve111_allparallel.txt" u 1:2 w l,\ "C_curve111_allparallel.txt" u 1:2 w l,\ "A_curve111_long_resynt.txt" u 1:2 w l,\ "B_curve111_long_resynt.txt" u 1:2 w l,\ "C_curve111_long_resynt.txt" u 1:2 w l plot [-0.0004:0.0004]"A_curve111_nominal_0.118000.txt" u 1:2 w p,\ "B_curve111_nominal_0.118000.txt" u 1:2 w p,\ "C_curve111_nominal_0.118000.txt" u 1:2 w p,\ "A_curve111_nominal_-0.400000.txt" u 1:2 w p,\ "B_curve111_nominal_-0.400000.txt" u 1:2 w p,\ "C_curve111_nominal_-0.400000.txt" u 1:2 w p,\ "A_curve111_nominal_0.100000.txt" u 1:2 w p,\ "B_curve111_nominal_0.100000.txt" u 1:2 w p,\ "C_curve111_nominal_0.100000.txt" u 1:2 w p,\ "xy111.txt" u 1:(0.3+$2/1.968e+7*0.432) w p,"rocking_curve111_nominal_-0.400000.txt" u 1:(0.3+$2) w lxrstools-0.15.0+git20210910+c147919d/README.rst000066400000000000000000000012041412732462000176360ustar00rootroot00000000000000XRStools =========================================== Main development website: https:// |Build Status| |Appveyor Status| Bla Bla References ---------- * reference Installation ------------ With PIP ........ As most Python packages, pyFAI is available via PIP:: pip install xrstools [--user] Provide the *--user* to perform an installation local to your user. Under UNIX, you may have to run the command via *sudo* to gain root access an perform a system wide installation. Documentation ------------- Documentation can be build using this command and Sphinx (installed on your computer):: python setup.py build build_doc xrstools-0.15.0+git20210910+c147919d/README.txt000066400000000000000000000012171412732462000176510ustar00rootroot00000000000000=========== XRSTools =========== XRSTools is a collection of Python functions, classes, and modules to extract and analyze x-ray Raman scattering (XRS) data from ID20 of the ESRF. Typical usage often looks like this:: #!/usr/bin/env python from xrstools import foo from xrstools import bar (Note the double-colon and 4-space indent formatting above.) Installation ========= to install the package extract xrstools-.tar.gz: tar xzf xrstools-.tar.gz and cd into the XRSTools directory: cd XRSTools For the installation type: sudo python setup.py install local installation: python setup.py install --home= xrstools-0.15.0+git20210910+c147919d/XRStools/000077500000000000000000000000001412732462000177075ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/Bragg.py000066400000000000000000000435011412732462000213060ustar00rootroot00000000000000## Only for Si so far from __future__ import absolute_import from __future__ import division from __future__ import print_function from ab2tds import dabax import numpy as np LAMBDAEV = dabax.LAMBDAEV def Arb_Rot(angle=None, rot_axis=None): assert(len(rot_axis)==3) x,y,z = rot_axis rot_mat = np.array([[ 1 + (1-np.cos(angle))*(x*x-1) , -z*np.sin(angle)+(1-np.cos(angle))*x*y, y*np.sin(angle)+(1-np.cos(angle))*x*z ], [ z*np.sin(angle)+(1-np.cos(angle))*x*y , 1 + (1-np.cos(angle))*(y*y-1), -x*np.sin(angle)+(1-np.cos(angle))*y*z ], [-y*np.sin(angle)+(1-np.cos(angle))*x*z, x*np.sin(angle)+(1-np.cos(angle))*y*z, 1 + (1-np.cos(angle))*(z*z-1) ]]) return rot_mat def Struct( hkl ) : center = np.array([1.0,1,1])/8.0 ps = np.array([ [0,0,0.0], [1,1,0], [1,0,1], [0,1,1] ] )*0.5 ps = np.concatenate( [ ps -center, ps+ center ] ) res = 0 for p in ps: res = res + np.exp( 2.0j * np.pi * np.dot( hkl, p ) ) return res def Refl( E0, FH, FmH, F0, nn, BH, T0, beam_direction=None ): ## BH est in Angstroems **-1 senza 2*pi k0 = 1/(LAMBDAEV/E0) K0_e = beam_direction*k0 b_inv = 1.0 + np.dot( nn, BH ) /np.dot( nn, K0_e ) b = 1.0/b_inv # print(" ########################### ") # print( K0_e ) # print( BH ) # print( np.linalg.norm(BH) ) # print(" ########################### ") alpha = 1.0*( np.dot( BH, BH+2*K0_e ) ) / k0/k0 ####### # risoluzione della formula q = b * FH*FmH z = (1-b)*F0/2.0 + b*alpha/2.0 delta0_a = 1.0/2.0*(F0 -z + np.sqrt( q+z*z ) ) delta0_b = 1.0/2.0*(F0 -z - np.sqrt( q+z*z ) ) ## # rapporto fra DH e D0 X_a = ( 2* delta0_a - F0 )/FmH X_b = ( 2* delta0_b - F0 )/FmH # gamma gamma = np.dot(K0_e, nn ) / np.linalg.norm(K0_e) ### c1,c2 psi_a = 2*np.pi * k0 *delta0_a / gamma psi_b = 2*np.pi * k0 *delta0_b / gamma # print(" PSIs ", psi_a, psi_b) if 0 : c_a = np.exp( - 1.0j *psi_a * T0) c_b = np.exp( - 1.0j *psi_b * T0) if psi_a.imag>b.imag: # A_ratio e' dato da X_b mu = -2*psi_b.imag c_a = 1.0 c_b = 0.0 else: # A_ratio e' dato da X_a mu = -2*psi_a.imag c_a = 0.0 c_b = 1.0 A_ratio = ( X_a*X_b*(c_a-c_b) )/(c_b*X_b -c_a*X_a ) I_ratio = (A_ratio*(A_ratio.conjugate())).real return I_ratio, A_ratio, mu def DoubleCrystalMonoSpectra( E0 = 7950.0, hkl = np.array( [ 1,1,1 ]), Dene = [0.0], NP = 1000, DeltaAngle = 0.0 ): Table_f0 = dabax.Dabax_f0_Table("f0_WaasKirf.dat") Table_f1f2 = dabax.Dabax_f1f2_Table("f1f2_Windt.dat") Sif0 = Table_f0.Element("Siva") Sif1f2= Table_f1f2.Element("Si") d_Si = 5.431 lamd0 = LAMBDAEV / E0 Ghkl = np.array( hkl,"d") G = 1.0/d_Si *np.linalg.norm(Ghkl) sint = lamd0*G /2.0 Brad = np.arcsin(sint) Drad_Dev = -1/np.sqrt(1-sint*sint ) * G /2.0 * LAMBDAEV / E0/E0 Brad2 = Brad+DeltaAngle*np.pi/180.0 print(" ANGOLO t , t2, ", Brad * 180.0 / np.pi , Brad2 * 180.0 / np.pi, DeltaAngle) Sh = Struct( hkl) S0 = Struct( [0,0,0]) anomalous_part = Sif1f2.f1f2Energy( E0 ) f0_H = Sif0.f0Energy(E0, Brad) f0_0 = Sif0.f0Energy(E0, 0) FH = Sh*( f0_H + anomalous_part ) F0 = S0*( f0_0 + anomalous_part ) CCmsec = 2.99792458e+10 mel_g = 9.1093837e-28 ee_stat = 4.803204e-10 Vcell = 1.0e-24 * (d_Si)**3 omega0 = E0 * 2*np.pi *CCmsec / LAMBDAEV *1.0e+8 Fconv_F2f = -4.0*np.pi*ee_stat*ee_stat / mel_g / omega0/omega0 / Vcell ## Per la simmetria centrale attorno al punto 1/8 1/8 1/8 non mi curo delle differenza fra Fh e F di meno h ## mi basta Fh F0 = Fconv_F2f* F0 FH = Fconv_F2f* FH ## prendiamo BH lungo Y BH = np.array([0.0,0,G]) nn = np.array( [ 0.0, 0, -1.0] ) # niente asimmetria res = [] beam_direction = np.array( [ -np.cos(Brad ), 0, -np.sin(Brad ) ] ) beam_direction_2 = np.array( [ -np.cos(Brad2), 0, -np.sin(Brad2) ] ) for offset in Dene : E = E0+offset I_ratio , A_ratio, mu = Refl( E, FH, FH, F0, nn, BH , 1.0e8, beam_direction = beam_direction ) ## BH est in Angstrom**-1 I_ratio2, A_ratio, mu = Refl( E, FH, FH, F0, nn, BH , 1.0e8, beam_direction = beam_direction_2 ) ## BH est in Angstrom**-1 res.append( [E, I_ratio* I_ratio2 ] ) return np.array(res) , Drad_Dev def CoefficientsReflection(E0 = 7950.0, Eused = 7950.0, hkl = [1,1,1], Drad = [0.0] , asymm_grad=0.0, tthv_grad=10.0, tth_grad=0.0, psi_grad=0.0): Table_f0 = dabax.Dabax_f0_Table("f0_WaasKirf.dat") Table_f1f2 = dabax.Dabax_f1f2_Table("f1f2_Windt.dat") Sif0 = Table_f0.Element("Si") Sif1f2= Table_f1f2.Element("Si") d_Si = 5.431 lamd0 = LAMBDAEV / E0 Ghkl = np.array( hkl,"d") G = 1.0/d_Si *np.linalg.norm(Ghkl) # the norm of the scattering vector is 1/d_S sint = lamd0*G /2.0 Brad = np.arcsin(sint) print(" NOMINAL BRAGG ANGLE ", Brad * 180.0 / np.pi ) Sh = Struct( hkl) S0 = Struct( [0,0,0]) anomalous_part = Sif1f2.f1f2Energy( E0 ) f0_H = Sif0.f0Energy(E0, Brad) f0_0 = Sif0.f0Energy(E0, 0) print( " Supposed ratio rho_G/rho for 111 ", (Sh*f0_H).real/ (S0*f0_0).real ) FH = Sh*( f0_H + anomalous_part ) F0 = S0*( f0_0 + anomalous_part ) print( " FH and F0 " , FH, F0) CCmsec = 2.99792458e+10 mel_g = 9.1093837e-28 ee_stat = 4.803204e-10 Vcell = 1.0e-24 * (d_Si)**3 omega0 = E0 * 2*np.pi *CCmsec / LAMBDAEV *1.0e+8 Fconv_F2f = -4.0*np.pi*ee_stat*ee_stat / mel_g / omega0/omega0 / Vcell ## Per la simmetria centrale attorno al punto 1/8 1/8 1/8 non mi curo delle differenza fra Fh e F di meno h ## mi basta Fh F0 = Fconv_F2f* F0 FH = Fconv_F2f* FH ## prendiamo BH lungo Y asymm_angle = asymm_grad * np.pi/180.0 ################################################################################################################## # questo per estrarre mu_norm2 # BH_simple = np.array( [ 0.0, 0, G ] ) nn_simple = np.array( [ 0.0, 0, -1 ] ) beam_direction = np.array( [ 0, 0, -1.0 ] ) dum1, dum2, mu_norm2 = Refl( E0, FH, FH, F0, nn_simple, BH_simple , 1.0e8, beam_direction = beam_direction ) ## BH est in Angstrom**-1 ################################################################################################################### # CALCOLO DEL COSENO FRA NN E DIREZIONE DETECTOR # prima dimensione orrizontale lungo X, seconda verticale lungo Y, terza verso l'osservatore della pagina, lungo z # # DECT_VERSOR nel sistema del laboratorio # tthv = tthv_grad *np.pi/180.0 tth = tth_grad *np.pi/180.0 dect_versor = np.array([ -np.cos(tthv) *np.cos(tth) , +(-1)*np.cos(tthv) *np.sin(tth) , #### qua forse errore di segno +np.sin(tthv) ] ) res = [] GEO0 = None for offset in Drad : thetarad = Brad+offset # # # G0_versor = np.array([ np.sin(thetarad), 0, np.cos(thetarad) ]) norm_versor_0 = (-1)*np.array([ np.sin(thetarad -asymm_angle), ## (-1) because we are pointing toward the interior of the sample 0, np.cos(thetarad -asymm_angle) ]) BH = G * G0_versor norm_versor = np.dot( Arb_Rot( angle = psi_grad*np.pi/180.0, rot_axis = G0_versor ), norm_versor_0 ) beam_direction = np.array( [ -1.0, 0, 0 ] ) beam_direction_out = beam_direction -2*G0_versor* (( G0_versor*beam_direction ).sum()) cos_in = abs( ( beam_direction * norm_versor ).sum() ) cos_out = abs( ( beam_direction_out * norm_versor ).sum() ) print (BH) print ( beam_direction_out / lamd0 - beam_direction / lamd0 ) print (lamd0) U = np.dot(BH,BH)/3.0 DK = ( dect_versor - beam_direction ) / lamd0 DKr = DK - BH DKr = ( dect_versor - beam_direction_out ) / lamd0 print( " U ", np.dot(BH,BH)/3.0, " Ldirect ", (np.dot(DK,DK)/U) , 2.25 , " Lreverse ", (np.dot(DKr,DKr)/U) , 2.25 , " Lcross ", (np.dot(DKr,DK)/U) , 3.0/4.0 ) I_ratio, A_ratio, mu = Refl( Eused, FH, FH, F0, norm_versor, BH , 1.0e8, beam_direction = beam_direction ) cos_dect_norm = abs( np.dot(dect_versor, norm_versor) ) print( " COSCOS ",np.dot(dect_versor, norm_versor) ) geo = 1.0/(mu + mu_norm2/cos_dect_norm) if GEO0 is None: GEO0 = geo res.append( [ I_ratio*cos_out/cos_in, geo/GEO0, geo*(A_ratio.real)*2.0/GEO0, geo* abs(A_ratio.real*A_ratio.real + A_ratio.imag*A_ratio.imag )/GEO0]) return np.array(res).T def calculate_RC_factors(E0 = None , hkl=None, hkl_mono=None, asymm_grad = None, tthv_grad=None, tth_grad=None, psi_grad= None, source_distance = None, magnification=None, DeltaAngle = 0 , nEpoints = 1000, nApoints= 1000, nHpoints = 100, Ew_source_ev = None, Aw_rocking_rad=None, Hw_slit_mm = None): """ This routine perform the calculation of the averaged coefficients considering different wavelenghts and different incoming directions. Contribution from different energies are considered being mutually incoherent. Moreover ech incoming direction is considered incoherent. This is a strong assumption because the beam in reality has a coherence lenght. But here we calculate the weight of all the contributions by a sort of ray-tracing, the weight being obtained from dynamical theory for a plane wave of a given energy and of a given direction calculating the reflection of the monochromators and what happens in the sample: A sampling is done over the energy, considering and energy width Ew_source_ev around nominal E0. A sampling is done also over the wavefront height. Each height define and incoming angle ( given by the source position and the distance from it). ** E0 is the nominal energy, eV ** source_distance is given in meters ** asymm_grad is a parameter of the sample cut ** tthv_grad, tth_grad, psi_grad are used to calculate the absorption coefficient of the way out to the detector after inelastic scattering ** magnification : converts the ray angular deviation from the central ray before the monochromator+mirror to the angular deviation after. ** Delta angle is zero for two parallel monochromator crystals ** Ew_source_ev, Aw_rocking_rad, Hw_slit_mm gives the widths of the samplings. Hw_slit_mm should be given and correspond the the height of the beam. Ew_source_ev should encompass the rocking curve arounf the nominal E0, and should encompass also the shift due to refraction index Aw_rocking curve also shoudl be large enough. ** The number of sampling points should be large enough for he sampling to be fine. So if Ew_source_ev and other widths are taken too large you might end up needing too large numbers of points. """ Ew = Ew_source_ev Aw = Aw_rocking_rad Hw = Hw_slit_mm if hkl_mono is None: hkl_mono = hkl DEpoints = np.linspace( - Ew/2.0 + Ew/nEpoints/2.0 , + Ew/2.0 - Ew/nEpoints/2.0 , num = nEpoints, endpoint= True) Drads_points = np.linspace( - Aw/2.0 + Aw/nApoints/2.0 , + Aw/2.0 - Aw/nApoints/2.0 , num=nApoints , endpoint= True) dHpoints = np.linspace( - Hw/2.0 + Hw/nHpoints/2.0 , + Hw/2.0 - Hw/nHpoints/2.0 , num=nHpoints , endpoint= True) print( "=================== " , DeltaAngle ) ########################################################################################## ## Drad_dEv is the derivative of the Bragg angle ( from the surface, i.e. 0 is grazing) ## versus the energy in ev. It is a negative number ## monochromator_response_energy, Drad_dEv = DoubleCrystalMonoSpectra( E0 = E0 , hkl = hkl_mono , Dene = DEpoints, DeltaAngle = DeltaAngle ) np.savetxt("monochromator_response_energy.txt",monochromator_response_energy) ref_curve_angle = 0.0 A_curve_angle = 0.0 B_curve_angle = 0.0 C_curve_angle = 0.0 ########################################################################################## ## calculations are done at different offset from theta Bragg given by Drads_points : ## drads are added to theta # # We calculate this curve at one energy : Eused # and for different grazing angles ref_0, A_0,C_0,B_0 = CoefficientsReflection(E0 = E0 , Eused = E0 +0.0, hkl = hkl , Drad = Drads_points , asymm_grad = asymm_grad, tthv_grad = tthv_grad , tth_grad = tth_grad, psi_grad = psi_grad ) np.savetxt("sample_ref.txt",np.array( [Drads_points, ref_0] ).T ) totsomma = 0.0 for dh in dHpoints : print(" dh ", dh) ################################################################################################## shiftA = dh*1e-3 / source_distance # dHpoints is given in millimiter this is the ray angle from the central ray in radians shiftA2 = shiftA / magnification # After reflections by the the monochromator and by the mirrors, this is the angular deviation # of the considered ray from the central ray assert( Drad_dEv < 0.0 ) ###################################################################################### # # The first monochromator mirror is considered facing downward. So imagine that shiftA is positive. # This means that ( considereing an horizontal central ray) our ray is pointing to the ceiling, # so this means an increase in the theta grasing angle. The center of the response curve shits by shiftE # and goes to lower energies (Drad_dEv is negative). The response of the crystal moves by +shiftE shiftE = shiftA / Drad_dEv # shift dell energia di Bragg. Immaginiamo un shiftA positivo. Tutta la curva si sposta ad energie piu basse for Ene, Beam in monochromator_response_energy: ## We interate over the reflectivity curve. totsomma += Beam # Beam is the reflectivity Ene = Ene + shiftE # the considered point of the reflectivity curve has moved by shiftE ################################################################################################################### ## ## Now on the side of the sample. As for the monochromator we have calculated the contributions of the sample ## for one fixed energy and different angles. We are hitting the sample with a deviation shiftA2 of the grazing angle ## from the nominal Bragg angle of the central ray at the nominal energy E0. ## We need to read the contribution for a given deviation from the bragg angle. ## Because the Bragg angle increase by (Ene-E0) *Drad_dEv ## then the deviation from the considered ray and this Bragg angle decreases by such term myshiftA = shiftA2 - (Ene-E0) *Drad_dEv ## se l'energia e' piu grande allora (Ene-E0) *Drad_dEv e' negativo ## Contemporaneamente se l'energia e' piu' alta incontro il massimo a piu' bassi angoli ## Quindi devo aggiungere agli angoli di calcolo un termine positivo in modo che il massimo venga letto ad un angolo piu basso ref = np.interp( Drads_points+myshiftA , Drads_points , ref_0 ) A = np.interp( Drads_points+myshiftA , Drads_points , A_0 ) B = np.interp( Drads_points+myshiftA , Drads_points , B_0 ) C = np.interp( Drads_points+myshiftA , Drads_points , C_0 ) ref_curve_angle = ref_curve_angle + Beam * ref A_curve_angle = A_curve_angle + Beam * A B_curve_angle = B_curve_angle + Beam * B C_curve_angle = C_curve_angle + Beam * C rocking_curve = np.array([ Drads_points, ref_curve_angle/totsomma ] ) A_curve = np.array([ Drads_points, A_curve_angle/totsomma ] ) B_curve = np.array([ Drads_points, B_curve_angle/totsomma ] ) C_curve = np.array([ Drads_points, C_curve_angle/totsomma ] ) np.savetxt("ref.txt",(rocking_curve).T ) return { "RC_curve": rocking_curve, "A_curve": A_curve, "B_curve": B_curve, "C_curve": C_curve } xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/000077500000000000000000000000001412732462000207075ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/Wizard.py000066400000000000000000003614231412732462000225320ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six import u version="" ########################################################################## # Copyright (C) 2016 European Synchrotron Radiation Facility # # European Synchrotron Radiation Facility, Grenoble,France # WIZARD.py # author : Alessandro Mirone # # This program is free software; you can redistribute it and/or modify it # under the terms of BSD License ########*/ import os import sys DEBUG = False import tokenize import imp import re import yaml import yaml.resolver yaml.resolver.Resolver Resolver = yaml.resolver.Resolver Resolver.add_implicit_resolver( u'tag:yaml.org,2002:float', re.compile(u(r'''^(?:[-+]?(?:[0-9][0-9_]*)(\.[0-9_]*)?(?:[eE][-+]?[0-9]+)? |\.[0-9_]+(?:[eE][-+][0-9]+)? |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\.[0-9_]* |[-+]?\.(?:inf|Inf|INF) |\.(?:nan|NaN|NAN))$'''), re.X), list(u'-+0123456789.')) os.environ['QT_API'] = 'pyqt' timerPila = [0] try: import sip sip.setapi("QString", 2) sip.setapi("QVariant", 2) except: print( " SIP NO ") pass import copy import collections from PyQt4 import Qt, QtCore import PyQt4 import PyMca5.PyMcaIO.specfilewrapper as SpecIO import h5py import pickle from multiprocessing import Process, Manager # manager_context=Manager() poneva dei problemi in fase di import per via del forking import subprocess import os import time import tempfile ## import thread import threading import string import signal import sys, os import traceback from PyQt4 import QtGui import numpy as np import math import matplotlib matplotlib.use('Qt4Agg') import pylab from . import Wizard_safe_module from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] global animation_file global dico_copy_buffer dico_copy_buffer = {} def split_hdf5_address(dataadress): pos = dataadress.rfind(":") if ( pos==-1): return None filename, groupname = dataadress[:pos], dataadress[pos+1:] return filename, groupname def initialise_specmem( spec_mem, specfile_name ): new_size = os.path.getsize(specfile_name) spec_mem["sf" ] = None spec_mem["sf" ] = SpecIO.Specfile( specfile_name ) spec_mem["size" ] = new_size scanlist = [] for t in spec_mem["sf" ]: scanlist.append(t.number()) spec_mem["scanlist" ] = scanlist def get_Specfile_Cached( specfile_name, buffer ={} ): if specfile_name in buffer: # print specfile_name , " eset in buffer " spec_mem = buffer[specfile_name] old_size = spec_mem["size"] new_size = os.path.getsize(specfile_name) if new_size != old_size: initialise_specmem( spec_mem, specfile_name ) return spec_mem try: spec_mem = {} initialise_specmem( spec_mem, specfile_name ) buffer[specfile_name] = spec_mem return spec_mem except: traceback.print_exc(file=sys.stdout) return None def specScan_exists(specfile_name , t ): # sf = SpecIO.Specfile( specfile_name ) sf = get_Specfile_Cached( specfile_name ) if sf is None: return 0 sf = sf["sf"] try: scan = sf.select(str(t)) good=1 except: good = 0 return good def render_numero_in_una_lista(value): return "[%s]" % value def render_numero_in_una_lista_piu_uno(value): return "[%s,%d]"%(value, int(value)+1) def RepresentsFloat(s): try: s="test : "+s test = yaml.load(s)["test"] if isinstance(test,float): return True else: return False except : return False def RepresentsInt(s): try: s="test : "+s test = yaml.load(s)["test"] if isinstance(test,int): return True else: return False except : return False def reduce_to_value(par) : if hasattr(par,"value" ): res = par.value else: res = par return res class Functor_append_numinterval_to_name: def __init__(self, plus=""): self.plus = plus pass def __call__(self,arg): nome, intervallo = arg if hasattr(nome,"value"): nome=nome.value seque = intervallo.getValue() nome=nome+"_" for n,i in enumerate(seque): nome = nome+str(i) if n=0: arg=arg[:pos] else: pos = arg.rfind(":") arg=arg[:pos+1] if (self.extract_numeric is not None): s="" while(len(remainder)): if remainder[-1].isdigit(): s=remainder[-1]+s remainder = remainder[:-1] else: remainder = "" return s return s if len(datagroupname): if self.transform_last is None: return arg+"/"+datagroupname elif (self.transform_last is not None) : s="" while(len(remainder)): if remainder[-1].isdigit(): s=remainder[-1]+s remainder = remainder[:-1] else: remainder = "" return arg+"/"+datagroupname+"_"+s else: return arg else: if self.add_basename is not None: return arg+"/"+ self.add_basename+ ":"+ datagroupname else: return arg+":"+datagroupname def simplecopy(par): if hasattr(par,"value" ): res = copy.deepcopy(par.value) else: res = copy.deepcopy(par) return res class Parametro: tipo = "text" isresult=0 mywarning="" enable=True automatic_forward = 0 master = False always_visible = False # guiding = False def pump_default(self): if self.defaults2 is not None: source, method = self.defaults2 self.value = method(source) def render(self): # print " A" , self # print self.rendering # print self.getValue() return self.rendering( self.getValue()) def getValue(self): return self.value def getFullValue(self): return self.value def depends_from(self): return [] class Functor_choicesFromSpecLabels: def __init__( self, spec_file_par , first_scan_par , last_scan_par , labels=1): self.spec_file_par = spec_file_par self.first_scan_par = first_scan_par self.last_scan_par = last_scan_par self.labels = labels def __call__(self): filename = self.spec_file_par.render() s1 = int(self.first_scan_par.render() ) s2 = int( self.last_scan_par.render() ) sf = get_Specfile_Cached( filename ) if sf is None: return [] scan = sf["sf"].select(str(s1)) if self.labels: res = scan.alllabels() else: res = scan.allmotors() return res class Parameter_Choices(Parametro): tipo ="multiplechoices" def __init__(self, doc="", choices = [], rendering = str, defaults2=None, choices_functor = None, initial_value = None): self.choices_functor = choices_functor self.rendering = rendering self.defaults2 = defaults2 self.doc = doc self.value = initial_value self.choices = choices self.pump_default() def getIndex(self): if self.value not in self.choices: self.value = self.choices[0] index = self.choices.index(self.value) return index def isReady(self): return self.isValid() def isValid(self): return self.value in self.choices def getValue(self): return self.value class FilePath(Parametro): tipo = "simplefile" isadir=0 def __init__(self, doc="" , defaults2=None, rendering = str, fileme=1, isadir=0, initial_value = None): self.rendering = rendering self.defaults2 = defaults2 self.doc = doc self.value = initial_value self.fileme = fileme self.isadir=isadir def isReady(self): path=self.value if self.fileme==1: if self.isadir==0: return self.isValid() and os.path.exists(path ) and os.path.isfile(path) else: return self.isValid() and os.path.exists(path ) and os.path.isdir(path) elif self.fileme==0: return self.isValid() and not os.path.exists(path ) elif self.fileme==0.5: return self.isValid() def isValid(self): path=self.value if type(path)!=type("") or len(path)==0: return 0 else: bd = os.path.dirname(path) if not os.path.exists(bd) or len(bd)==0: return 0 return 1 def getValue(self): return self.value def hdf5path_exists(filename, groupname): # os.system("touch %s"%filename) # print " checco ", filename dn = os.path.dirname(filename) if dn=="": dn="./" os.stat( dn ) os.system("touch %s" % filename) h5 = h5py.File(filename,"r" ) res = groupname in h5 h5.close() return res def is_hdf5(filename): try: h5 = h5py.File(filename,"r" ) h5.close() return 1 except: return 0 class Hdf5FilePath(FilePath): tipo = "simplefile hdf5" def __init__(self, doc="" , defaults2=None, rendering = str, fileme=1, gnameme=1, canbeNone=False, initial_value=None): self.rendering = rendering self.defaults2 = defaults2 self.doc = doc self.value = initial_value self.fileme = fileme self.gnameme = gnameme self.canbeNone = canbeNone def hdf5path_exists(self): bf, bg = split_hdf5_address(self.getValue()) if not is_hdf5(bf): return 0 if not hdf5path_exists(bf, bg): return 0 return 1 def fileexists(self): if self.canbeNone : if self.value=="None": return False if not self.isValid() : return 0 filename, groupname = split_hdf5_address(self.value) return os.path.exists(filename ) def isReady(self): if self.canbeNone : if self.value=="None": return True path=self.value if not self.isValid() : return 0 filename, groupname = split_hdf5_address(path) (fileme, gnameme) = (self.fileme, self.gnameme) if fileme ==0.5: if os.path.exists(filename ) and len(groupname): fileme = 1 else: fileme=0 gnameme=0 if (fileme, gnameme)==(1,1): if not is_hdf5(filename): return 0 if os.path.exists(filename ): pass else: return 0 return (hdf5path_exists( filename, groupname ))*1 elif (fileme, gnameme)==(0,0): if os.path.exists(filename ): return 0 else: return -1 elif (fileme, gnameme)==(1,0): if not is_hdf5(filename): return 0 if os.path.exists(filename ): return (not hdf5path_exists( filename, groupname ))*(-1) else: return 0 else: pass # raise Exception," invalid combination of fileme, gnameme in Hdf5FilePath definition " def corrected_dirname(self,path): pos = path.rfind(":") if pos==-1: return os.path.dirname(path) else: return os.path.dirname(path[:pos]) def isValid(self): if self.canbeNone : if self.value=="None": return True path=self.value isvalid=1 if type(path)!=type(""): return 0 res=split_hdf5_address(path) if res is None: return 0 path, groupname =res if type(path)!=type("") or len(path)==0 or len(groupname)==0 : return 0 else: bd = self.corrected_dirname(path) if not ( os.path.exists(bd) or len(bd)==0): return 0 return 1 class NameWithNormaliser(Parametro): def __init__(self, doc="" , defaults2=None, rendering = str, initial_value=""): self.doc = doc self.defaults2 = defaults2 self.rendering = rendering self.pump_default() self.value=initial_value def getatypelikethis(self): return "s" def isReady(self): return self.isValid() def isValid(self): value=self.value if type(value)!=type("") or len(value)==0: return 0 else: if not len(value.split())==1: return 0 if value.find("/")==-1: return 1 pos =value.find("/") number = value[pos+1:] return RepresentsInt(number) class hdf5_relative_path(Parametro): def __init__(self, base_h5file, doc="" , defaults2=None, rendering = str, gnameme=1, initial_value = None): self.gnameme = gnameme self.base_h5file = base_h5file self.rendering = rendering self.defaults2 = defaults2 self.doc = doc self.value = initial_value def depends_from(self): return [ self.base_h5file ] def getatypelikethis(self): return "s" def fileexists(self): return self.base_h5file.fileexists() def getFullValue(self): res = self.base_h5file.getValue()+"/"+self.getValue() return res def isReady(self): path=self.value if not self.isValid(): return 0 bf, bg = split_hdf5_address(self.base_h5file.getValue()) if not is_hdf5(bf): return 0 if not hdf5path_exists(bf, bg): return 0 if self.gnameme==1: return hdf5path_exists(bf, bg+"/"+path) else: if hdf5path_exists(bf, bg+"/"+path): return 0 else: return -1 def isValid(self): path=self.value if type(path)!=type("") or len(path)==0: return 0 else: return 1 def getValue(self): return self.value class aNumber(Parametro): def __init__(self, doc="", defaults2=None, rendering = str, float_also = False, nmin=-1.0e38, nmax=1.0e38 , initial_value = None): self.rendering = rendering self.defaults2 = defaults2 self.doc = doc self.value = initial_value self.float_also = float_also self.nmin = nmin self.nmax = nmax def getatypelikethis(self): return "1" def isReady(self): return self.isValid() def isValid__(self): if self.float_also : return RepresentsInt(self.value) or RepresentsFloat(self.value) else : return RepresentsInt(self.value) def isValid(self): if not self.isValid__(): return 0 val = self.getValue() return (val>=self.nmin and val<=self.nmax) def getValue(self): if RepresentsInt(self.value): return int(self.value) else: return float(self.value) class aNumberFloat(Parametro): def __init__(self, doc="", defaults2=None, rendering = str , initial_value = None): self.rendering = rendering self.defaults2 = defaults2 self.doc = doc self.value = initial_value def getatypelikethis(self): return "1.0" def isReady(self): return self.isValid() def isValid(self ): return RepresentsFloat(self.value) def getValue(self): return float(self.value) def intervals2sequence(scan_interval): # print " scan_interval " , scan_interval todo_list = [] ninterval = len(scan_interval)//2 for i in range(ninterval): todo_list = todo_list + list(range( int(scan_interval[2*i]) , scan_interval[2*i+1])) return todo_list class many_Number(Parametro): def __init__(self, doc="" , nmin=1, nmax=100, defaults2=None, rendering = str, isinterval = 0, float_also=0, initial_value = None): self.rendering = rendering self.defaults2 = defaults2 self.doc = doc self.value = initial_value self.nmin = nmin self.nmax = nmax self.float_also = float_also self.isinterval=isinterval def getatypelikethis(self): return "[1,2]" def isReady(self): return self.isValid() def getValue(self): test=None try: ss="test="+self.value if Wizard_safe_module.python_code_is_safe(ss): resdic = {"math":math,"numpy":np,"np":np,"pylab":pylab} exec(ss,{"math":math,"numpy":np,"np":np,"pylab":pylab},resdic) test = resdic["test"] else: print( "cowardly refusing to execude suspicious code :", ss) except: pass return test def isValid(self): test = self.getValue() if not isinstance(test,list): return 0 n=len(test) if nself.nmax: return 0 good = 1 for it,t in enumerate(test): if not ( isinstance(t, int) or ( self.isinterval and (int(t)==t) and it%2==0 ) or (self.float_also and isinstance(t, float) ) ): good=0 if good and self.isinterval: if ( n%2!=0): good = 0 return good def makeintervalfromfirst(value): value = reduce_to_value(value) test=None try: ss="test="+value if Wizard_safe_module.python_code_is_safe(ss): resdic={} exec(ss,{"math":math,"numpy":np,"np":np,"pylab":pylab},resdic) test = resdic["test"] val = test[0] else: print( "cowardly refusing to execude suspicious code :", ss) return "unsafe code : "+value return "[%d,%d]"%(val,val+1) except: print( " failed in makeintervalfromfirst with value = %s"% value) return value+" <<== PROBLEM " class many_Number4SpecScan(many_Number): def __init__(self, specfile, doc="" , nmin=1, nmax=100, defaults2=None, rendering = str,isinterval=0, initial_value = None): self.rendering = rendering self.defaults2 = defaults2 assert( isinstance(specfile, FilePath) ) self.doc = doc self.value = initial_value self.nmin = nmin self.nmax = nmax self.specfile=specfile self.isinterval=isinterval def isReady(self): specfile_name =self.specfile.value if not ( self.isValid() and self.specfile.isReady( ) ): return 0 test = self.getValue() if self.isinterval: test=intervals2sequence(test) good = 1 # print test for t in test[:4]+test[-4:] : if not specScan_exists(specfile_name , t ): good = 0 return good class aNumber4SpecScan(aNumber): def __init__(self, specfile, doc="", defaults2=None , rendering = str, float_also=0, nmin=-1.0e38, nmax=1.0e38, initial_value = None): self.nmin = nmin self.nmax = nmax self.float_also = float_also self.rendering = rendering self.defaults2 = defaults2 assert( isinstance(specfile, FilePath) ) self.doc = doc self.value = initial_value self.specfile=specfile def checkMyWarning(self): specfile_name =self.specfile.getValue() warning="" if not self.specfile.isReady( ): if DEBUG : print( "\nWARNING : cannot open file ", specfile_name) warning += "\nWARNING : cannot open file " self.mywarning = warning return if not self.isValid(): if DEBUG :print( "\nWARNING : not a valid number") warning += "\nWARNING : not a valid number" # sf = SpecIO.Specfile( specfile_name ) if not self.isReady(): if DEBUG :print( "\nWARNING : Scan Number is not in specfile") warning += "\nWARNING : Scan Number is not in specfile" sf_mem = get_Specfile_Cached( specfile_name ) # print " sf_mem " , sf_mem if sf_mem is not None: sf = sf_mem["sf"] slist = sf_mem["scanlist"] warning += "\nNOTE : scans range from %d to %d\n" %(min(slist), max(slist)) if DEBUG : print( warning) self.mywarning = warning def isReady(self): specfile_name =self.specfile.getValue() return self.isValid() and self.specfile.isReady( ) and specScan_exists(specfile_name , self.getValue() ) def DicRecursiveSearch(dico, key ): if key in dico: return dico[key] else: for k in dico.keys(): d=dico[k] if isinstance(d,dict): res = DicRecursiveSearch(d, key ) if res is not None: return res return None def aggiorna_dics(reloaded , defined): for key, t in defined.items(): if not ( key in reloaded): reloaded[key]=t else: r = reloaded[key] if isinstance(t,dict): assert( isinstance(r ,dict)) aggiorna_dics(r , t) elif not isinstance(t, Parametro) : reloaded[key]=t class widget_base: def show_w(self): self.activity=1 self.show() def hide_w(self): self.activity=0 self.hide() def show(self): if self.activity==1: # print " SHOW ", self super(multichoices_widget, self).show() def hide(self): if self.activity==0: # print " HIDE ", self super(multichoices_widget, self).hide() def post_initialisation(self): if self.par.master: self.par.always_visible = True if self.par.master: # print " METTO BORDO OOOOOOOOOOOOOOOOO " self.label.setStyleSheet("QLabel { border: 5px solid red ; }") # self.label.setStyleSheet("QLineEdit { background: rgb(200, 255, 200); selection-background-color: rgb(233, 99, 0); }") def textChangedFilter(self, nimportequoi): self.textChanged() class multichoices_widget(Qt.QWidget, widget_base): def __getstate__(self): return {} def __init__(self, parent, name , par, dicodic, map_par2w= {} ): self.map_par2w = map_par2w self.dicodic = dicodic # Qt.QDialog.__init__(self, parent) #Qt.QWidget.__init__(self, parent) super(multichoices_widget,self).__init__(parent) Qt.loadUi( os.path.join(installation_dir ,"resources" , my_relativ_path, "multichoices_widget.ui" ), self) self.name = name self.par = par self.label.setText(name) self.comboBox.insertItems( 0, par.choices ) self.comboBox.connect(self.comboBox, QtCore.SIGNAL("activated(int)"), self.textChangedFilter) extraActions = {} self.comboBox. contextMenuEvent = Functor_contextMenuEvent(par, dicodic,self.comboBox , extraActions = extraActions ) self.activity = 1 if not hasattr(self.par,"visibility_depends_on"): self.par.visibility_depends_on = {} self.post_initialisation() def init_delayed(self): self.take_par_value( par) def take_par_value(self, par, no_automatic = 0 ): if self.par.choices_functor is not None: newchoices = self.par.choices_functor() if newchoices != self.par.choices: self.par.choices = newchoices self.comboBox.clear() self.comboBox.insertItems( 0, self.par.choices) if str(self.comboBox.currentText()) != par.value: index = par.getIndex() self.comboBox.setCurrentIndex( index) self.par.value = par.value self.textChanged(no_automatic) def textChanged(self, no_automatic = 0): s = str(self.comboBox.currentText()) self.par.value=s valid = self.par.isValid() if valid: self.validentry_checkbox.setChecked(True) else: self.validentry_checkbox.setChecked(False) self.readyentry_checkbox.show() valid = self.par.isReady() if valid: self.readyentry_checkbox.setChecked(True) else: self.readyentry_checkbox.setChecked(False) self.readyentry_checkbox.show() value = self.par.value dico = self.par.visibility_depends_on for key, deps in dico.items(): for d in deps: if value in key: if self.isVisible(): self.map_par2w[d].show_w() else: self.map_par2w[d].hide_w() # if self.par.automatic_forward and not no_automatic : # self.lineEdit. contextMenuEvent.propagateForward(self.par) def dum(): print (" gestate qlineedit") return {} class filepath_widget(Qt.QWidget, widget_base): def __getstate__(self): return {} def __init__(self, parent, name , par, dicodic, map_par2w={}): self.map_par2w = map_par2w self.dicodic = dicodic #Qt.QWidget.__init__(self, parent) super(filepath_widget,self).__init__(parent) # Qt.QDialog.__init__(self, parent) Qt.loadUi( os.path.join(installation_dir ,"resources" , my_relativ_path, "filepath_widget.ui" ), self) self.name = name self.par = par self.lineEdit.__getstate__ = dum self.label.setText(name) # self.browseButton.connect(self.browseButton, QtCore.SIGNAL("clicked()"), self.browse) # self.pymcaviewButton.connect(self.pymcaviewButton, QtCore.SIGNAL("clicked()"), self.pymcaview) self.lineEdit.connect(self.lineEdit, QtCore.SIGNAL("textChanged(const QString &)"), self.textChangedFilter) extraActions = collections.OrderedDict([("Browse",self.browse),("pymcaview" ,self.pymcaview)] ) self.lineEdit. contextMenuEvent = Functor_contextMenuEvent(par, dicodic,self.lineEdit , extraActions = extraActions ) self.activity = 1 self.post_initialisation() def init_delayed(self): self.take_par_value( par) def take_par_value(self, par, no_automatic = 0 ): if str(self.lineEdit.text()) != par.value: self.lineEdit.setText(par.value ) self.par.value = par.value self.textChanged(no_automatic) def textChanged(self, no_automatic=0): diff = False if self.par.enable == False: s = self.par.value self.lineEdit.setText(s) #return else: s = str(self.lineEdit.text()) diff = (s!= self.par.value) self.par.value=s valid = self.par.isValid() if valid: self.validentry_checkbox.setChecked(True) else: self.validentry_checkbox.setChecked(False) self.readyentry_checkbox.show() valid = self.par.isReady() if valid: self.readyentry_checkbox.setChecked(True) else: self.readyentry_checkbox.setChecked(False) self.readyentry_checkbox.show() if self.par.automatic_forward and not no_automatic : if diff : self.lineEdit. contextMenuEvent.automatic_propagateForward(self.par) def pymcaview(self): filename = self.lineEdit.text() os.system("pymca -f %s &"%filename) def browse(self): if self.par.fileme==1: filename = self.lineEdit.text() if len(filename): if self.par.isadir : filename = Qt.QFileDialog.getExistingDirectory(None, "select" , filename) else: filename = Qt.QFileDialog.getOpenFileName(None, "select", filename) else: if self.par.isadir : if DEBUG : print( " SETTO DirectoryOnly " ) filename = Qt.QFileDialog.getExistingDirectory(None, "select" ) else: filename = Qt.QFileDialog.getOpenFileName(None, "select", ) elif self.par.fileme==0: if self.par.isadir : filename = Qt.QFileDialog.getExistingDirectory(None, "select" ) else: filename = Qt.QFileDialog.getSaveFileName() elif self.par.fileme==0.5: if self.par.isadir : filename = Qt.QFileDialog.getExistingDirectory(None, "select" ) else: filename = dialog.getSaveFileName() if isinstance(filename, tuple): filename = filename[0] filename=str(filename) if len(filename): self.lineEdit.setText(filename) def check_for_deps( papar, f): nm = [a for a in dir(f) if not a.startswith('__')] tl = [ getattr(f,a ) for a in nm ] return papar in tl class Functor_contextMenuEvent: def __init__(self, par, dicodic, ql, extraActions): self.par = par self.dicodic = dicodic self.ql=ql self.extraActions = extraActions def automatic_propagateForward(self, papar=None): if not papar.isValid(): return if papar.automatic_forward==1: self.propagateForward( papar) elif papar.automatic_forward==2: self.propagateForwardRecursive( ) def propagateForward(self, papar=None): if DEBUG : print( " propagateForward ", papar) timerS , global_dico = self.dicodic["refresher_stuff"] changed=[] if papar is None: papar = self.par for nn,tt in global_dico.items(): if nn == "CREATION_PARS": continue if isinstance(tt,collections.OrderedDict): stack=[iter(tt.items())] # for n,t in tt.items(): while(len(stack)): try: name, t = next(stack[-1]) if isinstance(t, collections.OrderedDict): stack.append( list(t.items()) ) except StopIteration: stack = stack[:-1] if isinstance(t,Parametro): if hasattr(t,"defaults2"): if t.defaults2 is not None: aa,f = t.defaults2 if papar is aa or ( isinstance(aa,(list,tuple)) and papar in aa) or papar in t.depends_from() or check_for_deps(papar , f): t. pump_default() changed.append(t) timerPila[0]+=1 timerS.start() return changed def propagateForwardRecursive(self): changed_all=[] changed = self.propagateForward() while( len(set(changed)-set(changed_all)) ): changed_new = [] for t in (set(changed)-set(changed_all)): changed_new.extend ( self.propagateForward(t) ) changed_all.extend(changed ) changed = changed_new def __call__(self, event): if not hasattr(self.ql,"createStandardContextMenu"): self.mem=[] testAction= Qt.QAction("Propagate Forward",None) self.ql.addAction(testAction); self.ql.setContextMenuPolicy(QtCore.Qt.ActionsContextMenu ) testAction.connect(testAction, Qt.SIGNAL("triggered()"), self.propagateForward) self.mem.append(testAction) testAction= Qt.QAction("Propagate Forward Recursively",None) self.ql.addAction(testAction); self.ql.setContextMenuPolicy(QtCore.Qt.ActionsContextMenu ) testAction.connect(testAction, Qt.SIGNAL("triggered()"), self.propagateForwardRecursive) self.mem.append(testAction) else: menu = self.ql.createStandardContextMenu() self.mem=[] menu.insertSeparator(menu.actions()[0]) testAction = Qt.QAction("Propagate Forward",None); menu.insertAction(menu.actions()[0], testAction) testAction.connect(testAction, Qt.SIGNAL("triggered()"), self.propagateForward) self.mem.append(testAction) testAction = Qt.QAction("Propagate Forward Recursively",None); menu.insertAction(menu.actions()[0], testAction) testAction.connect(testAction, Qt.SIGNAL("triggered()"), self.propagateForwardRecursive) self.mem.append(testAction) menu.insertSeparator(menu.actions()[0]) #menu.insertSeparator(menu.actions()[0]) for na, aa in list(self.extraActions.items())[::-1]: testAction = Qt.QAction( na ,None); self.mem.append(testAction) menu.insertAction( menu.actions()[0] , testAction) testAction.connect(testAction, Qt.SIGNAL("triggered()"), aa ) menu.exec_(event.globalPos()); del menu class hdf5filepath_widget(Qt.QWidget, widget_base): def __getstate__(self): return {} def __init__(self, parent, name , par, dicodic, map_par2w={}): self.map_par2w = map_par2w self.dicodic = dicodic super(hdf5filepath_widget,self).__init__(parent) # Qt.QWidget.__init__(self, parent) # Qt.QDialog.__init__(self, parent) Qt.loadUi( os.path.join( installation_dir ,"resources" , my_relativ_path, "hdf5filepath_widget.ui" ), self) self.name = name self.par = par self.label.setText(name) # self.browseButton.connect(self.browseButton, QtCore.SIGNAL("clicked()"), self.browse) # self.pymcaviewButton.connect(self.pymcaviewButton, QtCore.SIGNAL("clicked()"), self.pymcaview) # self.hdfviewButtaddAction(tr("My Menu Item")); # //... # menu->exec(event->globalPos()); # delete menu; # } # self.lineEdit.setFrameStyle(Qt.QFrame.Panel | Qt.QFrame.Sunken); self.activity = 1 def init_delayed(self): self.take_par_value( par) def take_par_value(self, par, no_automatic = 0): if str(self.lineEdit.text()) != par.value: self.lineEdit.setText(par.value ) self.par.value = par.value self.textChanged(no_automatic) def pymcaview(self): filename = str(self.lineEdit.text()) filename, groupname = split_hdf5_address(filename) os.system("pymca -f %s &"%filename) def hdfview(self): filename = str(self.lineEdit.text()) filename, groupname = split_hdf5_address(filename) os.system("hdfview %s &"%filename) def browse(self): filename = str(self.lineEdit.text()) fn, dg = hdf5_filedialog(filename, self.par.fileme) if fn is not None: filename=str(fn) if dg is not None: filename = filename +":"+str(dg) self.lineEdit.setText(filename) @staticmethod def myaction(fn, dg, varname="grabbed"): global MainWindow filename=str(fn) if dg is not None: groupname = str(dg) h5 = h5py.File(filename,"r") h5group = h5[groupname] runit, oggetto = Wizard_safe_module.make_obj( h5group ,h5, groupname ) if runit is not None: runit(oggetto) update = {varname:oggetto} h5.close() if runit is None: if MainWindow.console is None: msgBox = Qt.QMessageBox(None) msgBox.setText("Launch a Console First") msgBox.setInformativeText("You can launche the console from the mainwindow menu") msgBox.setStandardButtons( Qt. QMessageBox.Ok) msgBox.setDefaultButton( Qt.QMessageBox.Ok) ret = msgBox.exec_() return MainWindow.console.updateNamespace(update) MainWindow.console.showMessage("# %s is now in namespace "% list(update.keys())[0]) def toConsole(self): filename = str(self.lineEdit.text()) fn, dg = hdf5_filedialog(filename, 1, self.myaction, modal=1) if fn is not None: self.myaction(fn, dg) def textChanged(self, no_automatic=0): diff = False if self.par.enable == False: s = self.par.value self.lineEdit.setText(s) # return else: s = str(self.lineEdit.text()) diff = (s!= self.par.value) self.par.value=s valid = self.par.isValid() if valid: self.validentry_checkbox.setChecked(True) else: self.validentry_checkbox.setChecked(False) self.readyentry_checkbox.show() valid = self.par.isReady() if valid: self.readyentry_checkbox.setChecked(True) else: self.readyentry_checkbox.setChecked(False) self.readyentry_checkbox.show() if self.par.fileexists() and self.par.hdf5path_exists(): warning = "\n STATUS : file plus groupname point to an existing datagroup" else: if self.par.fileexists() : warning = "\n STATUS : file exists but groupname dont" else: warning = "\n STATUS : file does not exists" self.lineEdit.setToolTip( self.par.doc+warning ) if self.par.isresult: if self.par.fileexists() and self.par.hdf5path_exists(): self.lineEdit.setStyleSheet("QLineEdit { background: rgb(200, 255, 200); selection-background-color: rgb(233, 99, 0); }") else: self.lineEdit.setStyleSheet("QLineEdit { background: rgb(255, 200, 200); selection-background-color: rgb(233, 99, 0); }") # if valid==1 or (not self.par.fileexists()) : # self.lineEdit.setStyleSheet("QLineEdit { background: rgb(255, 200, 200); selection-background-color: rgb(233, 99, 0); }") # if self.par.fileexists(): # warning = "\n STATUS : file seems to exists and groupname seems to be free for writing over" # else: # warning = "\n STATUS : filename seems to be new" # elif ( self.par.fileexists()==1 ): # self.lineEdit.setStyleSheet("QLineEdit { background: rgb(200, 255, 200); selection-background-color: rgb(233, 99, 0); }") # warning = "\n STATUS : file plus groupname point to an existing datagroup" if self.par.automatic_forward and not no_automatic : if diff : self.lineEdit. contextMenuEvent.automatic_propagateForward(self.par) # lineEdit validentry_checkBox readyentry_checkBox browseButton pymcaviewButton class text_widget(Qt.QWidget, widget_base): def __getstate__(self): return {} def __getstate__(self): return {} def __init__(self, parent, name , par, dicodic , map_par2w={}): self.map_par2w = map_par2w self.dicodic = dicodic super(text_widget,self).__init__(parent) # Qt.QWidget.__init__(self, parent) # Qt.QDialog.__init__(self, parent) Qt.loadUi( os.path.join( installation_dir ,"resources" , my_relativ_path, "text_widget.ui" ), self) self.name = name self.par = par self.label.setText(name) self.lineEdit.connect(self.lineEdit, QtCore.SIGNAL("textChanged(const QString &)"), self.textChangedFilter) extraActions = collections.OrderedDict( ) self.lineEdit. contextMenuEvent = Functor_contextMenuEvent(par, dicodic,self.lineEdit , extraActions = extraActions ) self.activity = 1 self.post_initialisation() def init_delayed(self): self.take_par_value( par) def take_par_value(self, par, no_automatic = 0): if str(self.lineEdit.text()) != par.value: self.lineEdit.setText(par.value ) self.par.value = par.value self.textChanged(no_automatic) def textChanged(self, no_automatic=0): diff = False if self.par.enable == False: # print " NOT ENABLED " s = self.par.value self.lineEdit.setText(s) # return else: # print " IN TEXTCHANGED", str(self.lineEdit.text()), self.par.value, self.par.value.__class__, self.par.automatic_forward, no_automatic s = str(self.lineEdit.text()) diff = (s!= self.par.value) # print " diff " , diff self.par.value=s # if no_automatic not in [0,1,True, False]: # raise valid = self.par.isValid() if valid: self.validentry_checkbox.setChecked(True) else: self.validentry_checkbox.setChecked(False) self.readyentry_checkbox.show() valid = self.par.isReady() if valid: self.readyentry_checkbox.setChecked(True) else: self.readyentry_checkbox.setChecked(False) self.readyentry_checkbox.show() if isinstance(self.par, hdf5_relative_path): if self.par.isresult: if(valid==-1 or not self.par.fileexists() ): self.lineEdit.setStyleSheet("QLineEdit { background: rgb(255, 200, 200); selection-background-color: rgb(233, 99, 0); }") warning = "\nSTATUS : root path seems to be already there and relative path seems to be free " else: if self.par.base_h5file.hdf5path_exists(): self.lineEdit.setStyleSheet("QLineEdit { background: rgb(200, 255, 200); selection-background-color: rgb(233, 99, 0); }") warning = "\nSTATUS : root path seems to be already there and relative path seems to be exist already " else: self.lineEdit.setStyleSheet("QLineEdit { background: rgb(255, 20, 20); selection-background-color: rgb(233, 99, 0); }") if self.par.base_h5file.fileexists(): warning = "\nSTATUS : relative path seems to be non writable : the file exists but the root path is not ready" else: warning = "\nSTATUS : relative path seems to be non writable : the file does not exist" self.lineEdit.setToolTip( self.par.doc+warning ) elif hasattr(self.par, "checkMyWarning"): self.par.checkMyWarning() # print " MYWARNING " , self.par.mywarning if self.par.mywarning!="": # print self.par.doc+self.par.mywarning self.lineEdit.setToolTip( self.par.doc+self.par.mywarning ) if self.par.automatic_forward and not no_automatic : # print " forse propagate ", self.par, diff if diff : self.lineEdit. contextMenuEvent.automatic_propagateForward(self.par) class creationtab(Qt.QWidget): def __getstate__(self): return {} def __init__(self, parent): self.parent=parent # Qt.QWidget.__init__(self, parent) super(creationtab,self).__init__(parent) # Qt.QDialog.__init__(self, parent) Qt.loadUi( os.path.join( installation_dir ,"resources" , my_relativ_path, "creationtab.ui" ), self) class Functor_Copy_Dict: def __init__(self,dic): self.dic = dic def __call__(self): global dico_copy_buffer dico_copy_buffer = self.dic def GetYamlDicoTraduc( depth, subdic ) : if depth==0: foo = subdic["getyaml"] if foo.__class__ == tuple: foo, traduc = foo dico={} for key in subdic: dico[ traduc.get(key,key) ] = subdic[key] else: dico = subdic yamltext = foo(dico) else: yamltext="" for c,dico in subdic.items(): if isinstance(dico, dict) and dico.has_key("getyaml"): yamltext=yamltext+ GetYamlDicoTraduc( 0, dico ) +"\n" return yamltext class Functor_Show_Yaml: def __init__(self, subdic, depth=0): self.subdic=subdic self.depth=depth def __call__(self): yamltext = GetYamlDicoTraduc( self.depth, self.subdic ) self.mle = Qt.QTextEdit(None) self.mle.setLineWrapMode(0) self.mle.setText( yamltext ) self.mle.show() class MyProcess(Process): def __init__(self, target,args, toimport): Process.__init__(self) self.toimport = toimport self.target = pickle.dumps( target) self.args = args def run(self): if DEBUG : print( 'run called...') if DEBUG : print( "importo ", self.toimport) module=imp.load_source(self.toimport[0], self.toimport[1]) self.target = pickle.loads(self.target) self.target(*self.args) # print 'running...', module.step() class Functor_Run_Yaml: Runthis=0 Stopthis=1 Viewthis=2 Viewthiserr=3 Center=4 Pendings=5 PlugOut=6 ident =0 def __init__(self, subdic, depth=0, action = 0,sub_functors_list=[] , identity = None ): self.plugmeout = ( action == self.PlugOut ) self.run_pendings = ( action == self.Pendings ) self.subdic=subdic self.depth=depth self.action = action self.sub_functors_list = sub_functors_list self.identity = identity self.subdic["multifunctors_isstopped"] = 0 if identity is not None: self.identity_number, self.identity_widget = identity ## self.process={"mp":None, "dir":None} def viewthis(self, stderr=0): if len(self.sub_functors_list): return shadok_movie, shadok_action, shadok_returncode, run_informations, tb = self.subdic["shadok_data"] process_directory = run_informations["process_directory"] if process_directory is None: self.identity_widget.shadok_message_signal.emit("No information about previous runs") return p, where = process_directory if stderr==0: text = open(os.path.join(where,"stdout.txt") ,"r").read() else: text = open(os.path.join(where,"stderr.txt") ,"r").read() if stderr==0: title = 'stdout at %s'%where else: title = 'stderr at %s'%where self.identity_widget.shadok_textshow_signal.emit( title , text ) def __call__(self): if self.action == self.Runthis: if len(self.sub_functors_list): t = threading.Thread(target=self.runthis, args=()) # threads.append(t) t.start() ## thread.start_new_thread( self.runthis, () ) else: self.runthis() elif self.action in [ self.PlugOut, self.Pendings ]: self.runthis() elif self.action == self.Center: self.doCenter() elif self.action == self.Stopthis: self.stopthis() elif self.action == self.Viewthis: self.viewthis(0) elif self.action == self.Viewthiserr: self.viewthis(1) def get_yaml(self): yamltext = GetYamlDicoTraduc( self.depth , self. subdic ) # if self.depth==0: # yamltext = self.subdic["getyaml"](self.subdic) # else: # yamltext="" # for c,dico in self.subdic.items(): # if isinstance(dico, dict) and dico.has_key("getyaml"): # yamltext=yamltext+dico["getyaml"](dico)+"\n" return yamltext def stopthis(self): if DEBUG : print( " IN STOP THIS ") if len(self.sub_functors_list): if DEBUG : print( " PER TUTTI ") self.subdic["multifunctors_isstopped"]=1 msgBox = Qt.QMessageBox(None) msgBox.setText("Do you really want to stop all processes?") msgBox.setInformativeText("Do you really want to stop all processes?") msgBox.setStandardButtons( Qt.QMessageBox.Cancel | Qt. QMessageBox.Ok) msgBox.setDefaultButton( Qt.QMessageBox.Cancel) ret = msgBox.exec_() if not ret: return for f in self.sub_functors_list: f.stopthis() return shadok_movie, shadok_action, shadok_returncode, run_informations, tb = self.subdic["shadok_data"] process_directory = run_informations["process_directory"] if process_directory is not None: p, where = process_directory if p is not None: os.kill(p.pid, signal.SIGTERM) # p.terminate() def isrunning(self): shadok_main_movie, shadok_main_action, shadok_returncode, run_informations, tb = self.subdic["shadok_data"] pd = run_informations["process_directory"] if pd is not None: p, where =pd if p is not None: return 1 return 0 def get_method_name(self, yamltext ): pos = yamltext.find(":") if pos !=-1: yamltext= yamltext[:pos] yamltext=yamltext.strip() return yamltext def runthis(self): global MainWindow if self.plugmeout: if DEBUG : print( " PLUGOUT") global MainWindow copia = copy.copy(self) copia.plugmeout=0 update = {"grabbed":copia} copia.sysout = sys.stdout MainWindow.console.updateNamespace(update) MainWindow.console.showMessage("The new Functor object is available in the namespace with name grabbed") return if self.run_pendings: shadok_main_movie, shadok_main_action, shadok_returncode, run_informations, tb = self.subdic["shadok_data"] sub_functors_list = run_informations["sub_functors_list"] else: sub_functors_list = self.sub_functors_list if len(sub_functors_list): self.subdic["multifunctors_isstopped"] = 0 for f in sub_functors_list: if self.run_pendings: if f.identity < self.identity: continue todo = 0 for k,t in f.subdic.items(): if isinstance(t,Parametro): if t.isresult: nameval = t.getValue() if t.isReady(): todo=1 if not todo : continue if DEBUG :print (" runno " , f.identity, f) ## print " runno " , f, len( self.sub_functors_list ) f.runthis() while(f.isrunning()): # print " is running " time.sleep(0.2) if self.subdic["multifunctors_isstopped"]: # self.subdic["multifunctors_isstopped"]=0 return if DEBUG :print( " RETURN " ) return yamltext = self.get_yaml() ## methodname = self.get_method_name(yamltext) if self.isrunning(): if methodname != "view_Volume_myavi" : self.identity_widget.shadok_message_signal.emit("This Job is already running %s"%methodname) return if hasattr(self,"sysout"): oldsysout = sys.stdout sys.stdout = self.sysout where = os.path.join("wizardRUNS", methodname) if not os.path.exists(where): os.makedirs( where) where = tempfile.mkdtemp(suffix='', prefix='tmp', dir=where) self.manager_context=Manager() ret_dico = self.manager_context.dict() self.ret_dico=ret_dico self.identity_widget.shadok_returncode_signal.emit(self.identity_number, 0 ) # p = Process(target=self.subdic["swissknife_runner"] , args=(yamltext,where, ret_dico)) timerS , global_dico = self.subdic["refresher_stuff"] p = MyProcess(self.subdic["swissknife_runner"] , (yamltext,where, ret_dico), global_dico[ "tobeimported" ] ) p.start() if hasattr(self,"sysout"): sys.stdout = oldsysout shadok_main_movie, shadok_main_action, shadok_returncode, run_informations, tb = self.subdic["shadok_data"] run_informations["process_directory"] = (p,where) t = threading.Thread(target=self.ProcessMonitor, args=()) # threads.append(t) t.start() # thread.start_new_thread( self.ProcessMonitor, () ) self.identity_widget.shadok_start_signal.emit(self.identity_number) def doCenter(self): if len(self.sub_functors_list): return self.identity_widget.shadok_center_signal.emit(self.identity_number) return def ProcessMonitor(self): shadok_movie, shadok_action, shadok_returncode, run_informations, tb = self.subdic["shadok_data"] timerS , global_dico = self.subdic["refresher_stuff"] p,where = run_informations["process_directory"] p.join() run_informations["process_directory"]=[None,where] if DEBUG :print( " JOINED ") if DEBUG :print (" STOPPO " , self.identity_number) self.identity_widget.shadok_stop_signal.emit(self.identity_number) return_code = self.ret_dico["return_code"] self.identity_widget.shadok_returncode_signal.emit(self.identity_number, return_code ) if DEBUG :print (" il codice di ritorno est ", return_code) if return_code==0: if not MainWindow.actionOption.isChecked(): if DEBUG :print (" cancello " , [self.ret_dico["input"], self.ret_dico["stdout"], self.ret_dico["stderr"]]) for filename in [self.ret_dico["input"], self.ret_dico["stdout"], self.ret_dico["stderr"]]: if DEBUG :print( filename) if os.path.exists(filename): os.remove(filename) ## lanciare una thread per join. thread fa anche refresh e stop shadok dopo join ## Metter anche in stop refresh e stop shadok. Guardare che se stop anche sia join # def sleeper(name, seconds): # print 'starting child process with id: ', os.getpid() # print 'parent process:', os.getppid() # print 'sleeping for %s ' % seconds # time.sleep(seconds) # print "Done sleeping" # if __name__ == '__main__': # print "in parent process (id %s)" % os.getpid() # p = Process(target=sleeper, args=('bob', 5)) # p.start() # print dir(p) # print p.is_alive() # print p.terminate.__doc__ # print p.terminate() # raw_input() # print p.is_alive() # raw_input() # print "in parent process after child process start" # print "parent process about to join child process" # p.join() # print p.is_alive() # print "in parent process after child process join" # print "parent process exiting with id ", os.getpid() # print "The parent's parent process:", os.getppid() class Functor_Run_Yaml_and_pendings: def __init__(self, subdic): self.subdic=subdic def __call__(self): self.mle = Qt.QTextEdit(None) self.mle.setText( str(self.subdic) ) self.mle.show() def put_shadok_in_toolbar(toolBar): global animation_file movie = Qt.QMovie(animation_file); processLabel = Qt.QLabel(toolBar) processLabel.setToolTip("Les Shadoks\n at work \n for you") processLabel.setMovie(movie) action = toolBar.addWidget(processLabel) movie.start() movie.stop() action.setVisible(False) processLabel = Qt.QLabel(toolBar) processLabel.setText("ERROR") # processLabel.setToolTip("No return code yet") returncode = toolBar.addWidget(processLabel) returncode.setVisible(False) return movie, action, (returncode, processLabel) class creationtab2(Qt.QWidget): shadok_start_signal = QtCore.pyqtSignal(int) shadok_stop_signal = QtCore.pyqtSignal(int) shadok_returncode_signal = QtCore.pyqtSignal(int,int) shadok_message_signal = QtCore.pyqtSignal(str) shadok_textshow_signal = QtCore.pyqtSignal(str,str) shadok_center_signal = QtCore.pyqtSignal(int) def __getstate__(self): return {} def __init__(self, parent, dico = None): self.parent=parent super(creationtab2,self).__init__(parent) # Qt.QWidget.__init__(self, parent) # Qt.QDialog.__init__(self, parent) Qt.loadUi( os.path.join( installation_dir ,"resources" , my_relativ_path, "creationtab2.ui" ), self) self.shadok_dictios=[] self.shadok_start_signal.connect(self.start_shadok) self.shadok_center_signal.connect(self.center_shadok) self.shadok_stop_signal.connect (self.stop_shadok ) self.shadok_returncode_signal.connect (self.returncode_shadok ) self.shadok_message_signal.connect (self.message_shadok ) self.shadok_textshow_signal.connect (self.textshow_shadok ) if dico is None: return def textshow_shadok(self,title, message): self.mle = Qt.QTextEdit(None) self.mle.setLineWrapMode(0) self.mle.setText( message ) self.mle.setWindowTitle(title) self.mle.show() def message_shadok(self, message): msgBox = Qt.QMessageBox(None) msgBox.setText(message) msgBox.setInformativeText(message) msgBox.setStandardButtons( Qt. QMessageBox.Ok) msgBox.setDefaultButton( Qt.QMessageBox.Ok) ret = msgBox.exec_() def returncode_shadok(self, identity,returncode): if DEBUG :print( "returncode SHADOK" , returncode) subdic = self.shadok_dictios[identity] for sd in ["shadok_data","shadok_data_bis" ] : shadok_movie, shadok_action, (shadok_returncode, labello) = subdic[sd][:3] timerS , global_dico = subdic["refresher_stuff"] if returncode: labello.setText("!!! attention : ERROR : %d "% returncode) shadok_returncode.setVisible(True) else: shadok_returncode.setVisible(False) def stop_shadok(self, identity): if DEBUG :print( "STOP SHADOK" , identity) subdic = self.shadok_dictios[identity] shadok_movie, shadok_action, shadok_returncode, run_informations, tb = subdic["shadok_data"] timerS , global_dico = subdic["refresher_stuff"] timerS.start() shadok_movie.stop() shadok_action.setVisible(False) shadok_movie, shadok_action, shadok_returncode, tb = subdic["shadok_data_bis"] shadok_movie.stop() shadok_action.setVisible(False) def center_shadok(self, identity): if DEBUG :print( "CENTER SHADOK" , identity) subdic = self.shadok_dictios[identity] shadok_movie, shadok_action, shadok_returncode, run_informations, tb = subdic["shadok_data"] self.scrollArea.ensureWidgetVisible(tb) def start_shadok(self, identity): if DEBUG :print( "START SHADOK" , identity) subdic = self.shadok_dictios[identity] shadok_movie, shadok_action, shadok_returncode, run_informations, tb = subdic["shadok_data"] timerS , global_dico = subdic["refresher_stuff"] shadok_movie.start() shadok_action.setVisible(True) shadok_movie, shadok_action, shadok_returncode, tb = subdic["shadok_data_bis"] shadok_movie.start() shadok_action.setVisible(True) def run_everything(self): if DEBUG :print( " run everything ") def refresh_everything(self): self.timerS.start() def refresh(self): if self.visibleRegion().isEmpty(): return # print " NO REFRESH non est visibile " else: pass # print " REFRESH " dicodic = self.dico for edw in self.edws: if edw.par.value is None: edw.par.value = "" if type(edw.par.value) != type(""): if True or DEBUG : print( " in refresh normale " , edw.par.value) edw.par.value="problem" else: edw.take_par_value(edw.par, no_automatic=0 ) def refresh_from_forward(self): dicodic = self.dico for edw in self.edws: if type(edw.par.value) != type(""): if True or DEBUG : print( " in refresh forward ", edw.par.value) edw.par.value="problem" else: edw.take_par_value(edw.par, no_automatic=1 ) def save_parameters(self): filename = (Qt.QFileDialog.getSaveFileName(None, "select")) if isinstance(filename, tuple): filename = filename[0] filename = str(filename) if len(filename ): f=open(filename, "wb" ) s = pickle.dumps( self.dico) #s = s.replace( "XRStools"+version+".", "XRStools." ) f.write(s) # pickle.dump( self.dico, f ) f.close() def toggle_deps_visibility(self): if self.deps_vis_toggle_status: for w in self.w2toggle: w.hide_w() else: for w in self.w2toggle: w.show_w() self.deps_vis_toggle_status = 1- self.deps_vis_toggle_status def populate(self, dico, guided=False): self.guided=guided self.timer = Qt.QTimer() self.timerS = Qt.QTimer() self.dico=dico menu = Qt.QMenu() gAction = Qt.QAction("Run All", self); menu.addAction(gAction) gAction_stop = Qt.QAction("Stop All", self); menu.addAction(gAction_stop) gAction2 = Qt.QAction("Refresh", self); menu.addAction(gAction2) gAction3 = Qt.QAction("Display Yaml", self); menu.addAction(gAction3) gAction4 = Qt.QAction("Copy the dictionary", self); menu.addAction(gAction4) if guided: gAction_deps = Qt.QAction("Show/Hide depending parameters", self); menu.addAction(gAction_deps) self.w2toggle=[] self.deps_vis_toggle_status=0 toolButton = Qt.QToolButton() toolButton.setMenu(menu) toolButton.setPopupMode(Qt.QToolButton.InstantPopup) toolButton.setToolButtonStyle( 2) ## 1 per ToolButtonTextOnly toolButton.setText( "Global Actions" ) toolButton.setIcon( Qt.QIcon(Qt.QPixmap(bacchetta32_xpm)) ) toolBar = Qt.QToolBar() toolBar.addWidget(toolButton) but = Qt.QToolBar() toolBar.addWidget(but) shadok_main_movie, shadok_main_action, shadok_returncode = put_shadok_in_toolbar(toolBar ) self.verticalLayout_3.addWidget(toolBar ) run_informations = {"ALL_dic": self.dico, "process_directory":None} self.dico["shadok_data"] = [shadok_main_movie, shadok_main_action, shadok_returncode, run_informations, None ] identity = (len(self.shadok_dictios), self) self.shadok_dictios.append(self.dico) sub_functors_list = [] gAction .connect(gAction , Qt.SIGNAL("triggered()"), Functor_Run_Yaml(self.dico , depth=1, sub_functors_list = sub_functors_list, action = Functor_Run_Yaml.Runthis ,identity=identity ) ) gAction_stop .connect(gAction_stop , Qt.SIGNAL("triggered()"), Functor_Run_Yaml(self.dico , depth=1, sub_functors_list = sub_functors_list, action = Functor_Run_Yaml.Stopthis,identity=identity ) ) gAction2.connect(gAction2, Qt.SIGNAL("triggered()"), self.refresh_everything ) gAction3.connect(gAction3, Qt.SIGNAL("triggered()"), Functor_Show_Yaml( self.dico, depth=1)) gAction4.connect(gAction4, Qt.SIGNAL("triggered()"), Functor_Copy_Dict( self.dico)) if self.guided: gAction_deps.connect(gAction_deps, Qt.SIGNAL("triggered()"), self.toggle_deps_visibility ) chiavi = list(self.dico.keys())[1:] if DEBUG :print( " CHIAVI ", chiavi) self.edws=[] for c in chiavi : if DEBUG :print( " CONSIDERO ", c) subdic = self.dico[c] if not isinstance(subdic, dict): continue ####################################### identity = (len(self.shadok_dictios), self) for frame in range(2): menu = Qt.QMenu() testAction_center = Qt.QAction("Center", self); menu.addAction(testAction_center) testAction = Qt.QAction("display yaml inputs", self); menu.addAction(testAction) testAction2 = Qt.QAction("Run this", self); menu.addAction(testAction2) testAction3 = Qt.QAction("Stop this", self); menu.addAction(testAction3) testAction4 = Qt.QAction("View StdOutput", self); menu.addAction(testAction4) testAction5 = Qt.QAction("View StdErr", self); menu.addAction(testAction5) # testAction6 = Qt.QAction("Run this plus pending ones", self); # menu.addAction(testAction6) testAction7 = Qt.QAction("Plug out to Console", self); menu.addAction(testAction7) # toolButton = Qt.QToolButton() this = self class MyQToolButton(Qt.QToolButton): mywidget = self mysubdic = subdic # def mouseMoveEvent(self, event): # self.mywidget.scrollArea.ensureWidgetVisible(self.mysubdic["shadok_data"][-1] ) toolButton = MyQToolButton() toolButton.setMenu(menu) toolButton.setPopupMode(Qt.QToolButton.InstantPopup) toolButton.setToolButtonStyle( 2) ## 1 per ToolButtonTextOnly toolButton.setText( c ) toolButton.setIcon( Qt.QIcon(Qt.QPixmap(bacchetta32_xpm)) ) toolButton.setToolTip( subdic["HELP"] ) toolBar = Qt.QToolBar(self) toolBar.addWidget(toolButton) if frame : toolButton.setMouseTracking(True) if frame==0: self.verticalLayout_5.addWidget( toolBar ) shadok_movie, shadok_action, shadok_returncode = put_shadok_in_toolbar(toolBar ) subdic["shadok_data"] = [shadok_movie, shadok_action, shadok_returncode, run_informations, toolBar ] else: self.verticalLayout_50.addWidget( toolBar ) shadok_movie, shadok_action, shadok_returncode = put_shadok_in_toolbar(toolBar ) subdic["shadok_data_bis"] = [shadok_movie, shadok_action, shadok_returncode ,toolBar] testAction.connect(testAction, Qt.SIGNAL("triggered()"), Functor_Show_Yaml(subdic )) testAction.connect(testAction2, Qt.SIGNAL("triggered()"), Functor_Run_Yaml(subdic , action = Functor_Run_Yaml.Runthis ,identity=identity )) testAction.connect(testAction3, Qt.SIGNAL("triggered()"), Functor_Run_Yaml(subdic , action = Functor_Run_Yaml.Stopthis ,identity=identity )) testAction.connect(testAction4, Qt.SIGNAL("triggered()"), Functor_Run_Yaml(subdic , action = Functor_Run_Yaml.Viewthis ,identity=identity )) testAction.connect(testAction5, Qt.SIGNAL("triggered()"), Functor_Run_Yaml(subdic , action = Functor_Run_Yaml.Viewthiserr ,identity=identity )) testAction.connect(testAction_center, Qt.SIGNAL("triggered()"), Functor_Run_Yaml(subdic , action = Functor_Run_Yaml.Center ,identity=identity )) # testAction.connect(testAction6, Qt.SIGNAL("triggered()"), Functor_Run_Yaml(subdic , action = Functor_Run_Yaml.Pendings ,identity=identity )) testAction.connect(testAction7, Qt.SIGNAL("triggered()"), Functor_Run_Yaml(subdic , action = Functor_Run_Yaml.PlugOut ,identity=identity )) run_informations = {"ALL_dic": self.dico, "process_directory":None, "sub_functors_list":sub_functors_list} self.shadok_dictios.append(subdic) sub_functors_list.append(Functor_Run_Yaml(subdic , action = Functor_Run_Yaml.Runthis ,identity=identity )) #################################### subdic["refresher_stuff"] = [ self.timerS , self.dico ] lista = [] class ToggleVisibility(Qt.QToolButton ): ##Qt.QPushButton): def __init__(self, lista, nome, parent): self.lista = lista self.visible = 1 # super(ToggleVisibility,self).__init__(nome, parent) super(ToggleVisibility,self).__init__() self.setToolButtonStyle( 1) self.setText( nome ) menu = Qt.QMenu() testAction_toggle_visibility = Qt.QAction("toggle visibility", self); testAction.connect(testAction_toggle_visibility, Qt.SIGNAL("triggered()"), self.toggle ) menu.addAction(testAction_toggle_visibility) self.setMenu(menu) self.setPopupMode(Qt.QToolButton.InstantPopup) # butt.setToolButtonStyle(Qt::ToolButtonTextUnderIcon); def toggle(self): if self.visible: self.visible=0 for w in self.lista: w.hide() else: self.visible=1 for w in self.lista: w.show() # tbutton = ToggleVisibility(lista, "subparameters",self ) # menu = Qt.QMenu() # testAction_toggle_visibility = Qt.QAction("toggle visibility", tbutton); # testAction.connect(testAction_toggle_visibility, Qt.SIGNAL("triggered()"), tbutton.toggle ) # menu.addAction(testAction_toggle_visibility) # tbutton.setMenu(menu) # tbutton.setPopupMode(Qt.QToolButton.InstantPopup) # toolBar = Qt.QToolBar(self) # toolBar.addWidget(tbutton) # self.verticalLayout_5.addWidget(toolBar) map_par2w = collections.OrderedDict () lista = None stack=[iter(subdic.items())] # for name,par in subdic.items(): counttbutton=0 while( len(stack) ): try: name, par = next(stack[-1]) if isinstance(par, collections.OrderedDict): stack.append( iter(par.items()) ) else: if guided and isinstance(par, Parametro): if par.master: par.enable = True par.automatic_forward = 2 # par.guiding = True else: par.enable = False except StopIteration: stack = stack[:-1] if(len(stack)): if counttbutton : tbutton.toggle() counttbutton+=1 continue if isinstance(par, collections.OrderedDict): lista = [] tbutton = ToggleVisibility(lista, name ,self ) toolBar = Qt.QToolBar(self) toolBar.addWidget(tbutton) self.verticalLayout_5.addWidget(toolBar) else: if not isinstance(par, Parametro): continue if DEBUG :print( " >>>>>>>>>> " , par.tipo, edition_widgets[par.tipo], par.doc) nw = edition_widgets[par.tipo](self, name, par , subdic ,map_par2w= map_par2w ) if self.guided: if not nw.par.always_visible: self.w2toggle.append( nw) nw.hide_w() # lista.append(nw) self.edws.append(nw) nw.setToolTip( par.doc ) self.verticalLayout_5.addWidget(nw) map_par2w[par ] = nw if lista is not None: lista.append(nw) for par,nw in map_par2w.items(): if par.value is None: par.pump_default() if par.value is not None: nw.take_par_value(par) spacer = Qt.QSpacerItem(40, 20, Qt.QSizePolicy.Minimum, Qt.QSizePolicy.Expanding); self.verticalLayout_5.addSpacerItem(spacer); self.dico["refresher_stuff"] = [ self.timerS , self.dico ] self.connect( self.timer, QtCore.SIGNAL("timeout()"), self.refresh ); self.connect( self.timerS, QtCore.SIGNAL("timeout()"), self.refresh_from_forward ); self.timerS.setSingleShot(True) self.timer.start( 2000) self.refresh() class MyTree( QtGui.QTreeView): def __init__(self, *args): QtGui.QTreeView.__init__(self, *args) self.clicked[QtCore.QModelIndex].connect(self.itemClicked) self.doubleClicked[QtCore.QModelIndex].connect(self.itemDoubleClicked) def set_other(self, t): self.other=t def itemClicked(self, modelIndex): m = self.other.selectionModel() if m is not None: m.clearSelection() event ="itemClicked" if DEBUG :print( event, modelIndex) index = self.selectedIndexes()[0] if False and not( sys.platform =="win32"): index = self.model().mapToSource(index ) pippo = index.model().data(index) if DEBUG :print( pippo) pippo = index.model().filePath(index) print( pippo) pippo_name = os.path.basename(pippo) if pippo_name[-3:]==".py": pippo_name=pippo_name[:-3] foo = imp.load_source(pippo_name,pippo) parent = self.paparent for i in reversed(range(parent.scrolledGrid.count())): widgetToRemove = parent.scrolledGrid.itemAt( i ).widget() parent.scrolledGrid.removeWidget( widgetToRemove ) widgetToRemove.setParent( None ) if hasattr(foo, "OPTIONS"): options = foo.OPTIONS print( " LE OPZIONI SONO " , options) i=0 for chiave, (valore,aiuto) in options.items(): rb = QtGui.QCheckBox(chiave) rb.setChecked(valore) parent.scrolledGrid.addWidget( rb, i+1, 0,) rb.setToolTip( aiuto ) i=i+2 # parent.scrolledGrid.show()= def itemDoubleClicked(self, modelIndex): event ="itemDoubleClicked" if DEBUG :print( event, modelIndex) index = self.selectedIndexes()[0] if False and not( sys.platform =="win32"): index = self.model().mapToSource(index ) pippo = index.model().data(index) if DEBUG :print( pippo) pippo = index.model().filePath(index) if DEBUG : print( str(pippo)) pippo=str(pippo) if not( sys.platform =="win32"): os.system("emacs %s &"%pippo) else: comando = "emacs %s "%pippo child_process = subprocess.Popen(comando.split()) #os.system("emacs %s &"%pippo) class MethodsProxyModel(Qt.QAbstractProxyModel): def __init__(self, parent=None, model=None): Qt.QAbstractProxyModel.__init__(self, model) self.model=model def columnCount( self, index ): return 4 def rowCount( self, index ): sindex = self.mapToSource(index) res = self.model.rowCount(sindex) return res return 1 def setRoots(self, rootIndexes): self.rootIndexes = rootIndexes def mapToSource(self, proxyIndex): if DEBUG :print( " in map to source ") index = proxyIndex coords = [] while index != QtCore.QModelIndex(): coords.append( [ index.row(), index.column() ] ) index = index.parent() if DEBUG :print( index) coords.reverse() if len(coords)==0: if DEBUG :print( " RITORNO 0") return QtCore.QModelIndex() r,c = coords[0] if c>0 or r>= len(self.rootIndexes): if DEBUG :print( " RITORNO 0b") return QtCore.QModelIndex() index = self.rootIndexes[c] for r,c in coords[1:]: index = index.child(r,c) if DEBUG :print( " RITORNO ", index) return index def mapFromSource(self, sourceIndex): if DEBUG :print( " in map to proxy ") index = sourceIndex coords = [] while index not in [ None, QtCore.QModelIndex()] and index not in self.rootIndexes : coords.append( [ index.row(), index.column() ] ) index = index.parent() if index in [None, QtCore.QModelIndex()]: return QtCore.QModelIndex() r,c = self.rootIndexes.index(index) , 0 coords.append([r,c]) coords.reverse() index = QtCore.QModelIndex() for r,c in coords: index = index.child(r,c) return index def index( self,row, column, parent = QtCore.QModelIndex()): if (parent.isValid()): if parent==QtCore.QModelIndex(): r,c = row, column if c>0 or r>= len(self.rootIndexes): return QtCore.QModelIndex() index = self.rootIndexes[c] if DEBUG :print( " RITORNO UN O DEI PRIMI") return index if DEBUG :print( " DEVO MAPPAR " ) sourceParent = self.mapToSource(parent) return sourceParent.child(row,column) else: return QtCore.QModelIndex() class G_MethodsProxyModel(Qt.QSortFilterProxyModel): def __init__(self, parent=None, model=None, rootIndexes=[]): Qt.QSortFilterProxyModel.__init__(self, parent) self.model=model self.setSourceModel(model ) self.rootIndexes = rootIndexes # def columnCount( self, index ): # return 4 # def rowCount( self, index ): # sindex = self.mapToSource(index) # res = self.model.rowCount(sindex) # return res # return 1 def setRoots(self, rootIndexes): self.rootIndexes = rootIndexes path0 = rootIndexes[0] #proxyModel.setFilterRegExp(QRegExp(".png", Qt::CaseInsensitive, # QRegExp::FixedString)); def filterAcceptsRow(self , sourceRow, sourceParent): index0 = self.sourceModel().index(sourceRow, 0, sourceParent) filepath = index0.model().filePath(index0) accepted = False for root in self.rootIndexes: root = root.model().filePath(root) if False and sys.platform != "win32": if root[:len(filepath)] == filepath : if ((len(root)> len(filepath)) and (root[len(filepath)] not in ["/","\\"])) and filepath[-1] not in ["/","\\"] : pass else: accepted = True break if filepath[:len(root)] == root : if ((len(filepath)> len(root)) and (filepath[len(root)] not in ["/","\\"])) and root[-1] not in ["/","\\"]: pass else: accepted = True break else: if root[:len(filepath)].lower() == filepath.lower() : if ((len(root)> len(filepath)) and (root[len(filepath)] not in ["/","\\"])) and filepath[-1] not in ["/","\\"] : pass else: accepted = True break if filepath[:len(root)].lower() == root.lower() : if ((len(filepath)> len(root)) and (filepath[len(root)] not in ["/","\\"])) and root[-1] not in ["/","\\"]: pass else: accepted = True break return accepted # def mapToSource(self, proxyIndex): # print " in map to source " # index = proxyIndex # coords = [] # while index != QtCore.QModelIndex(): # coords.append( [ index.row(), index.column() ] ) # index = index.parent() # print index # coords.reverse() # if len(coords)==0: # print " RITORNO 0" # return QtCore.QModelIndex() # r,c = coords[0] # if c>0 or r>= len(self.rootIndexes): # print " RITORNO 0b" # return QtCore.QModelIndex() # index = self.rootIndexes[r] # for r,c in coords[1:]: # index = index.child(r,c) # print " RITORNO ", index # return index # def mapFromSource(self, sourceIndex): # print " in map to proxy " # index = sourceIndex # coords = [] # while index not in [ None, QtCore.QModelIndex()] and index not in self.rootIndexes : # coords.append( [ index.row(), index.column() ] ) # index = index.parent() # if index in [None, QtCore.QModelIndex()]: # return QtCore.QModelIndex() # r,c = self.rootIndexes.index(index) , 0 # coords.append([r,c]) # coords.reverse() # index = self.index(0,0, QtCore.QModelIndex() ) # for r,c in coords: # index = index.child(r,c) # return index # def index( self,row, column, parent = QtCore.QModelIndex()): # if (parent.isValid()): # if parent==QtCore.QModelIndex(): # r,c = row, column # if c>0 or r>= len(self.rootIndexes): # return QtCore.QModelIndex() # index = self.rootIndexes[c] # print " RITORNO UN O DEI PRIMI" # return index # print " DEVO MAPPAR " # sourceParent = self.mapToSource(parent) # return sourceParent.child(row,column) # else: # return QtCore.QModelIndex() def exit_confirmation(): msgBox = Qt.QMessageBox(None) msgBox.setText("The document has been modified.") msgBox.setInformativeText("DO you want to risk loosing unsaved work?") msgBox.setStandardButtons( Qt.QMessageBox.Cancel | Qt. QMessageBox.Ok) msgBox.setDefaultButton( Qt.QMessageBox.Cancel) ret = msgBox.exec_() return ret edition_widgets = { "simplefile": filepath_widget, "simplefile hdf5": hdf5filepath_widget, "text": text_widget, "multiplechoices": multichoices_widget } class mainwindow(Qt.QMainWindow): def __getstate__(self): return {} def Exit(self): ret = exit_confirmation() if ret == Qt.QMessageBox.Discard: self.close() def LaunchConsole(self): from .console import ExampleWidget namespace = {"Qt":Qt,"QtCore":QtCore, "QtGui":QtGui, "np":np, "pylab":pylab } self.namespace = namespace # self.console = LaunchAnotherConsole(namespace) self.consoleW = ExampleWidget() self.console = self.consoleW.console self.console.updateNamespace(self.namespace) self.consoleW.show() def __init__(self, InterfaceDescription , extrawpaths=[], options={}, parent=None): self.InterfaceDescription = copy.deepcopy(InterfaceDescription) self.console = None self.options=options Qt.QMainWindow.__init__(self, parent ) Qt.loadUi( os.path.join(installation_dir ,"resources" , my_relativ_path, "mainwindow.ui" ), self) self.actionExit.triggered.connect(self.Exit) self.actionConsole_1.triggered.connect(self.LaunchConsole) self.setWindowIcon(Qt.QIcon(Qt.QPixmap(bacchetta32_xpm))) ## self.AAA.setIcon( Qt.QIcon(Qt.QPixmap(bacchetta32_xpm)) ) # self.viewsTab.clear() # self.methodSelection = creationtab(self.viewsTab) # self.viewsTab.addTab(self.methodSelection , "Method Selection") # self.metodi_keys = list(self.InterfaceDescription.keys()) # self.method_selection_list = Qt.QComboBox(self.viewsTab) # self.method_selection_list.addItems(self.metodi_keys ) # self.methodSelection.verticalLayout_3.addWidget( self.method_selection_list ) # self.methodSelection.Creation.setText("Create Selected Method Wizard") # self.methodSelection.Creation .connect(self.methodSelection.Creation , QtCore.SIGNAL("clicked()"), self.displayMethodCreator) self.create_new_calculation .connect(self.create_new_calculation , QtCore.SIGNAL("clicked()"), self.displayMethodCreatorFromTree) self.methods_model = QtGui.QFileSystemModel() self.methods_model.setNameFilters(["winfo_*.py"]) self.methods_model.setNameFilterDisables(False) self.tree_view = MyTree(self ) self.tree_view.paparent = self # @@@@@@@@@@ self.vsplitter = QtGui.QSplitter() self.vsplitter.setOrientation(QtCore.Qt.Vertical) self.verticalLayout_5_b_3.addWidget(self.vsplitter) self.optionScroll = QtGui.QScrollArea() self.vsplitter.addWidget( self.optionScroll) scrolledWidget = QtGui.QWidget() self.scrolledGrid = QtGui.QGridLayout() self.scrolledGrid.addWidget( QtGui.QLabel("AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"), 0, 0); self.scrolledGrid.addWidget( QtGui.QLabel("B"), 1, 0); self.scrolledGrid.addWidget( QtGui.QLabel("C"), 2, 0); self.scrolledGrid.addWidget( QtGui.QLabel("D"), 3, 0); self.scrolledGrid.addWidget( QtGui.QLabel("E"), 4, 0); scrolledWidget.setLayout(self.scrolledGrid) self.optionScroll.setWidget(scrolledWidget) for i in reversed(range(self.scrolledGrid.count())): widgetToRemove = self.scrolledGrid.itemAt( i ).widget() self.scrolledGrid.removeWidget( widgetToRemove ) widgetToRemove.setParent( None ) # self.verticalLayout_5_b_3.addWidget( self.tree_view) self.vsplitter.addWidget( self.tree_view) self.methods_model_bis = QtGui.QFileSystemModel() self.methods_model_bis.setNameFilters(["winfo_*.py"]) self.methods_model_bis.setNameFilterDisables(False) self.tree_view_bis = MyTree(self ) self.tree_view_bis.paparent = self self.tree_view_bis.set_other(self.tree_view ) self.tree_view.set_other(self.tree_view_bis ) # self.verticalLayout_5_b_3.addWidget( self.tree_view_bis) self.vsplitter.addWidget( self.tree_view_bis) folder = os.path.join(os.path.dirname(__file__),"methods") # self.methods_model2 = QtGui.QFileSystemModel() # self.methods_model.appendRow( self.methods_model2 ) if False and (not (sys.platform=="win32")) : class MyLoop(QtCore.QEventLoop): def __init__(self): QtCore.QEventLoop.__init__(self) def quit(self,path): if DEBUG :print( " loaded " , path) QtCore.QEventLoop.quit(self) My_loop = MyLoop() self.methods_model.connect(self.methods_model,QtCore.SIGNAL("directoryLoaded(QString)"), My_loop.quit ) #My_loop.exec_() # self.methods_proxy.setSourceModel(self.methods_model) rindexes=[] wpaths =extrawpaths+ [folder] for p in wpaths: rindexes.append(self.methods_model.index(p)) self.methods_model.setRootPath("/") self.methods_proxy = G_MethodsProxyModel( model=self.methods_model, rootIndexes = rindexes ) # self.methods_model.setRootPath("\\") self.tree_view.setModel(self.methods_proxy) self.tree_view.expandAll() else: self.methods_model.setRootPath(folder) self.tree_view.setModel(self.methods_model) self.tree_view.setRootIndex(self.methods_model.index(folder)) if len(extrawpaths): folder_bis = extrawpaths[0] self.methods_model_bis.setRootPath(folder_bis) self.tree_view_bis.setModel(self.methods_model_bis) self.tree_view_bis.setRootIndex(self.methods_model_bis.index(folder_bis)) self.tree_view.header().setStretchLastSection(False); self.tree_view.header().setResizeMode(0, Qt.QHeaderView.Stretch); self.tree_view_bis.header().setStretchLastSection(False); self.tree_view_bis.header().setResizeMode(0, Qt.QHeaderView.Stretch); # view.setRootIndex(model.index("C:\\Users")) # self.setCentralWidget(view) def displayMethodCreator(self, ): metodo = str(self.method_selection_list.currentText()) interfaceDesc = copy.deepcopy( self.InterfaceDescription[metodo] ) view = mainwindow2( interfaceDesc, self.viewsTab ) self.viewsTab.addTab(view, "Go create : %s"% metodo) def displayMethodCreatorFromTree(self, count=[0] ): count[0]+=1 if len( self.tree_view.selectedIndexes() ): tree = self.tree_view elif len( self.tree_view_bis.selectedIndexes() ): tree = self.tree_view_bis else: return index = tree.selectedIndexes()[0] if False and not (sys.platform == "win32"): index = tree.model().mapToSource(index ) pippo = index.model().data(index) # print pippo pippo = index.model().filePath(index) pippo = str(pippo) pippo_name = os.path.basename(pippo) if pippo_name[-3:]==".py": pippo_name=pippo_name[:-3] # modulename = 'pippo'+str(count[0]) modulename = pippo_name foo = imp.load_source(modulename,pippo) options={} for i in reversed(range(self.scrolledGrid.count())): widget = self.scrolledGrid.itemAt( i ).widget() options[str(widget.text())]= widget.isChecked() interfaceDesc = copy.deepcopy( foo.getMethod(options) ) # interfaceDesc ["tobeimported"] = [ 'pippo'+str(count[0]), pippo ] interfaceDesc ["tobeimported"] = [ modulename , pippo ] view = mainwindow2( interfaceDesc, self.viewsTab ) self.viewsTab.addTab(view, "Go create : %s"% os.path.basename( pippo )) class mainwindow2(Qt.QMainWindow): def __getstate__(self): return {} def Exit(self): ret = exit_confirmation() if ret == Qt.QMessageBox.Ok: self.paparent.removeTab( self.paparent.currentIndex() ) self.close() def closetab(self,i): if i: self.viewsTab.widget(i).timer.stop() self.viewsTab.removeTab(i) def __init__(self, InterfaceDescription , parent=None): self.timerS = Qt.QTimer() self.paparent = parent self.InterfaceDescription = copy.deepcopy(InterfaceDescription) Qt.QMainWindow.__init__(self, parent ) Qt.loadUi( os.path.join( installation_dir ,"resources" , my_relativ_path, "mainwindow2.ui" ), self) self.actionExit.triggered.connect(self.Exit) self.action_load_start_parameters.triggered.connect(self.load_start_parameters) self.action_load_parameters.triggered.connect(self.load_parameters) self.action_load_parametersGuided.triggered.connect(self.load_parametersGuided) self.action_save_parameters.triggered.connect(self.save_parameters) self.viewsTab.clear() #### styleTabs="QTabBar::tab { width:120px; height:30px; color:#333399; font-family:Tahoma; font-size:14px; font-weight:bold; border-top:2px solid #E68B2C; margin-top:-1px; }" styleTabs="QTabBar::tab { width:120px; color:#333399; font-family:Tahoma; font-size:7px; font-weight:bold; border-top:2px solid #E68B2C; margin-top:-1px; }" self.viewsTab.setStyleSheet(styleTabs); view = creationtab(self) self.viewsTab.addTab(view, "Go create it") self.viewsTab.setTabsClosable(True); self.viewsTab.connect( self.viewsTab , QtCore.SIGNAL("tabCloseRequested(int)"), self.closetab) dicodic = self.InterfaceDescription["CREATION_PARS"] # dicodic["refresher_stuff"] = [ self.timerS , self.dico ] self.edition_widgets={} self.edws = [] map_par2w = collections.OrderedDict () for name, par in dicodic.items(): if not isinstance(par, Parametro): continue if 1: mydico = collections.OrderedDict ( [ ["mydico", dicodic]] ) mydico[ "refresher_stuff"] = [self.timerS , mydico ] # print par.tipo, edition_widgets[par.tipo], par.doc nw = edition_widgets[par.tipo](self, name, par , mydico ,map_par2w= map_par2w ) map self.edws.append(nw) self.edition_widgets[name]= nw map_par2w[par ] = nw nw.setToolTip( par.doc ) view.verticalLayout.addWidget(nw) for par,nw in map_par2w.items(): par.pump_default() if par.value is not None: nw.take_par_value(par) view.Creation.setText("Create Inputs Lists accordingly") view.Creation .connect(view.Creation , QtCore.SIGNAL("clicked()"), self.CreateInputsLists) view.CreationGuided.setText("GUIDED MODE : Create Inputs Lists accordingly") view.CreationGuided .connect(view.CreationGuided , QtCore.SIGNAL("clicked()"), self.CreateInputsListsGuided) view.CreationInit.setText("Init from dict in the buffer (if any) ") view.CreationInit.connect(view.CreationInit , QtCore.SIGNAL("clicked()"), self.pick_the_dict) # view = creationtab(self) # self.viewsTab.addTab(view, "1") self.connect( self.timerS, QtCore.SIGNAL("timeout()"), self.refresh ); self.timerS.setSingleShot(True) def refresh(self): if DEBUG :print( " rinfresco self " , self) for edw in self.edws: if type(edw.par.value) != type(""): print( " PROBLEMA in refresh mainwindow2 ", edw.par.value) edw.par.value="problem" else: edw.take_par_value(edw.par ) def pick_the_dict(self): global dico_copy_buffer if DEBUG :print( dico_copy_buffer) if DEBUG :print( " era il dict ") if "init_initialiser" in self.InterfaceDescription: self.InterfaceDescription["init_initialiser"]( dico_copy_buffer ) self.refresh() else: print( " non predisposto per init_initialiser") def CreateInputsListsGuided(self): self.CreateInputsLists(guided=True) def CreateInputsLists(self, guided=False): sintesi = str(self.InterfaceDescription["CREATION_PARS"]["define_new"]()) sintesi=sintesi[-24:] dicodic = copy.deepcopy(self.InterfaceDescription) view = creationtab2(self) self.viewsTab.addTab(view, sintesi) view.populate(dicodic , guided) def load_start_parameters(self): itab = self.viewsTab.currentIndex() filename = str(Qt.QFileDialog.getOpenFileName(None, "select", filter = "All Files (*);;Par Files (*.par);;Pick Files (*.pick)" )) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) if len(filename ): try: s=open(filename, "rb" ).read() dicodic = pickle.loads( s ) except: s=open(filename, "r" ).read() dicodic = pickle.loads( bytearray(s, 'utf-8') ) # s = s.replace( "XRStools.", "XRStools"+version+"." ) for name, par in dicodic.items(): if not isinstance(par, Parametro): continue else: if par.value is not None: self.edition_widgets[name].take_par_value(par ) # f.close() def load_parametersGuided(self): self.load_parameters( guided=True) def load_parameters(self, guided=False): itab = self.viewsTab.currentIndex() filename = (Qt.QFileDialog.getOpenFileName(None, "select", filter = "All Files (*);;Par Files (*.par);;Pick Files (*.pick)" ) ) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) if len(filename ): # f=open(filename, "r" ) # dicodic = pickle.load( f ) try: s=open(filename, "rb" ).read() dicodic = pickle.loads( s,) except: s=open(filename, "r" ).read() dicodic = pickle.loads( bytearray(s,'utf-8' )) # s = s.replace( "XRStools.", "XRStools"+version+"." ) sintesi=filename[-24:] view = creationtab2(self) self.viewsTab.addTab(view, sintesi) aggiorna_dics(dicodic, self.InterfaceDescription) view.populate(dicodic, guided) def _finditems(self, obj, keys, results, already_done ): if obj in already_done: return already_done.append(obj) for key in keys: if key in obj: results.append(obj[key]) for k, v in obj.items(): if isinstance(v,dict): self._finditems(v, keys, results, already_done) def save_parameters(self): filename = (Qt.QFileDialog.getSaveFileName(None, "select")) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) itab = self.viewsTab.currentIndex() if len(filename ): if itab==0: dico2save = self.InterfaceDescription["CREATION_PARS"] else: results=[] dico2save = copy.deepcopy(self.viewsTab.widget(itab).dico) self._finditems( dico2save, ["refresher_stuff", "shadok_data", "shadok_data_bis"], results, [] ) for t in results: for i in range(len(t)): t[i]=None f=open(filename, "wb" ) s = pickle.dumps( dico2save ) # s = s.replace( "XRStools"+version+".", "XRStools." ) f.write(s) f.close() # f=open(filename, "w" ) # pickle.dump( dico2save, f ) # f.close() class hdf5dialog(Qt.QDialog): def __getstate__(self): return {} def __init__(self, parent=None): Qt.QDialog.__init__(self, parent) Qt.loadUi( os.path.join( installation_dir ,"resources" , my_relativ_path, "hdf5dialog.ui" ), self) self.hdf5File=None def closeEvent(self,event): print( " CLOSE EVENT " ) if self.hdf5File is not None: self.hdf5File.close() event.accept() def hdf5_filedialog(hint, fileme=1, myaction = None, modal=1): sphint= split_hdf5_address(hint) groupname="/" if sphint is not None and len(sphint): hint , groupname = sphint if fileme in [1]: if len(hint): filename = Qt.QFileDialog.getOpenFileName(None,'Open hdf5 file and then choose groupname', hint,filter="hdf5 (*h5)\nall files ( * )" ) else: filename = Qt.QFileDialog.getOpenFileName(None,'Open hdf5 file and then choose groupname',filter="hdf5 (*h5)\nall files ( * )" ) else: if len(hint): filename = Qt.QFileDialog.getSaveFileName(None,'Open hdf5 file and then choose groupname', hint,filter="hdf5 (*h5)\nall files ( * )" ) else: filename = Qt.QFileDialog.getSaveFileName(None,'Open hdf5 file and then choose groupname',filter="hdf5 (*h5)\nall files ( * )" ) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) if len(filename) : ret =None if os.path.exists(filename): import PyMca5.PyMcaGui.HDF5Widget as HDF5Widget storage=[None] __hdf5Dialog = hdf5dialog() def mySlot(ddict): name = ddict["name"] if ddict["event"] =='itemDoubleClicked': if myaction is not None: vn = str(__hdf5Dialog.varname.text()) if len(vn)==0: vn="grabbed" myaction(filename, name, varname = vn) storage[0]=name if myaction is None: __hdf5Dialog.varname.hide() __hdf5Dialog.setWindowTitle('Select a Group containing roi_definitions by a double click') __hdf5Dialog.mainLayout = __hdf5Dialog.verticalLayout_2 fileModel = HDF5Widget.FileModel() fileView = HDF5Widget.HDF5Widget(fileModel) hdf5File = fileModel.openFile(filename) shiftsDataset = None fileView.sigHDF5WidgetSignal.connect(mySlot) __hdf5Dialog.mainLayout.addWidget(fileView) __hdf5Dialog.resize(400, 700) indice = fileModel.index(0,0,QtCore.QModelIndex()) fileView.expand(indice) if groupname !="": groupname.replace("//","/") gn_l = groupname.split("/") hf=h5py.File(filename,"r") h5 = hf if DEBUG :print( " gn_l " , gn_l) i=None for tn in gn_l: if tn=="": continue if DEBUG :print( tn) nl = list(h5.keys()) nl.sort() if tn not in nl: break i = nl.index( tn ) indice = fileModel.index(i,0,indice) fileView.expand(indice) h5=h5[tn] if i is not None: fileView.setCurrentIndex(indice) hf.close() if modal: ret = __hdf5Dialog.exec_() hdf5File.close() else: __hdf5Dialog.hdf5file=hdf5File __hdf5Dialog.show() if ret: name = storage[0] return filename, name else: # return filename , None return None , None else: return None,None bacchetta32_xpm = [ "32 32 212 2", " c None", ". c #000000", "+ c #482D02", "@ c #160B00", "# c #FADE55", "$ c #FFF3A7", "% c #807149", "& c #ECEDEE", "* c #BAB5AE", "= c #CDC3B1", "- c #D7C7AC", "; c #FFE720", "> c #FFFFEF", ", c #FFFDB4", "' c #FFFFFF", ") c #D3CEC5", "! c #825E03", "~ c #DEC018", "{ c #FFFCA6", "] c #FFFFB1", "^ c #655100", "/ c #0B0700", "( c #372505", "_ c #EFDA98", ": c #FFFFFA", "< c #FFF87B", "[ c #FFFAB6", "} c #614E21", "| c #634700", "1 c #FFF7BB", "2 c #FFF9B5", "3 c #FFF6A2", "4 c #FFF773", "5 c #694600", "6 c #A77710", "7 c #FFEE71", "8 c #FFF6B5", "9 c #FFF8A9", "0 c #FFF7A4", "a c #FFF4AB", "b c #E4B828", "c c #271600", "d c #FFF9AF", "e c #FFF096", "f c #FFF184", "g c #A08B43", "h c #442F00", "i c #E2D5AE", "j c #FFFCE8", "k c #FFF090", "l c #FFF5BD", "m c #FFE755", "n c #220E00", "o c #291801", "p c #FFD918", "q c #FFF20D", "r c #FFF523", "s c #523D03", "t c #77643A", "u c #FFEE96", "v c #BC8F05", "w c #05060C", "x c #090608", "y c #F4D23B", "z c #FFF89D", "A c #E5C465", "B c #1C1100", "C c #8B5E05", "D c #393634", "E c #F7F7F7", "F c #2B2A29", "G c #050305", "H c #DAB232", "I c #FFF166", "J c #FFF79D", "K c #373331", "L c #110F0E", "M c #D5D0C8", "N c #B7B7B7", "O c #010101", "P c #535353", "Q c #F7F2D2", "R c #533F0F", "S c #352501", "T c #7C5A07", "U c #000208", "V c #6B5500", "W c #F5F3DD", "X c #413100", "Y c #281900", "Z c #DCDCDC", "` c #FDFAEE", " . c #FFF6A1", ".. c #FEFDD4", "+. c #B5982A", "@. c #180800", "#. c #FFF0BD", "$. c #FFFFFC", "%. c #FFF77D", "&. c #FFF78B", "*. c #FFF874", "=. c #C89A0C", "-. c #939393", ";. c #F3F3F3", ">. c #CFCFCF", ",. c #766649", "'. c #E5C23B", "). c #FFEF4C", "!. c #1A1208", "~. c #090100", "{. c #E9CF83", "]. c #FFF8C3", "^. c #FFF890", "/. c #FFF79E", "(. c #FFF3A3", "_. c #302100", ":. c #2E2E2E", "<. c #E6E6E6", "[. c #FFE268", "}. c #FFE96B", "|. c #FFEA63", "1. c #FFDD32", "2. c #E1E1E1", "3. c #323232", "4. c #FFCC1C", "5. c #FED117", "6. c #92721C", "7. c #E0A713", "8. c #0B0300", "9. c #878787", "0. c #FEFEFE", "a. c #8D8D8D", "b. c #ACA59A", "c. c #EBE07E", "d. c #FFFF18", "e. c #625311", "f. c #2C2B2B", "g. c #F2F2F2", "h. c #D7D6D6", "i. c #C0B69C", "j. c #FEF8B7", "k. c #FFFDFB", "l. c #FFFFC0", "m. c #C7C6C6", "n. c #EAE9E9", "o. c #2D2D2D", "p. c #916E15", "q. c #FFF156", "r. c #FFF38C", "s. c #817756", "t. c #7B7B7B", "u. c #D8D7D7", "v. c #767575", "w. c #100900", "x. c #1A0E00", "y. c #755709", "z. c #1D1400", "A. c #2E2D2D", "B. c #D1D0D0", "C. c #B6B5B5", "D. c #040404", "E. c #AFAEAE", "F. c #C8C7C7", "G. c #292929", "H. c #695104", "I. c #FEEA11", "J. c #B6A425", "K. c #090704", "L. c #6D6D6D", "M. c #B7B6B5", "N. c #646464", "O. c #030303", "P. c #C6A62D", "Q. c #FFFBCA", "R. c #4A3810", "S. c #292829", "T. c #AAA8A8", "U. c #939292", "V. c #080808", "W. c #8E6D19", "X. c #F7D855", "Y. c #DCC262", "Z. c #020307", "`. c #8C8B8B", " + c #9D9C9D", ".+ c #252424", "++ c #585757", "@+ c #8E8C8C", "#+ c #545353", "$+ c #232323", "%+ c #7F7D7D", "&+ c #757373", "*+ c #181717", "=+ c #686768", "-+ c #878586", ";+ c #605E5F", ">+ c #434242", ",+ c #969696", "'+ c #B5B5B5", ")+ c #161616", "!+ c #454545", "~+ c #A6A5A5", "{+ c #B1B1B1", "]+ c #5F5F5F", "^+ c #0E0E0E", "/+ c #FBFBFB", "(+ c #9B9B9B", "_+ c #616161", ":+ c #AFAFAF", "<+ c #949494", "[+ c #0F0F0F", "}+ c #313131", ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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"] class Console(QtGui.QPlainTextEdit): def __init__(self, prompt='$> ', startup_message='', parent=None): QtGui.QPlainTextEdit.__init__(self, parent) self.prompt = prompt self.history = [] self.namespace = {"QtCore":QtCore ,"QtGui":QtGui } self.construct = [] self.setGeometry(50, 75, 600, 400) self.setWordWrapMode(QtGui.QTextOption.WrapAnywhere) self.setUndoRedoEnabled(False) self.document().setDefaultFont(QtGui.QFont("monospace", 10, QtGui.QFont.Normal)) self.showMessage(startup_message) def updateNamespace(self, namespace): self.namespace.update(namespace) def showMessage(self, message): self.appendPlainText(message) self.newPrompt() def newPrompt(self): if self.construct: prompt = '.' * len(self.prompt) else: prompt = self.prompt self.appendPlainText(prompt) self.moveCursor(QtGui.QTextCursor.End) def getCommand(self): doc = self.document() curr_line = unicode(doc.findBlockByLineNumber(doc.lineCount() - 1).text()) curr_line = curr_line.rstrip() curr_line = curr_line[len(self.prompt):] return curr_line def setCommand(self, command): if self.getCommand() == command: return self.moveCursor(QtGui.QTextCursor.End) self.moveCursor(QtGui.QTextCursor.StartOfLine, QtGui.QTextCursor.KeepAnchor) for i in range(len(self.prompt)): self.moveCursor(QtGui.QTextCursor.Right, QtGui.QTextCursor.KeepAnchor) self.textCursor().removeSelectedText() self.textCursor().insertText(command) self.moveCursor(QtGui.QTextCursor.End) def getConstruct(self, command): if self.construct: prev_command = self.construct[-1] self.construct.append(command) if not prev_command and not command: ret_val = '\n'.join(self.construct) self.construct = [] return ret_val else: return '' else: if command and command[-1] == (':'): self.construct.append(command) return '' else: return command def getHistory(self): return self.history def setHisory(self, history): self.history = history def addToHistory(self, command): if command and (not self.history or self.history[-1] != command): self.history.append(command) self.history_index = len(self.history) def getPrevHistoryEntry(self): if self.history: self.history_index = max(0, self.history_index - 1) return self.history[self.history_index] return '' def getNextHistoryEntry(self): if self.history: hist_len = len(self.history) self.history_index = min(hist_len, self.history_index + 1) if self.history_index < hist_len: return self.history[self.history_index] return '' def getCursorPosition(self): return self.textCursor().columnNumber() - len(self.prompt) def setCursorPosition(self, position): self.moveCursor(QtGui.QTextCursor.StartOfLine) for i in range(len(self.prompt) + position): self.moveCursor(QtGui.QTextCursor.Right) def runCommand(self): command = self.getCommand() self.addToHistory(command) command = self.getConstruct(command) if command: tmp_stdout = sys.stdout class stdoutProxy(): def __init__(self, write_func): self.write_func = write_func self.skip = False def flush(self): return None def write(self, text): if not self.skip: stripped_text = text.rstrip('\n') self.write_func(stripped_text) QtCore.QCoreApplication.processEvents() self.skip = not self.skip sys.stdout = stdoutProxy(self.appendPlainText) try: try: result = eval(command, self.namespace, self.namespace) if result != None: self.appendPlainText(repr(result)) except SyntaxError: exec( command , self.namespace, self.namespace) except SystemExit: self.close() except: traceback_lines = traceback.format_exc().split('\n') # Remove traceback mentioning this file, and a linebreak for i in (3,2,1,-1): traceback_lines.pop(i) self.appendPlainText('\n'.join(traceback_lines)) sys.stdout = tmp_stdout self.newPrompt() def keyPressEvent(self, event): if event.key() in (QtCore.Qt.Key_Enter, QtCore.Qt.Key_Return): self.runCommand() return if event.key() == QtCore.Qt.Key_Home: self.setCursorPosition(0) return if event.key() == QtCore.Qt.Key_PageUp: return elif event.key() in (QtCore.Qt.Key_Left, QtCore.Qt.Key_Backspace): if self.getCursorPosition() == 0: return elif event.key() == QtCore.Qt.Key_Up: self.setCommand(self.getPrevHistoryEntry()) return elif event.key() == QtCore.Qt.Key_Down: self.setCommand(self.getNextHistoryEntry()) return elif event.key() == QtCore.Qt.Key_D and event.modifiers() == QtCore.Qt.ControlModifier: self.close() super(Console, self).keyPressEvent(event) console_welcome_message = ''' --------------------------------------------------------------- Welcome to a primitive Python interpreter. grabbed from : http://stackoverflow.com/questions/2758159/how-to-embed-a-python-interpreter-in-a-pyqt-widget --------------------------------------------------------------- ''' def LaunchAnotherConsole(namespace = {}): message = console_welcome_message+"\n\nINITIAL NAMESPACE\n\n"+str(namespace) console = Console(startup_message=message) console.updateNamespace( namespace ) console.show(); return console def wizardMain(filename, metodi, extrawpaths, options ): global animation_file animation_file = filename app=Qt.QApplication([]) w = mainwindow(metodi,extrawpaths, options ) w.show() global MainWindow MainWindow = w app.exec_() # wizardMain("../../data/shadok_eb-003.gif", metodi ) xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/Wizard_safe_module.py000066400000000000000000000045131412732462000250670ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import h5py import tokenize import math, pylab, numpy import numpy as np class funct_Readline: def __init__(self,s): self.sl=s.split("\n") def __call__(self): if len(self.sl)==0: raise StopIteration res = self.sl[0] self.sl = self.sl[1:] return res def python_code_is_safe(s): rl=funct_Readline(s) tk = tokenize.generate_tokens(rl) for t in tk: t=t[1] if t in ["import","exec"]: return 0 if t[:1]=="_": return 0 if t[:4]=="exec": return 0 return 1 def make_obj( h5group , h5, groupname ): runit=None if isinstance(h5group,h5py._hl.dataset.Dataset): pos = groupname.rfind("python_") if pos>=0 and "/" not in groupname[pos:]: func_name = str(groupname[pos:][len("python_"):]) groupname= str(groupname[:pos]) h5group = h5[groupname] runit=func_name if isinstance(h5group,h5py._hl.dataset.Dataset): oggetto = h5group.value else: dictio={} for key in h5group: if isinstance(h5group[key],h5py._hl.dataset.Dataset): dictio[key]= h5group[key].value if key[:len("python_")]=="python_": func_name = key[len("python_"):] sfunc = h5group[key].value print( " SFUNC " , sfunc) s="def %s(self):\n"%func_name sl = sfunc.split("\n") for l in sl: s+=" "+l+"\n" s+="foo=%s\n"%func_name if python_code_is_safe(s): print( " ESEGUO " , s) resdic = {"math":math,"numpy":np,"np":np,"pylab":pylab} exec(s , resdic, resdic) foo = resdic["foo"] dictio[func_name] = foo if func_name == runit: runit = foo else: print( "cowardly refusing to execute suspicious python code ", s) oggetto = type('MyObject',(object,),dictio)() return runit, oggetto xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/XRS_wizard.py000066400000000000000000000044251412732462000233220ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .Wizard import * from . import Wizard # from .methods.RIXS_spectra import winfo_RIXS_spectra_extraction_deconvolve # from .methods.RIXS_spectra import winfo_RIXS_spectra_extraction_preparation # from .methods.RIXS_spectra import winfo_RIXS_spectra_extraction # from .methods.predictions import winfo_xrsprediction ## from .methods.predictions winfo_response_denoiser import metodo as metodo_reponse_denoiser installation_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),"..") animation_filename = os.path.join(installation_dir,'data/shadok_eb-003.gif') def main(): import sys, getopt usage = "USAGE : XRS_wizard --shift_the_reference <0/1>(defaults 1) --do_deconvolution <0/1>(defaults 0) --wroot " shift_the_reference = 1 do_deconvolution = 0 try: opts, args = getopt.getopt(sys.argv[1:],"h",["shift_the_reference=","do_deconvolution=","wroot="]) except getopt.GetoptError: print( usage) sys.exit(2) print( opts, args) extrawpaths=[] for opt, arg in opts: if opt == '-h': print( usage) sys.exit() elif opt in ("--shift_the_reference"): shift_the_reference = int(arg) elif opt in ("--do_deconvolution"): do_deconvolution = int(arg) elif opt in ("--wroot"): extrawpaths.append(arg) options = { "shift_the_reference" :shift_the_reference, "do_deconvolution":do_deconvolution } # metodo_RIXS_spectra_extraction_preparation = winfo_RIXS_spectra_extraction_preparation.getMethod(options) # metodo_RIXS_spectra_extraction = winfo_RIXS_spectra_extraction.getMethod(options) # metodo_xrsprediction = winfo_xrsprediction.getMethod(options) import collections metodi = collections.OrderedDict() # metodi["RIXS_spectra_extraction_preparation"] = metodo_RIXS_spectra_extraction_preparation # metodi["RIXS_spectra_extraction"] = metodo_RIXS_spectra_extraction # metodi["XRS Prediction"] = metodo_xrsprediction print( animation_filename) wizardMain(animation_filename, metodi, extrawpaths, options) if __name__=="__main__": main() xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/__init__.py000066400000000000000000000001641412732462000230210ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/console.py000066400000000000000000000062221412732462000227250ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #/############################################################################ # # This module is based on an answer published in: # # http://stackoverflow.com/questions/11513132/embedding-ipython-qt-console-in-a-pyqt-application # # by Tim Rae # #############################################################################*/ import os import sys os.environ['QT_API'] = 'pyqt' try: import sip sip.setapi("QString", 2) sip.setapi("QVariant", 2) except: pass from PyQt4 import QtGui import IPython print( IPython.__version__) if IPython.__version__.startswith("2"): QTCONSOLE = False else: try: import qtconsole QTCONSOLE = True except ImportError: QTCONSOLE = False if QTCONSOLE: try: from qtconsole.rich_ipython_widget import RichJupyterWidget as RichIPythonWidget except: from qtconsole.rich_ipython_widget import RichIPythonWidget from qtconsole.inprocess import QtInProcessKernelManager else: from IPython.qt.console.rich_ipython_widget import RichIPythonWidget from IPython.qt.inprocess import QtInProcessKernelManager from IPython.lib import guisupport class QIPythonWidget(RichIPythonWidget): """ Convenience class for a live IPython console widget. We can replace the standard banner using the customBanner argument""" def __init__(self,customBanner=None,*args,**kwargs): super(QIPythonWidget, self).__init__(*args,**kwargs) if customBanner != None: self.banner = customBanner self.setWindowTitle(self.banner) self.kernel_manager = kernel_manager = QtInProcessKernelManager() kernel_manager.start_kernel() kernel_manager.kernel.gui = 'qt4' self.kernel_client = kernel_client = self._kernel_manager.client() kernel_client.start_channels() def stop(): kernel_client.stop_channels() kernel_manager.shutdown_kernel() guisupport.get_app_qt4().exit() self.exit_requested.connect(stop) def updateNamespace(self,variableDict): """ Given a dictionary containing name / value pairs, push those variables to the IPython console widget """ self.kernel_manager.kernel.shell.push(variableDict) def clearTerminal(self): """ Clears the terminal """ self._control.clear() def showMessage(self,text): self._append_plain_text(text) def executeCommand(self,command): """ Execute a command in the frame of the console widget """ self._execute(command,False) class ExampleWidget(QtGui.QWidget): def __init__(self, parent=None): super(ExampleWidget, self).__init__(parent) layout = QtGui.QVBoxLayout(self) ipyConsole = QIPythonWidget(customBanner="Welcome to the embedded ipython console\n") layout.addWidget(ipyConsole) ## ipyConsole.showMessage(message) self.console = ipyConsole if __name__ =="__main__": app = QtGui.QApplication([]) widget = ExampleWidget() widget.show() ## widget.console.printText(message) app.exec_() xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/fileview/000077500000000000000000000000001412732462000225215ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/fileview/file_view.py000066400000000000000000000034131412732462000250450ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys from PyQt4 import Qt class ProxyModel(Qt.QSortFilterProxyModel): def __init__(self, model, parent=None): Qt.QSortFilterProxyModel.__init__(self, parent) self.setSourceModel(model) def setRoots(self, roots): self.roots = roots self.base_root = os.path.commonprefix(roots) self.sourceModel().setRootPath(self.base_root) self.all_dirs = set() if 'posix' in os.name: for root in roots: last = root while last != '/': self.all_dirs.add(last) last = os.path.dirname(last) def get_root(self, path): for i, root in enumerate(self.roots): if root == path: return root, i if root in path: return root, -1 return None, False def mapFromSource(self, sourceIndex): # return super(ProxyModel, self).mapFromSource(sourceIndex) model = sourceIndex.model() if model is None: return Qt.QModelIndex() path = model.filePath(sourceIndex) root, root_idx = self.get_root(path) if root_idx >= 0: return self.createIndex(root_idx, 0) return Qt.QModelIndex() def mapToSource(self, proxyIndex): # print 'maptosource', proxyIndex return super(ProxyModel, self).mapToSource(proxyIndex) app = Qt.QApplication([]) view = Qt.QTreeView() roots = ['/usr/local/include', '/usr/local/share'] model = Qt.QFileSystemModel() proxy = ProxyModel(model, view) proxy.setRoots(roots) view.setModel(proxy) view.show() sys.exit(app.exec_()) xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/fileview/main.cc000066400000000000000000000120211412732462000237500ustar00rootroot00000000000000#include #include QList list; class SortProxy : public QAbstractProxyModel { Q_OBJECT public: SortProxy(QObject *parent = 0) : QAbstractProxyModel(parent), hideThem(false) { fixModel(); } int rowCount(const QModelIndex &parent) const { QModelIndex sourceParent; if (parent.isValid()) sourceParent = mapToSource(parent); int count = 0; QMapIterator it(proxySourceParent); while (it.hasNext()) { it.next(); if (it.value() == sourceParent) count++; } return count; } int columnCount(const QModelIndex &) const { return 1; } QModelIndex index(int row, int column, const QModelIndex &parent = QModelIndex()) const { QModelIndex sourceParent; if (parent.isValid()) sourceParent = mapToSource(parent); QMapIterator it(proxySourceParent); while (it.hasNext()) { it.next(); if (it.value() == sourceParent && it.key().row() == row && it.key().column() == column) return it.key(); } return QModelIndex(); } QModelIndex parent(const QModelIndex &child) const { QModelIndex mi = proxySourceParent.value(child); if (mi.isValid()) return mapFromSource(mi); return QModelIndex(); } QModelIndex mapToSource(const QModelIndex &proxyIndex) const { if (!proxyIndex.isValid()) return QModelIndex(); return mapping.key(proxyIndex); } QModelIndex mapFromSource(const QModelIndex &sourceIndex) const { if (!sourceIndex.isValid()) return QModelIndex(); return mapping.value(sourceIndex); } public slots: void hideEverythingButA1AndChildren() { hideThem = !hideThem; // Now we set up the proxy <-> source mappings emit layoutAboutToBeChanged(); fixModel(); emit layoutChanged(); } private: void fixModel() { mapping.clear(); proxySourceParent.clear(); for (int i=0;itext().startsWith("A") || !si->parent()) continue; QModelIndex proxy = createIndex(si->row(), si->column(), si->index().internalPointer()); mapping.insert(QPersistentModelIndex(si->index()), proxy); QModelIndex sourceParent; if (si->parent()->parent()) sourceParent = si->parent()->index(); proxySourceParent.insert(proxy, sourceParent); } else { QModelIndex proxy = createIndex(si->row(), si->column(), si->index().internalPointer()); mapping.insert(QPersistentModelIndex(si->index()), proxy); QModelIndex sourceParent; if (si->parent()) sourceParent = si->parent()->index(); proxySourceParent.insert(proxy, sourceParent); } } } QMap mapping; QMap proxySourceParent; bool hideThem; }; SortProxy *proxyModel = 0; class Tree : public QTreeView { Q_OBJECT public: Tree(QWidget *parent = 0) : QTreeView(parent) { QStandardItemModel *sourceModel = new QStandardItemModel(this); QStandardItem *parentA = sourceModel->invisibleRootItem(); for (int i = 0; i < 2;i++) { itemA = new QStandardItem(QString("A %0").arg(i)); parentA->appendRow(itemA); parentA = itemA; list.append(itemA); } itemA = new QStandardItem(QString("A 2")); parentA->appendRow(itemA); list.append(itemA); itemA3 = new QStandardItem(QString("A 3")); list.append(itemA3); parentA->appendRow(itemA3); itemA4 = new QStandardItem(QString("A 4")); list.append(itemA4); parentA->appendRow(itemA4); itemNonA = new QStandardItem(QString("Non A")); list.append(itemNonA); parentA->appendRow(itemNonA); QStandardItem *parentB = sourceModel->invisibleRootItem(); for (int i = 0; i < 3;i++) { itemB = new QStandardItem(QString("B %0").arg(i)); parentB->appendRow(itemB); parentB = itemB; list.append(itemB); } QStandardItem *parentC = sourceModel->invisibleRootItem(); for (int i = 0; i < 3;i++) { itemC = new QStandardItem(QString("C %0").arg(i)); parentC->appendRow(itemC); parentC = itemC; list.append(itemC); } proxyModel = new SortProxy(this); proxyModel->setSourceModel(sourceModel); setModel(proxyModel); expandAll(); } QStandardItem *itemA; QStandardItem *itemA3; QStandardItem *itemA4; QStandardItem *itemNonA; QStandardItem *itemB; QStandardItem *itemC; }; #include "main.moc" int main(int argc, char **argv) { QApplication app(argc, argv); QWidget widget; QPushButton *button = new QPushButton("Make only A1 A children visible", &widget); printf(" Tree \n"); Tree *tree = new Tree(&widget); printf(" tree OK \n"); QVBoxLayout *lay = new QVBoxLayout(&widget); lay->addWidget(button); QObject::connect(button, SIGNAL (clicked()), proxyModel, SLOT (hideEverythingButA1AndChildren())); lay->addWidget(tree); printf(" SHOW \n"); widget.show(); return app.exec(); } xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/fileview/qfile.py000066400000000000000000000251001412732462000241710ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from PyQt4 import Qt, QtGui, QtCore import sys from six.moves import range if sys.platform =="win32": rootdir = "c:\\users\\aless\\src\\xrstoolssuperresolution\\xrstools\\WIZARD\\methods" else: rootdir = "/scisoft/users/mirone/WORKS/Christoph/XRStoolsSuperResolution/XRStools/WIZARD/methods" class MyLoop(QtCore.QEventLoop): def __init__(self): QtCore.QEventLoop.__init__(self) def quit(self,path): print( " loaded " , path) QtCore.QEventLoop.quit(self) My_loop = MyLoop() class MyTree( QtGui.QTreeView): def __init__(self, *args): QtGui.QTreeView.__init__(self, *args) self.clicked[QtCore.QModelIndex].connect(self.itemClicked) def itemClicked(self, modelIndex): event ="itemDoubleClicked" print( event, modelIndex) index = self.selectedIndexes()[0] # if not( sys.platform =="win32"): # index = self.model().mapToSource(index ) pippo = index.model().data(index) print( pippo) pippo = index.model().filePath(index) print( str(pippo)) class MethodsProxyModel(Qt.QAbstractProxyModel): def __init__(self, parent=None, model=None, rootIndexes=[]): self.rootIndexes = rootIndexes self.mapping = {} self.model=model Qt.QSortFilterProxyModel.__init__(self, model) # self.emit(QtCore.SIGNAL("layoutAboutToBeChanged()")) self.fixModel() # self.emit(QtCore.SIGNAL("layoutChanged()")) self.setSourceModel(model ) def fixModel(self): print( " fix model ") self.mapping.clear() for ir, root in enumerate(self.rootIndexes): print( root) sourceIndex = self.model.index(root) print( " sourceIndex.internalPointer() " , sourceIndex.internalId()) proxy = self.createIndex(ir, 0, sourceIndex.internalId()) tmp = Qt.QPersistentModelIndex(sourceIndex) self.mapping[tmp] = proxy print( " STACK AGGIUNGO ", sourceIndex.data().toString(), " con rowcount ", self.model.rowCount(sourceIndex ) ) todo = list(range( self.model.rowCount(sourceIndex ))) stack = [ ( iter(todo) , sourceIndex ) ] print( stack) while len(stack): try: it, si = stack[-1] ir = next(it) for ic in range(4): print( ic) sourceIndex = self.model.index( ir,ic, si) if sourceIndex and sourceIndex != Qt.QModelIndex(): print( " AGGIUNGO per ir ic ", ir, ic, " ITEM sourceIndex ", sourceIndex.data().toString()) proxy = self.createIndex(ir, ic, sourceIndex.internalId()) tmp = Qt.QPersistentModelIndex(sourceIndex) self.mapping[tmp] = proxy if ic==0: # if sourceIndex.model().isDir(sourceIndex): if self.model.isDir(sourceIndex): percorso = self.model.filePath( sourceIndex ) self.model.setRootPath(percorso) My_loop.exec_() print( " STACK AGGIUNGO ", sourceIndex.data().toString(), " con rowcount ", self.model.rowCount(sourceIndex ) ) todo = list(range( self.model.rowCount(sourceIndex ))) stack.append( ( iter(todo) , sourceIndex ) ) except StopIteration: stack = stack[:-1] def columnCount( self, index ): print( " column count " ) return 4 def rowCount( self, parent ): print( " row count A") sourceParent = Qt.QModelIndex() if parent.isValid(): sourceParent = self.mapToSource(parent) if sourceParent ==Qt.QModelIndex(): print( " row count B", 1) return 1 count = self.model.rowCount(sourceParent) print( " row count C", count) return count def mapToSource(self, proxyIndex): print( "DB1 map to source ") index = proxyIndex coords = [] while index != QtCore.QModelIndex(): coords.append( [ index.row(), index.column() ] ) index = index.parent() coords.reverse() print( "DB1 coords ", coords) if len(coords)==0: print( "DB1 RITORNO 0") return QtCore.QModelIndex() r,c = coords[0] if c>0 or r>= len(self.rootIndexes): print( "DB1 RITORNO 0b None") return QtCore.QModelIndex() # return None # index = self.rootIndexes[c] index = self.model.index(self.rootIndexes[0]) print( "DB1 RADICE ", self.model.filePath(index)) for r,c in coords[1:]: index = index.child(r,c) print( "DB1 in profondita ", self.model.filePath(index)) print( "DB1 RITORNO ", index) print( "DB1 RITORNO ", self.model.filePath(index)) return index def mapFromSource(self, sourceIndex): print( "DB1 map from source ") if not sourceIndex.isValid(): print( "DB1 map from source non valido ") return Qt.QModelIndex() print( "DB1 map from sourc filePath ", self.model.filePath(sourceIndex)) pi = Qt.QPersistentModelIndex(sourceIndex) if pi in self.mapping: print( "DB1 mapfrom source ben in mapping") return self.mapping[pi] else: print( " DB1 non c'e'") return Qt.QModelIndex() # def mapFromSource(self, sourceIndex): # print " in map to proxy " # index = sourceIndex # coords = [] # while index not in [ None, QtCore.QModelIndex()] and index not in self.rootIndexes : # coords.append( [ index.row(), index.column() ] ) # index = index.parent() # if index in [None, QtCore.QModelIndex()]: # return QtCore.QModelIndex() # r,c = self.rootIndexes.index(index) , 0 # coords.append([r,c]) # coords.reverse() # index = QtCore.QModelIndex() # for r,c in coords: # index = index.child(r,c) # return index def index(self, row, column, parent): print( " IndEX , parent", parent.data().toString()) sourceParent= Qt.QModelIndex() if parent.isValid() : sourceParent = self.mapToSource(parent) print( "DEB2 good ", sourceParent.model().filePath(sourceParent), row, column) else: print( "DEB2 fault") if (row,column)==(0,0): sourceParent = self.model.index(self.rootIndexes[row]) return self.mapFromSource(sourceParent) else: sourceParent = None return sourceParent #sourceParent = self.model.invisibleRootItem() print( " IndEX , parent", sourceParent.model().filePath(sourceParent)) sind = self.model.index(row, column, sourceParent ) return self.mapFromSource(sind) if(1): app = Qt.QApplication(sys.argv) widget = Qt.QWidget() button = Qt.QPushButton("Make only A1 + 'A' children visible", widget); tree_view = MyTree(widget) lay = Qt.QVBoxLayout(widget) lay.addWidget(button) # button.connect(button, QtCore.SIGNAL("clicked()"), proxyModel.hideEverythingButA1AndChildren) lay.addWidget(tree_view) methods_model = QtGui.QFileSystemModel() # methods_model.setNameFilters(["winfo_*.py"]) # methods_model.setNameFilterDisables(False) methods_model.setRootPath(rootdir) methods_model.connect(methods_model,QtCore.SIGNAL("directoryLoaded(QString)"), My_loop.quit ) My_loop.exec_() folder = rootdir extrawpaths=[] if ( 1) : # self.methods_proxy.setSourceModel(self.methods_model) rindexes=[] wpaths =extrawpaths+ [folder] for p in wpaths: rindexes.append(methods_model.index(p.lower())) # methods_proxy = MethodsProxyModel( model=methods_model, rootIndexes = rindexes ) print( " CREO PROXY") methods_proxy = MethodsProxyModel( model=methods_model, rootIndexes = wpaths ) # methods_proxy.fixModel() print( " CREO OK ") # tree_view.setRootIndex(Qt.QModelIndex()) # self.methods_model.setRootPath("\\") tree_view.setModel(methods_proxy) # self.tree_view.expandAll() else: methods_model.setRootPath(folder) tree_view.setModel(methods_model) tree_view.setRootIndex(methods_model.index(folder)) tree_view.header().setStretchLastSection(False); tree_view.header().setResizeMode(0, Qt.QHeaderView.Stretch); widget.show() app.processEvents(QtCore.QEventLoop.AllEvents) app.exec_() if (0): def displayMethodCreator(self, ): metodo = str(self.method_selection_list.currentText()) interfaceDesc = copy.deepcopy( self.InterfaceDescription[metodo] ) view = mainwindow2( interfaceDesc, self.viewsTab ) self.viewsTab.addTab(view, "Go create : %s"% metodo) def displayMethodCreatorFromTree(self, count=[0] ): count[0]+=1 index = self.tree_view.selectedIndexes()[0] # if not (sys.platform == "win32"): # index = self.tree_view.model().mapToSource(index ) pippo = index.model().data(index) print( pippo) pippo = index.model().filePath(index) pippo = str(pippo) foo = imp.load_source('pippo'+str(count[0]),pippo) interfaceDesc = copy.deepcopy( foo.getMethod(self.options) ) interfaceDesc ["tobeimported"] = [ 'pippo'+str(count[0]), pippo ] view = mainwindow2( interfaceDesc, self.viewsTab ) self.viewsTab.addTab(view, "Go create : %s"% os.path.basename( pippo )) xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/fileview/qtmodel.py000066400000000000000000000243421412732462000245450ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #include import os import sys from PyQt4 import Qt, QtCore, QtGui from six.moves import range list = [] # QList list; # class SortProxy : public QAbstractProxyModel # { # Q_OBJECT print( " SORTYPROXY " ) class SortProxy(Qt.QAbstractProxyModel): def __init__(self, parent=0): Qt.QAbstractProxyModel.__init__(self, parent) self.hideThem = False # self.hideThem = True self.mapping = {} self.mapping2key = {} self.proxySourceParent = {} self.fixModel() # SortProxy(QObject *parent = 0) : QAbstractProxyModel(parent), hideThem(false) # { # fixModel(); # } def rowCount(self, parent): sourceParent = Qt.QModelIndex() if parent.isValid(): sourceParent = self.mapToSource(parent) count = 0 for key,value in self.proxySourceParent.items(): if value == sourceParent: count+=1 return count # int rowCount(const QModelIndex &parent) const # { # QModelIndex sourceParent; # if (parent.isValid()) sourceParent = mapToSource(parent); # int count = 0; # QMapIterator it(proxySourceParent); # while (it.hasNext()) { # it.next(); # if (it.value() == sourceParent) # count++; # } # return count; # } def columnCount(self, index): return 2 # int columnCount(const QModelIndex &) const # { # return 1; # } def index(self, row, column, parent): sourceParent= Qt.QModelIndex() if parent.isValid() : sourceParent = self.mapToSource(parent) for key, value in self.proxySourceParent.items(): if value == sourceParent and key.row() == row and key.column() == column: return key return Qt.QModelIndex() # QModelIndex index(int row, int column, const QModelIndex &parent = QModelIndex()) const # { # QModelIndex sourceParent; # if (parent.isValid()) # sourceParent = mapToSource(parent); # QMapIterator it(proxySourceParent); # while (it.hasNext()) { # it.next(); # if (it.value() == sourceParent && it.key().row() == row && # it.key().column() == column) # return it.key(); # } # return QModelIndex(); # } def parent(self, child): mi = self.proxySourceParent[ child ] if mi.isValid(): return self.mapFromSource(mi) return Qt.QModelIndex() # QModelIndex parent(const QModelIndex &child) const # { # QModelIndex mi = proxySourceParent.value(child); # if (mi.isValid()) # return mapFromSource(mi); # return QModelIndex(); # } def mapToSource( self,proxyIndex): if not proxyIndex.isValid(): # print " RITORNO INVALIDO " return Qt.QModelIndex() for a,b in self.mapping.items(): # print a,b, ( b == proxyIndex) , proxyIndex if b == proxyIndex: # print " RITORNO ", a return Qt.QModelIndex(a) print( " NON TROVATO ") return Qt.QModelIndex() # QModelIndex mapToSource(const QModelIndex &proxyIndex) const # { # if (!proxyIndex.isValid()) return QModelIndex(); # return mapping.key(proxyIndex); # } def mapFromSource(self, sourceIndex): if not sourceIndex.isValid(): return Qt.QModelIndex() if sourceIndex in self.mapping: return self.mapping[sourceIndex] else: return Qt.QModelIndex() # QModelIndex mapFromSource(const QModelIndex &sourceIndex) const{ # if (!sourceIndex.isValid()) # return QModelIndex(); # return mapping.value(sourceIndex); # } def hideEverythingButA1AndChildren(self): self.hideThem = not self.hideThem self.emit(QtCore.SIGNAL("layoutAboutToBeChanged()")) self.fixModel() self.emit(QtCore.SIGNAL("layoutChanged()")) # public slots: # void hideEverythingButA1AndChildren() # { # hideThem = !hideThem; # // Now we set up the proxy <-> source mappings # emit layoutAboutToBeChanged(); # fixModel(); # emit layoutChanged(); # } def fixModel(self): # if self.amodel is not None: # print " OK " # raise self.mapping.clear() self.mapping2key.clear() self.proxySourceParent.clear() oldrow=0 for si in list: if (self.hideThem): # if ( (not si.text().startsWith(Qt.QString("A"))) or (not si.parent())): # continue if ( (not si.text().startsWith(Qt.QString("A")))): continue print( si.text(), si.row(), si.column()) row = si.row() if row-oldrow>1: row = oldrow+1 proxy = self.createIndex(row, si.column(), si.index().internalPointer()) oldrow = si.row() tmp = Qt.QPersistentModelIndex(si.index()) self.mapping[tmp] = proxy self.mapping2key [ proxy ] = tmp sourceParent = Qt.QModelIndex() if (si.parent()) : # if (si.parent().parent()) : sourceParent = Qt.QPersistentModelIndex(si.parent().index()) self.proxySourceParent[proxy] = sourceParent else : # if ( (not si.text().startsWith(Qt.QString("A"))) ): # break proxy = self.createIndex(si.row(), si.column(), si.index().internalPointer()) tmp = Qt.QPersistentModelIndex(si.index()) self.mapping[tmp] = proxy self.mapping2key [ proxy ] = tmp sourceParent = Qt.QModelIndex() if (si.parent()) : sourceParent = Qt.QPersistentModelIndex(si.parent().index()) self.proxySourceParent[proxy] = sourceParent print( " Tree " ) global proxyModel class Tree(Qt.QTreeView): def __init__(self, parent=None): global proxyModel Qt.QTreeView.__init__(self, parent) self.amodel = None if 1: methods_model = QtGui.QFileSystemModel() rootdir = "/scisoft/users/mirone/WORKS/Christoph/XRStoolsSuperResolution/XRStools/WIZARD/methods" methods_model.setRootPath(rootdir) class MyLoop(QtCore.QEventLoop): def __init__(self): QtCore.QEventLoop.__init__(self) def quit(self,path): print( " loaded " , path) QtCore.QEventLoop.quit(self) loop = MyLoop() methods_model.connect(methods_model,QtCore.SIGNAL("directoryLoaded(QString)"), loop.quit ) loop.exec_() self.amodel = methods_model sourceModel = Qt.QStandardItemModel(self) parentA = sourceModel.invisibleRootItem() for i in range(2): self.itemA = Qt.QStandardItem(Qt.QString("AAA %d"%i)); self.itemAb = Qt.QStandardItem(Qt.QString("Abis %d"%i)); parentA.appendRow([ self.itemA, self.itemAb]) parentA = self.itemA list.append(self.itemA) list.append(self.itemAb) self.itemA = Qt.QStandardItem(Qt.QString("A 2")) parentA.appendRow(self.itemA) list.append(self.itemA) self.itemA3 = Qt.QStandardItem(Qt.QString("A 3")) list.append(self.itemA3) parentA.appendRow(self.itemA3) self.itemNonA = Qt.QStandardItem(Qt.QString("Non A")) list.append(self.itemNonA) parentA.appendRow(self.itemNonA) self.itemA4 = Qt.QStandardItem(Qt.QString("A 4")) list.append(self.itemA4) parentA.appendRow(self.itemA4) # parentB = sourceModel.invisibleRootItem() # self.itemB = Qt.QStandardItem(Qt.QString("B %d"%100 )) # parentB.appendRow(self.itemB) # list.append(self.itemB); parentB = sourceModel.invisibleRootItem() self.itemB = Qt.QStandardItem(Qt.QString("B %d"%0 )) parentB.appendRow(self.itemB) parentB = self.itemB; list.append(self.itemB) self.itemB = Qt.QStandardItem(Qt.QString("B %d"%1 )) parentB.appendRow(self.itemB) list.append(self.itemB) self.itemB = Qt.QStandardItem(Qt.QString("B %d"%2 )) parentB.appendRow(self.itemB) parentB = self.itemB; list.append(self.itemB) if(1): parentB = sourceModel.invisibleRootItem() for i in range(3): self.itemB = Qt.QStandardItem(Qt.QString("B %d"%i )) parentB.appendRow(self.itemB) parentB = self.itemB; list.append(self.itemB); parentC = sourceModel.invisibleRootItem() for i in range(3): self.itemC = Qt.QStandardItem(Qt.QString("C %d"%i )) parentC.appendRow(self.itemC) parentC = self.itemC list.append(self.itemC) proxyModel = SortProxy(self) proxyModel.setSourceModel(sourceModel) self.setModel(proxyModel) self.expandAll() #include "main.moc" def main() : print( " qui " ) app = Qt.QApplication(sys.argv) widget = Qt.QWidget() button = Qt.QPushButton("Make only A1 + 'A' children visible", widget); tree = Tree(widget) lay = Qt.QVBoxLayout(widget) lay.addWidget(button) button.connect(button, QtCore.SIGNAL("clicked()"), proxyModel.hideEverythingButA1AndChildren) lay.addWidget(tree) widget.show() app.exec_() print( " cucu ") main() xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/000077500000000000000000000000001412732462000223525ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/RIXS_spectra/000077500000000000000000000000001412732462000246605ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/RIXS_spectra/__init__.py000066400000000000000000000001641412732462000267720ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/RIXS_spectra/setup.py000066400000000000000000000032171412732462000263750ustar00rootroot00000000000000# coding: utf-8 # /*########################################################################## # Copyright (C) 2016-2018 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # ############################################################################*/ __authors__ = ["V. Valls"] __license__ = "MIT" __date__ = "03/01/2019" from numpy.distutils.misc_util import Configuration def configuration(parent_package='', top_path=None): config = Configuration('RIXS_spectra', parent_package, top_path) return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration=configuration) winfo_RIXS_spectra_extraction.py000066400000000000000000000461761412732462000331410ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/RIXS_spectrafrom __future__ import absolute_import from __future__ import division from __future__ import print_function import collections ######## Managing versioning postpending to project name ################ # Get the name of the launching script, # the launching script (an principal module) # is post pended by version name # We use this to retrieve the version import inspect import os import importlib curframe = inspect.currentframe() calframe = inspect.getouterframes(curframe, 2) global version caller = calframe[-1][1] if caller[-3:]==".py": print( " caller :" , caller , " // " , os.path.dirname(os.path.dirname(caller))) # imported by Wizard.py to see what are the subtasks version = os.path.basename(os.path.dirname(os.path.dirname(caller)))[len("XRStools"):] else: # called by the XRS_wizard script caller = os.path.basename(caller) version = caller[len("XRS_wizard"):] print( " Caller version==", version) XRStools = importlib.import_module( "XRStools"+version ) exec("from XRStools%s.WIZARD.Wizard import *"%version) ################################################################### OPTIONS = { "do_deconvolution":(False,"activate advanced deconvolution"), "shift_the_reference":(True,"correct for a precalculated shift\n" "between reference and data") } class functor_sigterm_handler: def __init__(self, p): self.p = p def __call__(self, *x): print( x) print( " KILLO " , self.p.pid) os.kill(self.p.pid, signal.SIGKILL) def swissknife_runner( yamltext, where , ret_dico): inputname = os.path.join(where,"input.yaml") stdname = os.path.join(where,"stdout.txt") errname = os.path.join(where,"stderr.txt") ret_dico["input"] =inputname ret_dico["stdout"]=stdname ret_dico["stderr"]=errname open(inputname,"w").write(yamltext) # os.system("XRS_swissknife %s 1> %s 2>%s"%(inputname,stdname, errname ) ) #p = subprocess.call( string.split("XRS_swissknife %s 1> %s 2> %s"%(inputname,stdname, errname )) , shell=False, stdout= subprocess .PIPE ) # preexec_fn=os.setsid # p= subprocess.Popen(string.split( "XRS_swissknife %s 1> %s 2>%s"%(inputname,stdname, errname )) , shell=False, stdout= subprocess .PIPE , stderr= subprocess .PIPE ) stdout = open(stdname,"w") stderr = open(errname,"w") if not sys.platform=="win32": p= subprocess.Popen(( "XRS_swissknife%s %s 1> %s 2>%s"%(version, inputname,stdname, errname )).split() , shell=False, stdout= stdout , stderr= stderr ) else: packages = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(XRStools.__file__ ) )))) script = os.path.join(packages,"scripts","XRS_swissknife%s.bat"%version ) p= subprocess.Popen(( "%s %s"%(script, inputname)) .split() , shell=False, stdout= stdout , stderr= stderr ) signal.signal(signal.SIGTERM, functor_sigterm_handler(p)) p.communicate() ret_dico["return_code"]=p.returncode # import time # time.sleep(100) # p.communicate() class Functor_adapt: def __init__(self, dico): self.dico=dico def __call__(self): chiavi = list(self.dico.keys())[1:] for c in chiavi : subdic = self.dico[c] if not isinstance(subdic,dict): continue for name,par in subdic.items(): if isinstance(par,Parametro): if par.defaults2 is not None: source, method = par.defaults2 if hasattr(source,"value" ): par.value = method(source.value) else: par.value = method(source) return self.dico["CREATION_PARS"]["resynth_scan"].value+"__"+ self.dico["CREATION_PARS"]["scans"] .value def dic2yaml_extraction_scan_with_recentering(dicodic): s = "loadscan_2Dimages :\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() s = s+ " roiaddress : %s \n" % dicodic["roiaddress"].render() s = s+ " scan_interval : %s \n" % dicodic["scans"].render() s = s+ " monitor_column : %s \n" % dicodic["monitor_column"].render() ## s = s+ " recenterings : %s \n" % dicodic["recenterings"].render() s = s+ " signaladdress : %s \n" % dicodic["signaladdress"].render() s = s+""" sumto1D : 0 energycolumn : 'Anal Energy' monitorcolumn : %s """ % dicodic["monitor_column"].render() return s def dic2yaml_generic(dicodic): s = "%s :\n"%dicodic["method_name"] for n,t in dicodic.items(): if isinstance(t, Parametro): s = s +" %s : %s\n"%(n,t.render()) return s class functor_finalise_last_part_of_the_path: def __init__(self, cp_num): self.cp_num=cp_num def __call__(self, cp_add ) : cp_num = self.cp_num path = cp_add num = cp_num.getValue() path_items = path.split("/")[-3:] # res = path + "/" + path_items[0] + "/" + path_items[1] +"/" + "Scan%03d" % num res = path + "/" + path_items[0] + "/" print( " RITORNO " , res) return res class Functor_initialiser: def __init__(self, c): self.c_p = c def __call__(self, dico): l = [ ("SpecExpFile","spec_file"),("ResynthScan","resynth_scan"), ("Reference_scan", "reference_scan"), ("monitor_column", "monitor_column")] for source,target in l: par = DicRecursiveSearch(dico,source) if par is not None: self.c_p[target].value = par.value def getMethod(options): if "shift_the_reference" in options: shift_the_reference = options["shift_the_reference"] if "do_deconvolution" in options: do_deconvolution = options["do_deconvolution"] metodo = collections.OrderedDict() c_p = collections.OrderedDict() metodo["CREATION_PARS"] = c_p metodo["init_initialiser"] = Functor_initialiser(c_p) c_p["spec_file"] = FilePath(doc="The path to the specfile\n file must exist", fileme=1) # c_p["roiaddress"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ # " where the ROI for calibration extraction can be found \n"+ # " Format toto.hdf5:datagroupname ", # fileme=1, gnameme=1) c_p["scans"] = many_Number4SpecScan( c_p["spec_file"] ,isinterval=1, doc= "This is the list of two number (**Use Python syntax [start,end+1]**)\n"+ " defining the interval scan numbers containing data.\n"+ "You can also give more intervals in the same list.\n"+ # " Must containing two numbers : [ start,end+1] \n"+ "", # "Will also be used for ROI selection", nmin=2,nmax=200) if not shift_the_reference: c_p["recenterings"] = Hdf5FilePath(doc=("The hdf5 file where recentering informations can be found\n" "Better if you give a refined one so that it will be the same\n" "for all spectra (will not be refined each time" ), fileme=1, gnameme=1) c_p["signaladdress"] = Hdf5FilePath(doc=("The hdf5 file where extracted data will be written\n" "Must be a free name\n"), fileme=0.5, gnameme=0, initial_value="dummy_extracted_data.h5:/data") c_p["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", initial_value = "kapraman/1000" ) c_p["resynth_scan"] =Hdf5FilePath(doc="The full path where the references can be found\n It is the last address in the preparation phase where \n resynthetizeed scan has been written\n", fileme=1, gnameme=1) c_p["reference_scan"] = aNumber( doc="This was the original Reference scan Number") # c_p["reference_scan"] = aNumber( doc="This is the scan number containing references.\n") c_p["target"] = Hdf5FilePath(doc="The hdf5 full path on which spectra will be written. Must NOT exist ", fileme=0.5, gnameme=0, initial_value = "SPECTRA.h5:/myexp") c_p["signaladdress"] .isresult = 1 c_p["target"] .isresult = 1 c_p["define_new"] = Functor_adapt( metodo ) ## ============================================================================== es_moved = collections.OrderedDict() metodo["EXTRACTION_DATA_SAMPLE_CENTERED"] = es_moved es_moved["getyaml"] = dic2yaml_extraction_scan_with_recentering es_moved["HELP"] = " Recollect the sample data, with\n the rois centered on calibration scan\n" ## , and with the recentering displacement" es_moved["method_name"] = "loadscan_2Dimages" es_moved["expdata"] = FilePath(doc="The path to the specfile", defaults2 = (c_p["spec_file"], simplecopy) ) # es_moved["roiaddress"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ # " where the ROI for calibration extraction can be found \n"+ # " Format toto.hdf5:datagroupname ", # defaults2 = ( c_p["reference_address"] , Functor_add_datagroup("", remplace_last= 3) ), # fileme=1, gnameme=1) # es_moved["roiaddress"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ # " where the ROI for calibration extraction can be found \n"+ # " Format toto.hdf5:datagroupname ", # defaults2 = ( c_p["reference_address"] , simplecopy ), # fileme=1, gnameme=1) es_moved["roiaddress"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ " where the ROI for calibration extraction can be found \n"+ " Format toto.hdf5:datagroupname ", defaults2 = ( c_p["resynth_scan"], simplecopy ), fileme=1, gnameme=1) es_moved["scans"] = many_Number4SpecScan( es_moved["expdata"] ,isinterval=1, doc= "This is the list of two number (**Use Python syntax [start,end+1]**)\n"+ " defining the interval scan numbers containing data.\n"+ "You can also give more intervals in the same list.\n"+ # " Must containing two numbers : [ start,end+1] \n"+ "", # "Will also be used for ROI selection", nmin=2,nmax=200, defaults2 = ( c_p["scans"] , simplecopy)) es_moved["scans"] .master = True if not shift_the_reference: es_moved["recenterings"] = Hdf5FilePath(doc=("The hdf5 file where recentering informations can be found\n" "Better if you give a refined one so that it will be the same\n" "for all spectra (will not be refined each time" ), defaults2 = ( c_p["recenterings"], simplecopy), fileme=1, gnameme=1) es_moved["signaladdress"] = Hdf5FilePath(doc=("The hdf5 file where extracted data will be written\n" "Must be a free name\n"), defaults2 = ( ( c_p["signaladdress"], es_moved["scans"] ) , Functor_append_numinterval_to_name() ) , fileme=0.5, gnameme=0) es_moved["signaladdress"] .always_visible = True es_moved["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", defaults2=(c_p["monitor_column"], simplecopy) ) es_moved["signaladdress"] .isresult = 1 es_moved["signaladdress"] .always_visible = True es_moved["swissknife_runner"] = swissknife_runner # ============================================================================== extract_spectra = collections.OrderedDict() metodo["EXTRACT_SPECTRA"] = extract_spectra extract_spectra["getyaml"] = dic2yaml_generic extract_spectra["HELP"] = " Extract the spectra by fitting\nthe data with superpositions\ of references" extract_spectra["method_name"] = "extract_spectra" # extract_spectra["reference_address"] =Hdf5FilePath(doc="Where the references can be foun.d\nIn general a subgroup of ROI_something\n"+ # "containing a group called scans.\n The precise scan will be indicated by reference_scan below", # defaults2 = ( c_p["reference_address"] , Functor_add_datagroup("", remplace_last= 2) ), # fileme=1, gnameme=1) extract_spectra["reference_address"] =Hdf5FilePath(doc="Where the references can be foun.d\nIn general a subgroup of ROI_something\n"+ "containing a group called scans.\n The precise scan will be indicated by reference_scan below", defaults2 = ( c_p["resynth_scan"] , functor_finalise_last_part_of_the_path(c_p["reference_scan"] ) ), # defaults2 = ( c_p["resynth_scan"] , simplecopy ), fileme=1, gnameme=1) extract_spectra["sample_address"] =Hdf5FilePath(doc="Where the sample data can be found.\nIn general a subgroup of ROI_something\n"+ "containing a goup called scans", defaults2 = ( es_moved["signaladdress"],simplecopy ), fileme=1, gnameme=1) extract_spectra["roiaddress"] =Hdf5FilePath(doc="Where the roi can be found\nA datagroup containing rois_definition", defaults2 = (es_moved["roiaddress"], simplecopy), fileme=1, gnameme=1) # extract_spectra["reference_scan"] = aNumber( doc="This is the scan number containing references.\n", # defaults2 = (c_p["reference_address"], Functor_add_datagroup("", extract_numeric= 1) )) extract_spectra["reference_scan"] = aNumber( doc="This is the scan number containing references.\n", defaults2 = ( c_p["reference_scan"] , simplecopy )) extract_spectra["scan_interval"] = many_Number4SpecScan( es_moved["expdata"] , isinterval=1, doc= "This is the list of two number (**Use Python syntax [start,end+1 ,start2,end2+1 ]**)\n"+ " defining the interval scan numbers containing data.\n"+ "Will also be used for ROI selection\n", nmin=2,nmax=200, defaults2 = ( es_moved["scans"] , simplecopy)) ## if do_deconvolution : extract_spectra["DE"] = aNumberFloat( doc="You can let this parameter to zero.\n" "In this case the step will be calculated as the smallest step from experimental scans.\n" "Otherwise this will be the energy step in eV for the resulting spectra", defaults2 = ("0.0", simplecopy) ) extract_spectra["DE"].master = True extract_spectra["zmargin"] = aNumber( doc="The used ROIs will be reduced by this number of pixels vertically, at both top and bottom of the ROI", defaults2 = ("0", simplecopy) ) extract_spectra["zmargin"].master = True if do_deconvolution : extract_spectra["niterLip"] = aNumber( doc="The number of power method iterations used for estimation of the Lipschitz factor", defaults2 = ("20", simplecopy) ) extract_spectra["niter"] = aNumber( doc="The number of Fista iterations", defaults2 = ("50", simplecopy) ) extract_spectra["beta"] = aNumberFloat( doc="Optionally you can use this L1 penalization factor if you need to regularize the problem", defaults2 = ("0.0", simplecopy) ) extract_spectra["target"] = Hdf5FilePath(doc="The hdf5 full path on which spectra will be written. Must NOT exist ", defaults2 = ( (c_p["target"], extract_spectra["scan_interval"] ) , Functor_append_numinterval_to_name() ), fileme=0.5, gnameme=0) extract_spectra["final_plot"] = Parameter_Choices( " keyword, can be 'slab' or 'sphere' " , ["PLOT", "NOPLOT" ], defaults2=( "PLOT" , simplecopy) ) extract_spectra["target"].isresult = 1 extract_spectra["target"].always_visible = True extract_spectra["swissknife_runner"] = swissknife_runner return metodo winfo_RIXS_spectra_extraction_deconvolve.py000066400000000000000000000351031412732462000353510ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/RIXS_spectrafrom __future__ import absolute_import from __future__ import division from __future__ import print_function import collections ######## Managing versioning postpending to project name ################ # Get the name of the launching script, # the launching script (an principal module) # is post pended by version name # We use this to retrieve the version import inspect import os import importlib curframe = inspect.currentframe() calframe = inspect.getouterframes(curframe, 2) global version caller = calframe[-1][1] if caller[-3:]==".py": print( " caller :" , caller , " // " , os.path.dirname(os.path.dirname(caller))) # imported by Wizard.py to see what are the subtasks version = os.path.basename(os.path.dirname(os.path.dirname(caller)))[len("XRStools"):] else: # called by the XRS_wizard script caller = os.path.basename(caller) version = caller[len("XRS_wizard"):] print( " Caller version==", version) XRStools = importlib.import_module( "XRStools"+version ) exec("from XRStools%s.WIZARD.Wizard import *"%version) ################################################################### def simplecopy(par): if hasattr(par,"value" ): res = copy.deepcopy(par.value) else: res = copy.deepcopy(par) return res class functor_sigterm_handler: def __init__(self, p): self.p = p def __call__(self, *x): print( x) print( " KILLO " , self.p.pid) os.kill(self.p.pid, signal.SIGKILL) def swissknife_runner( yamltext, where ): inputname = os.path.join(where,"input.yaml") stdname = os.path.join(where,"stdout.txt") errname = os.path.join(where,"stderr.txt") open(inputname,"w").write(yamltext) # os.system("XRS_swissknife %s 1> %s 2>%s"%(inputname,stdname, errname ) ) #p = subprocess.call( string.split("XRS_swissknife %s 1> %s 2> %s"%(inputname,stdname, errname )) , shell=False, stdout= subprocess .PIPE ) # preexec_fn=os.setsid # p= subprocess.Popen(string.split( "XRS_swissknife %s 1> %s 2>%s"%(inputname,stdname, errname )) , shell=False, stdout= subprocess .PIPE , stderr= subprocess .PIPE ) stdout = open(stdname,"w") stderr = open(errname,"w") p= subprocess.Popen(( "XRS_swissknife%s %s 1> %s 2>%s"%(version,inputname,stdname, errname )).split() , shell=False, stdout= stdout , stderr= stderr ) # p= subprocess.Popen( "XRS_swissknife %s 1> %s 2>%s"%(inputname,stdname, errname ) , shell=True ) signal.signal(signal.SIGTERM, functor_sigterm_handler(p)) p.communicate() # import time # time.sleep(100) # p.communicate() class Functor_adapt: def __init__(self, dico): self.dico=dico def __call__(self): chiavi = list(self.dico.keys())[1:] for c in chiavi : subdic = self.dico[c] for name,par in subdic.items(): if isinstance(par,Parametro): if par.defaults2 is not None: source, method = par.defaults2 if hasattr(source,"value" ): par.value = method(source.value) else: par.value = method(source) return self.dico["CREATION_PARS"]["reference_address"].value+"__"+ self.dico["CREATION_PARS"]["scans"] .value def dic2yaml_extraction_scan_with_recentering(dicodic): s = "loadscan_2Dimages :\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() s = s+ " roiaddress : %s \n" % dicodic["roiaddress"].render() s = s+ " scan_interval : %s \n" % dicodic["scans"].render() s = s+ " monitor_column : %s \n" % dicodic["monitor_column"].render() ## s = s+ " recenterings : %s \n" % dicodic["recenterings"].render() s = s+ " signaladdress : %s \n" % dicodic["signaladdress"].render() s = s+""" sumto1D : 0 energycolumn : 'Anal Energy' monitorcolumn : %s """ % dicodic["monitor_column"].render() return s def dic2yaml_generic(dicodic): s = "%s :\n"%dicodic["method_name"] for n,t in dicodic.items(): if isinstance(t, Parametro): s = s +" %s : %s\n"%(n,t.render()) return s def getMethod(options): metodo = collections.OrderedDict() c_p = collections.OrderedDict() metodo["CREATION_PARS"] = c_p c_p["spec_file"] = FilePath(doc="The path to the specfile\n file must exist", fileme=1) # c_p["roiaddress"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ # " where the ROI for calibration extraction can be found \n"+ # " Format toto.hdf5:datagroupname ", # fileme=1, gnameme=1) c_p["scans"] = many_Number4SpecScan( c_p["spec_file"] ,isinterval=1, doc= "This is the list of two number (**Use Python syntax [start,end+1]**)\n"+ " defining the interval scan numbers containing data.\n"+ "You can also give more intervals in the same list.\n"+ # " Must containing two numbers : [ start,end+1] \n"+ "", # "Will also be used for ROI selection", nmin=2,nmax=200) if 0: c_p["recenterings"] = Hdf5FilePath(doc=("The hdf5 file where recentering informations can be found\n" "Better if you give a refined one so that it will be the same\n" "for all spectra (will not be refined each time" ), fileme=1, gnameme=1) c_p["signaladdress"] = Hdf5FilePath(doc=("The hdf5 file where extracted data will be written\n" "Must be a free name\n"), fileme=0.5, gnameme=0) c_p["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", initial_value = "kapraman/1000" ) c_p["reference_address"] =Hdf5FilePath(doc="The full path where the references can be found\n It is a groupname ending by ScanXXX contained in \n"+ " group called scans", fileme=1, gnameme=1) # c_p["reference_scan"] = aNumber( doc="This is the scan number containing references.\n") c_p["target"] = Hdf5FilePath(doc="The hdf5 full path on which spectra will be written. Must NOT exist ", fileme=0.5, gnameme=0) c_p["signaladdress"] .isresult = 1 c_p["target"] .isresult = 1 c_p["define_new"] = Functor_adapt( metodo ) ## ============================================================================== es_moved = collections.OrderedDict() metodo["EXTRACTION_DATA_SAMPLE_CENTERED"] = es_moved es_moved["getyaml"] = dic2yaml_extraction_scan_with_recentering es_moved["HELP"] = " Recollect the sample data, with\n the rois centered on calibration scan\n" ## , and with the recentering displacement" es_moved["method_name"] = "loadscan_2Dimages" es_moved["expdata"] = FilePath(doc="The path to the specfile", defaults2 = (c_p["spec_file"], simplecopy) ) es_moved["roiaddress"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ " where the ROI for calibration extraction can be found \n"+ " Format toto.hdf5:datagroupname ", defaults2 = ( c_p["reference_address"] , Functor_add_datagroup("", remplace_last= 3) ), fileme=1, gnameme=1) es_moved["scans"] = many_Number4SpecScan( es_moved["expdata"] ,isinterval=1, doc= "This is the list of two number (**Use Python syntax [start,end+1]**)\n"+ " defining the interval scan numbers containing data.\n"+ "You can also give more intervals in the same list.\n"+ # " Must containing two numbers : [ start,end+1] \n"+ "", # "Will also be used for ROI selection", nmin=2,nmax=200, defaults2 = ( c_p["scans"] , simplecopy)) if 0: es_moved["recenterings"] = Hdf5FilePath(doc=("The hdf5 file where recentering informations can be found\n" "Better if you give a refined one so that it will be the same\n" "for all spectra (will not be refined each time" ), defaults2 = ( c_p["recenterings"], simplecopy), fileme=1, gnameme=1) es_moved["signaladdress"] = Hdf5FilePath(doc=("The hdf5 file where extracted data will be written\n" "Must be a free name\n"), defaults2 = ( ( c_p["signaladdress"], es_moved["scans"] ) , Functor_append_numinterval_to_name() ) , fileme=0.5, gnameme=0) es_moved["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", defaults2=(c_p["monitor_column"], simplecopy) ) es_moved["signaladdress"] .isresult = 1 es_moved["swissknife_runner"] = swissknife_runner # ============================================================================== extract_spectra = collections.OrderedDict() metodo["EXTRACT_SPECTRA"] = extract_spectra extract_spectra["getyaml"] = dic2yaml_generic extract_spectra["HELP"] = " Extract the spectra by fitting\nthe data with superpositions\ of references" extract_spectra["method_name"] = "extract_spectra" extract_spectra["reference_address"] =Hdf5FilePath(doc="Where the references can be foun.d\nIn general a subgroup of ROI_something\n"+ "containing a group called scans.\n The precise scan will be indicated by reference_scan below", defaults2 = ( c_p["reference_address"] , Functor_add_datagroup("", remplace_last= 2) ), fileme=1, gnameme=1) extract_spectra["sample_address"] =Hdf5FilePath(doc="Where the sample data can be found.\nIn general a subgroup of ROI_something\n"+ "containing a goup called scans", defaults2 = ( es_moved["signaladdress"],simplecopy ), fileme=1, gnameme=1) extract_spectra["roiaddress"] =Hdf5FilePath(doc="Where the roi can be found\nA datagroup containing rois_definition", defaults2 = (es_moved["roiaddress"], simplecopy), fileme=1, gnameme=1) extract_spectra["reference_scan"] = aNumber( doc="This is the scan number containing references.\n", defaults2 = (c_p["reference_address"], Functor_add_datagroup("", extract_numeric= 1) )) extract_spectra["scan_interval"] = many_Number4SpecScan( es_moved["expdata"] , isinterval=1, doc= "This is the list of two number (**Use Python syntax [start,end+1 ,start2,end2+1 ]**)\n"+ " defining the interval scan numbers containing data.\n"+ "Will also be used for ROI selection\n", nmin=2,nmax=200, defaults2 = ( es_moved["scans"] , simplecopy)) extract_spectra["DE"] = aNumberFloat( doc="The energy stem in eV for the resulting spectra", defaults2 = ("0.02", simplecopy) ) extract_spectra["zmargin"] = aNumber( doc="The used ROIs will be reduced by this number of pixels vertically, at both top and bottom of the ROI", defaults2 = ("4", simplecopy) ) extract_spectra["niterLip"] = aNumber( doc="The number of power method iterations used for estimation of the Lipschitz factor", defaults2 = ("20", simplecopy) ) extract_spectra["niter"] = aNumber( doc="The number of Fista iterations", defaults2 = ("50", simplecopy) ) extract_spectra["beta"] = aNumberFloat( doc="Optionally you can use this L1 penalization factor if you need to regularize the problem", defaults2 = ("0.0", simplecopy) ) extract_spectra["target"] = Hdf5FilePath(doc="The hdf5 full path on which spectra will be written. Must NOT exist ", defaults2 = ( ("extracted_spectra.h5:/spectra_scan", extract_spectra["scan_interval"] ) , Functor_append_numinterval_to_name() ), fileme=0.5, gnameme=0) extract_spectra["target"].isresult = 1 extract_spectra["swissknife_runner"] = swissknife_runner return metodo winfo_RIXS_spectra_extraction_preparation.py000066400000000000000000000777431412732462000355510ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/RIXS_spectrafrom __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import os import inspect ######## Managing versioning postpending to project name ################ # Get the name of the launching script, # the launching script (an principal module) # is post pended by version name # We use this to retrieve the version import inspect import os import importlib curframe = inspect.currentframe() calframe = inspect.getouterframes(curframe, 2) global version caller = calframe[-1][1] if caller[-3:]==".py": print( " caller :" , caller , " // " , os.path.dirname(os.path.dirname(caller))) # imported by Wizard.py to see what are the subtasks version = os.path.basename(os.path.dirname(os.path.dirname(caller)))[len("XRStools"):] else: # called by the XRS_wizard script caller = os.path.basename(caller) version = caller[len("XRS_wizard"):] print( " Caller version==", version) version = caller[len("XRS_wizard"):] print( " Caller version", version) XRStools = importlib.import_module( "XRStools"+version ) exec("from XRStools%s.WIZARD.Wizard import *"%version) ################################################################### import yaml " modification to test branching " def dic2yaml_generic(dicodic): s = "%s :\n"%dicodic["method_name"] for n,t in dicodic.items(): if isinstance(t, Parametro): s = s +" %s : %s\n"%(n,t.render()) return s class functor_sigterm_handler: def __init__(self, p): self.p = p def __call__(self, *x): print( x) print( " KILLO " , self.p.pid) os.kill(self.p.pid, signal.SIGKILL) def swissknife_runner( yamltext, where, ret_dico ): mydata = yaml.load(yamltext) mname, mydata = list(mydata.items())[0] print( mydata) if "MPI_N_PROCS" in mydata: MPI_N_PROCS = mydata["MPI_N_PROCS"] else: MPI_N_PROCS = 1 print( " MPI_N_PROCS " , MPI_N_PROCS) inputname = os.path.join(where,"input.yaml") stdname = os.path.join(where,"stdout.txt") errname = os.path.join(where,"stderr.txt") ret_dico["input"] =inputname ret_dico["stdout"]=stdname ret_dico["stderr"]=errname open(inputname,"w").write(yamltext) stdout = open(stdname,"w") stderr = open(errname,"w") if not sys.platform=="win32": if MPI_N_PROCS==1: p= subprocess.Popen(( "XRS_swissknife%s %s 1> %s 2>%s"%(version, inputname,stdname, errname )).split() , shell=False, stdout= stdout , stderr= stderr ) else: p= subprocess.Popen(( "mpirun -n %d XRS_swissknife%s %s 1> %s 2>%s"%(version, MPI_N_PROCS , inputname,stdname, errname )) .split() , shell=False, stdout= stdout , stderr= stderr ) else: packages = (os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(XRStools.__file__ ) )))) script = os.path.join(packages,"scripts","XRS_swissknife%s.bat"%version ) if MPI_N_PROCS==1: p= subprocess.Popen(( "%s %s"%(script, inputname)) .split() , shell=False, stdout= stdout , stderr= stderr ) else: p= subprocess.Popen(( "mpiexec -n %d %s %s"%(MPI_N_PROCS ,script, inputname) ) .split() , shell=False, stdout= stdout , stderr= stderr ) signal.signal(signal.SIGTERM, functor_sigterm_handler(p)) p.communicate() ret_dico["return_code"]=p.returncode class Functor_adapt: def __init__(self, dico): self.dico=dico def __call__(self): chiavi = list(self.dico.keys())[1:] for c in chiavi : subdic = self.dico[c] for name,par in subdic.items(): if isinstance(par,Parametro): if par.defaults2 is not None: source, method = par.defaults2 print( source) par.value = method(source) return self.dico["CREATION_PARS"]["result_file"].value def dic2yaml_create_rois(dicodic): s = "create_rois:\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() tok = dicodic["filter_path"].render() if tok not in ["None",None]: s = s+ " filter_path : %s \n" % tok s = s+ " scans : %s \n" % dicodic["scans"].render () s = s+ " roiaddress : %s \n" % dicodic["result_file"].render() return s def dic2yaml_extraction_scan(dicodic): s = "loadscan_2Dimages :\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() s = s+ " roiaddress : %s \n" % dicodic["roiaddress"].render() s = s+ " monitor_column : %s \n" % dicodic["monitor_column"].render() s = s+ " scan_interval : %s \n" % dicodic["scans"].render() s = s+ " signaladdress : %s \n" % dicodic["signaladdress"].render() s = s+""" sumto1D : 0 energycolumn : 'Anal Energy' monitorcolumn : %s """ % dicodic["monitor_column"].render() return s class Functor_Compose_A_Scan_Address: def __init__(self, take_first=0): self.take_first = take_first def __call__(self,par): a,b = par print( " ====================== " ) print( a,b) va=a.getFullValue() vb=b.getValue() print( va, vb) if isinstance(vb,int): return va+"/scans/Scan%03d/"%vb elif isinstance(vb,list): return va+"/scans/Scan%03d/"%vb[0] else: raise Exception(" unprepared for this instance : "+str(vb) +" from "+ str(b.value)) class Functor_Compose_An_Absolute_Address: def __init__(self, take_first=0, is_retrieved_scan=0): self.take_first = take_first self.is_retrieved_scan = is_retrieved_scan def __call__(self,par): if self.is_retrieved_scan: par, N = par if hasattr(N,"value"): N=N.value if isinstance(par, hdf5_relative_path): b = par a = b.base_h5file else: a,b = par print( " ====================== " ) print( a,b) va=a.getFullValue() vb=b.getValue() print( va, vb) res = va+"/"+vb if self.is_retrieved_scan: res = res+"/scans/Scan%03d"%int(N) return res class Functor_compose_recentering_name: def __init__(self,expd, scans): self.expd = expd self.scans = scans def __call__(self, fn): if isinstance(fn, Parametro): fn = fn.render() # a,b = string.split(fn,"/") expd = self.expd.render() expd=expd.replace("/","_") expd=expd.replace("\\","_") scans = self.scans.getValue() i,f = scans[:2] # name = a+expd+"/"+b+"_"+str(i)+"_"+str(f) name = fn +"/recentering"+"_"+str(i)+"_"+str(f) return name def dic2yaml_calculate_recenterings(dicodic): s = "calculate_recenterings :\n" s = s+ " bariA : %s \n" % dicodic["bariA"].render() s = s+ " bariB : %s \n" % dicodic["bariB"].render() s = s+ " target : %s \n" % dicodic["recenterings"].render() return s def dic2yaml_extraction_scan_with_recentering(dicodic): s = "loadscan_2Dimages :\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() s = s+ " roiaddress : %s \n" % dicodic["roiaddress"].render() s = s+ " scan_interval : %s \n" % dicodic["scans"].render() s = s+ " monitor_column : %s \n" % dicodic["monitor_column"].render() s = s+ " recenterings : %s \n" % dicodic["recenterings"].render() s = s+ " recenterings_confirmed : %s \n" % dicodic["recenterings_refined"].render() s = s+ " signaladdress : %s \n" % dicodic["signaladdress"].render() s = s+""" sumto1D : 0 energycolumn : 'Anal Energy' monitorcolumn : %s """ % dicodic["monitor_column"].render() return s def dic2yaml_response_fit(dicodic): print( list(dicodic.keys())) s = "superR_fit_responses:\n" s = s+ " foil_scan_address : %s \n" % dicodic["response_scan_address"].render() s = s+ " nref : %s \n" % dicodic["nref"].render () s = s+ " niter_optical : %s \n" % dicodic["niter_optical"].render () s = s+ " beta_optical : %s \n" % dicodic["beta"].render () s = s+ " niter_global : %s \n" % dicodic["niter_global"].render () s = s+ " pixel_dim : 1 \n" s = s+ " niter_pixel : 0 \n" s = s+ " beta_pixel : 0 \n" s = s+ " do_refine_trajectory : 1 \n" s = s+ " simmetrizza : 1\n" s = s+ " filter_rois : 0\n" s = s+ " target_file : %s \n" % dicodic["result_file"].render() s = s+ " MPI_N_PROCS : %s \n" % dicodic["MPI_N_PROCS"].render () s = s+ " fit_lines : 1 \n" return s class Functor_set_new_root: def __init__(self): pass def __call__(self,par): a,b = par va=a.getFullValue() print( va, b) return va+"/"+b def dic2yaml_resynt_fit(dicodic): s = "superR_recreate_rois :\n" s = s+ " responsefilename : %s \n" % dicodic["responsefilename"].render() s = s+ " nex : 0 \n" s = s+ " old_scan_address : %s \n" % dicodic["response_scan_address"].render () if "recenterings_refined" in dicodic and dicodic["recenterings_refined"].render () not in ["", None]: s = s+ " recenterings_refined : %s \n" % dicodic["recenterings_refined"].render () s = s+ " filter_rois : 0\n" s = s+ " target_filename : %s \n" % dicodic["result_file"].render() return s def getMethod(options): shift_the_reference = True if "shift_the_reference" in options: shift_the_reference = options["shift_the_reference"] metodo = collections.OrderedDict() c_p = collections.OrderedDict() metodo["CREATION_PARS"] = c_p c_p["spec_file"] = FilePath(doc="The path to the specfile\n file must exist", fileme=1) c_p["reference_scan"] = aNumber4SpecScan(c_p["spec_file"] ,"This is the scan number containing references.\n Will also be used for ROI selection") c_p["sample_scans"] = many_Number4SpecScan(c_p["spec_file"] , "This defines the list(**Use Python syntax [1,2,3]**)\n"+ "of scan numbers containing data.\n"+ # " Must containing two numbers : [ start,end+1] \n"+ "Will also be used for ROI selection", nmin=1,nmax=100) c_p["filter_path"] = Hdf5FilePath(doc="OPTIONAL(you can let it to None)\nThe default hdf5 file AND path where the filter is stored\n must be a matrix dataset with 1 for good pixels , 0 for bad ones.\n the format is filename:groupname. ", fileme=1, gnameme=1, canbeNone=True) c_p["result_file"] = Hdf5FilePath(doc="The default hdf5 file where results will be stored\n must be a new file.\n the format is filename:groupname. The datagroup name must be a new one for the entry to be ready", fileme=0.5, gnameme=0) c_p["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", initial_value = "kapraman/1000" ) c_p["result_file"] .isresult = 1 c_p["define_new"] = Functor_adapt( metodo ) c_r = collections.OrderedDict() ## ====================================================================== metodo["CREATION_ROIS"] = c_r c_r["HELP"] = """ Create the ROIS for sample data""" c_r["getyaml"] = dic2yaml_create_rois , {"Reference_scan":"scans","SpecExpFile":"expdata"} c_r["swissknife_runner"] = swissknife_runner c_r["method_name"] = "create_rois" c_r["SpecExpFile"] = FilePath(doc="The path to the specfile", defaults2 = (c_p["spec_file"], simplecopy) ) c_r["SpecExpFile"].master = 1 c_r["Reference_scan"] = aNumber4SpecScan( c_r["SpecExpFile"] , doc="This is the scan number containing references.\n"+ " Will be used here for ROI selection", defaults2 = (c_p["reference_scan"], simplecopy), rendering = render_numero_in_una_lista ) c_r["filter_path"] = Hdf5FilePath(doc="OPTIONAL(you can let it to None)\nThe default hdf5 file AND path where the filter is stored\n must be a matrix dataset with 1 for good pixels , 0 for bad ones.\n the format is filename:groupname. ", fileme=1, gnameme=1, canbeNone=True, defaults2 = (c_p["filter_path"], simplecopy)) c_r["Reference_scan"].master = 1 c_r["result_file"] = Hdf5FilePath(doc="The hdf5 file and datagroup where results will be stored. Format toto.hdf5:datagroupname", defaults2 = (c_p["result_file"], Functor_add_datagroup("ROI_AS_SELECTED") ), fileme=0.5, gnameme=0) c_r["result_file"].isresult = 1 c_r["result_file"].always_visible = True ## ============================================================================== c_r_sample = collections.OrderedDict() c_r_sample["HELP"] = """ Create the ROIS for sample data""" c_r_sample["getyaml"] = dic2yaml_create_rois metodo["CREATION_ROIS_SAMPLE"] = c_r_sample c_r_sample["method_name"] = "create_rois" c_r_sample["expdata"] = FilePath(doc="The path to the specfile", defaults2 = (c_p["spec_file"], simplecopy) ) c_r_sample["scans"] = many_Number4SpecScan( c_r_sample["expdata"] , "This is the list(**Use Python syntax [1,2,3]**)\n"+ "of scan numbers containing data.\n"+ "Will be used here for ROI selection\n", # " Must containing two numbers : [ start,end+1] \n", nmin=1,nmax=100, defaults2 = (c_p["sample_scans"], simplecopy)) c_r_sample["scans"] .master = True c_r_sample["filter_path"] = Hdf5FilePath(doc="OPTIONAL(you can let it to None)\nThe default hdf5 file AND path where the filter is stored\n must be a matrix dataset with 1 for good pixels , 0 for bad ones.\n the format is filename:groupname. ", fileme=1, gnameme=1, canbeNone=True, defaults2 = (c_p["filter_path"], simplecopy)) c_r_sample["result_file"] = Hdf5FilePath(doc="The hdf5 file and datagroup where results will be stored. Format toto.hdf5:datagroupname", defaults2 = (c_p["result_file"], Functor_add_datagroup("ROI_FROMSAMPLE") ), fileme=0.5, gnameme=0) c_r_sample["result_file"] .always_visible = True c_r_sample["result_file"].isresult = 1 c_r_sample["swissknife_runner"] = swissknife_runner ## ============================================================================== ed_calib = collections.OrderedDict() ed_calib["HELP"] = """ Collects the calibration scans """ ed_calib["getyaml"] = dic2yaml_extraction_scan metodo["EXTRACTION_DATA_CALIBRATION"] = ed_calib ed_calib["method_name"] = "loadscan_2Dimages" ed_calib["expdata"] = FilePath(doc="The path to the specfile", defaults2 = (c_r["SpecExpFile"], simplecopy) ) ed_calib["roiaddress"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ " where the ROI for calibration extraction can be found \n"+ " Format toto.hdf5:datagroupname ", defaults2 = (c_r["result_file"], simplecopy ), fileme=1, gnameme=1) ed_calib["scans"] = aNumber4SpecScan( ed_calib["expdata"] , doc="This is the scan number containing references.\n", defaults2 = (c_r["Reference_scan"], simplecopy), rendering = render_numero_in_una_lista_piu_uno ) ed_calib["scans"] .master = True ed_calib["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", defaults2=(c_p["monitor_column"], simplecopy) ) ed_calib["monitor_column"] .master = True ed_calib["signaladdress"] = hdf5_relative_path( ed_calib["roiaddress"] , doc="The datagroup path, relative to roiaddress,\n"+ "where extracted data will be written. Must be a new name", defaults2 = ( "calibration_scan" , simplecopy ) , gnameme = 0) ed_calib["signaladdress"] .always_visible = True ed_calib["swissknife_runner"] = swissknife_runner ed_calib["signaladdress"] .isresult = 1 ## ============================================================================== es_nonmoved = collections.OrderedDict() es_nonmoved["HELP"] = """ Collects the scans data for the sample, based on the sample ROIS""" es_nonmoved["getyaml"] = dic2yaml_extraction_scan metodo["EXTRACTION_DATA_SAMPLE_UNCENTERED"] = es_nonmoved es_nonmoved["method_name"] = "loadscan_2Dimages" es_nonmoved["expdata"] = FilePath(doc="The path to the specfile", defaults2 = (ed_calib["expdata"], simplecopy) ) es_nonmoved["roiaddress"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ " where the ROI for calibration extraction can be found \n"+ " Format toto.hdf5:datagroupname ", defaults2 = (c_r_sample["result_file"] , simplecopy ), fileme=1, gnameme=1) es_nonmoved["roiaddress"] .always_visible = True es_nonmoved["scans"] = many_Number4SpecScan( es_nonmoved["expdata"] ,isinterval=1, doc= "This is the list of two number (**Use Python syntax [start,end+1]**)\n"+ " defining the intervals scan numbers containing data.\n"+ "Will also be used for ROI selection\n"+ "You can also give more intervals : [start,end+1,start2,end2+1] in the same list \n" "EXAMPLE [263,264] will read only scan 263\n" " [263,264, 270, 276] will read 263, 270,271,272,273,274,275\n" # " Must containing two numbers : [ start,end+1] \n"+ "", # "Will also be used for ROI selection", nmin=2,nmax=200, defaults2 = (c_r_sample["scans"], makeintervalfromfirst)) # es_nonmoved["scans"].master = True es_nonmoved["signaladdress"] = hdf5_relative_path( c_r_sample["result_file"] , doc="The datagroup path, relative to roiaddress,\n"+ "where extracted data will be written. Must be a new name", defaults2 = ( "uncentered_sample_scans" , simplecopy ) , gnameme = 0) es_nonmoved["signaladdress"].always_visible = True es_nonmoved["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", defaults2=(c_p["monitor_column"], simplecopy) ) es_nonmoved["signaladdress"] .isresult = 1 es_nonmoved["swissknife_runner"] = swissknife_runner ## ============================================================================== calc_rec = collections.OrderedDict() metodo["CALCULATE_RECENTERINGS"] = calc_rec calc_rec["HELP"] = "Calculates the horizontal shifts to go from baricenters A to baricenters B" calc_rec["getyaml"] = dic2yaml_calculate_recenterings calc_rec["bariA"] = Hdf5FilePath(doc=" The scan on which baricenters are calculated for sample. (hdf5 full path. must exist) ", defaults2 = ( (es_nonmoved["signaladdress"], es_nonmoved["scans"] ) , Functor_Compose_A_Scan_Address(take_first=1) ), fileme=1, gnameme=1) calc_rec["bariB"] = Hdf5FilePath(doc=" The scan on which baricenters are calculated for reference. (hdf5 full path. must exist) ", defaults2 = ( (ed_calib["signaladdress"], ed_calib["scans"] ) , Functor_Compose_A_Scan_Address() ), fileme=1, gnameme=1) calc_rec["recenterings"] = Hdf5FilePath(doc=("The hdf5 full path on which shifts will be written. Must NOT exist\n" "The shifts informations will consist in the baricenter goal B,\n" " and the starting baricenter B"), defaults2 = ( c_p["result_file"] , Functor_compose_recentering_name( c_r["SpecExpFile"] ,es_nonmoved["scans"] ) ), fileme=0.5, gnameme=0) calc_rec["recenterings"] .always_visible = True calc_rec["recenterings"].isresult = 1 calc_rec["swissknife_runner"] = swissknife_runner ## ============================================================================== es_moved = collections.OrderedDict() metodo["EXTRACTION_DATA_SAMPLE_CENTERED"] = es_moved es_moved["getyaml"] = dic2yaml_extraction_scan_with_recentering es_moved["HELP"] = " Recollect the sample data, with\n the rois centered on calibration scan\n, and with the recentering displacement" es_moved["method_name"] = "loadscan_2Dimages" es_moved["expdata"] = FilePath(doc="The path to the specfile", defaults2 = (c_r["SpecExpFile"], simplecopy) ) es_moved["roiaddress"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ " where the ROI for calibration extraction can be found \n"+ " Format toto.hdf5:datagroupname ", defaults2 = (c_r["result_file"], simplecopy ), fileme=1, gnameme=1) es_moved["scans"] = many_Number4SpecScan( es_moved["expdata"] ,isinterval=1, doc= "This is the list of two number (**Use Python syntax [start,end+1]**)\n"+ " defining the interval scan numbers containing data.\n"+ "You can also give more intervals in the same list.\n"+ # " Must containing two numbers : [ start,end+1] \n"+ "", # "Will also be used for ROI selection", nmin=2,nmax=200, defaults2 = ( es_nonmoved["scans"] , simplecopy)) # es_moved["scans"].master = True es_moved["recenterings"] = Hdf5FilePath(doc="The hdf5 file where recentering informations can be found", defaults2 = ( calc_rec["recenterings"], simplecopy), fileme=1, gnameme=1) es_moved["recenterings_refined"] = Hdf5FilePath(doc=("The hdf5 full path on which shifts will be written. Must NOT exist\n" "The shift informations will consist of the refined shift"), defaults2 = ( es_moved["recenterings"] , Functor_add_datagroup("confirmed_shift", remplace_last= True, keep_last_numbers=True)), fileme=0.5, gnameme=0) es_moved["recenterings_refined"] .always_visible = True es_moved["signaladdress"] = hdf5_relative_path( es_moved["roiaddress"] , doc="The datagroup path, relative to roiaddress,\n"+ "where extracted data will be written. Must be a new name", defaults2 = ( "Centered_sample_scans" , simplecopy ) , gnameme = 0) es_moved["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", defaults2=(es_nonmoved["monitor_column"], simplecopy) ) es_moved["signaladdress"] .isresult = 1 es_moved["recenterings_refined"] .isresult = 1 es_moved["swissknife_runner"] = swissknife_runner # ============================================================================================================= f_r = collections.OrderedDict() metodo["RESPONSE_FIT"] = f_r f_r["HELP"] = """ The method first find an estimation of the response scan trajectory on each roi then, based on this, obtain a fit of the response function assuming a flat pixel response. There is a final phase where a global optimisation is done in niter_global steps. Each step is composed of response functionfit, followed by a trajectory fit. """ f_r["getyaml"] = dic2yaml_response_fit f_r["swissknife_runner"] = swissknife_runner f_r["method_name"] = "response_fit" f_r["response_scan_address"] = Hdf5FilePath(doc="The full address of the scan which measures the response", defaults2 = ((ed_calib["signaladdress"], ed_calib["scans"] ) , Functor_Compose_An_Absolute_Address( is_retrieved_scan=1)), fileme=1, gnameme=1) f_r["nref"] = aNumber( doc="The number of subdivision per pixel dimension used to represent\n"+ "the optical response function at higher resolution", defaults2 = ("2", simplecopy) ) f_r["nref"].master = True f_r["niter_optical"] = aNumber( doc="the number of iterations used in the \n"+ " optimisation of the optical response", defaults2 = ("50", simplecopy) ) f_r["niter_optical"] .master = True f_r["beta"] = aNumberFloat( doc="The L1 norm factor in the regularisation\n"+ "term for the response functions", defaults2 = ("0.1", simplecopy) ) f_r["beta"].master = True f_r["niter_global"] = aNumber( doc="Each step is composed of optical response fit, followed \n"+ " trajectory reoptimisation. All this niter_global times ", defaults2 = ("2", simplecopy) ) f_r["niter_global"].master = True f_r["MPI_N_PROCS"] = aNumber( doc="If bigger than 1, run it with mpi.\n" " useful if you have several ROIs \n" " It is not useful to haveMPI_N_PROCS > n rois \n", defaults2 = ("1", simplecopy) ) f_r["MPI_N_PROCS"].master = True f_r["result_file"] = Hdf5FilePath(doc=("The default hdf5 file where results will be stored\n" " must be a new address.\n" " the format is filename:groupname.\n" " The datagroup name must be a new one for the entry to be ready") , defaults2 = ( f_r["response_scan_address"] , Functor_add_datagroup("ScanFittedResponse", transform_last= 1)), fileme=0.5, gnameme=0) f_r["result_file"].isresult = 1 f_r["result_file"].always_visible = True ## ====================================================================== r_r = collections.OrderedDict() metodo["RESYNTHETISE_FIT"] = r_r r_r["HELP"] = """ Recreate the reference scan """ r_r["getyaml"] = dic2yaml_resynt_fit, {"ResynthScan":"result_file"} r_r["swissknife_runner"] = swissknife_runner r_r["method_name"] = "Scan Resynth." r_r["responsefilename"] = Hdf5FilePath(doc="The address on which the fitted responsed was written", defaults2 = (f_r["result_file"] , simplecopy), fileme=1, gnameme=1) r_r["response_scan_address"] = Hdf5FilePath(doc="The original address of the scan which measures the response", defaults2 = (f_r["response_scan_address"] , simplecopy), fileme=1, gnameme=1) if shift_the_reference: r_r["recenterings_refined"] = Hdf5FilePath(doc=("The hdf5 full path containing the refined shift"), defaults2 = ( es_moved["recenterings_refined"] , simplecopy ), fileme=1, gnameme=1) r_r["ResynthScan"] = Hdf5FilePath(doc="The default hdf5 file where results will be stored\n"+ "must be a new file.\n"+ " The format is filename:groupname. \n"+ "The datagroup name must be a new one\n"+ "for the entry to be ready", defaults2 = ( f_r["result_file"], Functor_add_datagroup("ScanReSynth", transform_last= 1) ), fileme=0.5, gnameme=0) r_r["ResynthScan"].isresult = 1 r_r["ResynthScan"].always_visible = True return metodo xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/RIXS_spectra/winfo_response_denoiser.py000066400000000000000000000221761412732462000321720ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections ######## Managing versioning postpending to project name ################ # Get the name of the launching script, # the launching script (an principal module) # is post pended by version name # We use this to retrieve the version import inspect import os import importlib curframe = inspect.currentframe() calframe = inspect.getouterframes(curframe, 2) global version caller = calframe[-1][1] if caller[-3:]==".py": print( " caller :" , caller , " // " , os.path.dirname(os.path.dirname(caller))) # imported by Wizard.py to see what are the subtasks version = os.path.basename(os.path.dirname(os.path.dirname(caller)))[len("XRStools"):] else: # called by the XRS_wizard script caller = os.path.basename(caller) version = caller[len("XRS_wizard"):] print( " Caller version==", version) XRStools = importlib.import_module( "XRStools"+version ) exec("from XRStools%s.WIZARD.Wizard import *"%version) ################################################################### import yaml class functor_sigterm_handler: def __init__(self, p): self.p = p def __call__(self, *x): print( x) print( " KILLO " , self.p.pid) os.kill(self.p.pid, signal.SIGKILL) def swissknife_runner( yamltext, where ): mydata = yaml.load(yamltext) mname, mydata = list(mydata.items())[0] print( mydata) if "MPI_N_PROCS" in mydata: MPI_N_PROCS = mydata["MPI_N_PROCS"] else: MPI_N_PROCS = 1 print( " MPI_N_PROCS " , MPI_N_PROCS) inputname = os.path.join(where,"input.yaml") stdname = os.path.join(where,"stdout.txt") errname = os.path.join(where,"stderr.txt") open(inputname,"w").write(yamltext) stdout = open(stdname,"w") stderr = open(errname,"w") if MPI_N_PROCS==1: p= subprocess.Popen(( "XRS_swissknife%s %s 1> %s 2>%s"%(version, inputname,stdname, errname )) .split() , shell=False, stdout= stdout , stderr= stderr ) else: p= subprocess.Popen(( "mpirun -n %d XRS_swissknife%s %s 1> %s 2>%s"%(version, MPI_N_PROCS , inputname,stdname, errname )).split() , shell=False, stdout= stdout , stderr= stderr ) signal.signal(signal.SIGTERM, functor_sigterm_handler(p)) p.communicate() # import time # time.sleep(100) # p.communicate() class Functor_adapt: def __init__(self, dico): self.dico=dico def __call__(self): chiavi = list(self.dico.keys())[1:] for c in chiavi : subdic = self.dico[c] for name,par in subdic.items(): if isinstance(par,Parametro): if par.defaults2 is not None: source, method = par.defaults2 if hasattr(source,"value" ): par.value = method(source.value) else: par.value = method(source) return self.dico["CREATION_PARS"]["result_file"].value class Functor_set_new_root: def __init__(self): pass def __call__(self,par): a,b = par va=a.getFullValue() print( va, b) return va+"/"+b def dic2yaml_resynt_fit(dicodic): s = "superR_recreate_rois :\n" s = s+ " responsefilename : %s \n" % dicodic["responsefilename"].render() s = s+ " nex : 0 \n" s = s+ " old_scan_address : %s \n" % dicodic["response_scan_address"].render () s = s+ " filter_rois : 0\n" s = s+ " target_filename : %s \n" % dicodic["result_file"].render() return s def dic2yaml_response_fit(dicodic): print( list(dicodic.keys())) s = "superR_fit_responses:\n" s = s+ " foil_scan_address : %s \n" % dicodic["response_scan_address"].render() s = s+ " nref : %s \n" % dicodic["nref"].render () s = s+ " niter_optical : %s \n" % dicodic["niter_optical"].render () s = s+ " beta_optical : %s \n" % dicodic["beta"].render () s = s+ " niter_global : %s \n" % dicodic["niter_global"].render () s = s+ " pixel_dim : 1 \n" s = s+ " niter_pixel : 0 \n" s = s+ " beta_pixel : 0 \n" s = s+ " do_refine_trajectory : 1 \n" s = s+ " simmetrizza : 1\n" s = s+ " filter_rois : 0\n" s = s+ " target_file : %s \n" % dicodic["result_file"].render() s = s+ " MPI_N_PROCS : %s \n" % dicodic["MPI_N_PROCS"].render () s = s+ " fit_lines : 1 \n" return s def getMethod(options): metodo = collections.OrderedDict() c_p = collections.OrderedDict() metodo["CREATION_PARS"] = c_p c_p["response_scan_address"] = Hdf5FilePath(doc="The full address of the scan which measures the response", fileme=1, gnameme=1) c_p["result_file"] = Hdf5FilePath(doc="The default hdf5 file where results will be stored\n must be a new file.\n the format is filename:groupname. The datagroup name must be a new one for the entry to be ready", fileme=0.5, gnameme=0) c_p["result_file"] .isresult = 1 c_p["define_new"] = Functor_adapt( metodo) f_r = collections.OrderedDict() ## ====================================================================== metodo["RESPONSE_FIT"] = f_r f_r["HELP"] = """ The method first find an estimation of the response scan trajectory on each roi then, based on this, obtain a fit of the response function assuming a flat pixel response. There is a final phase where a global optimisation is done in niter_global steps. Each step is composed of response functionfit, followed by a trajectory fit. """ f_r["getyaml"] = dic2yaml_response_fit f_r["swissknife_runner"] = swissknife_runner f_r["method_name"] = "response_fit" f_r["response_scan_address"] = Hdf5FilePath(doc="The full address of the scan which measures the response", defaults2 = (c_p["response_scan_address"], copy.deepcopy), fileme=1, gnameme=1) f_r["nref"] = aNumber( doc="The number of subdivision per pixel dimension used to represent\n"+ "the optical response function at higher resolution", defaults2 = ("2", copy.deepcopy) ) f_r["niter_optical"] = aNumber( doc="the number of iterations used in the \n"+ " optimisation of the optical response", defaults2 = ("50", copy.deepcopy) ) f_r["beta"] = aNumberFloat( doc="The L1 norm factor in the regularisation\n"+ "term for the response functions", defaults2 = ("0.1", copy.deepcopy) ) f_r["niter_global"] = aNumber( doc="Each step is composed of optical response fit, followed \n"+ " trajectory reoptimisation. All this niter_global times ", defaults2 = ("10", copy.deepcopy) ) f_r["MPI_N_PROCS"] = aNumber( doc="If bigger than 1, run it with mpi \n", defaults2 = ("1", copy.deepcopy) ) f_r["result_file"] = Hdf5FilePath(doc="The default hdf5 file where results will be stored\n must be a new file.\n the format is filename:groupname. The datagroup name must be a new one for the entry to be ready", defaults2 = (c_p["result_file"], copy.deepcopy), fileme=0.5, gnameme=0) f_r["result_file"].isresult = 1 r_r = collections.OrderedDict() ## ====================================================================== metodo["RESYNTHETISE_FIT"] = r_r r_r["HELP"] = """ Recreate the reference scan """ r_r["getyaml"] = dic2yaml_resynt_fit r_r["swissknife_runner"] = swissknife_runner r_r["method_name"] = "Scan Resynth." r_r["responsefilename"] = Hdf5FilePath(doc="The address on which the fitted responsed was written", defaults2 = (f_r["result_file"] , copy.deepcopy), fileme=1, gnameme=1) r_r["response_scan_address"] = Hdf5FilePath(doc="The original address of the scan which measures the response", defaults2 = (f_r["response_scan_address"] , copy.deepcopy), fileme=1, gnameme=1) r_r["result_file"] = Hdf5FilePath(doc="The default hdf5 file where results will be stored\n"+ "must be a new file.\n"+ " The format is filename:groupname. \n"+ "The datagroup name must be a new one\n"+ "for the entry to be ready", defaults2 = ( (f_r["result_file"],"syntesis") , Functor_set_new_root()), fileme=0.5, gnameme=0) r_r["result_file"].isresult = 1 return metodo xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/SuperResolution/000077500000000000000000000000001412732462000255345ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/SuperResolution/setup.py000066400000000000000000000032221412732462000272450ustar00rootroot00000000000000# coding: utf-8 # /*########################################################################## # Copyright (C) 2016-2018 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # ############################################################################*/ __authors__ = ["V. Valls"] __license__ = "MIT" __date__ = "03/01/2019" from numpy.distutils.misc_util import Configuration def configuration(parent_package='', top_path=None): config = Configuration('SuperResolution', parent_package, top_path) return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration=configuration) winfo_RIXS_wholespots2spectra.py000066400000000000000000000303411412732462000337520ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/SuperResolutionfrom __future__ import absolute_import from __future__ import division from __future__ import print_function import collections ######## Managing versioning postpending to project name ################ # Get the name of the launching script, # the launching script (an principal module) # is post pended by version name # We use this to retrieve the version import inspect import os import importlib curframe = inspect.currentframe() calframe = inspect.getouterframes(curframe, 2) global version caller = calframe[-1][1] if caller[-3:]==".py": print( " caller :" , caller , " // " , os.path.dirname(os.path.dirname(caller))) # imported by Wizard.py to see what are the subtasks version = os.path.basename(os.path.dirname(os.path.dirname(caller)))[len("XRStools"):] else: # called by the XRS_wizard script caller = os.path.basename(caller) version = caller[len("XRS_wizard"):] print( " Caller version==", version) print( " Caller version", version) XRStools = importlib.import_module( "XRStools"+version ) exec("from XRStools%s.WIZARD.Wizard import *"%version) ################################################################### import yaml import PyMca5.PyMcaIO.specfilewrapper as SpecIO def dic2yaml_generic(dicodic): s = "%s :\n"%dicodic["method_name"] for n,t in dicodic.items(): if isinstance(t, Parametro): s = s +" %s : %s\n"%(n,t.render()) return s class functor_sigterm_handler: def __init__(self, p): self.p = p def __call__(self, *x): print( x) print( " KILLO " , self.p.pid) os.kill(self.p.pid, signal.SIGKILL) def swissknife_runner( yamltext, where, ret_dico ): mydata = yaml.load(yamltext) mname, mydata = list(mydata.items())[0] print( mydata) if "MPI_N_PROCS" in mydata: MPI_N_PROCS = mydata["MPI_N_PROCS"] else: MPI_N_PROCS = 1 print( " MPI_N_PROCS " , MPI_N_PROCS) inputname = os.path.join(where,"input.yaml") stdname = os.path.join(where,"stdout.txt") errname = os.path.join(where,"stderr.txt") ret_dico["input"] =inputname ret_dico["stdout"]=stdname ret_dico["stderr"]=errname open(inputname,"w").write(yamltext) stdout = open(stdname,"w") stderr = open(errname,"w") if MPI_N_PROCS==1: p= subprocess.Popen(( "XRS_swissknife%s %s 1> %s 2>%s"%(version,inputname,stdname, errname )).split() , shell=False, stdout= stdout , stderr= stderr ) else: p= subprocess.Popen(( "mpirun -n %d XRS_swissknife%s %s 1> %s 2>%s"%(version,MPI_N_PROCS , inputname,stdname, errname )) .split() , shell=False, stdout= stdout , stderr= stderr ) signal.signal(signal.SIGTERM, functor_sigterm_handler(p)) p.communicate() ret_dico["return_code"]=p.returncode class Functor_adapt: def __init__(self, dico): self.dico=dico def __call__(self): chiavi = list(self.dico.keys())[1:] for c in chiavi : subdic = self.dico[c] for name,par in subdic.items(): if isinstance(par,Parametro): if par.defaults2 is not None: source, method = par.defaults2 print( source) par.value = method(source) return self.dico["CREATION_PARS"]["result_directory"].value def dic2yaml_create_rois(dicodic): s = "create_rois:\n" s = s+ " expdata : %s \n" % dicodic["spec_file"].render() s = s+ " scans : %s \n" % dicodic["elastic_scan"].render () s = s+ " roiaddress : %s \n" % dicodic["mask_file"].render() # tok = dicodic["filter_path"].render() # if tok !="None": # s = s+ " filter_path : %s \n" % tok # # s = s+ " masktype : filter \n" return s def dic2yaml_extraction_scan(dicodic): s = "loadscan_2Dimages :\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() s = s+ " roiaddress : %s \n" % dicodic["roiaddress"].render() s = s+ " monitor_column : %s \n" % dicodic["monitor_column"].render() s = s+ " scan_interval : %s \n" % dicodic["scans"].render() s = s+ " signaladdress : %s \n" % dicodic["signaladdress"].render() s = s+ " isolateSpot : %s \n" % dicodic["isolateSpot"].render() s = s+""" sumto1D : 0 energycolumn : 'stx' monitorcolumn : %s """ % dicodic["monitor_column"].render() return s class Functor_Compose_A_Scan_Address: def __init__(self, take_first=0): self.take_first = take_first def __call__(self,par): a,b = par print( " ====================== " ) print( a,b) va=a.getFullValue() vb=b.getValue() print( va, vb) if isinstance(vb,int): return va+"/scans/Scan%03d/"%vb elif isinstance(vb,list): return va+"/scans/Scan%03d/"%vb[0] else: raise Exception(" unprepared for this instance : "+str(vb) +" from "+ str(b.value)) class Functor_Compose_An_Absolute_Address: def __init__(self, take_first=0, is_retrieved_scan=0): self.take_first = take_first self.is_retrieved_scan = is_retrieved_scan def __call__(self,par): if self.is_retrieved_scan: par, N = par if hasattr(N,"value"): N=N.value if isinstance(par, hdf5_relative_path): b = par a = b.base_h5file else: a,b = par print( " ====================== " ) print( a,b) va=a.getFullValue() vb=b.getValue() print( va, vb) res = va+"/"+vb if self.is_retrieved_scan: res = res+"/scans/Scan%03d"%int(N) return res def dic2yaml_response_fit(dicodic): print( list(dicodic.keys())) s = "superR_fit_responses:\n" s = s+ " foil_scan_address : %s \n" % dicodic["response_scan_address"].render() s = s+ " nref : %s \n" % dicodic["nref"].render () s = s+ " niter_optical : %s \n" % dicodic["niter_optical"].render () s = s+ " beta_optical : %s \n" % dicodic["beta"].render () s = s+ " niter_global : %s \n" % dicodic["niter_global"].render () s = s+ " pixel_dim : 1 \n" s = s+ " niter_pixel : 0 \n" s = s+ " beta_pixel : 0 \n" s = s+ " do_refine_trajectory : 1 \n" s = s+ " simmetrizza : 1\n" s = s+ " filter_rois : 0\n" s = s+ " target_file : %s \n" % dicodic["result_file"].render() s = s+ " MPI_N_PROCS : %s \n" % dicodic["MPI_N_PROCS"].render () s = s+ " fit_lines : 1 \n" return s class Functor_set_new_root: def __init__(self): pass def __call__(self,par): a,b = par va=a.getFullValue() print( va, b) return va+"/"+b def dic2yaml_resynt_fit(dicodic): s = "superR_recreate_rois :\n" s = s+ " responsefilename : %s \n" % dicodic["responsefilename"].render() s = s+ " nex : 0 \n" s = s+ " old_scan_address : %s \n" % dicodic["response_scan_address"].render () if "recenterings_refined" in dicodic: s = s+ " recenterings_refined : %s \n" % dicodic["recenterings_refined"].render () s = s+ " filter_rois : 0\n" s = s+ " target_filename : %s \n" % dicodic["result_file"].render() return s def getMethod(options): metodo = collections.OrderedDict() c_p = collections.OrderedDict() metodo["CREATION_PARS"] = c_p c_p["spec_file"] = FilePath(doc="The path to the specfile\n file must exist", fileme=1) c_p["elastic_scan"] = aNumber4SpecScan(c_p["spec_file"] ,"This is the scan number containing the elastic scan.\n Will also be for ROI selection") c_p["first_scan"] = aNumber4SpecScan(c_p["spec_file"] ,"This is the first scan of the to-be-summed interval.") c_p["last_scan"] = aNumber4SpecScan(c_p["spec_file"] ,"This is the first scan of the to-be-summed interval.") c_p["result_directory"] = FilePath(doc="The path to the directory\n where results will be written", fileme=1, isadir=1) c_p["define_new"] = Functor_adapt( metodo ) c_r = collections.OrderedDict() ## ====================================================================== metodo["CREATION_ROIS"] = c_r c_r["HELP"] = """ Create the ROIS for sample data""" c_r["getyaml"] = dic2yaml_create_rois c_r["swissknife_runner"] = swissknife_runner c_r["method_name"] = "create_rois" c_r["spec_file"] = FilePath(doc="The path to the specfile", defaults2 = (c_p["spec_file"], simplecopy) ) c_r["elastic_scan"] = aNumber4SpecScan( c_r["spec_file"] , doc="This is the scan number containing the elastic scan.\n"+ " Will be used here for ROI selection", defaults2 = (c_p["elastic_scan"], simplecopy), rendering = render_numero_in_una_lista ) c_r["mask_file"] = Hdf5FilePath(doc="The hdf5 file and datagroup where results the MASK will be WRITTEN. Format toto.hdf5:datagroupname", defaults2 = (c_p["result_directory"], Functor_add_datagroup("ROI", add_basename="roifile.h5") ), fileme=0.5, gnameme=0.5) c_r["mask_file"].master= True ########################## c_r["mask_file"].automatic_forward = 2 c_r["mask_file"].isresult = 1 # ## ============================================================================== s_s = collections.OrderedDict() ## ====================================================================== metodo["SUM_SCANS"] = s_s s_s["HELP"] = """ Do the sum over scans providing two dimensional maps A label is selected for the Y axis. A motor is selected for the X axis. Scans having all equals motors and equal range for Y are summed together, otherwise they are summed into new maps """ s_s["getyaml"] = dic2yaml_generic s_s["swissknife_runner"] = swissknife_runner s_s["method_name"] = "sum_scans2maps" s_s_spec_file = FilePath(doc="The path to the specfile", defaults2 = (c_p["spec_file"], simplecopy) ) s_s["first_scan"] = aNumber4SpecScan(s_s_spec_file ,"This is the first scan of the to-be-summed interval.", defaults2=(c_p["first_scan"], simplecopy)) s_s["last_scan"] = aNumber4SpecScan(s_s_spec_file ,"This is the first scan of the to-be-summed interval.", defaults2=(c_p["last_scan"] , simplecopy)) s_s["first_scan"].master= True s_s["last_scan"].master= True s_s["Scan_Variable"] = Parameter_Choices( "Variable moving along the scan. To be chosed between the scans labels" , choices_functor = Functor_choicesFromSpecLabels( s_s_spec_file , s_s["first_scan"] , s_s["last_scan"] ), defaults2=( "Anal Energy" , simplecopy) ) s_s["Motor_Variable"] = Parameter_Choices( "Motor changing from scan to scan. To be chosed between the specfile motors" , choices_functor = Functor_choicesFromSpecLabels( s_s_spec_file , s_s["first_scan"] , s_s["last_scan"] , labels=0 ), defaults2=( "energy" , simplecopy) ) s_s["Scan_Variable"] .master= True s_s["Motor_Variable"].master= True s_s["spec_file"] = s_s_spec_file s_s["mask_file"] = Hdf5FilePath(doc="The hdf5 file and datagroup where results the MASK will be WRITTEN. Format toto.hdf5:datagroupname", defaults2 = (c_r["mask_file"], simplecopy ), fileme=0.5, gnameme=0.5) s_s["scans_file"] = Hdf5FilePath(doc="The hdf5 file and datagroup where the SUMs will be WRITTEN. Format toto.hdf5:datagroupname", defaults2 = ((c_p["result_directory"], c_r["mask_file"] ), Functor_add_datagroup("SCAN", add_basename="scansfile.h5") ), fileme=0.5, gnameme=0.5 ) s_s["scans_file"] .always_visible = True s_s["scans_file"].isresult = 1 # ## ============================================================================== return metodo winfo_SuperR_extraction_deconvolution.py000066400000000000000000000510101412732462000356560ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/SuperResolutionfrom __future__ import absolute_import from __future__ import division from __future__ import print_function import collections ######## Managing versioning postpending to project name ################ # Get the name of the launching script, # the launching script (an principal module) # is post pended by version name # We use this to retrieve the version import inspect import os import importlib curframe = inspect.currentframe() calframe = inspect.getouterframes(curframe, 2) global version caller = calframe[-1][1] if caller[-3:]==".py": print( " caller :" , caller , " // " , os.path.dirname(os.path.dirname(caller))) # imported by Wizard.py to see what are the subtasks version = os.path.basename(os.path.dirname(os.path.dirname(caller)))[len("XRStools"):] else: # called by the XRS_wizard script caller = os.path.basename(caller) version = caller[len("XRS_wizard"):] print( " Caller version==", version) XRStools = importlib.import_module( "XRStools"+version ) exec("from XRStools%s.WIZARD.Wizard import *"%version) ################################################################### import yaml def dic2yaml_generic(dicodic): s = "%s :\n"%dicodic["method_name"] for n,t in dicodic.items(): if isinstance(t, Parametro): s = s +" %s : %s\n"%(n,t.render()) return s class functor_sigterm_handler: def __init__(self, p): self.p = p def __call__(self, *x): print( x) print( " KILLO " , self.p.pid) os.kill(self.p.pid, signal.SIGKILL) def swissknife_runner( yamltext, where, ret_dico ): mydata = yaml.load(yamltext) mname, mydata = list(mydata.items())[0] print( mydata) if "MPI_N_PROCS" in mydata: MPI_N_PROCS = mydata["MPI_N_PROCS"] else: MPI_N_PROCS = 1 print( " MPI_N_PROCS " , MPI_N_PROCS) inputname = os.path.join(where,"input.yaml") stdname = os.path.join(where,"stdout.txt") errname = os.path.join(where,"stderr.txt") ret_dico["input"] =inputname ret_dico["stdout"]=stdname ret_dico["stderr"]=errname open(inputname,"w").write(yamltext) stdout = open(stdname,"w") stderr = open(errname,"w") if not sys.platform=="win32": if MPI_N_PROCS==1: p= subprocess.Popen(( "XRS_swissknife%s %s 1> %s 2>%s"%(version, inputname,stdname, errname )) .split() , shell=False, stdout= stdout , stderr= stderr ) else: p= subprocess.Popen(( "mpirun -n %d XRS_swissknife%s %s 1> %s 2>%s"%(version, MPI_N_PROCS , inputname,stdname, errname )) .split() , shell=False, stdout= stdout , stderr= stderr ) else: packages = (os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(XRStools.__file__ ) )))) script = os.path.join(packages,"scripts","XRS_swissknife%s.bat"%version ) if MPI_N_PROCS==1: p= subprocess.Popen(( "%s %s"%(script, inputname)) .split() , shell=False, stdout= stdout , stderr= stderr ) else: p= subprocess.Popen(( "mpiexec -n %d %s %s"%(MPI_N_PROCS ,script, inputname) ) .split() , shell=False, stdout= stdout , stderr= stderr ) signal.signal(signal.SIGTERM, functor_sigterm_handler(p)) p.communicate() ret_dico["return_code"]=p.returncode class Functor_adapt: def __init__(self, dico): self.dico=dico def __call__(self): chiavi = list(self.dico.keys())[1:] for c in chiavi : subdic = self.dico[c] for name,par in subdic.items(): if isinstance(par,Parametro): if par.defaults2 is not None: source, method = par.defaults2 if hasattr(source,"value" ): par.value = method(source.value) else: par.value = method(source) return self.dico["CREATION_PARS"]["target_address"].value def dic2yaml_create_rois(dicodic): s = "create_rois:\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() print( dicodic["scans"].render) print( dicodic["scans"].rendering) print( dicodic["scans"].getValue()) print( dicodic["scans"].rendering(dicodic["scans"].getValue())) s = s+ " scans : %s \n" % dicodic["scans"].render () s = s+ " roiaddress : %s \n" % dicodic["result_file"].render() tok = dicodic["filter_path"].render() if tok !="None": s = s+ " filter_path : %s \n" % tok # s = s+ " masktype : filter \n" return s def dic2yaml_extraction_scan(dicodic): s = "loadscan_2Dimages :\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() s = s+ " roiaddress : %s \n" % dicodic["roiaddress"].render() s = s+ " monitor_column : %s \n" % dicodic["monitor_column"].render() s = s+ " scan_interval : %s \n" % dicodic["scans"].render() s = s+ " signaladdress : %s \n" % dicodic["signaladdress"].render() s = s+""" sumto1D : 0 energycolumn : 'stx' monitorcolumn : %s """ % dicodic["monitor_column"].render() return s class Functor_Compose_A_Scan_Address: def __init__(self, take_first=0): self.take_first = take_first def __call__(self,par): a,b = par print( " ====================== " ) print( a,b) va=a.getFullValue() vb=b.getValue() print( va, vb) if isinstance(vb,int): return va+"/scans/Scan%03d/"%vb elif isinstance(vb,list): return va+"/scans/Scan%03d/"%vb[0] else: raise Exception(" unprepared for this instance : "+str(vb) +" from "+ str(b.value)) class Functor_Compose_An_Absolute_Address: def __init__(self, take_first=0, is_retrieved_scan=0): self.take_first = take_first self.is_retrieved_scan = is_retrieved_scan def __call__(self,par): if self.is_retrieved_scan: par, N = par if hasattr(N,"value"): N=N.value if isinstance(par, hdf5_relative_path): b = par a = b.base_h5file else: a,b = par print( " ====================== " ) print( a,b) va=a.getFullValue() vb=b.getValue() print( va, vb) res = va+"/"+vb if self.is_retrieved_scan: res = res+"/scans/Scan%03d"%int(N) return res class functor_finalise_last_part_of_the_path: def __init__(self, cp_num): self.cp_num=cp_num def __call__(self, cp_add ) : cp_num = self.cp_num path = cp_add num = cp_num.getValue() # path_items = path.split("/")[-3:] res = path + "/scans/Scan%03d" % num # res = path + "/" + path_items[0] + "/" + path_items[1] +"/" + "Scan%03d" % num # res = path + "/" + path_items[0] + "/" print( " RITORNO " , res) return res def dic2yaml_extraction_scan(dicodic): s = "loadscan_2Dimages :\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() s = s+ " roiaddress : %s \n" % dicodic["roiaddress"].render() s = s+ " monitor_column : %s \n" % dicodic["monitor_column"].render() s = s+ " scan_interval : %s \n" % dicodic["scans"].render() s = s+ " energy_column : %s \n" % 'sty' s = s+ " signaladdress : %s \n" % dicodic["signaladdress"].render() s = s+""" sumto1D : 0 monitorcolumn : %s """ % dicodic["monitor_column"].render() return s def dic2yaml_response_fit(dicodic): print( list(dicodic.keys())) s = "superR_fit_responses:\n" s = s+ " foil_scan_address : %s \n" % dicodic["response_scan_address"].render() s = s+ " nref : %s \n" % dicodic["nref"].render () s = s+ " niter_optical : %s \n" % dicodic["niter_optical"].render () s = s+ " beta_optical : %s \n" % dicodic["beta"].render () s = s+ " niter_global : %s \n" % dicodic["niter_global"].render () s = s+ " pixel_dim : 1 \n" s = s+ " niter_pixel : 0 \n" s = s+ " beta_pixel : 0 \n" s = s+ " do_refine_trajectory : 1 \n" s = s+ " simmetrizza : 1\n" s = s+ " filter_rois : 0\n" s = s+ " target_file : %s \n" % dicodic["result_file"].render() s = s+ " MPI_N_PROCS : %s \n" % dicodic["MPI_N_PROCS"].render () s = s+ " fit_lines : 1 \n" return s class Functor_set_new_root: def __init__(self): pass def __call__(self,par): a,b = par va=a.getFullValue() print( va, b) return va+"/"+b def dic2yaml_resynt_fit(dicodic): s = "superR_recreate_rois :\n" s = s+ " responsefilename : %s \n" % dicodic["responsefilename"].render() s = s+ " nex : 0 \n" s = s+ " old_scan_address : %s \n" % dicodic["response_scan_address"].render () if "recenterings_refined" in dicodic: s = s+ " recenterings_refined : %s \n" % dicodic["recenterings_refined"].render () s = s+ " filter_rois : 0\n" s = s+ " target_filename : %s \n" % dicodic["result_file"].render() return s def getMethod(options): metodo = collections.OrderedDict() c_p = collections.OrderedDict() metodo["CREATION_PARS"] = c_p c_p["spec_file"] = FilePath(doc="The path to the specfile\n file must exist", fileme=1) c_p["scans"] = many_Number4SpecScan( c_p["spec_file"] ,isinterval=1, doc= "This is the list of two number (**Use Python syntax [start,end+1]**)\n"+ " defining the interval scan numbers containing data.\n"+ "You can also give more intervals in the same list.\n"+ # " Must containing two numbers : [ start,end+1] \n"+ "", # "Will also be used for ROI selection", nmin=2,nmax=200) c_p["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", initial_value = "kapraman/1000" ) c_p["reference_address"] =Hdf5FilePath(doc= "The full path where the references can be found\n" "It is the hdf5 file plus the path pointing to a datagroup\n" " this datagroup must contain a datagroup named 'scans' \n" " which at its turn contains ScanXXX for each reference scan " , fileme=1, gnameme=1) c_p["reference_scan"] = aNumber( doc= "This is the Reference scan Number\n" " It is used to point to ScanXXX and select the scan number " ) c_p["signaladdress"] = Hdf5FilePath(doc=("The hdf5 file where extracted data will be written\n" "Must be a free name\n"), fileme=0.5, gnameme=0) c_p["target_address"] = Hdf5FilePath(doc="The hdf5 full path on which Volumes will be written. Must NOT exist ", fileme=0.5, gnameme=0) c_p["signaladdress"] .isresult = 1 c_p["target_address"] .isresult = 1 c_p["define_new"] = Functor_adapt( metodo ) ## ============================================================================== es = collections.OrderedDict() metodo["DATA_EXTRACTION"] = es es["getyaml"] = dic2yaml_extraction_scan es["HELP"] = " Collect the sample data\n" es["method_name"] = "loadscan_2Dimages" es["expdata"] = FilePath(doc="The path to the specfile", defaults2 = (c_p["spec_file"], simplecopy) ) es["roiaddress"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ " where the ROI for calibration extraction can be found \n"+ " Format toto.hdf5:datagroupname ", defaults2 = ( c_p["reference_address"], Functor_add_datagroup( "", remplace_last=1) ), fileme=1, gnameme=1) es["scans"] = many_Number4SpecScan( es["expdata"] ,isinterval=1, doc= "This is the list of two number (**Use Python syntax [start,end+1]**)\n"+ " defining the interval scan numbers containing data.\n"+ "You can also give more intervals in the same list.\n"+ # " Must containing two numbers : [ start,end+1] \n"+ "", # "Will also be used for ROI selection", nmin=2,nmax=200, defaults2 = ( c_p["scans"] , simplecopy)) es["signaladdress"] = Hdf5FilePath(doc=("The hdf5 file where extracted data will be written\n" "Must be a free name\n"), defaults2 = ( ( c_p["signaladdress"], es["scans"] ) , Functor_append_numinterval_to_name() ) , fileme=0.5, gnameme=0) es["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", defaults2=(c_p["monitor_column"], simplecopy) ) es["signaladdress"] .isresult = 1 es["swissknife_runner"] = swissknife_runner # ============================================================================== scal = collections.OrderedDict() metodo["SCALAR_PRODUCTS"] = scal scal["getyaml"] = dic2yaml_generic scal["method_name"] = "superR_scal_deltaXimages" scal["sample_address"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ " where the extracted scans can be found \n"+ " It contains ScanXXX for every extracted scan ", defaults2 = ( es["signaladdress"] , Functor_add_datagroup( "scans") ), fileme=1, gnameme=1) scal["delta_address"] = Hdf5FilePath(doc="Where the references can be found\nIn general a subgroup of ROI_something\n"+ "containing a group called scans.\n The precise scan will be indicated by reference_scan below", defaults2 = ( c_p["reference_address"] , functor_finalise_last_part_of_the_path(c_p["reference_scan"] ) ), fileme=1, gnameme=1) scal["nbin"] = aNumber( doc="If different from 1 :\n"+ " the reference scan positions will be binned", defaults2 = ("1", simplecopy),nmin=1, nmax=40 ) scal["optional_solution"] = Hdf5FilePath(doc=("OPTIONAL : The hdf5 file where scalar volumes has been written\n" "If given, used for balancing analyzer factors\n"), defaults2 = ( "", simplecopy ) , fileme=0.5, gnameme=0.5) scal["target_address"] = Hdf5FilePath(doc=("The hdf5 file where scalar products will be written\n" "Must be a free name\n"), defaults2 = ( ( c_p["target_address"], es["scans"] ) , Functor_append_numinterval_to_name( plus="scal_prods") ) , fileme=0.5, gnameme=0) scal["HELP"] = " Scalar products between sample data ROIs and reference scan\n" scal["target_address"] .isresult = 1 scal["swissknife_runner"] = swissknife_runner # ============================================================================== gv = collections.OrderedDict() metodo["GET_VOLUME"] = gv gv["getyaml"] = dic2yaml_generic gv["method_name"] = "superR_getVolume" gv["scalprods_address"] = Hdf5FilePath(doc=" The hdf5 file and datagroup where\n"+ " scalar products can be found", defaults2 = (scal["target_address"] ,simplecopy), fileme=1, gnameme=1) gv["target_address"] = Hdf5FilePath(doc=("The hdf5 file where volumes will be written\n" "Must be a free name\n"), defaults2 = ( ( c_p["target_address"], es["scans"] ) , Functor_append_numinterval_to_name( plus="Volume") ) , fileme=0.5, gnameme=0) gv["niter"] = aNumber( doc="The number of iterations", defaults2 = ("100", simplecopy),nmin=1, nmax=40000000 ) gv["beta"] = aNumber( doc="the TV preconditioning factor", float_also=True, defaults2 = ("0.06", simplecopy),nmin=0, nmax=1.0e38 ) gv["eps"] = aNumber( doc="a parameter for the convergence of the Chambolle-Pock TV denoising phase", float_also=True, defaults2 = (" 0.000002", simplecopy),nmin=0, nmax=1.0e38 ) gv["debin"] = many_Number( doc="The parameter debin dafaults to [1,1]\n" "It is used to increase a dimension Z,Y or both , to make it match with X", defaults2 = ("[1,1]", simplecopy), nmin=2, nmax=2) gv["HELP"] = "Obtaining the volume \n" gv["target_address"] .isresult = 1 gv["swissknife_runner"] = swissknife_runner # ============================================================================== vis = collections.OrderedDict() metodo["VIS_VOLUME"] = vis vis["getyaml"] = dic2yaml_generic vis["method_name"] = "view_Volume_myavi" vis["volume_address"] = Hdf5FilePath(doc=" The hdf5 file and datagroup where volume is \n", defaults2 = ( gv["target_address"] ,simplecopy), fileme=1, gnameme=1) vis["isolevel"] = aNumber( doc="The isolevel : a number between 0 and 1 ( 0 represents minimum, 1, maximum)", float_also=True, defaults2 = ("0.1", simplecopy),nmin=0, nmax=1.0 ) vis["opacity"] = aNumber( doc="The opacity : a number between 0 and 1 ", float_also=True, defaults2 = ("0.6", simplecopy),nmin=0, nmax=1.0) vis["HELP"] = "Visualising teh volume \n" vis["swissknife_runner"] = swissknife_runner vis["opacity"] .master = True vis["isolevel"] .master = True gv["beta"] .master = True gv["niter"] .master = True gv["target_address"] . master = True return metodo winfo_SuperR_makeFilterMask.py000066400000000000000000000232231412732462000334320ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/SuperResolutionfrom __future__ import absolute_import from __future__ import division from __future__ import print_function import collections ######## Managing versioning postpending to project name ################ # Get the name of the launching script, # the launching script (an principal module) # is post pended by version name # We use this to retrieve the version import inspect import os import importlib curframe = inspect.currentframe() calframe = inspect.getouterframes(curframe, 2) global version caller = calframe[-1][1] if caller[-3:]==".py": print( " caller :" , caller , " // " , os.path.dirname(os.path.dirname(caller))) # imported by Wizard.py to see what are the subtasks version = os.path.basename(os.path.dirname(os.path.dirname(caller)))[len("XRStools"):] else: # called by the XRS_wizard script caller = os.path.basename(caller) version = caller[len("XRS_wizard"):] print( " Caller version==", version) XRStools = importlib.import_module( "XRStools"+version ) exec("from XRStools%s.WIZARD.Wizard import *"%version) ################################################################### import yaml def dic2yaml_generic(dicodic): s = "%s :\n"%dicodic["method_name"] for n,t in dicodic.items(): if isinstance(t, Parametro): s = s +" %s : %s\n"%(n,t.render()) return s class functor_sigterm_handler: def __init__(self, p): self.p = p def __call__(self, *x): print( x) print( " KILLO " , self.p.pid) os.kill(self.p.pid, signal.SIGKILL) def swissknife_runner( yamltext, where, ret_dico ): mydata = yaml.load(yamltext) mname, mydata = list(mydata.items())[0] print( mydata) if "MPI_N_PROCS" in mydata: MPI_N_PROCS = mydata["MPI_N_PROCS"] else: MPI_N_PROCS = 1 print( " MPI_N_PROCS " , MPI_N_PROCS) inputname = os.path.join(where,"input.yaml") stdname = os.path.join(where,"stdout.txt") errname = os.path.join(where,"stderr.txt") ret_dico["input"] =inputname ret_dico["stdout"]=stdname ret_dico["stderr"]=errname open(inputname,"w").write(yamltext) stdout = open(stdname,"w") stderr = open(errname,"w") if MPI_N_PROCS==1: p= subprocess.Popen(( "XRS_swissknife%s %s 1> %s 2>%s"%(version,inputname,stdname, errname )) .split() , shell=False, stdout= stdout , stderr= stderr ) else: p= subprocess.Popen(( "mpirun -n %d XRS_swissknife%s %s 1> %s 2>%s"%(version, MPI_N_PROCS , inputname,stdname, errname )) .split() , shell=False, stdout= stdout , stderr= stderr ) signal.signal(signal.SIGTERM, functor_sigterm_handler(p)) p.communicate() ret_dico["return_code"]=p.returncode class Functor_adapt: def __init__(self, dico): self.dico=dico def __call__(self): chiavi = list(self.dico.keys())[1:] for c in chiavi : subdic = self.dico[c] if not isinstance(subdic,dict): continue for name,par in subdic.items(): if isinstance(par,Parametro): if par.defaults2 is not None: source, method = par.defaults2 print( source) par.value = method(source) return self.dico["CREATION_PARS"]["result_file"].value def dic2yaml_create_rois(dicodic): s = "create_rois:\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() print( dicodic["scans"].render) print( dicodic["scans"].rendering) print( dicodic["scans"].getValue()) print( dicodic["scans"].rendering(dicodic["scans"].getValue())) s = s+ " scans : %s \n" % dicodic["scans"].render () s = s+ " roiaddress : %s \n" % dicodic["result_file"].render() s = s+ " masktype : filter \n" return s def dic2yaml_extraction_scan(dicodic): s = "loadscan_2Dimages :\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() s = s+ " roiaddress : %s \n" % dicodic["roiaddress"].render() s = s+ " monitor_column : %s \n" % dicodic["monitor_column"].render() s = s+ " scan_interval : %s \n" % dicodic["scans"].render() s = s+ " signaladdress : %s \n" % dicodic["signaladdress"].render() s = s+""" sumto1D : 0 energycolumn : 'Anal Energy' monitorcolumn : %s """ % dicodic["monitor_column"].render() return s class Functor_Compose_A_Scan_Address: def __init__(self, take_first=0): self.take_first = take_first def __call__(self,par): a,b = par print( " ====================== " ) print( a,b) va=a.getFullValue() vb=b.getValue() print( va, vb) if isinstance(vb,int): return va+"/scans/Scan%03d/"%vb elif isinstance(vb,list): return va+"/scans/Scan%03d/"%vb[0] else: raise Exception(" unprepared for this instance : "+str(vb) +" from "+ str(b.value)) class Functor_Compose_An_Absolute_Address: def __init__(self, take_first=0, is_retrieved_scan=0): self.take_first = take_first self.is_retrieved_scan = is_retrieved_scan def __call__(self,par): if self.is_retrieved_scan: par, N = par if hasattr(N,"value"): N=N.value if isinstance(par, hdf5_relative_path): b = par a = b.base_h5file else: a,b = par print( " ====================== " ) print( a,b) va=a.getFullValue() vb=b.getValue() print( va, vb) res = va+"/"+vb if self.is_retrieved_scan: res = res+"/scans/Scan%03d"%int(N) return res def dic2yaml_response_fit(dicodic): print( list(dicodic.keys())) s = "superR_fit_responses:\n" s = s+ " foil_scan_address : %s \n" % dicodic["response_scan_address"].render() s = s+ " nref : %s \n" % dicodic["nref"].render () s = s+ " niter_optical : %s \n" % dicodic["niter_optical"].render () s = s+ " beta_optical : %s \n" % dicodic["beta"].render () s = s+ " niter_global : %s \n" % dicodic["niter_global"].render () s = s+ " pixel_dim : 1 \n" s = s+ " niter_pixel : 0 \n" s = s+ " beta_pixel : 0 \n" s = s+ " do_refine_trajectory : 1 \n" s = s+ " simmetrizza : 1\n" s = s+ " filter_rois : 0\n" s = s+ " target_file : %s \n" % dicodic["result_file"].render() s = s+ " MPI_N_PROCS : %s \n" % dicodic["MPI_N_PROCS"].render () s = s+ " fit_lines : 1 \n" return s class Functor_set_new_root: def __init__(self): pass def __call__(self,par): a,b = par va=a.getFullValue() print( va, b) return va+"/"+b def dic2yaml_resynt_fit(dicodic): s = "superR_recreate_rois :\n" s = s+ " responsefilename : %s \n" % dicodic["responsefilename"].render() s = s+ " nex : 0 \n" s = s+ " old_scan_address : %s \n" % dicodic["response_scan_address"].render () if "recenterings_refined" in dicodic: s = s+ " recenterings_refined : %s \n" % dicodic["recenterings_refined"].render () s = s+ " filter_rois : 0\n" s = s+ " target_filename : %s \n" % dicodic["result_file"].render() return s def getMethod(options): metodo = collections.OrderedDict() c_p = collections.OrderedDict() metodo["CREATION_PARS"] = c_p c_p["spec_file"] = FilePath(doc="The path to the specfile\n file must exist", fileme=1) c_p["reference_scan"] = aNumber4SpecScan(c_p["spec_file"] ,"This is the scan number containing references.\n Will also be used for ROI selection") c_p["result_file"] = Hdf5FilePath(doc="The default hdf5 file where results will be stored\n must be a new file.\n the format is filename:groupname. The datagroup name must be a new one for the entry to be ready", fileme=0.5, gnameme=0) c_p["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", initial_value = "kapraman/1000" ) c_p["result_file"] .isresult = 1 c_p["define_new"] = Functor_adapt( metodo ) c_r = collections.OrderedDict() ## ====================================================================== metodo["CREATION_ROIS"] = c_r c_r["HELP"] = """ Create the ROIS for sample data""" c_r["getyaml"] = dic2yaml_create_rois c_r["swissknife_runner"] = swissknife_runner c_r["method_name"] = "create_rois" c_r["expdata"] = FilePath(doc="The path to the specfile", defaults2 = (c_p["spec_file"], simplecopy) ) c_r["scans"] = aNumber4SpecScan( c_r["expdata"] , doc="This is the scan number containing references.\n"+ " Will be used here for ROI selection", defaults2 = (c_p["reference_scan"], simplecopy), rendering = render_numero_in_una_lista ) c_r["result_file"] = Hdf5FilePath(doc="The hdf5 file and datagroup where results will be stored. Format toto.hdf5:datagroupname", defaults2 = (c_p["result_file"], Functor_add_datagroup("FILTER_MASK") ), fileme=0.5, gnameme=0) c_r["result_file"].isresult = 1 ## ============================================================================== return metodo winfo_SuperR_preparation.py000066400000000000000000000321531412732462000330610ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/SuperResolutionfrom __future__ import absolute_import from __future__ import division from __future__ import print_function import collections ######## Managing versioning postpending to project name ################ # Get the name of the launching script, # the launching script (an principal module) # is post pended by version name # We use this to retrieve the version import inspect import os import importlib curframe = inspect.currentframe() calframe = inspect.getouterframes(curframe, 2) global version caller = calframe[-1][1] if caller[-3:]==".py": print( " caller :" , caller , " // " , os.path.dirname(os.path.dirname(caller))) # imported by Wizard.py to see what are the subtasks version = os.path.basename(os.path.dirname(os.path.dirname(caller)))[len("XRStools"):] else: # called by the XRS_wizard script caller = os.path.basename(caller) version = caller[len("XRS_wizard"):] print( " Caller version==", version) XRStools = importlib.import_module( "XRStools"+version ) exec("from XRStools%s.WIZARD.Wizard import *"%version) ################################################################### import yaml def dic2yaml_generic(dicodic): s = "%s :\n"%dicodic["method_name"] for n,t in dicodic.items(): if isinstance(t, Parametro): s = s +" %s : %s\n"%(n,t.render()) return s class functor_sigterm_handler: def __init__(self, p): self.p = p def __call__(self, *x): print( x) print( " KILLO " , self.p.pid) os.kill(self.p.pid, signal.SIGKILL) def swissknife_runner( yamltext, where, ret_dico ): mydata = yaml.load(yamltext) mname, mydata = list(mydata.items())[0] print( mydata) if "MPI_N_PROCS" in mydata: MPI_N_PROCS = mydata["MPI_N_PROCS"] else: MPI_N_PROCS = 1 print( " MPI_N_PROCS " , MPI_N_PROCS) inputname = os.path.join(where,"input.yaml") stdname = os.path.join(where,"stdout.txt") errname = os.path.join(where,"stderr.txt") ret_dico["input"] =inputname ret_dico["stdout"]=stdname ret_dico["stderr"]=errname open(inputname,"w").write(yamltext) stdout = open(stdname,"w") stderr = open(errname,"w") if MPI_N_PROCS==1: p= subprocess.Popen(( "XRS_swissknife%s %s 1> %s 2>%s"%(version,inputname,stdname, errname )).split() , shell=False, stdout= stdout , stderr= stderr ) else: p= subprocess.Popen(( "mpirun -n %d XRS_swissknife%s %s 1> %s 2>%s"%(version, MPI_N_PROCS , inputname,stdname, errname )).split() , shell=False, stdout= stdout , stderr= stderr ) signal.signal(signal.SIGTERM, functor_sigterm_handler(p)) p.communicate() ret_dico["return_code"]=p.returncode class Functor_adapt: def __init__(self, dico): self.dico=dico def __call__(self): chiavi = list(self.dico.keys())[1:] for c in chiavi : subdic = self.dico[c] if not isinstance(subdic,dict): continue for name,par in subdic.items(): if isinstance(par,Parametro): if par.defaults2 is not None: source, method = par.defaults2 print( source) par.value = method(source) return self.dico["CREATION_PARS"]["result_file"].value def dic2yaml_create_rois(dicodic): s = "create_rois:\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() print( dicodic["scans"].render) print( dicodic["scans"].rendering) print( dicodic["scans"].getValue()) print( dicodic["scans"].rendering(dicodic["scans"].getValue())) s = s+ " scans : %s \n" % dicodic["scans"].render () s = s+ " roiaddress : %s \n" % dicodic["result_file"].render() tok = dicodic["filter_path"].render() if tok !="None": s = s+ " filter_path : %s \n" % tok # s = s+ " masktype : filter \n" return s def dic2yaml_extraction_scan(dicodic): s = "loadscan_2Dimages :\n" s = s+ " expdata : %s \n" % dicodic["expdata"].render() s = s+ " roiaddress : %s \n" % dicodic["roiaddress"].render() s = s+ " monitor_column : %s \n" % dicodic["monitor_column"].render() s = s+ " scan_interval : %s \n" % dicodic["scans"].render() s = s+ " signaladdress : %s \n" % dicodic["signaladdress"].render() s = s+ " isolateSpot : %s \n" % dicodic["isolateSpot"].render() s = s+""" sumto1D : 0 energycolumn : 'stx' monitorcolumn : %s """ % dicodic["monitor_column"].render() return s class Functor_Compose_A_Scan_Address: def __init__(self, take_first=0): self.take_first = take_first def __call__(self,par): a,b = par print( " ====================== " ) print( a,b) va=a.getFullValue() vb=b.getValue() print( va, vb) if isinstance(vb,int): return va+"/scans/Scan%03d/"%vb elif isinstance(vb,list): return va+"/scans/Scan%03d/"%vb[0] else: raise Exception(" unprepared for this instance : "+str(vb) +" from "+ str(b.value)) class Functor_Compose_An_Absolute_Address: def __init__(self, take_first=0, is_retrieved_scan=0): self.take_first = take_first self.is_retrieved_scan = is_retrieved_scan def __call__(self,par): if self.is_retrieved_scan: par, N = par if hasattr(N,"value"): N=N.value if isinstance(par, hdf5_relative_path): b = par a = b.base_h5file else: a,b = par print( " ====================== " ) print( a,b) va=a.getFullValue() vb=b.getValue() print( va, vb) res = va+"/"+vb if self.is_retrieved_scan: res = res+"/scans/Scan%03d"%int(N) return res def dic2yaml_response_fit(dicodic): print( list(dicodic.keys())) s = "superR_fit_responses:\n" s = s+ " foil_scan_address : %s \n" % dicodic["response_scan_address"].render() s = s+ " nref : %s \n" % dicodic["nref"].render () s = s+ " niter_optical : %s \n" % dicodic["niter_optical"].render () s = s+ " beta_optical : %s \n" % dicodic["beta"].render () s = s+ " niter_global : %s \n" % dicodic["niter_global"].render () s = s+ " pixel_dim : 1 \n" s = s+ " niter_pixel : 0 \n" s = s+ " beta_pixel : 0 \n" s = s+ " do_refine_trajectory : 1 \n" s = s+ " simmetrizza : 1\n" s = s+ " filter_rois : 0\n" s = s+ " target_file : %s \n" % dicodic["result_file"].render() s = s+ " MPI_N_PROCS : %s \n" % dicodic["MPI_N_PROCS"].render () s = s+ " fit_lines : 1 \n" return s class Functor_set_new_root: def __init__(self): pass def __call__(self,par): a,b = par va=a.getFullValue() print( va, b) return va+"/"+b def dic2yaml_resynt_fit(dicodic): s = "superR_recreate_rois :\n" s = s+ " responsefilename : %s \n" % dicodic["responsefilename"].render() s = s+ " nex : 0 \n" s = s+ " old_scan_address : %s \n" % dicodic["response_scan_address"].render () if "recenterings_refined" in dicodic: s = s+ " recenterings_refined : %s \n" % dicodic["recenterings_refined"].render () s = s+ " filter_rois : 0\n" s = s+ " target_filename : %s \n" % dicodic["result_file"].render() return s def getMethod(options): metodo = collections.OrderedDict() c_p = collections.OrderedDict() metodo["CREATION_PARS"] = c_p c_p["spec_file"] = FilePath(doc="The path to the specfile\n file must exist", fileme=1) c_p["reference_scan"] = aNumber4SpecScan(c_p["spec_file"] ,"This is the scan number containing references.\n Will also be used for ROI selection") c_p["filter_path"] = Hdf5FilePath(doc="OPTIONAL(you can let it to None)\nThe default hdf5 file AND path where the filter is stored\n must be a matrix dataset with 1 for good pixels , 0 for bad ones.\n the format is filename:groupname. ", fileme=1, gnameme=1, canbeNone=True) c_p["result_file"] = Hdf5FilePath(doc="The default hdf5 file where results will be stored\n must be a new file.\n the format is filename:groupname. The datagroup name must be a new one for the entry to be ready", fileme=0.5, gnameme=0) c_p["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", initial_value = "kapraman/1000" ) c_p["result_file"] .isresult = 1 c_p["define_new"] = Functor_adapt( metodo ) c_r = collections.OrderedDict() ## ====================================================================== metodo["CREATION_ROIS"] = c_r c_r["HELP"] = """ Create the ROIS for sample data""" c_r["getyaml"] = dic2yaml_create_rois c_r["swissknife_runner"] = swissknife_runner c_r["method_name"] = "create_rois" c_r["expdata"] = FilePath(doc="The path to the specfile", defaults2 = (c_p["spec_file"], simplecopy) ) c_r["scans"] = aNumber4SpecScan( c_r["expdata"] , doc="This is the scan number containing references.\n"+ " Will be used here for ROI selection", defaults2 = (c_p["reference_scan"], simplecopy), rendering = render_numero_in_una_lista ) c_r["filter_path"] = Hdf5FilePath(doc="OPTIONAL(you can let it to None)\nThe default hdf5 file AND path where the filter is stored\n must be a matrix dataset with 1 for good pixels , 0 for bad ones.\n the format is filename:groupname. ", fileme=1, gnameme=1, canbeNone=True, defaults2 = (c_p["filter_path"], simplecopy)) c_r["result_file"] = Hdf5FilePath(doc="The hdf5 file and datagroup where results will be stored. Format toto.hdf5:datagroupname", defaults2 = (c_p["result_file"], Functor_add_datagroup("ROI_AS_SELECTED") ), fileme=0.5, gnameme=0) c_r["result_file"].isresult = 1 ## ============================================================================== ed_calib = collections.OrderedDict() ed_calib["HELP"] = """ Collects the calibration scans """ ed_calib["getyaml"] = dic2yaml_extraction_scan metodo["EXTRACTION_DATA_CALIBRATION"] = ed_calib ed_calib["method_name"] = "loadscan_2Dimages" ed_calib["expdata"] = FilePath(doc="The path to the specfile", defaults2 = (c_r["expdata"], simplecopy) ) ed_calib["roiaddress"] = Hdf5FilePath(doc=" The hdf5 file and datagroup \n"+ " where the ROI for calibration extraction can be found \n"+ " Format toto.hdf5:datagroupname ", defaults2 = (c_r["result_file"], simplecopy ), fileme=1, gnameme=1) ed_calib["scans"] = aNumber4SpecScan( ed_calib["expdata"] , doc="This is the scan number containing references.\n", defaults2 = (c_r["scans"], simplecopy), rendering = render_numero_in_una_lista_piu_uno ) ed_calib["monitor_column"] = NameWithNormaliser(doc="The name of the spec monitor, used to renormalise the intensities\n"+ " this name maybe absent from the spec file, in this case no normalisation will be done\n"+ " You can also write something like kapraman/100000 to renormalise too big monitor intensities ", defaults2=(c_p["monitor_column"], simplecopy) ) ed_calib["isolateSpot"] = aNumber( doc="If different from zero :\n"+ " clean the area outside a radius=isolateSpot from the maxium", defaults2 = ("7", simplecopy),nmin=0, nmax=40 ) ed_calib["signaladdress"] = hdf5_relative_path( ed_calib["roiaddress"] , doc="The datagroup path, relative to roiaddress,\n"+ "where extracted data will be written. Must be a new name", defaults2 = ( "calibration_scan" , simplecopy ) , gnameme = 0) ed_calib["swissknife_runner"] = swissknife_runner ed_calib["signaladdress"] .isresult = 1 ## ============================================================================== return metodo xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/__init__.py000066400000000000000000000001641412732462000244640ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/predictions/000077500000000000000000000000001412732462000246755ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/predictions/__init__.py000066400000000000000000000001641412732462000270070ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/predictions/setup.py000066400000000000000000000032161412732462000264110ustar00rootroot00000000000000# coding: utf-8 # /*########################################################################## # Copyright (C) 2016-2018 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # ############################################################################*/ __authors__ = ["V. Valls"] __license__ = "MIT" __date__ = "03/01/2019" from numpy.distutils.misc_util import Configuration def configuration(parent_package='', top_path=None): config = Configuration('predictions', parent_package, top_path) return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration=configuration) xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/predictions/winfo_xrsprediction.py000066400000000000000000000520041412732462000313470ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections ######## Managing versioning postpending to project name ################ # Get the name of the launching script, # the launching script (an principal module) # is post pended by version name # We use this to retrieve the version import inspect import os import importlib from six.moves import map from six.moves import zip curframe = inspect.currentframe() calframe = inspect.getouterframes(curframe, 2) global version caller = calframe[-1][1] if caller[-3:]==".py": print( " caller :" , caller , " // " , os.path.dirname(os.path.dirname(caller))) # imported by Wizard.py to see what are the subtasks version = os.path.basename(os.path.dirname(os.path.dirname(caller)))[len("XRStools"):] else: # called by the XRS_wizard script caller = os.path.basename(caller) version = caller[len("XRS_wizard"):] print( " Caller version==", version) XRStools = importlib.import_module( "XRStools"+version ) exec("from XRStools%s.WIZARD.Wizard import *"%version) xrs_utilities = importlib.import_module( "XRStools"+version +".xrs_utilities" ) ################################################################### import yaml import glob class functor_sigterm_handler: def __init__(self, p): self.p = p def __call__(self, *x): print( x) print( " KILLO " , self.p.pid) os.kill(self.p.pid, signal.SIGKILL) def swissknife_runner( yamltext, where , ret_dico): inputname = os.path.join(where,"input.yaml") stdname = os.path.join(where,"stdout.txt") errname = os.path.join(where,"stderr.txt") ret_dico["stdout"] = stdname ret_dico["stderr"] = errname ret_dico["input"] = inputname open(inputname,"w").write(yamltext) # os.system("XRS_swissknife %s 1> %s 2>%s"%(inputname,stdname, errname ) ) #p = subprocess.call( string.split("XRS_swissknife %s 1> %s 2> %s"%(inputname,stdname, errname )) , shell=False, stdout= subprocess .PIPE ) # preexec_fn=os.setsid # p= subprocess.Popen(string.split( "XRS_swissknife %s 1> %s 2>%s"%(inputname,stdname, errname )) , shell=False, stdout= subprocess .PIPE , stderr= subprocess .PIPE ) stdout = open(stdname,"w") stderr = open(errname,"w") if not sys.platform=="win32": p= subprocess.Popen( ("XRS_swissknife%s %s 1> %s 2>%s"%(version,inputname,stdname, errname )).split() , shell=False, stdout= stdout , stderr= stderr ) else: packages = (os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(XRStools.__file__ ) )))) script = os.path.join(packages,"scripts","XRS_swissknife%s.bat"%version ) print( " SCRIPT " , script) p= subprocess.Popen(( "%s %s"%(script, inputname)).split() , shell=False, stdout= stdout , stderr= stderr ) # p= subprocess.Popen(string.split( "XRS_swissknife %s 1> %s 2> %s"%(inputname,stdname, errname )) , shell=False, stdout= stdout , stderr= stderr ) # p= subprocess.Popen(string.split( "c:\\users\aless\\packaes\\scripts\\XRS_swissknife") , shell=False, stdout= stdout , stderr= stderr ) # p= subprocess.Popen( "XRS_swissknife %s 1> %s 2>%s"%(inputname,stdname, errname ) , shell=True ) signal.signal(signal.SIGTERM, functor_sigterm_handler(p)) p.communicate() ret_dico["return_code"]=p.returncode # import time # time.sleep(100) # p.communicate() class Functor_adapt: def __init__(self, dico): self.dico=dico def __call__(self): chiavi = list(self.dico.keys())[1:] for c in chiavi : subdic = self.dico[c] for name,par in subdic.items(): if isinstance(par,Parametro): if par.defaults2 is not None: source, method = par.defaults2 if hasattr(source,"value" ): par.value = method(source.value) else: par.value = method(source) return self.dico["CREATION_PARS"]["chem_formulas"].value+"__" class many_ChemFormula(Parametro): def __init__(self, doc="" , nmin=1, nmax=100, defaults2=None, rendering = str, just_one = False): self.rendering = rendering self.defaults2 = defaults2 self.doc = doc self.value = None self.nmin = nmin self.nmax = nmax self.just_one = just_one data_dir = os.path.join( os.path.dirname( XRStools.__file__ ) , "data" ) afile = os.path.join(data_dir,"AtomicData.yaml") self.adata = yaml.load(open(afile,"r").read()) def prevalid(self,value): value=value.strip() if not self.just_one: if value[0]!="[" or value[-1]!="]": return None value = value[1:-1] return value.split(",") else: return [value] def isReady(self): return self.isValid() def getValue(self): res = self.prevalid(self.value) return res def render(self): if not self.just_one: return self.rendering( self.getValue()) else: return self.rendering( self.getValue()[0]) def isValid(self): res = self.prevalid( self.value) if res is None: return 0 test = self.getValue() if (not self.just_one) and (not isinstance(test,list)): return 0 n=len(test) if(not self.just_one) and ( nself.nmax): return 0 good = 1 for t in test: elements,stoichiometries = xrs_utilities.parseformula(t) for el in elements: if el not in self.adata: good=0 return good class Functor_list_from_list: def __init__(self,method): self.method=method def __call__(self, source): print( source) vals = source.getValue() if hasattr(self.method,"__call__"): print( vals) res = [self.method(t) for t in vals ] else: print( " num ", vals) res = [self.method for t in vals ] return str(res) class Functor_retrieveDensity: def __init__(self, fobj): self.fobj = fobj def __call__(self, t): elements,stoichiometries = xrs_utilities.parseformula(t) if len(elements)>1: return -1.0 else: el = elements[0] return self.fobj.adata[el]["Density_g__ccm_"] class Functor_retrieveMolarMasses: def __init__(self, fobj): self.fobj = fobj def __call__(self, t): elements,stoichiometries = xrs_utilities.parseformula(t) massa=0.0 for el,st in zip(elements,stoichiometries): massa += st*self.fobj.adata[el]["AtomicMass"] return massa def dic2yaml_prediction(dicodic): s = "XRSprediction :\n" s = s + " sample :\n" s = s + " chem_formulas : %s \n" % dicodic["sample"]["chem_formulas"].render() s = s + " concentrations : %s \n" % dicodic["sample"]["concentrations"].render() s = s + " densities : %s \n" % dicodic["sample"]["densities"].render() s = s + " angle_tth : %s \n" % dicodic["sample"]["angle_tth"].render() s = s + " sample_thickness : %s \n" % dicodic["sample"]["sample_thickness"].render() s = s + " shape : %s \n" % dicodic["sample"]["shape"].render() if dicodic["sample"]["shape"].render()=="sphere": s = s + " angle_in : %s \n" % None s = s + " angle_out : %s \n" % None else: s = s + " angle_in : %s \n" % dicodic["sample"]["angle_in"].render() s = s + " angle_out : %s \n" % dicodic["sample"]["angle_out"].render() s = s + " molar_masses : %s \n" % dicodic["sample"]["molar_masses"].render() s = s + " incident_beam :\n" s = s + " i0_intensity : %s \n" % dicodic["incident_beam"]["i0_intensity"].render() s = s + " beam_height : %s \n" % dicodic["incident_beam"]["beam_height"].render() s = s + " beam_width : %s \n" % dicodic["incident_beam"]["beam_width"].render() tavola = ((dicodic["analyzer"]["crystal_reflection"]).render()[len("chitable_"):])[:-len(".dat")] elemento=tavola numpart="" while(elemento[-1].isdigit()): numpart=elemento[-1]+numpart elemento = elemento[:-1] splitting_scheme = {3:[1,1,1], 4:[2,1,1],5:[2,2,1],6:[2,2,2]}[len(numpart)] ss = splitting_scheme indexes = [int(numpart[0:ss[0]]), int(numpart[ss[0]: ss[0]+ss[1]]) , int(numpart[ ss[0]+ss[1]: ss[0]+ss[1]+ss[2]]) ] elemento = elemento[0].upper()+elemento[1:] s = s + " analyzer :\n" s = s + " material : %s \n" % elemento s = s + " hkl : %s \n" % indexes s = s + " mask_d : %s \n" % dicodic["analyzer"]["mask_d"].render() s = s + " bend_r : %s \n" % dicodic["analyzer"]["bend_r"].render() s = s + " energy_resolution : %s \n" % dicodic["analyzer"]["energy_resolution"].render() s = s + " diced : %s \n" % dicodic["analyzer"]["diced"].render() s = s + " thickness : %s \n" % dicodic["analyzer"]["thickness"].render() datadir = os.path.join( os.path.dirname( XRStools.__file__ ) , "data" ) s = s + " database_dir : %s \n" % datadir s = s + " compton_profiles :\n" s = s + " eloss_range : np.arange(%s,%s,%s) \n" % ( dicodic["compton_profiles"]["eloss_start"].render(), dicodic["compton_profiles"]["eloss_end"].render(), dicodic["compton_profiles"]["eloss_step"].render()) s = s + " E0 : %s \n" % dicodic["compton_profiles"]["E0"] .render() s = s + " detector :\n" s = s + " energy : %s \n" % dicodic["detector"]["energy"] .render() s = s + " thickness : %s \n" % dicodic["detector"]["thickness"] .render() s = s + " material : %s \n" % dicodic["detector"]["material"] .render() s = s + " thomson :\n" s = s + " scattering_plane : %s \n" % dicodic["thomson"]["scattering_plane"] .render() s = s + " polarization : %s \n" % dicodic["thomson"]["polarization"] .render() saveaddress = dicodic["saveaddress"].render() if isinstance(saveaddress,str): if len(saveaddress): s = s + " saveaddress : %s \n" % saveaddress return s def getMethod(options): if "shift_the_reference" in options: shift_the_reference = options["shift_the_reference"] if "do_deconvolution" in options: do_deconvolution = options["do_deconvolution"] metodo = collections.OrderedDict() c_p = collections.OrderedDict() metodo["CREATION_PARS"] = c_p c_p["chem_formulas"] = many_ChemFormula( doc=" a list of formulas like [SiO2,C] ", nmin=1,nmax=200, defaults2=("[C]",simplecopy)) c_p["define_new"] = Functor_adapt( metodo ) ######################################################3 xrs_p = collections.OrderedDict() metodo["xrs_prediction"] = xrs_p xrs_p["swissknife_runner"] = swissknife_runner xrs_p["getyaml"] = dic2yaml_prediction xrs_p["HELP"] = """ This launches the XRS prediction routines. If yamlData contains information about: the sample, the incident beam, the analyzer, the detector, the polarization, and the HF compton profiles, this will create the desired predicted XRS data. At the end you have the possibility to write the predicted profiles into a container hdf5 file. """ sample = collections.OrderedDict() metodo["xrs_prediction"]["sample"] = sample sample["chem_formulas"] = many_ChemFormula( doc=" a list of formulas like [SiO2,C] ", nmin=1,nmax=200, defaults2 = ( c_p["chem_formulas"], simplecopy ) ) sample["concentrations"] = many_Number( "list of concentrations, should contain values between 0.0 and 1.0" , nmin=1, nmax=200, float_also = 1, defaults2=( sample["chem_formulas"] , Functor_list_from_list( 1.0 )) ) sample["densities"] = many_Number( "list of densities of the constituents [g/cm^3]" , nmin=1, nmax=200, float_also = 1, defaults2=( sample["chem_formulas"] , Functor_list_from_list( Functor_retrieveDensity(sample["chem_formulas"]) ) ) ) if 0: sample["angle_tth"] = aNumber(doc = "scattering angle [deg]", defaults2=( "35.0" , simplecopy) , float_also = True) else: sample["angle_tth"] = many_Number(doc = "scattering angles [deg1,deg2, deg2], dont foret the square brakets or try if it works", defaults2=( "[35.0]" , simplecopy) , float_also = True) sample["sample_thickness"] = aNumber(doc = "sample thickness/diameter in [cm]", defaults2=( "0.1" , simplecopy) , float_also = True) sample["shape"] = Parameter_Choices( " keyword, can be 'slab' or 'sphere' " , ["sphere", "slab" ], defaults2=( "sphere" , simplecopy) ) sample["angle_in"] = aNumber(doc = "beam exit angle in [deg] relative to sample surface normal", defaults2=( "45.0" , simplecopy) , float_also = True) sample["angle_out"] = aNumber(doc = "incident beam angle in [deg] relative to sample surface normal", defaults2=( "45.0" , simplecopy) , float_also = True) sample["shape"] . visibility_depends_on = {("slab", ):[sample["angle_in"], sample["angle_out"] ] } sample["molar_masses"] = many_Number( "list of densities of the constituents [g/cm^3]" , nmin=1, nmax=200, float_also = 1, defaults2=( sample["chem_formulas"] , Functor_list_from_list( Functor_retrieveMolarMasses(sample["chem_formulas"]) ) ) ) incident_beam = collections.OrderedDict() metodo["xrs_prediction"]["incident_beam"] = incident_beam incident_beam["i0_intensity"] = aNumber(doc = " number of incident photons [1/sec]", defaults2=( "1.0e+13" , simplecopy) , float_also = True) incident_beam["beam_height"] = aNumber(doc = "in micron", defaults2=( "10.0" , simplecopy) , float_also = True) incident_beam["beam_width"] = aNumber(doc = "in micron ", defaults2=( "20.0" , simplecopy) , float_also = True) analyzer = collections.OrderedDict() metodo["xrs_prediction"]["analyzer"] = analyzer ##### datadir = os.path.join( os.path.dirname( XRStools.__file__ ) , "data" ) chitables = glob.glob(datadir+"/chitable_*.dat") chitables=list(map(os.path.basename,chitables)) #### analyzer["crystal_reflection"] = Parameter_Choices( "analyzer material (e.g. 'Si', 'Ge') and reflection.\n" " Possibilities are given by the chitables in data directory : " +datadir, chitables, defaults2=( "chitable_si660.dat" , simplecopy) ) analyzer["mask_d"] = aNumber(doc = "analyzer mask diameter in [mm] ", defaults2=( "60.0" , simplecopy) , float_also = True) analyzer["bend_r"] = aNumber(doc = "bending radius of the crystal [mm] ", defaults2=( "1.0" , simplecopy) , float_also = True) analyzer["energy_resolution"] = aNumber(doc = "energy resolution [eV] ", defaults2=( "0.5 " , simplecopy) , float_also = True) analyzer["diced"] = Parameter_Choices( "boolean (True or False) if a diced crystal is used or not (defalt is False)" , ["False","True"], defaults2=( "False" , simplecopy)) analyzer["thickness"] = aNumber(doc = " thickness of the analyzer crystal", defaults2=( "500.0" , simplecopy) , float_also = True) compton_profiles = collections.OrderedDict() metodo["xrs_prediction"]["compton_profiles"] = compton_profiles compton_profiles["eloss_start"] = aNumber(doc = "eloss range start eV", defaults2=( "0.0" , simplecopy) , float_also = True) compton_profiles["eloss_end"] = aNumber(doc = " eloss range end eV", defaults2=( "1000.0" , simplecopy) , float_also = True) compton_profiles["eloss_step"] = aNumber(doc = " eloss range step eV ", defaults2=( "0.1" , simplecopy) , float_also = True) compton_profiles["E0"] = aNumber(doc = "E0 KeV ", defaults2=( "9.7" , simplecopy) , float_also = True) detector = collections.OrderedDict() metodo["xrs_prediction"]["detector"] = detector detector["energy"] = aNumber(doc = "analyzer energy [keV] ", defaults2=( "9.7" , simplecopy) , float_also = True) detector["thickness"] = aNumber(doc = "thickness of the active material [microns] ", defaults2=( " 500.0" , simplecopy) , float_also = True) detector["material"] = many_ChemFormula( doc="detector active material " , nmin=1, nmax=100, defaults2=( "Si" , simplecopy), rendering = str, just_one = True) thomson = collections.OrderedDict() metodo["xrs_prediction"]["thomson"] = thomson thomson["scattering_plane"] = Parameter_Choices( "keyword to indicate scattering plane relative to lab frame ('vertical' or 'horizontal')detector active material", ["vertical","horizontal"], defaults2=( "vertical" , simplecopy) ) thomson["polarization"] = aNumber(doc = "degree of polarization (close to 1.0 for undulator radiation)", defaults2=( "0.99" , simplecopy) , float_also = True) metodo["xrs_prediction"]["saveaddress"] = Hdf5FilePath(doc="the target destination, if data should be saved\n" "let a blanck line if not", defaults2 = ( "myfile.hdf5:/path/to/hdf5/group" , simplecopy ), fileme=0.5, gnameme=0) return metodo xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/methods/setup.py000066400000000000000000000032121412732462000240620ustar00rootroot00000000000000# coding: utf-8 # /*########################################################################## # Copyright (C) 2016-2018 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # ############################################################################*/ __authors__ = ["V. Valls"] __license__ = "MIT" __date__ = "03/01/2019" from numpy.distutils.misc_util import Configuration def configuration(parent_package='', top_path=None): config = Configuration('methods', parent_package, top_path) return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration=configuration) xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/prova.py000066400000000000000000000025011412732462000224060ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys from PyQt4 import QtGui,QtCore class MyTree( QtGui.QTreeView): def __init__(self, *args): QtGui.QTreeView.__init__(self, *args) self.clicked[QtCore.QModelIndex].connect(self.itemClicked) def itemClicked(self, modelIndex): event ="itemDoubleClicked" print( event, modelIndex) index = self.selectedIndexes()[0] pippo = index.model().data(index) print( pippo) print( str(pippo)) print( dir(pippo)) print( pippo.toString()) pippo = index.model().filePath(index) print( str(pippo)) class Myview(QtGui.QMainWindow): def __init__(self,parent=None): QtGui.QMainWindow.__init__(self) model = QtGui.QFileSystemModel() model.setNameFilters(["winfo_*.py"]) self.model=model model.setRootPath('C:\Myfolder') model.setNameFilterDisables(False) # view = QtGui.QTreeView() view = MyTree() view.setModel(model) view.setRootIndex(model.index("C:\\Users")) self.setCentralWidget(view) if __name__ == '__main__': app = QtGui.QApplication(sys.argv) myview = Myview() myview.show() sys.exit(app.exec_()) xrstools-0.15.0+git20210910+c147919d/XRStools/WIZARD/setup.py000066400000000000000000000032111412732462000224160ustar00rootroot00000000000000# coding: utf-8 # /*########################################################################## # Copyright (C) 2016-2018 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # ############################################################################*/ __authors__ = ["V. Valls"] __license__ = "MIT" __date__ = "03/01/2019" from numpy.distutils.misc_util import Configuration def configuration(parent_package='', top_path=None): config = Configuration('WIZARD', parent_package, top_path) return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration=configuration) xrstools-0.15.0+git20210910+c147919d/XRStools/XES_spectra_extraction.py000066400000000000000000000171131412732462000247040ustar00rootroot00000000000000import os import json import h5py import numpy as np def runit( s , work_dir, filename ) : open( os.path.join(work_dir, filename) ,"w" ).write(s) os.system( "cd %s; XRS_swissknife %s ; cd .." % (work_dir, filename) ) def prepare( work_dir="preparation_run4_16", data = "/data/id20/inhouse/data/run4_16/run6_ihr/rixs" , scan_for_roi= 66 , do_roi = True, do_roi_sample = True, reference_scan = 66 , # the scan used for the reference monitorcolumn = "izero", do_recentering=False, scan_for_sample_roi = 268 , # Not necessary is recentering is not needed. If not given the rois file is assumed (when needed if needed) to be already there beta_response = 0 ) : if not os.path.exists(work_dir): os.makedirs(work_dir) assert (os.path.exists( work_dir) and os.path.isdir( work_dir) ), "Cannot get work directory %s "%work_dir assert (os.access(work_dir, os.W_OK | os.X_OK)), " directory %s is not writable"%work_dir preparation_parameters = { "work_dir" : work_dir, "data" : data, "scan_for_roi" : scan_for_roi, "reference_scan" : reference_scan, "monitorcolumn" : monitorcolumn, "do_recentering" : do_recentering, "scan_for_sample_roi" : scan_for_sample_roi } json.dump(preparation_parameters, open(os.path.join(work_dir, "preparation_parameters.json"),"w") ) if do_roi: s=""" create_rois: expdata : "{data_file}" scans : [{scan_for_roi}] roiaddress : "roi.h5:/ROI_AS_SELECTED" """.format( data_file = data, scan_for_roi = scan_for_roi ) runit( s , work_dir, "01_create_roi.yaml" ) if do_recentering and do_roi_sample: s=""" create_rois: expdata : "{data_file}" scans : [{scan_for_roi} ] roiaddress : roi_sample.h5:/ROI_AS_SELECTED """.format( data_file = data, scan_for_roi = scan_for_sample_roi ) runit( s , work_dir, "02_create_roi_sample.yaml" ) s=""" loadscan_2Dimages : expdata : "{data_file}" roiaddress : roi.h5:/ROI_AS_SELECTED scan_interval : [{reference_scan},{reference_scan_P1}] signaladdress : calibration_scan sumto1D : 0 energycolumn : 'Anal Energy' monitorcolumn : {monitorcolumn} save_also_roi : 1 """.format( data_file = data, reference_scan = reference_scan , reference_scan_P1 = reference_scan +1 , monitorcolumn = monitorcolumn ) runit( s , work_dir, "03_load_scan_reference.yaml" ) if do_recentering : s=""" loadscan_2Dimages : expdata : "{data_file}" roiaddress : roi_sample.h5:/ROI_AS_SELECTED scan_interval : [{scan_for_roi},{scan_for_roi_P1}] signaladdress : uncentered_sample_scan sumto1D : 0 energycolumn : 'Anal Energy' monitorcolumn : {monitorcolumn} save_also_roi : 1 """.format( data_file = data, scan_for_roi = scan_for_sample_roi , scan_for_roi_P1 = scan_for_sample_roi +1 , monitorcolumn = monitorcolumn ) runit( s , work_dir, "04_load_scan_sample.yaml" ) s=""" calculate_recenterings : bariA : roi_sample.h5:/ROI_AS_SELECTED/uncentered_sample_scan/scans/Scan%03d/ bariB : roi.h5:/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d/ target : recentering.h5:/recentering """%( scan_for_sample_roi, reference_scan ) runit( s , work_dir, "05_setup_recentering.yaml" ) s=""" loadscan_2Dimages : expdata : {data_file} roiaddress : roi.h5:/ROI_AS_SELECTED scan_interval : [{scan_for_roi},{scan_for_roi_P1}] monitorcolumn : {monitorcolumn} recenterings : recentering.h5:/recentering recenterings_confirmed : recentering.h5:/confirmed_shift signaladdress : Centered_sample_scans sumto1D : 0 energycolumn : 'Anal Energy' """.format( data_file = data, scan_for_roi = scan_for_sample_roi , scan_for_roi_P1 = scan_for_sample_roi +1 , monitorcolumn = monitorcolumn ) runit( s , work_dir, "06_calculate_recentering.yaml" ) s=""" superR_fit_responses: foil_scan_address : roi.h5:/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d nref : 2 niter_optical : 50 beta_optical : %e niter_global : 2 pixel_dim : 1 niter_pixel : 0 beta_pixel : 0 do_refine_trajectory : 1 simmetrizza : 1 filter_rois : 0 target_file : fitted_response.h5:/ScanFittedResponse_%03d fit_lines : 1 """%( reference_scan, beta_response, reference_scan ) runit( s , work_dir, "07_fit.yaml" ) s=""" superR_recreate_rois : responsefilename : fitted_response.h5:/ScanFittedResponse_%03d nex : 0 old_scan_address : roi.h5:/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d filter_rois : 0 recenterings_refined : recentering.h5:/confirmed_shift target_filename : newscan.h5:/ROI_AS_SELECTED/calibration_scan/scans/ScanReSynth_%03d """%( reference_scan, reference_scan, reference_scan ) runit( s , work_dir, "08_resynth.yaml" ) def extract( work_dir="preparation_run4_16", scan_interval = [130, 132] , target_file = "SPECTRA.h5", temporary_file = "temporary.h5", niter = 0, niterLip = 20, beta = 0, ) : assert (os.path.exists( work_dir) and os.path.isdir( work_dir) ), "Cannot get work directory %s "%work_dir assert (os.access(work_dir, os.W_OK | os.X_OK)), " directory %s is not writable"%work_dir preparation_parameters = json.load( open(os.path.join(work_dir, "preparation_parameters.json"),"r") ) data = preparation_parameters["data"] reference_scan = preparation_parameters["reference_scan"] monitorcolumn = preparation_parameters["monitorcolumn"] scan_for_roi = preparation_parameters["scan_for_roi"] start = scan_interval[0] end = scan_interval[1] s=""" loadscan_2Dimages : expdata : {data_file} roiaddress : newscan.h5:/ROI_AS_SELECTED/calibration_scan/scans/ScanReSynth_{reference_scan:03d} scan_interval : [{start}, {end}] signaladdress : ../{temporary_file}:/data_{start}_{end} sumto1D : 0 energycolumn : 'Anal Energy' monitorcolumn : {monitorcolumn} """.format(data_file = data, reference_scan = reference_scan , monitorcolumn = monitorcolumn, start = start, end = end, temporary_file=temporary_file) runit( s , work_dir, "31_harvest.yaml" ) s=""" extract_spectra : reference_address : newscan.h5:/ROI_AS_SELECTED/calibration_scan/scans/ScanReSynth_{reference_scan:03d} sample_address : ../{temporary_file}:/data_{start}_{end} roiaddress : newscan.h5:/ROI_AS_SELECTED/calibration_scan/scans/ScanReSynth_{reference_scan:03d} reference_scan : {reference_scan} scan_interval : [{start}, {end}] zmargin : 0 target : ../{result_file}:/fromscans_{start}_{end} # final_plot : PLOT DE : 0.0 niterLip : {niterLip} niter : {niter} beta : {beta} """.format(reference_scan=reference_scan, start = start, end = end , niterLip = niterLip, niter = niter, beta = beta, result_file = target_file, temporary_file=temporary_file) runit( s , work_dir, "32_extract_spectra.yaml" ) xrstools-0.15.0+git20210910+c147919d/XRStools/XRS_swissknife.py000066400000000000000000004657121412732462000232210ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import scipy import json ### __doc__=""" generality_doc = 1 """ Documentation of XRS_swissknife ------------------------------- The script is called in this way :: XRS_swissknife yourinput.yaml The input file is in *yaml* format. In *yaml* format each line introduces an item and the indentation expresses the hierarchy. An example is :: Fancy_Reduction : parameterTom : 3.14 parameterJerry : False choicesBob : [1,2,3] In this example we create an item called *Fancy_Reduction* which contains three subitems. The *XRS_swissknife* expects that for each operation that you want to apply, you provide an item named as the operation, and the associated subitems that provide that values for the parameters. *XRS_swissknife* will do what you want provided you respect the proper indentation. A thing which helps is using emacs and activating the *python mode*, because python uses the same indentation principle to structure the code. Each processing item has the additional, optional, key **active**. This key can be set to **0** or **1** to desactivate or not (default is **1**, active) the processing. Here a desactivation example :: Fancy_Reduction : active : 0 parameterTom : 3.14 parameterJerry : False choicesBob : [1,2,3] The following documentation has been created automatically, for each functionality, based on the documentation string written in the code for the fonctionality itself. You can also write mathematical expressions : :math:`\\int{ x dx} = \\frac { x^2 }{ 2 }` and even include graphics. """ import collections try: from mayavi import mlab except: print( " WAS not able to load mayavi, some feature might be missing ") import string import numpy as np import math import yaml from yaml import load, dump import numbers import re import yaml import yaml.resolver import PyMca5.PyMcaIO.specfilewrapper as SpecIO import fabio from six import u from silx.gui import qt as Qt ## from PyQt4 import Qt, QtCore from XRStools.roiNmaSelectionGui import roiNmaSelectionWidget from XRStools import roiSelectionWidget import h5py import sys yaml.resolver.Resolver Resolver = yaml.resolver.Resolver Resolver.add_implicit_resolver( u'tag:yaml.org,2002:float', re.compile(u(r"""^(?:[-+]?(?:[0-9][0-9_]*)(\.[0-9_]*)?(?:[eE][-+]?[0-9]+)? |\.[0-9_]+(?:[eE][-+][0-9]+)? |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\.[0-9_]* |[-+]?\.(?:inf|Inf|INF) |\.(?:nan|NaN|NAN))$"""), re.X), list(u'-+0123456789.')) try: from mpi4py import MPI myrank = MPI.COMM_WORLD.Get_rank() nprocs = MPI.COMM_WORLD.Get_size() procnm = MPI.Get_processor_name() comm = MPI.COMM_WORLD print( "MPI LOADED , nprocs = ", nprocs) except: class FakeComm: def Barrier(self): pass def barrier(self): pass def allreduce(self,number, operation): assert ( isinstance(number, numbers.Number) ) return number def Get_size(self): return 1 myrank=0 nprocs = 1 comm = FakeComm() print( "no MPI LOADED , nprocs = ", nprocs) def stripROI(t): if "ROI" in t: return t[3:] else: return t def filterRoiList(l, strip=False, prefix="ROI"): if strip: result = [ str(int(stripROI(t))) for t in l if ( t not in [ "motorDict", "response"] and t[:len(prefix)]==prefix )] else: result = [ t for t in l if ( t not in [ "motorDict", "response"] and t[:len(prefix)]==prefix ) ] result = sorted( result , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) ### result.sort() return result def filterScanList(l, prefix="Scan"): result = [ t for t in l if ( t not in [ "motorDict", "response"] and t[:len(prefix)]==prefix ) ] result = sorted( result , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) # result.sort() return result def checkNoParallel( routineName): if nprocs>1: msg = " ERROR : %s feature not yet parallel : relaunch it with 1 process only " print( msg) raise Exception( msg) # try: # from yaml import CLoader as Loader, CDumper as Dumper # except ImportError: # from yaml import Loader, Dumper nprocs = comm.Get_size() if nprocs>1: # circumvent issus with mpi4py not stopping mpirun def excepthook(type, value, traceback): res = sys.__excepthook__(type, value, traceback) comm.Abort(1) return res sys.excepthook=excepthook import os from XRStools import xrs_rois from XRStools import roifinder_and_gui from XRStools import xrs_scans from XRStools import xrs_read from XRStools import rixs_read from XRStools import theory from XRStools import extraction from XRStools import xrs_prediction from XRStools import xrs_imaging from XRStools import xrs_imaging from XRStools import superr ################################################################# ## THIS redefinition of yaml is used to keep the entry ordering ## when accessing yamlData keys ## #yaml_anydict.py import yaml from yaml.representer import Representer from yaml.constructor import Constructor, MappingNode, ConstructorError from XRStools import fit_spectra from XRStools import reponse_percussionelle def check_allowed_keys( mydata, allowed_keys ) : for k in mydata.keys(): if not k in allowed_keys: raise ValueError( (" key "+str(k) +" not in allowed keys :"+ str(allowed_keys)) ) def dump_anydict_as_map( anydict): yaml.add_representer( anydict, _represent_dictorder) def _represent_dictorder( self, data): return self.represent_mapping('tag:yaml.org,2002:map', data.items() ) class Loader_map_as_anydict( object): 'inherit + Loader' anydict = None #override @classmethod #and call this def load_map_as_anydict( klas): yaml.add_constructor( 'tag:yaml.org,2002:map', klas.construct_yaml_map) 'copied from constructor.BaseConstructor, replacing {} with self.anydict()' def construct_mapping(self, node, deep=False): if not isinstance(node, MappingNode): raise ConstructorError(None, None, "expected a mapping node, but found %s" % node.id, node.start_mark) mapping = self.anydict() for key_node, value_node in node.value: key = self.construct_object(key_node, deep=deep) try: hash(key) except TypeError as exc: raise ConstructorError("while constructing a mapping", node.start_mark, "found unacceptable key (%s)" % exc, key_node.start_mark) value = self.construct_object(value_node, deep=deep) mapping[key] = value return mapping def construct_yaml_map( self, node): data = self.anydict() yield data value = self.construct_mapping(node) data.update(value) class myOrderedDict (collections.OrderedDict): def __setitem__(self,a,b): if type(a)==type("") and a in self: self[a+"_TAG_this_key_is_given_twice"]=b else: ## print super(myOrderedDict, self) super(myOrderedDict, self).__setitem__(a,b ) def cleaned(key): while key[-28:]=="_TAG_this_key_is_given_twice": key=key[:-8] return key dictOrder = myOrderedDict class Loader( Loader_map_as_anydict, yaml.Loader): anydict = dictOrder Loader.load_map_as_anydict() dump_anydict_as_map( dictOrder) ## ## END of yaml redefinition ############################### def check_libre( h5 , groupname ) : print("check_libre DESACTIVATED. RESULTS CAN BE OVERWRITTEN") return if type(h5)==type(""): h5f = h5py.File(h5, "r" ) h5f.close() if groupname in h5f: msg=(("ERROR: %s key already present in the hdf5 file %s. Erase it before if you dont need it.\n" % (groupname, h5))*10 ) print( msg) raise Exception( msg) else: if groupname in h5: msg = (("ERROR: %s key already present in the hdf5 file. Erase it before if you dont need it.\n"%groupname)*10 ) print( msg) raise Exception( msg) inputtext="" def yamlFileToDic(fn): global inputtext filename = fn inputfile = open(filename,"r") yamlData = load(inputfile, Loader=Loader) return yamlData def main(): global inputtext filename = sys.argv[1] inputfile = open(filename,"r") yamlData = load(inputfile, Loader=Loader) inputtext = open(filename,"r").read() for key in list(yamlData.keys()): mydata = yamlData[key] if isinstance(mydata,dict) and "active" in mydata : if mydata["active"]==0: continue if key != "help": swissknife_operations[cleaned(key)]( mydata ) else: if cleaned(key) not in parallelised_operations: checkNoParallel( cleaned(key) ) swissknife_operations[cleaned(key)]( yamlData ) # workbench_file = yamlData["workbench_file"] def help(yamlData): """ **help** Displays doc on the operations. In the input file :: help : will trigger printing of all the available operation names :: help : create_rois load_scans will print the help on *create_rois* and the help about *load_scans*. By the way, it is the same that you can read here because the *sphinx* doc-generation tool reads the same docstrings contained in the code. """ print( " HELP " *15) if yamlData ["help"] is None: print( """ Printing all the function names To get help on a specific function: help : "functionName" """) for key,func in swissknife_operations.items(): print( " FUNCTION : " , key) else: func = swissknife_operations[ yamlData ["help"]] print( "---------------------------------------") print( func.__doc__) def split_hdf5_address(dataadress): pos = dataadress.rfind(":") if ( pos==-1): raise Exception( """ roiaddress must be given in the form roiaddress : "myfile.hdf5:/path/to/hdf5/group" but : was not found """) filename, groupname = dataadress[:pos], dataadress[pos+1:] if( len(groupname) and groupname[0:1] !="/" ): groupname = "/"+groupname return filename, groupname # def load_scans(mydata): # """ # **load_scans** # This command harvest the selected signals. # the instructions on the scans to be taken must be in the form( as submembers ofload_scans ) :: # load_scans : # roiaddress : "hdf5filename:nameofroigroup" # the same given in create_rois # expdata : "absolutepathtoaspecfile" # this points to a spec file # elastic_scans : [623] # fine_scans : [626,630,634,638,642] # n_loop : 4 # long_scan : 624 # signaladdress : "nameofsignalgroup" # Target group for writing Relative to ROI (and in the same file)!!!! # ############################################################# # # OPTIONALS # # # order : [0,1,2,3,4,5] # list of integers (0-5) which describes the order of modules in which the # # ROIs were defined (default is VD, VU, VB, HR, HL, HB; i.e. [0,1,2,3,4,5]) # rvd : -41 # mean tth angle of HL module (default is 0.0) # rvu : 85 # mean tth angle of HR module (default is 0.0) # rvb : 121.8 # mean tth angle of HB module (default is 0.0) # rhl : 41.0 # mean tth angle of VD module (default is 0.0) # rhr : 41.0 # mean tth angle of VU module (default is 0.0) # rhb : 121.8 # mean tth angle of VB module (default is 0.0) # # # """ # roiaddress=None # roiaddress = mydata["roiaddress"] # filename, groupname = split_hdf5_address (roiaddress) # file= h5py.File(filename,"r") # rois = {} # shape=xrs_rois.load_rois_fromh5(file[groupname],rois) # file.close() # roiob = xrs_rois.roi_object() # roiob.load_rois_fromMasksDict(rois , newshape = shape, kind="zoom") # reader = xrs_read.read_id20(mydata["expdata"] , monitorcolumn='kapraman') # reader.set_roiObj(roiob) # elastic_scans = mydata["elastic_scans"][:] # fine_scans = mydata["fine_scans"][:] # n_loop = mydata["n_loop"] # long_scan = mydata["long_scan"] # reader.loadelasticdirect(elastic_scans) # reader.loadloopdirect(fine_scans,n_loop) # print( " LUNGO " ) # reader.loadlongdirect(long_scan) # reader.getspectrum() # reader.geteloss() # reader.gettths( # rvd = gvord(mydata,"rvd",0.0) , # rvu = gvord(mydata,"rvu",0.0) , # rvb = gvord(mydata,"rvb",0.0) , # rhl = gvord(mydata,"rhl",0.0) , # rhr = gvord(mydata,"rhr",0.0) , # rhb = gvord(mydata,"rhb",0.0) , # order = gvord(mydata,"order", [0,1,2,3,4,5]) # ) # groupname = groupname+"/"+ mydata["signaladdress"] # check_libre( filename , groupname ) # reader.save_state_hdf5( filename, groupname , comment = inputtext ) # def volume_from_2Dimages(mydata): # """ # imagesaddress : "test_imaging.hdf5:/ROI_A/images" # where the data have been saved # scan_interval : [372,375] # OPTIONAL : can be shorter then the scans effectively present in the file # roi_n : 0 # OPTIONAL. if not given, the first non empty found roi. Starts from 0 # imagesaddress : "myfile.hdf5:/path/to/hdf5/data" # OPTIONAL. the target destination for volume. if not given mayavi is launched on the fly. # """ # reader = xrs_imaging.oneD_imaging( "bidon" , "bidon", "bidon" , "bidon") # imagesaddress = mydata["imagesaddress"] # filename, groupname = split_hdf5_address(imagesaddress) # reader.load_state_hdf5( filename, groupname) # scan_names = list( reader.twoDimages.keys() ) # scan_ids = map(int, [name[4:] for name in scan_names ] ) # order = np.argsort(scan_ids) # if not ('scan_interval') in mydata : # scan_names = [ scan_names[id] for id in order ] # else: # scan_interval = mydata['scan_interval'] # print( order) # print( scan_names) # print( scan_interval) # scan_names = [ scan_names[id] for id in order if scan_ids[id]>=scan_interval[0] and scan_ids[id]0.0: # # print nn, dd # if True or (np.abs(np.array(kOM)-np.array(oOM)).sum()==0.0): # print( " OM equal " , len(kV) , len(oV)) # if( len(kV) == len(oV)): # # print np.abs(kV-oV) # # if np.abs(kV-oV).sum()==0.0: # assert( kM!=oM ) # DONE[oscan]=1 # # print " odata.shape " , odata.shape # myscans.append(odata) # # print " myscans.shape " , np.array(myscans).shape # myM.append(oM) # myV.append(oV) # DONE[kscan]=1 # myscans = np.array(myscans) # if len(myM)>1: # order = np.argsort(myM) # myM = np.array(myM)[order] # myscans = np.array(myscans)[order] # myV = np.array(myV) # else: # myM = np.array(myM) # myV = np.array(myV) # ts = "scan_"+str(int(s1)+kscan) # h5t = h5.require_group(ts) # h5t["Variable"] = myV # h5t["Motor"] = myM # s="f, axarr = pylab.plt.subplots(%d, sharex=True)\n"%len(roi_names) # ly = myM[0] # hy = myM[-1] # if ( np.abs(myV[-1,:]-myV[0,:]).sum()==0.0 ): # xlabel = "An. Energy" # lx = myV[0,0] # hx = myV[0,-1] # else: # xlabel = "Energy Loss" # diff = myM[0]- myV[0,:] # lx = diff[0] # hx = diff[-1] # for i,rn in enumerate(roi_names): # tok = myscans[:,:,i] # h5t["signal_"+rn] = tok # s=s+"axarr[%d].imshow( %s ,extent=( %e ,%e ,%e ,%e) ) \n"%(i, "self.signal_"+rn, lx,hx,ly,hy) # s=s+"axarr[%d].set_title('%s')\n"%(i,rn) # if i==0: # s=s+"axarr[%d].set_xlabel('%s')\n"%(i,xlabel) # s=s+"pylab.plt.show()\n" # h5t["python_plot"]=s # h5f.flush() # h5f.close() # """ # sum_scans2maps : # spec_file : /mntdirect/_data_visitor/hc2892/id20/run7_hc2892/rixs # first_scan : 127 # last_scan : 136 # Scan_Variable : Anal Energy # Motor_Variable : energy # scans_file : /scisoft/users/mirone/WORKS/matlabID20/rawdata/scansfile.h5:SCAN # """ def loadscan_2Dimages(mydata): """ **loadscan_2Dimages** This command harvest the selected signals. the instructions on the scans to be taken must be in the form( as submembers ofload_scans ) :: loadscan_2Dimages : roiaddress : "hdf5filename:nameofroigroup" # the same given in create_rois expdata : "absolutepathtoaspecfile" # this points to a spec file scan_interval : [372,423] # from 372 to 422 included signaladdress : "nameofsignalgroup" # Target group for writing Relative to nameofroigroup/ (and in the same file)!!!! # unless it is in the format filename:groupname energycolumn : 'sty' # OPTIONAL, if not give defaults to sty monitorcolumn : 'kapraman' # OPTIONAL , default is kapraman. If the key is not found in spec file, then no normalisation will be done # You can also write kapraman/1000 in this case the monito will be divided by 1000 # (or the other number that you write) edfName : 'edfprefix' # OPTIONAL, if not given autonmatically determined sumto1D : 1 # OPTIONAL, default 0 isolateSpot : 0 # is different from zero selects on each image ( when sumto1d=0 ) ROI the spot region and sets to zero outside a radius = isolateSpot # the following defaults to None recenterings : "recenterings.h5:/recenterings4rois" # """ allowed_keys = ["roiaddress", 'monitorcolumn', 'recenterings', 'recenterings_confirmed', 'energycolumn', 'edfName', 'isolateSpot', "expdata","scan_interval","scan_list","signaladdress","save_also_roi", 'sumto1D',] check_allowed_keys(mydata, allowed_keys) roiaddress=None roiaddress = mydata["roiaddress"] filename, groupname = split_hdf5_address( roiaddress) file= h5py.File(filename,"r") rois = {} shape=xrs_rois.load_rois_fromh5(file[groupname],rois) file.close() roiob = xrs_rois.roi_object() roiob.load_rois_fromMasksDict(rois , newshape = shape, kind="zoom") monitor_divider = 1.0 if ('monitorcolumn') in mydata : monitorcolumn = mydata['monitorcolumn'] else: monitorcolumn = 'kapraman' pos = monitorcolumn.find("/") if pos != -1: monitor_divider = float(monitorcolumn[pos+1:]) monitorcolumn = monitorcolumn[:pos] print( "monitorcolumn : " , monitorcolumn) print( "monitor_divider : " , monitor_divider) is_by_refinement =False if ('recenterings') in mydata : recenterings = mydata['recenterings'] recenterings_filename, recenterings_groupname = split_hdf5_address( recenterings ) h5f = h5py.File(recenterings_filename,"r") h5 = h5f[recenterings_groupname] recenterings= {} chiavi = filterRoiList(h5.keys(), prefix="") for c in chiavi: recenterings[int(c)]= h5[c][:] if recenterings[int(c)].shape == (2,2): if nprocs>1: raise Exception("When using recentering with refinement parallelism cannote be used") is_by_refinement = True h5f.close() if is_by_refinement: recenterings_confirmed = mydata['recenterings_confirmed'] recenterings_confirmed_filename, recenterings_confirmed_groupname = split_hdf5_address( recenterings_confirmed ) else: recenterings = None if ('energycolumn') in mydata : energycolumn = mydata['energycolumn'] else: energycolumn = 'sty' if ('edfName') in mydata : edfName = mydata['edfName'] else: edfName = None if ('sumto1D') in mydata : sumto1D = mydata['sumto1D'] else: sumto1D = 0 if ('isolateSpot') in mydata : isolateSpot = mydata['isolateSpot'] else: isolateSpot = 0 reader = xrs_imaging.oneD_imaging( mydata["expdata"] ,monitorcolumn = monitorcolumn , monitor_divider=monitor_divider, energycolumn = energycolumn , edfName = edfName, sumto1D = sumto1D, recenterings=recenterings) reader.set_roiObj(roiob) todo_list = [] if "scan_interval" in mydata: scan_interval = mydata["scan_interval"] ninterval = len(scan_interval)//2 for i in range(ninterval): todo_list = todo_list + list(range( int(scan_interval[2*i]), int(scan_interval[2*i+1])) ) # *scan_interval[2*i :2*i+2]) else: scan_list = mydata["scan_list"] for i in scan_list: todo_list = todo_list + [int(i)] mytodo = np.array_split(todo_list, nprocs) [myrank] print( " Process ", myrank, " is going to read the following scans ", mytodo) maxvalue=0.0 if(len(mytodo)): maxvalue = reader.loadscan_2Dimages( list(mytodo) ,scantype=energycolumn, isolateSpot = isolateSpot) if nprocs>1: maxvalue = comm.allreduce(maxvalue, op=MPI.MAX) if is_by_refinement : if nprocs>1: raise Exception("When using recentering with refinement parallelism cannote be used") if os.path.exists(recenterings_confirmed_filename): check_libre( recenterings_confirmed_filename , recenterings_confirmed_groupname ) h5f = h5py.File(recenterings_confirmed_filename,"a") else: h5f = h5py.File(recenterings_confirmed_filename,"w") h5 = h5f.require_group(recenterings_confirmed_groupname) for c in chiavi: if c in h5: del h5[c] h5[c] = reader.recenterings[int(c)][()] h5f.flush() h5f.close() h5f = None signaladdress = mydata["signaladdress"] if ":" not in signaladdress: groupname = groupname+"/"+ mydata["signaladdress"]+"/" check_libre( filename , groupname ) if "save_also_roi" in mydata: if mydata["save_also_roi"]: save_also_roi = "for_resynth" else: save_also_roi = False else: filename , groupname = split_hdf5_address(signaladdress ) save_also_roi = True for iproc in range(nprocs): comm.Barrier() if iproc==myrank: reader.save_state_hdf5( filename, groupname, comment = inputtext, myrank = myrank, save_also_roi = save_also_roi )# factor = 16000.0/maxvalue) comm.Barrier() if myrank==0: if save_also_roi == "for_resynth": myfile = h5py.File(filename,'r+') myfile[os.path.join( groupname,"image")] = h5py.SoftLink( os.path.join( os.path.dirname( groupname[:-1]) , "rois_definition/image" ) ) myfile[os.path.join( groupname,"rois_dict")] = h5py.SoftLink( os.path.join( os.path.dirname( groupname[:-1]) , "rois_definition/rois_dict" ) ) myfile.close() def loadscan_2Dimages_galaxies(mydata): """ **loadscan_2Dimages_galaxies** This command harvest the selected signals. the instructions on the scans to be taken must be in the form( as submembers of load_scans ) :: loadscan_2Dimages_galaxies : roiaddress : "hdf5filename:nameofroigroup" # the same given in create_rois expdata : "kapton_%05d_01.nxs:/root_spyc_config1d_RIXS_00001/scan_data/data_07" monitor_address : "kapton_%05d_01_monitor.nxs:/monitor" # oppure None scan_interval : [1,2] # from 1 to 1 included ( kapton_00001_01.nxs) Ydim : 16 Zdim : 16 Edim : 19 signalfile : "filename" # Target file for the signals # """ allowed_keys = [ "roiaddress",'Ydim','Zdim','Edim',"signalfile","monitor_address","expdata", "scan_interval" ] check_allowed_keys(mydata, allowed_keys) roiaddress=None roiaddress = mydata["roiaddress"] filename, groupname = split_hdf5_address( roiaddress) file= h5py.File(filename,"r") rois = {} shape, image=xrs_rois.load_rois_fromh5(file[groupname],rois, retrieveImage = True) file.close() roiob = xrs_rois.roi_object() roiob.load_rois_fromMasksDict(rois , newshape = shape, kind="zoom") roiob.input_image = image monitor_divider = 1.0 scan_interval = mydata["scan_interval"] todo_list = [] ninterval = len(scan_interval)//2 for i in range(ninterval): todo_list = todo_list + list(range( int(scan_interval[2*i]), int(scan_interval[2*i+1])) ) # *scan_interval[2*i :2*i+2]) Ydim = mydata['Ydim'] Zdim = mydata['Zdim'] Edim = mydata['Edim'] hf = h5py.File(mydata["signalfile"],"w") for iE in range(Edim): egroup = "E%d/"%iE hf.require_group(egroup) hf[ egroup+ "image" ] = roiob.input_image for roikey, (origin, box) in roiob.red_rois.items(): roigroup=roikey+"/" hf.require_group( egroup+roigroup ) hf[ egroup+roigroup +"origin" ] = origin hf[ egroup+roigroup +"mask" ] = box for iZ in range( Zdim ): scangroup = "Scan%d/"% iZ hf.require_group(egroup+scangroup) for roikey, (origin, box) in roiob.red_rois.items(): roigroup=roikey[3:]+"/" # with "ROI" removed, nly numerical part hf.require_group(egroup+scangroup+roigroup) hf.require_dataset(egroup+scangroup+roigroup+"matrix", [Ydim, box.shape[0], box.shape[1]] , "f",exact="True") hf[egroup+scangroup+roigroup+"cornerpos"] = origin if mydata["monitor_address"] not in [None,"None"] : averaged_monitor = 0 for iscan in todo_list: monitor_filename, monitor_dataname = split_hdf5_address( mydata["monitor_address"] % iscan ) monitor = np.array(h5py.File(monitor_filename,"r")[monitor_dataname][:]) averaged_monitor += monitor averaged_monitor = averaged_monitor / len(todo_list) for iscan in todo_list: iZ = (iscan-scan_interval[0]) % Zdim iY = (iscan-scan_interval[0]) // Zdim filename, dataname = split_hdf5_address( mydata["expdata"] % iscan ) data = np.array(h5py.File(filename,"r")[dataname][:]) if mydata["monitor_address"] not in [None,"None"] : monitor_filename, monitor_dataname = split_hdf5_address( mydata["monitor_address"] % iscan ) monitor = np.array(h5py.File(monitor_filename,"r")[monitor_dataname][:]) monitor[:] = monitor / averaged_monitor data[:] = data / monitor[:, None, None] for roikey, (origin, box) in roiob.red_rois.items(): ##roigroup=roikey+"/" roigroup=roikey[3:]+"/" # with "ROI" removed, nly numerical part sliced = data[:, origin[0]:origin[0]+box.shape[0], origin[1]:origin[1]+box.shape[1]] * box for iE in range(Edim): egroup = "E%d/"%iE scangroup = "Scan%d/"% iZ hf[ egroup+scangroup+roigroup+"matrix" ][ iY ] = sliced[iE] hf.close() def loadscan_2Dimages_galaxies_foilscan(mydata): """ **loadscan_2Dimages_galaxies_foilscan** This command harvest the selected signals. the instructions on the scans to be taken must be in the form( as submembers ofload_scans ) :: loadscan_2Dimages_galaxies_foilscan : roiaddress : "hdf5filename:nameofroigroup" # the same given in create_rois expdata : "kapton_00001_01.nxs:/root_spyc_config1d_RIXS_00001/scan_data/data_07" signalfile : "filename" # Target file for the signals isolateSpot : 0 # if different from zero selects on each image ( when sumto1d=0 ) ROI the spot region and sets to zero outside a radius = isolateSpot scan_list : None # if given the expdata input will be used as a template to load the scans in the scan_list # """ allowed_keys = ["roiaddress",'isolateSpot',"signalfile","expdata", "scan_list" ] check_allowed_keys(mydata, allowed_keys) roiaddress=None roiaddress = mydata["roiaddress"] filename, groupname = split_hdf5_address( roiaddress) file= h5py.File(filename,"r") rois = {} shape, image=xrs_rois.load_rois_fromh5(file[groupname],rois,retrieveImage = True) file.close() roiob = xrs_rois.roi_object() roiob.load_rois_fromMasksDict(rois , newshape = shape, kind="zoom") roiob.input_image = image monitor_divider = 1.0 if ('isolateSpot') in mydata : isolateSpot = mydata['isolateSpot'] else: isolateSpot = 0 hf = h5py.File(mydata["signalfile"],"w") hf[ "image" ] = roiob.input_image rois_dict_group = hf.require_group("rois_dict" ) for roikey, (origin, box) in roiob.red_rois.items(): roigroup=roikey+"/" hf.require_group( roigroup ) hf[ roigroup +"origin" ] = origin hf[ roigroup +"mask" ] = box if roikey in rois_dict_group: del rois_dict_group[roikey] rois_dict_group[roikey] = h5py.SoftLink( "/"+ roigroup ) if mydata["expdata"] not in [None, "None"]: if "scan_list" not in mydata or mydata["scan_list"] in [None, "None"]: filename, dataname = split_hdf5_address( mydata["expdata"] ) data = np.array(h5py.File(filename,"r")[dataname][:]) else: data = 0 for scan_number in mydata["scan_list"]: filename, dataname = split_hdf5_address( mydata["expdata"]%scan_number ) data = data + np.array(h5py.File(filename,"r")[dataname][:]) else: # per scan puramente energetici data = None isolateSpot = 0 for roikey, (origin, box) in roiob.red_rois.items(): roigroup=roikey[3:]+"/" # with "ROI" removed, nly numerical part if data is not None: sliced = data[:, origin[0]:origin[0]+box.shape[0], origin[1]:origin[1]+box.shape[1]] * box else: sliced = np.ones([1, box.shape[0], box.shape[1]],"f") sliced[:] = box if isolateSpot: imageLines = np.sum(sliced,axis=1) imageLines =imageLines- scipy.ndimage.filters.gaussian_filter( imageLines ,[0,isolateSpot],mode='constant',cval=0) poss = np.argmax(imageLines,axis=1) for i in range(len(poss)): sliced[i,:, : max(0,poss[i]-isolateSpot) ]=0 sliced[i,:, poss[i]+isolateSpot : ]=0 scangroup = "Scan%d/"% 0 hf[ scangroup+roigroup+"matrix" ] = sliced hf[ scangroup+roigroup+"cornerpos" ] = origin hf.close() def extract_spectra(mydata): """ **extract_spectra** parameters :: extract_spectra : reference_address : "demo_rois.h5:/ROI_AS_SELECTED/energy_scanb/scans/Scan237/" sample_address : "demo_rois.h5:/ROI_AS_SELECTED/images2/scans/" roiaddress : "demo_rois.h5:/ROI_AS_SELECTED/" reference_scan : 237 scan_interval : [342,343] DE : 5 zmargin : 4 niterLip : 100 niter : 500 beta : 0.0 target : "extracted_spectra.h5:/spectra_scan_342" final_plot : "PLOT" # or "NOPLOT" """ allowed_keys = [ "target","sample_address","scan_interval","roiaddress","DE","discard_threshold","threshold_fraction","zmargin", "flatTriggerer","slope","fitted_response","niter","niterLip","beta","weight_by_response","reference_scan","reference_address" ] check_allowed_keys(mydata, allowed_keys) target_filename , target_groupname = split_hdf5_address( mydata["target"]) sample_address = mydata["sample_address"] sample_file, sample_groupname = split_hdf5_address(sample_address) roiaddress = mydata["roiaddress"] rois_file, rois_groupname = split_hdf5_address(roiaddress) scan_interval = mydata["scan_interval"] scans = [] extratags = [] for i in range( len(scan_interval)//2 ): tok = list(range( int(scan_interval[2*i]), scan_interval[2*i+1] )) scans = scans + tok if( isinstance( scan_interval[2*i] , int ) ) : extratags = extratags + [0]*len(tok) else: extratags = extratags + tok if "DE" in mydata: DE = mydata["DE"] else: DE = 0 if "discard_threshold" in mydata: discard_threshold = mydata["discard_threshold"] else: discard_threshold =0 if "threshold_fraction" in mydata: threshold_fraction = mydata["threshold_fraction"] else: threshold_fraction =0 if "zmargin" in mydata: zmargin = mydata["zmargin"] else: zmargin = 0 h5frois = h5py.File(rois_file,"r" ) h5rois = h5frois[rois_groupname]["rois_definition/rois_dict"] rois_keys_orig = filterRoiList(h5rois.keys(), strip=True) references = {} niter = 0 niterLip = 0 beta = 0 slopeInfos = {} if "flatTriggerer" in mydata: rois_keys = rois_keys_orig for k,c in enumerate( rois_keys_orig ): slopeInfos[c] = { "slope":0.0 , "zrate":1.0e38, "estep":1.0 , "mask": h5rois["ROI%02d"%int(c)]["mask"][:] } references[c] = None elif "slope" in mydata: rois_keys = rois_keys_orig for k,c in enumerate( rois_keys_orig ): slopeInfos[c] = { "slope":mydata["slope"][k] , "zrate":mydata["dh4estep"][k] , "estep":mydata["estep"], "mask": h5rois["ROI%02d"%int(c)]["mask"][:] } references[c] = None else: for k,c in enumerate( rois_keys_orig ): slopeInfos[c] = None if "fitted_response" in mydata: deconvolve = False chiave = "fitted_response" optical_response_name = "data" else: deconvolve = True chiave = "reference_address" optical_response_name = "optical_response" if "niter" in mydata: niter = mydata["niter"] niterLip = mydata["niterLip"] beta = mydata["beta"] if "weight_by_response" in mydata: weight_by_response = mydata["weight_by_response"] else: weight_by_response = 1 reference_address = mydata[chiave] reference_file, reference_groupname = split_hdf5_address(reference_address) if deconvolve: nrefscan = mydata["reference_scan"] reference_groupname = reference_groupname +"/scans/Scan%03d"%nrefscan references = {} h5f = h5py.File(reference_file,"r" ) if reference_groupname not in h5f: raise ValueError("Key %s not present in file %s"%(reference_groupname, reference_file) ) h5 = h5f[reference_groupname] rois_keys = filterRoiList(h5.keys(),prefix="") rois_keys = list(set.intersection( set(rois_keys), set(rois_keys_orig) ) ) print(" After filtering the list of rois to be used is ", rois_keys ) incidentE = None if "motorDict/energy" in h5: incidentE = h5["motorDict/energy"][()] for k in rois_keys: if deconvolve: mm = h5[k]["matrix"][:] mm[np.isnan(mm)] = 0.0 else: mm = None zscale = h5[k]["xscale"][()]*1000.0 mask = h5rois["ROI%02d"%int(k)]["mask"][:] cummask = np.cumsum(mask,axis=0) mask[cummask<=zmargin]=0 mask[(cummask.max(axis=0) -cummask)>>>>>> refining ROIs.' ) # # make refinement_key iterable: # ref_keys = [] # if not isinstance(refinement_key,list): # ref_keys.append( refinement_key ) # else: # ref_keys = refinement_key # # go through keys and do refinements # for ref_key in ref_keys: # if ref_key == 'NNMF': # roifinder_obj.refine_rois_MF( Fourc_obj, refinement_scan ) # elif ref_key == 'pw': # roifinder_obj.refine_rois_PW( Fourc_obj, refinement_scan ) # elif ref_key == 'cw': # roifinder_obj.refine_rois_CW( Fourc_obj, refinement_scan ) # else: # print ('Unknown refinement keyword, will end here.') # return # Fourc_obj.set_roiObj( roifinder_obj.roi_obj ) # if save_address: # roifinder_obj.roi_obj.writeH5(save_address) # # manage elastic line scan # try: # elastic = mydata["elastic"] # print( '>>>>>>> Integrating elastic line scan.' ) # scan_number = gvord( elastic, "scan_number", None ) # roi_number = gvord( elastic, "roi_number", 0 ) # method = gvord( elastic, "method", 'sum' ) # Fourc_obj.get_compensation_factor( scan_number, method=method, roi_number=roi_number ) # except: # elastic = {} # # manage scans to be read # try: # scans = mydata["scans"] # except: # scans = {} # print( '>>>>>>> Reading scans.' ) # scan_numbers = gvord(scans, "scan_numbers",None) # scan_type = gvord(scans, "scan_type", 'inelastic') # comp_factor = gvord(scans, "comp_factor", None) # rot_angles = gvord(scans, "rot_angles", None) # if comp_factor: # Fourc_obj.load_scan(scan_numbers, direct=True, comp_factor=comp_factor, scan_type=scan_type, rot_angles=rot_angles) # else: # Fourc_obj.load_scan(scan_numbers, direct=True, scan_type=scan_type, rot_angles=rot_angles) # # should there be a spectrum constructed? # try: # spectrum = mydata["spectrum"] # xes = gvord( spectrum, "xes", False ) # if xes: # Fourc_obj.get_XES_spectrum() # if rixs: # print (' NOT implemented yet!' ) # except: # spectrum = {} # # manage saving of scans # try: # print( '>>>>>>> Saving scans.' ) # saving = mydata["saving"] # path = gvord(saving, "path",None) # f_name = gvord(saving, "f_name", None) # post_fix = gvord(saving, "post_fix", ".dat") # scan_numbers = gvord(saving, "scan_numbers", None) # print( '>>>>>>>', scan_numbers, type(scan_numbers)) # header = gvord(saving, "header", "") # Fourc_obj.dump_scans_ascii( scan_numbers, path, f_name, post_fix, header ) # except: # saving = {} # print( '>>>>>>> All finished.' ) # def Hydra_extraction( yamlData ): # """ **Hydra_extraction** # Launches the data extraction from the FOURC spectrometer. # example :: # Hydra_extracion : # active : 1 # data : # path (str): Absolute path to directory holding the data. # SPECfname (str): Name of the SPEC-file ('rixs' is the default). # EDFprefix (str): Prefix for the EDF-files ('/edf/' is the default). # EDFname (str): Filename of the EDF-files ('rixs_' is the default). # EDFpostfix (str): Postfix for the EDF-files ('.edf' is the default). # en_column (str): Counter mnemonic for the energy motor ('energy' is the default). # moni_column (str): Mnemonic for the monitor counter ('izero' is the default). # rois : # scan_number (int): Scan number (as in SPEC file) of scan to be used for ROI definition. # zoom_roi (boolean): Keyword if ROIs should be defined by zooming (default is True). # poly_roi (boolean): Keyword if ROIs should be defined by selecting polygons (default is False). # auto_roi (boolean): Keyword if ROIs should be defined automatically (default is False). # load_address (str): Absolute filename for loading ROIs (HDF5 format). # save_address (str): Absolute filename for saving ROIs (HDF5 format). # elastic : # scan_number (int): Scan number (as in SPEC file). # line_comp (boolean): Keyword, if line-by-line compensation should be used (default is True). # roi_number (int): Number of ROI for which to find the compensation factor (default is 0). # scans : # scan_numbers (int or list): Integer or list of scan numbers that should be integrated. # scan_type (str): Type of scans to be loaded (default is 'inelastic'). # comp_factor (float): Optional compensation factor to be used (default is None). # saving : # path (str): Absolute path to directory where the data should be saved as ASCII files. # f_name (str): Base file name for the files to be created. # post_fix (str): Post-fix for the files to be created (default is '.dat'). # scan_numbers (int or list): Integer or list of integers of scans to be written into the files. # header (str): Optional string defining a header-line for the files (default is ''). # """ # mydata = yamlData#["Hydra_extraction"] # if mydata is not None and ("active" in mydata) : # if mydata["active"]==0: # return # if mydata is not None: # # manage file locations and naming conventions # try: # data = mydata["data"] # except: # data = {} # path = gvord(data,"path",None) # SPECfname = gvord(data,"SPECfname", "hydra") # EDFprefix = gvord(data,"EDFprefix", "/edf/") # EDFname = gvord(data,"EDFname", "hydra_") # EDFpostfix = gvord(data,"EDFpostfix", ".edf") # en_column = gvord(data,"en_column", "energy") # moni_column = gvord(data,"moni_column", "izero") # Hydra_obj = xrs_read.Hydra(path, SPECfname, EDFprefix, EDFname, EDFpostfix, en_column, moni_column ) # # manage ROIs # try: # rois = mydata["rois"] # except: # rois = {} # scan_number = gvord(rois,"scan_number",None) # roi_type = gvord(rois,"roi_type",'zoom') # load_address = gvord(rois,"load_address",None) # save_address = gvord(rois,"save_address",None) # refinement_key = gvord(rois,"refinement_key",None) # refinement_scan= gvord(rois,"refinement_scan",None) # if load_address: # roifinder_obj = roifinder_and_gui.roi_finder() # roifinder_obj.roi_obj.loadH5(load_address) # else: # roifinder_obj = roifinder_and_gui.roi_finder() # image4rois = Hydra_obj.SumDirect(scan_number) # if roi_type == 'zoom': # roifinder_obj.get_zoom_rois(image4rois) # elif roi_type == 'poly': # roifinder_obj.get_polygon_rois(image4rois) # elif roi_type == 'auto': # roifinder_obj.get_auto_rois(image4rois) # elif roi_type == 'line': # roifinder_obj.get_linear_rois(image4rois) # else: # print( 'ROI type ' + roi_type + ' not supported. Will end here.' ) # return # Hydra_obj.set_roiObj(roifinder_obj.roi_obj) # if refinement_key is not None and refinement_scan is not None: # print( '>>>>>>> refining ROIs.' ) # # make refinement_key iterable: # ref_keys = [] # if not isinstance(refinement_key,list): # ref_keys.append(refinement_key) # else: # ref_keys = refinement_key # # go through keys and do refinements # for ref_key in ref_keys: # if ref_key == 'NNMF': # roifinder_obj.refine_rois_MF( Hydra_obj, refinement_scan ) # elif ref_key == 'pw': # roifinder_obj.refine_rois_PW( Hydra_obj, refinement_scan ) # elif ref_key == 'cw': # roifinder_obj.refine_rois_CW( Hydra_obj, refinement_scan ) # else: # print ('Unknown refinement keyword, will end here.') # return # Hydra_obj.set_roiObj(roifinder_obj.roi_obj) # if save_address: # roifinder_obj.roi_obj.writeH5(save_address) # # manage scans to be read # try: # scans = mydata["scans"] # except: # scans = {} # scan_meth = gvord(scans, "method", 0) # print ('>>>>>>>>>>>>>>>> ', type(scan_meth)) # if scan_meth == 0: # scan_method = 'sum' # elif scan_meth == 1: # scan_method = 'pixel' # elif scan_method == 2: # scan_mehtod = 'row' # direct = gvord(scans, "direct", True) # scaling = gvord(scans, "scaling", None) # scan_numbers = gvord(scans, "scan_numbers", None) # scan_types = gvord(scans, "scan_types", 'generic') # comp_factor = gvord(scans, "comp_factor", None) # include_elastic = gvord(scans, "include_elastic", True) # for scan_num, scan_type in zip(scan_numbers, scan_types): # print( '>>>>>>>>>>HHHHHH>>>>>>> ', scan_type, type(scan_type)) # Hydra_obj.load_scan(scan_num, scan_type=scan_type, direct=direct, scaling=scaling, method=scan_method ) # Hydra_obj.get_spectrum_new( method=scan_method, include_elastic=include_elastic, abs_counts=False, interpolation=False ) # # manage saving of scans # try: # saving = mydata["output"] # except: # saving = {} # if saving: # print( '>>>>>>> saving results.' ) # save_format = gvord(saving, "format", 'ascii') # file_name = gvord(saving, "file_name", 'default.dat') # group_name = gvord(saving, "group_name", 'spectrum') # comment = gvord(saving, "comment", '') # if save_format == 'ascii': # Hydra_obj.dump_spectrum_ascii( file_name ) # elif save_format == 'hdf5': # Hydra_obj.dump_spectrum_hdf5( file_name, group_name, comment=comment ) # else: # print( 'Format not supported. Will end here.' ) # return # print( '>>>>>>> All finished.' ) def superR_getVolume_fullfit(mydata): """ Here an example of the input file dedicated section :: superR_getVolume_fullfit : sample_address : "demo_imaging.hdf5:ROI_B_FIT8/images/scans/" delta_address : "demo_imaging.hdf5:ROI_B_FIT8/scanXX/scans/Scan273/" scalprods_address : "scalprods.hdf5:scal_prods/" target_filename : "volume.hdf5" niter : 20 beta : 1.0e-8 eps : 0.000002 ################################### # optional nbin : 5 # defaults to 1 # it will bin 5 xpixels in one roi_keys : [60, 64, 35, 69, 34, 24, 5, 6, 71, 70, 39, 58, 56, 33] # roi_keys default to all the keys present in delta_address optional_solution : /mntdirect/_scisoft/users/mirone/WORKS/Christoph/XRSTools/volumes_gasket.h5:/ref408_bis_423_548/Volume0p03 ## a solution with dimensions [ZDIM,YDIM,XDIM] ## If given, will be used to balance analyzer factors The volume will be written in file target_filename( which must not exist already), in the datagroup Volume. The parameter debin dafaults to [1,1] It is used to increase a dimension Z,Y or both , to make it match with X The parameters for the Fista optimisation cicle are : - niter : the number of fista cycles - beta : the factor of the Total Variation penalisation term - eps : a parameter for the convergence of the Chambolle-Pock TV denoising phase """ allowed_keys = ["delta_address",'nbin','optional_solution','roi_keys',"sample_address",'scan_interval',"scalprods_address","target_address","niter","beta","eps",'debin',] check_allowed_keys(mydata, allowed_keys) delta_address = mydata["delta_address"] delta_filename, delta_groupname = split_hdf5_address(delta_address) h5f = h5py.File(delta_filename,"r") h5 = h5f[delta_groupname] if not ('nbin' in mydata) : width = 1 else: width = mydata['nbin'] if not ('optional_solution' in mydata ): solution = None else: solution_address = str(mydata["optional_solution"]) if solution_address=="None" or solution_address is None or solution_address.strip()=="": solution = None else: solution_filename, solution_groupname = split_hdf5_address(solution_address) solution = h5py.File(solution_filename,"r") solution = solution[solution_groupname][:] roi_keys = filterRoiList(h5.keys(),prefix="") roi_keys = [str(t) for t in roi_keys] if ('roi_keys' in mydata) : u_roi_keys = mydata['roi_keys'] u_roi_keys = [str(t) for t in roi_keys] roi_keys = [ t for t in u_roi_keys if t in roi_keys] roi_keys = [str(t) for t in roi_keys] ## ########################## ## DELTA >>>>>>>>>>>>>> sonde = {} ## The response is added to sonde (probe) XDIM = None for t in roi_keys: m = np.array(h5[t+"/matrix"][:],"d") if width != 1 : # REBINNING nbin = width assert(nbin>1) m=m[:(m.shape[0]//nbin)*nbin].reshape(-1, nbin, m.shape[1],m.shape[2]).sum(axis=1)/nbin sonde [t] = m ## PROBE STUFF GOES HERE if XDIM is None: XDIM = m.shape[0] else: assert (XDIM==m.shape[0]), "The probes ( references) dont have the same X lenght ( scan lenght). One is %s, anoter other %s "%(XDIM, m.shape[0] ) ## DELTA <<<<<<<<<<<<<<<<<<<<< ## ############################# h5f.close() ## ########################## ## >>>>>>>>>>>>>>>>>>> SAMPLE ## sample_address = mydata["sample_address"] sample_filename, sample_groupname = split_hdf5_address(sample_address) h5f = h5py.File(sample_filename,"r") h5 = h5f[sample_groupname] if not ('scan_interval' in mydata) : zscan_keys = sorted( filterScanList(h5.keys()) , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) else: zscan_keys =[ "Scan%03d"%i for i in range(*list(mydata['scan_interval']))] ZDIM = len(zscan_keys) ## in case of parallelism the workload is splitted between processes ## myrange = np.array_split( np.arange( ZDIM ), nprocs )[myrank] myrange = np.arange( ZDIM ) myZDIM = len(myrange) YDIM = None rois_to_be_removed=[] for ro in roi_keys: if ro in h5[zscan_keys[0]]: m = h5[zscan_keys[0]][ro]["matrix"][:] if YDIM is not None: assert (YDIM == m.shape[0]), "The probes ( references) dont have the same X lenght ( scan lenght). One is %s, anoter other %s "%(XDIM, m.shape[0] ) ## we take the Y lenght from the first roi of the first scan : ## this lenght is supposed to be the same are supposed to be the same for all scans else: YDIM = m.shape[0] else: rois_to_be_removed.append(ro) del sonde[ro] del integrated_images[ro] for ro in rois_to_be_removed: roi_keys.remove(ro) if YDIM is None: return fattori = {} ## This is used for balancing. Will stay to 1.0 for all rois if solution is not given in input for i,rk in enumerate(roi_keys): fattori[rk] = 1.0 ## IF solution is given then balancing factors are calculated if solution is not None: for rk in roi_keys: scal_dd=np.array([0.0],"d") scal_ds = np.array([0.0],"d") scal_ss = np.array([0.0],"d") probes = sonde [rk] SS = np.tensordot( probes, probes, axes = [ [1,2], [1,2] ] ) for iz in range(myZDIM): zkey = zscan_keys[myrange[iz] ] m = np.array(h5[ zkey ][ rk ]["matrix"][:],"d") msum = m.sum(axis=0) probes = sonde [rk] assert( probes.shape[1:] == m.shape[1:]) assert( XDIM == probes.shape[0] ) assert( YDIM == m.shape[0] ) plane_contrib = np.tensordot( m, probes, axes = [ [1,2], [1,2] ] ) scal_dd += (m*m).sum() keypos = zscan_keys.index( zkey) scal_ds[:] = scal_ds + np.tensordot( plane_contrib , solution[keypos], axes = [ [0,1], [0,1] ] ) scal_ss[:] = scal_ss + np.tensordot( np.tensordot(SS,solution[keypos],axes=[[1],[1]]) , solution[keypos],axes=[[0,1],[1,0]]) fattori[rk] = scal_ds/scal_ss ### Renormalising the overall strenght of all the factors sum = 0.0 for rk in roi_keys: sum = sum + fattori[rk]*fattori[rk] for rk in roi_keys: fattori[rk] = fattori[rk]/np.sqrt( sum/len(roi_keys) ) scalprods_address = mydata["scalprods_address"] scalprods_filename, scalprods_groupname = split_hdf5_address(scalprods_address) target_address = mydata["target_address"] target_filename, target_groupname = split_hdf5_address(target_address) niter = mydata["niter"] beta = mydata["beta"] eps = mydata["eps"] if not ('debin' in mydata) : debin = [1,1] else: debin = mydata['debin'] ## moltiplica per fattori sonde for rk in roi_keys: sonde[rk][:] = sonde[rk] * fattori[rk] h5f = h5py.File(scalprods_filename, "r") h5_sampledata = h5f [scalprods_groupname] ## modo di impiego # per ogni rk in roi_keys # sonde[rk] e' una serie di risposte Nech * Ny * Nx # per ogni zk in zscan_keys # m = np.array(h5[ zkey ][ rk ]["matrix"][:],"d") # ydim = m.shape[0] # probes.shape[1:] == m.shape[1:] ( le immagini) # # Zdim = len( zscan_keys ) # Ydim = m.shape[0] # Xdim = probes.shape[0] Volume = superr.superr_fullfit( ZDIM, YDIM, XDIM, roi_keys, sonde, zscan_keys, h5_sampledata, niter=niter, beta=beta) if os.path.exists(target_filename): h5 = h5py.File(target_filename,"a") else: h5 = h5py.File(target_filename,"w") if target_groupname in h5: del h5[target_groupname] h5[target_groupname] = Volume h5.close() def superR_getVolume_Esynt(mydata): """ Here an example of the input file dedicated section :: superR_getVolume_Esynt : scalprods_address : "scalprods.hdf5:/groupname" target_filename : "volume.hdf5:/volumes" dict_interp : interpolation.json output_prefix : /mydir/pippo_ scalprods_address points to the scalDS, scalDD, scalSS scalar products calculated by superR_scal_deltaXimages. The volume will be written in file target_filename( which must not exist already), in the datagroup Volume. It is used to increase a dimension Z,Y or both , to make it match with X """ allowed_keys = ["scalprods_address","output_prefix","dict_interp", ] check_allowed_keys(mydata, allowed_keys) scalprods_address = mydata["scalprods_address"] scalprods_filename, scalprods_groupname = split_hdf5_address(scalprods_address) output_prefix = mydata["output_prefix"] interpolation_dict = json.load( open(mydata["dict_interp"],"r") ) h5f = h5py.File(scalprods_filename, "r") h5 = h5f [scalprods_groupname] vkeys = list(h5.keys()) vkeys = sorted( vkeys , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) ### vkeys.sort() DS=[] DD=[] SS=None roi_keys = None for k in vkeys: DS.append(h5[k]["scal_prods"]["scalDS"][()]) DD.append(h5[k]["scal_prods"]["scalDD"][()]) if SS is None: SS = h5[k]["scal_prods"]["scalSS"][()] if roi_keys is None: roi_keys = h5[k]["scal_prods"]["roi_keys"][()] else: tmp = h5[k]["scal_prods"]["roi_keys"][()] assert abs((tmp-roi_keys)).sum()==0 DS = np.array(DS,"f") DD = np.array(DD,"f") SS = np.array(SS,"f") h5f.close() NV, NROI, DIMZ,DIMY,DIMX = DS.shape print(" NV, NROI, DIMZ,DIMY,DIMX " , NV, NROI, DIMZ,DIMY,DIMX ) print( " DS SHAPE ", DS.shape) print( " DD SHAPE ", DD.shape) print( " SS SHAPE ", SS.shape) indexes_custom_energies = [k for k in interpolation_dict.keys() if k.isdigit() ] indexes_custom_energies = sorted( indexes_custom_energies , key = int) NE = len(indexes_custom_energies) coefficients = np.zeros([NE, NV, NROI ], "f") roi_map={} for i,k in enumerate(roi_keys ): roi_map[str(k)]=i id = interpolation_dict for iE in range(NE): ide = id[str(iE)]["coefficients"] for iv,vk in enumerate(vkeys): idev=ide[vk] used_rois = list(idev.keys()) for rk,c in idev.items(): if(rk in roi_map): coefficients[iE, iv, roi_map[rk]] = c yamlname = output_prefix+"inputforcpp.yaml" DSname = output_prefix+"DS.h5" DDname = output_prefix+"DD.h5" SSname = output_prefix+"SS.h5" COEFname = output_prefix+"coefficients.h5" DSname_relative = os.path.basename(DSname) DDname_relative = os.path.basename(DDname) SSname_relative = os.path.basename(SSname) coeffname_relative = os.path.basename(COEFname) f=open(yamlname,"w") # f.write("NE : %d\n"%NE) # f.write("NV : %d\n"%NV) # f.write("NROI : %d\n"%NROI) # f.write("DIMZ : %d\n"%DIMZ) # f.write("DIMY : %d\n"%DIMY) # f.write("DIMX : %d\n"%DIMX) f.write("DSname : %s\n"%DSname_relative) f.write("DDname : %s\n"%DDname_relative) f.write("SSname : %s\n"%SSname_relative) f.write("COEFFSname : %s\n"%coeffname_relative) f.close() # coefficients.tofile(COEFname) # DS.tofile(DSname) # DD.tofile(DDname) # SS.tofile(SSname) h5py.File(DSname ,"w")["data"] = DS.astype("f") h5py.File(DDname ,"w")["data"] = DD.astype("f") h5py.File(SSname ,"w")["data"] = SS.astype("f") h5py.File(COEFname,"w")["data"] = coefficients.astype("f") def superR_getVolume(mydata): """ Here an example of the input file dedicated section :: superR_getVolume : scalprods_address : "scalprods.hdf5:scal_prods/" target_filename : "volume.hdf5" debin: : [2,1] niter : 20 beta : 1.0e-8 eps : 0.000002 scalprods_address points to the scalDS, scalDD, scalSS scalar products calculated by superR_scal_deltaXimages. The volume will be written in file target_filename( which must not exist already), in the datagroup Volume. The parameter debin dafaults to [1,1] It is used to increase a dimension Z,Y or both , to make it match with X The parameters for the Fista optimisation cicle are : - niter : the number of fista cycles - beta : the factor of the Total Variation penalisation term - eps : a parameter for the convergence of the Chambolle-Pock TV denoising phase """ allowed_keys = ["scalprods_address","target_address","niter","beta","eps",'debin',] check_allowed_keys(mydata, allowed_keys) scalprods_address = mydata["scalprods_address"] scalprods_filename, scalprods_groupname = split_hdf5_address(scalprods_address) target_address = mydata["target_address"] target_filename, target_groupname = split_hdf5_address(target_address) niter = mydata["niter"] beta = mydata["beta"] eps = mydata["eps"] if not ('debin' in mydata) : debin = [1,1] else: debin = mydata['debin'] h5f = h5py.File(scalprods_filename, "r") h5 = h5f [scalprods_groupname] scalDS = h5["scalDS"][:] scalDD = h5["scalDD"][:] scalSS = h5["scalSS"][:] h5f.close() DIMZ,DIMY,DIMX = scalDS.shape if debin != [1,1]: nuovoDS = np.zeros([DIMZ, debin[0], DIMY, debin[1], DIMX ], "d") scalDS.shape = DIMZ,1,DIMY,1,DIMX nuovoDS[:] = scalDS scalDS = nuovoDS scalDS.shape = DIMZ*debin[0], DIMY*debin[1], DIMX Volume = superr.superr( scalDD, scalDS, scalSS, niter=niter, beta=beta) if os.path.exists(target_filename): h5 = h5py.File(target_filename,"a") else: h5 = h5py.File(target_filename,"w") if target_groupname in h5: del h5[target_groupname] h5[target_groupname] = Volume h5.close() def superR_scal_deltaXimages(mydata): """ This step supposes that you have: - already extracted 2D images with the **loadscan_2Dimages** command. The **loadscan_2Dimages** has then already accomplished the following requirements which are listed below for informative purposes : - these images must reside at *sample_address* - Under *sample_address* there must be a a set of datagroups with name *ScanZ* where Z is an integer. The number of these datagroups will be called ZDIM - Inside each *ScanZ* there must be a a set of datagroup with name N where N is the ROI number. - inside each roi datagroup there is the dataset *matrix*. This is a three_dimensional array : - first dimension is YDIM : the number of steps in the Y direction - the other two dimensions are the dimensions of the ROI - Obtained the optical PSF of all desired analyzers, and the maxipix response function. This can be done with the **iPSF** commands which will have provided the responses for a dirac Delta placed at different positions along X direction. The **iPSF** has then already taken care of placing in the *delta_address* data_group the following(listed for informational purposes): - a list of datagroup with name N, N being the number of the ROI. - Inside each datagroup a dataset called *matrix* exists - the matrix has 3 Dimensions - The first dimension is the number for steps done with the thin foil in the X direction to get super-resolution. This will be called XDIM - The other two dimensions are the dimensions of the ROI. They must be equal to those appearing in the the sample datas describe informatively above. Here an example of the input file dedicated section :: superR_scal_deltaXimages : sample_address : "demo_imaging.hdf5:ROI_B_FIT8/images/scans/" delta_address : "demo_imaging.hdf5:ROI_B_FIT8/scanXX/scans/Scan273/" target_address : "scalprods.hdf5:scal_prods/" ################################### # optional nbin : 5 # defaults to 1 # it will bin 5 xpixels in one roi_keys : [60, 64, 35, 69, 34, 24, 5, 6, 71, 70, 39, 58, 56, 33] # roi_keys default to all the keys present in delta_address orig_delta_address : "demo_imaging.hdf5:ROI_B/foil_scanXX/scans/Scan273/" # defaults to None. If given the integrated image and the average line will be written # to check the superposability between the foil scans and the sample scans ### ## optional optional_solution : /mntdirect/_scisoft/users/mirone/WORKS/Christoph/XRSTools/volumes_gasket.h5:/ref408_bis_423_548/Volume0p03 ## a solution with dimensions [ZDIM,YDIM,XDIM] ## If given, will be used to balance analyzer factors If nbin is given the dimensios of the superresolution axis, will be reduced or increased, by binning together the foil PSFs. What the program will produce, under *target_address* datagroup, is - scalDS which is an array [ZDIM,YDIM,XDIM] , type "d" . - scalDD which is the total sum of the squared datas. - scalSS which is an array [XDIM,XDIM] , type "d" . From these three quantities the volume can be reconstructed with iterative procedure in subsequent steps. Here what they are : - scalSS is a 2D matrix, one of its elements is the scalar product of the response function for a given position of the foil, along X, with the response function for another position of the foil. The sum over ROIS is implicitely done. - scalDS is a 3D array. One of its element is the scalar product of the sample image for a given Z,Y position of the sample, with the reponse function for a given X position of the foil. The sum over the ROIs is implicitely done. """ allowed_keys = [ "delta_address",'orig_delta_address','nbin','optional_solution' ,'roi_keys',"sample_address",'scan_interval',"load_factors_from","save_factors_on","target_address", ] check_allowed_keys(mydata, allowed_keys) delta_address = mydata["delta_address"] delta_filename, delta_groupname = split_hdf5_address(delta_address) if ('orig_delta_address' in mydata) : orig_delta_address = mydata["orig_delta_address"] orig_delta_filename, orig_delta_groupname = split_hdf5_address(orig_delta_address) else: orig_delta_filename, orig_delta_groupname = None, None h5f = h5py.File(delta_filename,"r") h5 = h5f[delta_groupname] if not ('nbin' in mydata) : width = 1 else: width = mydata['nbin'] if not ('optional_solution' in mydata ): solution = None else: solution_address = str(mydata["optional_solution"]) if solution_address=="None" or solution_address is None or solution_address.strip()=="": solution = None else: solution_filename, solution_groupname = split_hdf5_address(solution_address) try: solution = h5py.File(solution_filename,"r") solution = solution[solution_groupname][:] except: raise ValueError("You asked for the utilisation of the optional solution to fix factors but could not read file %s groupname %s "%(solution_filename, solution_groupname) ) roi_keys = filterRoiList(h5.keys(),prefix="") roi_keys = [str(t) for t in roi_keys] if ('roi_keys' in mydata) : u_roi_keys = mydata['roi_keys'] u_roi_keys = [int(t) for t in u_roi_keys] roi_keys = [ t for t in roi_keys if int(t) in u_roi_keys] roi_keys = [str(t) for t in roi_keys] ## ########################## ## DELTA >>>>>>>>>>>>>> sonde = {} ## The response is added to sonde (probe) XDIM = None ## integrated_images :: ### much of the code is just for checking the trajectory and compare between the sample measurement ### and the foil measurement. ### integrated_images will be used only if is given in input. ### If you are interested only in the building of the scalar products then just skip everything related to integrated_image integrated_images={} ## to monitor if the foil move along a line which superimposes unto the sample integrated image for t in roi_keys: m = np.array(h5[t+"/matrix"][:],"d") integrated_images [t]=[ m.sum(axis=0) ,0, 0, None, None] # the second entry is left free for the sample integrated image, the third for the orig # the 4th the cornerpos, to be initialised by sample data, the fifth cornerpos by origdelta images ( if origdelta is read) if width != 1 : # REBINNING nbin = width assert(nbin>1) m=m[:(m.shape[0]//nbin)*nbin].reshape(-1, nbin, m.shape[1],m.shape[2]).sum(axis=1)/nbin sonde [t] = m ## PROBE STUFF GOES HERE if XDIM is None: XDIM = m.shape[0] else: assert(XDIM==m.shape[0]), "The probes ( references) dont have the same X lenght ( scan lenght). One is %s, anoter other %s "%(XDIM, m.shape[0] ) ## DELTA <<<<<<<<<<<<<<<<<<<<< ## ############################# h5f.close() ## ############################################### ## >>>>>>>>>>>>>> ORIG : checking related thing. ## The idea is that, originally, the delta files was a large scan resintetised ## from a small reference scan. So in this section, that you can skip, ## on is taking information about the original scan. If delta and original delta are the same it's OK ## In most cases , if you want to do check, the original delta file is the same file as the delta file ## and the interesting part will be comparing the integrated line of the reference and the sample if orig_delta_filename is not None: h5f = h5py.File(orig_delta_filename,"r") h5 = h5f[orig_delta_groupname] for t in roi_keys: m = np.array(h5[t+"/matrix"][:],"d") integrated_images [t][2] += m.sum(axis=0) cornerpos = np.array(h5[t+"/cornerpos"][:]) integrated_images [t][4] = cornerpos h5f.close() ## ORIG <<<<<<<<<<<<<<<<<<<<<<<<< ## ############################# ## ########################## ## >>>>>>>>>>>>>>>>>>> SAMPLE ## sample_address = mydata["sample_address"] sample_filename, sample_groupname = split_hdf5_address(sample_address) h5f = h5py.File(sample_filename,"r") h5 = h5f[sample_groupname] if not ('scan_interval' in mydata) : zscan_keys = sorted( filterScanList(h5.keys()) , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) else: zscan_keys =[ "Scan%03d"%i for i in range(*list(mydata['scan_interval']))] ZDIM = len(zscan_keys) ## in case of parallelism the workload is splitted between processes myrange = np.array_split( np.arange( ZDIM ), nprocs )[myrank] myZDIM = len(myrange) YDIM = None rois_to_be_removed=[] for ro in roi_keys: if ro in h5[zscan_keys[0]]: m = h5[zscan_keys[0]][ro]["matrix"][:] if YDIM is not None: assert(YDIM == m.shape[0]) , " " else: YDIM = m.shape[0] else: rois_to_be_removed.append(ro) del sonde[ro] del integrated_images[ro] for ro in rois_to_be_removed: roi_keys.remove(ro) if YDIM is None: return fattori = {} ## This is used for balancing. Will stay to 1.0 for all rois if solution is not given in input for i,rk in enumerate(roi_keys): fattori[rk] = 1.0 ## IF solution is given then balancing factors are calculated if solution is not None: for rk in roi_keys: ## This are scalars, so why am I using a np.array? ## I am doing that in prevision of mpi AllReduce with mpi4py scal_dd=np.array([0.0],"d") scal_ds = np.array([0.0],"d") scal_ss = np.array([0.0],"d") probes = sonde [rk] SS = np.tensordot( probes, probes, axes = [ [1,2], [1,2] ] ) for iz in range(myZDIM): zkey = zscan_keys[myrange[iz] ] m = np.array(h5[ zkey ][ rk ]["matrix"][:],"d") msum = m.sum(axis=0) probes = sonde [rk] assert( probes.shape[1:] == m.shape[1:]) assert( XDIM == probes.shape[0] ) assert( YDIM == m.shape[0] ) plane_contrib = np.tensordot( m, probes, axes = [ [1,2], [1,2] ] ) scal_dd += (m*m).sum() keypos = zscan_keys.index( zkey) scal_ds[:] = scal_ds + np.tensordot( plane_contrib , solution[keypos], axes = [ [0,1], [0,1] ] ) scal_ss[:] = scal_ss + np.tensordot( np.tensordot(SS,solution[keypos],axes=[[1],[1]]) , solution[keypos],axes=[[0,1],[1,0]]) if nprocs>1: comm.Allreduce([np.array(scal_ss), MPI.DOUBLE], [scal_ss, MPI.DOUBLE], op=MPI.SUM) comm.Allreduce([np.array(scal_dd), MPI.DOUBLE], [scal_dd, MPI.DOUBLE], op=MPI.SUM) comm.Allreduce([np.array(scal_ds), MPI.DOUBLE], [scal_ds, MPI.DOUBLE], op=MPI.SUM) comm.Barrier() fattori[rk] = scal_ds/scal_ss ### Renormalising the overall strenght of all the factors sum = 0.0 for rk in roi_keys: sum = sum + fattori[rk]*fattori[rk] for rk in roi_keys: fattori[rk] = float(fattori[rk]/np.sqrt( sum/len(roi_keys) )) if "load_factors_from" in mydata: fattori = json.load( open(mydata["load_factors_from"],"r") ) if "save_factors_on" in mydata: json.dump( fattori, open(mydata["save_factors_on"],"w") ) ## These arrays below will contain, after summation and for each process, ## the contribution of the process's workload ## They will be mpi Reduced to get the final result scalDS = np.zeros( [myZDIM,YDIM,XDIM] ,"d" ) scalDD = 0.0 scalSS = np.zeros( [XDIM,XDIM] ,"d" ) for i,rk in enumerate(roi_keys): if i%nprocs == myrank: probes = sonde [rk] ## Consider that, below, factor is a factor which is applied to the probe to better adapt it to the sample strenght ## variations from roi to roi. scalSS[:] = scalSS[:] + np.tensordot( probes, probes, axes = [ [1,2], [1,2] ] ) *fattori[rk]*fattori[rk] doppio_filtro_done=0 for iz in range(myZDIM): ## each scan is at fixed Z and contains many ys. So we proceed along z zkey = zscan_keys[myrange[iz] ] print( " process %d analyzing scan : "%myrank , zkey) my_roi_keys = filterRoiList(h5[ zkey ].keys(),prefix="") my_roi_keys = [str(t) for t in my_roi_keys] ## The following piece of code makes sure that our roi_keys list ## contains rois which can be found in the datas if not doppio_filtro_done: new_roi_keys=[] for ok in roi_keys: if ok not in my_roi_keys: del integrated_images[ok] else: new_roi_keys.append(ok) doppio_filtro_done=1 roi_keys = new_roi_keys for rk in roi_keys: m = np.array(h5[ zkey ][ rk ]["matrix"][:],"d") msum = m.sum(axis=0) if iz: if msum.shape != integrated_images[rk][1].shape: msg = " ERROR : the yscan elements have different shapes.\n selects homogeneous scans." print( msg) raise Exception( msg) integrated_images[rk][1] = integrated_images[rk][1]+msum cornerpos = np.array(h5 [ zkey ][ rk ]["cornerpos"][:]) integrated_images [rk][3] = cornerpos probes = sonde [rk] assert( probes.shape[1:] == m.shape[1:]) assert( XDIM == probes.shape[0] ) assert( YDIM == m.shape[0] ) ## At the end all boils down to these three lines of code. Note that they sum-up ## contributions from all the rois alltogether plane_contrib = np.tensordot( m, probes, axes = [ [1,2], [1,2] ] ) scalDS[iz] = scalDS[iz]+ plane_contrib*fattori[rk] scalDD += (m*m).sum() ## sum-Reducing the final result if nprocs>1: comm.Reduce([np.array(scalSS), MPI.DOUBLE], [scalSS, MPI.DOUBLE], op=MPI.SUM, root=0) comm.Barrier() h5f.close() ## ## ###################### ## stuffs for checking (integrated_images) if nprocs>1: for n in list(integrated_images.keys()): if myrank: comm.Reduce([integrated_images[n][0], MPI.DOUBLE], None, op=MPI.SUM, root=0) comm.Reduce([integrated_images[n][1], MPI.DOUBLE], None, op=MPI.SUM, root=0) else: comm.Reduce( [integrated_images[n][1], MPI.DOUBLE] , [integrated_images[n][0], MPI.DOUBLE], op=MPI.SUM, root=0) comm.Reduce( [np.array(integrated_images[n][1]), MPI.DOUBLE], [integrated_images[n][1], MPI.DOUBLE], op=MPI.SUM, root=0) ## All the remaining part is just writing target_address = mydata["target_address"] target_filename, target_groupname = split_hdf5_address(target_address) for iproc in range(nprocs): comm.barrier() if iproc != myrank: continue h5f = h5py.File(target_filename,"a") if myrank==0: if h5f.__contains__( target_groupname ): del h5f[target_groupname] h5f.close() h5f = h5py.File(target_filename,"a") h5f.require_group(target_groupname ) h5 = h5f[target_groupname] h5["scalSS"] = scalSS h5.create_dataset("scalDS", ( ZDIM , YDIM , XDIM ), dtype='d') h5.create_dataset("scalDD", ( 1, ), dtype='d') h5["scalDS"][:]=0 h5["scalDD"][:]=0 for n in list(integrated_images.keys()): B=integrated_images[n][1] A=integrated_images[n][0] # B=B.sum(axis=0) pesiA = A.sum(axis=0) pesiB = B.sum(axis=0) medieA = (np.arange(A.shape[0])[:,None]*A).sum(axis=0)/pesiA medieB = (np.arange(B.shape[0])[:,None]*B).sum(axis=0)/pesiB h5.require_group(n) h5n=h5[n] h5n["delta_poss"] = medieA h5n["sample_poss"] = medieB h5n["delta_integrated" ] = integrated_images[n][0] h5n["sample_integrated" ] = integrated_images[n][1] h5n["sample_integrated_weight" ] = pesiB if orig_delta_filename is not None: corner_C = np.array(integrated_images[n][4]) corner_B = np.array(integrated_images[n][3]) diff = corner_C-corner_B C = integrated_images[n][2] pesiC = C.sum(axis=0) medieC = (np.arange(C.shape[0])[:,None]*C).sum(axis=0)/pesiC coords = np.arange(len( medieC )) + diff[1] h5n["orig_delta_poss" ] = np.array( medieC+diff[0] ) h5n["orig_delta_poss_coord" ] = np.array( coords ) inset = integrated_images[n][2] tmp = np.zeros_like( integrated_images[n][1] ) target = tmp [ diff[0]:diff[0]+ inset.shape[0], diff[1]:diff[1]+ inset.shape[1]] target[:] = inset[ :target.shape[0], :target.shape[1] ] h5n["orig_delta_integrated" ] = tmp h5f.require_group(target_groupname ) h5 = h5f[target_groupname] h5["scalDD"][:] += scalDD h5["scalDS"][myrange[0]:myrange[-1]+1] += scalDS h5.require_group("Mean_Poss") h5=h5["Mean_Poss"] h5f.flush() h5f.close() return fattori def superR_scal_deltaXimages_Esynt(mydata): """ This step supposes that you have: - already extracted 2D images with the **loadscan_2Dimages** command. The **loadscan_2Dimages** has then already accomplished the following requirements which are listed below for informative purposes : - these images must reside at *sample_address* - Under *sample_address* there must be a a set of datagroups with name *ScanZ* where Z is an integer. The number of these datagroups will be called ZDIM - Inside each *ScanZ* there must be a a set of datagroup with name N where N is the ROI number. - inside each roi datagroup there is the dataset *matrix*. This is a three_dimensional array : - first dimension is YDIM : the number of steps in the Y direction - the other two dimensions are the dimensions of the ROI - Obtained the optical PSF of all desired analyzers, and the maxipix response function. This can be done with the **iPSF** commands which will have provided the responses for a dirac Delta placed at different positions along X direction. The **iPSF** has then already taken care of placing in the *delta_address* data_group the following(listed for informational purposes): - a list of datagroup with name N, N being the number of the ROI. - Inside each datagroup a dataset called *matrix* exists - the matrix has 3 Dimensions - The first dimension is the number for steps done with the thin foil in the X direction to get super-resolution. This will be called XDIM - The other two dimensions are the dimensions of the ROI. They must be equal to those appearing in the the sample datas describe informatively above. Here an example of the input file dedicated section :: superR_scal_deltaXimages : sample_address : "demo_imaging.hdf5:ROI_B_FIT8/images/scans/" delta_address : "demo_imaging.hdf5:ROI_B_FIT8/scanXX/scans/Scan273/" target_address : "scalprods.hdf5:scal_prods/" ################################### # optional nbin : 5 # defaults to 1 # it will bin 5 xpixels in one roi_keys : [60, 64, 35, 69, 34, 24, 5, 6, 71, 70, 39, 58, 56, 33] # roi_keys default to all the keys present in delta_address orig_delta_address : "demo_imaging.hdf5:ROI_B/foil_scanXX/scans/Scan273/" # defaults to None. If given the integrated image and the average line will be written # to check the superposability between the foil scans and the sample scans ### ## optional optional_solution : /mntdirect/_scisoft/users/mirone/WORKS/Christoph/XRSTools/volumes_gasket.h5:/ref408_bis_423_548/Volume0p03 ## a solution with dimensions [ZDIM,YDIM,XDIM] ## If given, will be used to balance analyzer factors If nbin is given the dimensios of the superresolution axis, will be reduced or increased, by binning together the foil PSFs. What the program will produce, under *target_address* datagroup, is - scalDS which is an array [ZDIM,YDIM,XDIM] , type "d" . - scalDD which is the total sum of the squared datas. - scalSS which is an array [XDIM,XDIM] , type "d" . From these three quantities the volume can be reconstructed with iterative procedure in subsequent steps. Here what they are : - scalSS is a 2D matrix, one of its elements is the scalar product of the response function for a given position of the foil, along X, with the response function for another position of the foil. The sum over ROIS is implicitely done. - scalDS is a 3D array. One of its element is the scalar product of the sample image for a given Z,Y position of the sample, with the reponse function for a given X position of the foil. The sum over the ROIs is implicitely done. """ allowed_keys = [ "delta_address",'orig_delta_address','nbin','roi_keys',"sample_address",'scan_interval',"load_factors_from","target_address",] check_allowed_keys(mydata, allowed_keys) delta_address = mydata["delta_address"] delta_filename, delta_groupname = split_hdf5_address(delta_address) if ('orig_delta_address' in mydata) : orig_delta_address = mydata["orig_delta_address"] orig_delta_filename, orig_delta_groupname = split_hdf5_address(orig_delta_address) else: orig_delta_filename, orig_delta_groupname = None, None h5f = h5py.File(delta_filename,"r") h5 = h5f[delta_groupname] if not ('nbin' in mydata) : width = 1 else: width = mydata['nbin'] solution = None roi_keys = filterRoiList(h5.keys(),prefix="") roi_keys = [str(t) for t in roi_keys] if ('roi_keys' in mydata) : u_roi_keys = mydata['roi_keys'] u_roi_keys = [str(t) for t in u_roi_keys] roi_keys = [ t for t in u_roi_keys if t in roi_keys] roi_keys = [str(t) for t in roi_keys] ## ########################## ## DELTA >>>>>>>>>>>>>> sonde = {} ## The response is added to sonde (probe) XDIM = None ## integrated_images :: ### much of the code is just for checking the trajectory and compare between the sample measurement ### and the foil measurement. ### integrated_images will be used only if is given in input. ### If you are interested only in the building of the scalar products then just skip everything related to integrated_image integrated_images={} ## to monitor if the foil move along a line which superimposes unto the sample integrated image for t in roi_keys: m = np.array(h5[t+"/matrix"][:],"d") integrated_images [t]=[ m.sum(axis=0) ,0, 0, None, None] # the second entry is left free for the sample integrated image, the third for the orig # the 4th the cornerpos, to be initialised by sample data, the fifth cornerpos by origdelta images ( if origdelta is read) if width != 1 : # REBINNING nbin = width assert(nbin>1) m=m[:(m.shape[0]//nbin)*nbin].reshape(-1, nbin, m.shape[1],m.shape[2]).sum(axis=1)/nbin sonde [t] = m ## PROBE STUFF GOES HERE if XDIM is None: XDIM = m.shape[0] else: assert(XDIM==m.shape[0]) ## DELTA <<<<<<<<<<<<<<<<<<<<< ## ############################# h5f.close() ## ############################################### ## >>>>>>>>>>>>>> ORIG : checking related thing. ## The idea is that, originally, the delta files was a large scan resintetised ## from a small reference scan. So in this section, that you can skip, ## on is taking information about the original scan. If delta and original delta are the same it's OK ## In most cases , if you want to do check, the original delta file is the same file as the delta file ## and the interesting part will be comparing the integrated line of the reference and the sample if orig_delta_filename is not None: h5f = h5py.File(orig_delta_filename,"r") h5 = h5f[orig_delta_groupname] for t in roi_keys: m = np.array(h5[t+"/matrix"][:],"d") integrated_images [t][2] += m.sum(axis=0) cornerpos = np.array(h5[t+"/cornerpos"][:]) integrated_images [t][4] = cornerpos h5f.close() ## ORIG <<<<<<<<<<<<<<<<<<<<<<<<< ## ############################# ## ########################## ## >>>>>>>>>>>>>>>>>>> SAMPLE ## sample_address = mydata["sample_address"] sample_filename, sample_groupname = split_hdf5_address(sample_address) h5f = h5py.File(sample_filename,"r") h5 = h5f[sample_groupname] if not ('scan_interval' in mydata) : zscan_keys = sorted( filterScanList(h5.keys()) , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) else: zscan_keys =[ "Scan%03d"%i for i in range(*list(mydata['scan_interval']))] ZDIM = len(zscan_keys) ## in case of parallelism the workload is splitted between processes myrange = np.array_split( np.arange( ZDIM ), nprocs )[myrank] myZDIM = len(myrange) YDIM = None rois_to_be_removed=[] for ro in roi_keys: if ro in h5[zscan_keys[0]]: m = h5[zscan_keys[0]][ro]["matrix"][:] if YDIM is not None: assert(YDIM == m.shape[0]) ## we take the Y lenght from the first roi of the first scan : ## this lenght is supposed to be the same are supposed to be the same for all scans else: YDIM = m.shape[0] else: rois_to_be_removed.append(ro) del sonde[ro] del integrated_images[ro] for ro in rois_to_be_removed: roi_keys.remove(ro) if YDIM is None: return doppio_filtro_done=0 for iz in range(myZDIM): ## each scan is at fixed Z and contains many ys. So we proceed along z zkey = zscan_keys[myrange[iz] ] my_roi_keys = filterRoiList(h5[ zkey ].keys(),prefix="") my_roi_keys = [str(t) for t in my_roi_keys] ## The following piece of code makes sure that our roi_keys list ## contains rois which can be found in the datas if not doppio_filtro_done: new_roi_keys=[] for ok in roi_keys: if ok not in my_roi_keys: del integrated_images[ok] else: new_roi_keys.append(ok) doppio_filtro_done=1 roi_keys = new_roi_keys roi_keys=sorted(roi_keys, key = int) fattori = {} ## This is used for balancing. Will stay to 1.0 for all rois if solution is not given in input for i,rk in enumerate(roi_keys): fattori[rk] = 1.0 if "load_factors_from" in mydata and mydata["load_factors_from"] not in [None, "None"]: loaded_fattori = json.load( open(mydata["load_factors_from"],"r") ) for i,rk in enumerate(roi_keys): if rk in loaded_fattori: fattori[rk] *= loaded_fattori[rk] else: fattori[rk] = 0 ## These arrays below will contain, after summation and for each process, ## the contribution of the process's workload ## They will be mpi Reduced to get the final result Nrois = len( roi_keys ) scalDS = np.zeros( [Nrois,myZDIM,YDIM,XDIM] ,"d" ) scalDD = np.zeros( [Nrois] ,"d" ) scalSS = np.zeros( [Nrois, XDIM,XDIM] ,"d" ) for i,rk in enumerate(roi_keys): if i%nprocs == myrank: probes = sonde [rk] ## Consider that, below, factor is a factor which is applied to the probe to better adapt it to the sample strenght ## variations from roi to roi. scalSS[i, :] = scalSS[i, :] + np.tensordot( probes, probes, axes = [ [1,2], [1,2] ] ) *fattori[rk]*fattori[rk] for iz in range(myZDIM): ## each scan is at fixed Z and contains many ys. So we proceed along z zkey = zscan_keys[myrange[iz] ] print( " process %d analyzing scan : "%myrank , zkey) my_roi_keys = filterRoiList(h5[ zkey ].keys(),prefix="") my_roi_keys = [str(t) for t in my_roi_keys] for iroi, rk in enumerate(roi_keys): m = np.array(h5[ zkey ][ rk ]["matrix"][:],"d") msum = m.sum(axis=0) if iz: if msum.shape != integrated_images[rk][1].shape: msg = " ERROR : the yscan elements have different shapes.\n selects homogeneous scans." print( msg) raise Exception( msg) integrated_images[rk][1] = integrated_images[rk][1]+msum cornerpos = np.array(h5 [ zkey ][ rk ]["cornerpos"][:]) integrated_images [rk][3] = cornerpos probes = sonde [rk] assert( probes.shape[1:] == m.shape[1:]) assert( XDIM == probes.shape[0] ) assert( YDIM == m.shape[0] ) ## At the end all boils down to these three lines of code. Note that they sum-up ## contributions from all the rois alltogether plane_contrib = np.tensordot( m, probes, axes = [ [1,2], [1,2] ] ) scalDS[iroi,iz] = scalDS[iroi, iz]+ plane_contrib*fattori[rk] scalDD[iroi] += (m*m).sum() h5f.close() ## ## ###################### ## stuffs for checking (integrated_images) if nprocs>1: for n in list(integrated_images.keys()): if myrank: comm.Reduce([integrated_images[n][0], MPI.DOUBLE], None, op=MPI.SUM, root=0) comm.Reduce([integrated_images[n][1], MPI.DOUBLE], None, op=MPI.SUM, root=0) else: comm.Reduce( [integrated_images[n][1], MPI.DOUBLE] , [integrated_images[n][0], MPI.DOUBLE], op=MPI.SUM, root=0) comm.Reduce( [np.array(integrated_images[n][1]), MPI.DOUBLE], [integrated_images[n][1], MPI.DOUBLE], op=MPI.SUM, root=0) ## All the remaining part is just writing target_address = mydata["target_address"] target_filename, target_groupname = split_hdf5_address(target_address) for iproc in range(nprocs): comm.barrier() if iproc != myrank: continue h5f = h5py.File(target_filename,"a") if myrank==0: if h5f.__contains__( target_groupname ): del h5f[target_groupname] h5f.close() h5f = h5py.File(target_filename,"a") h5f.require_group(target_groupname ) h5 = h5f[target_groupname] h5.create_dataset("scalDS", ( Nrois, ZDIM , YDIM , XDIM ), dtype='d') h5.create_dataset("scalDD", ( Nrois, ), dtype='d') h5.create_dataset("scalSS", ( Nrois, XDIM,XDIM), dtype='d') h5["scalDS"][:]=0 h5["scalDD"][:]=0 h5["scalSS"][:]=0 for n in list(integrated_images.keys()): B=integrated_images[n][1] A=integrated_images[n][0] # B=B.sum(axis=0) pesiA = A.sum(axis=0) pesiB = B.sum(axis=0) ## print(" pesi ", pesiA, pesiB) medieA = (np.arange(A.shape[0])[:,None]*A).sum(axis=0)/pesiA medieB = (np.arange(B.shape[0])[:,None]*B).sum(axis=0)/pesiB h5.require_group(n) h5n=h5[n] h5n["delta_poss"] = medieA h5n["sample_poss"] = medieB h5n["delta_integrated" ] = integrated_images[n][0] h5n["sample_integrated" ] = integrated_images[n][1] h5n["sample_integrated_weight" ] = pesiB if orig_delta_filename is not None: corner_C = np.array(integrated_images[n][4]) corner_B = np.array(integrated_images[n][3]) diff = corner_C-corner_B C = integrated_images[n][2] pesiC = C.sum(axis=0) medieC = (np.arange(C.shape[0])[:,None]*C).sum(axis=0)/pesiC coords = np.arange(len( medieC )) + diff[1] h5n["orig_delta_poss" ] = np.array( medieC+diff[0] ) h5n["orig_delta_poss_coord" ] = np.array( coords ) inset = integrated_images[n][2] tmp = np.zeros_like( integrated_images[n][1] ) target = tmp [ diff[0]:diff[0]+ inset.shape[0], diff[1]:diff[1]+ inset.shape[1]] target[:] = inset[ :target.shape[0], :target.shape[1] ] h5n["orig_delta_integrated" ] = tmp h5f.require_group(target_groupname ) h5 = h5f[target_groupname] if myrank == 0 : h5["roi_keys"] = np.array(list( map(int,roi_keys))) h5["scalSS"][:] += scalSS h5["scalDD"][:] += scalDD h5["scalDS"][:, myrange[0]:myrange[-1]+1] += scalDS h5.require_group("Mean_Poss") h5=h5["Mean_Poss"] h5f.flush() h5f.close() # def XRSprediction( yamlData ): # """ **prediction** # This launches the XRS prediction routines. # If yamlData contains information about: the sample, the incident beam, # the analyzer, the detector, the polarization, and the HF compton profiles, # this will create the desired predicted XRS data. # At the end you have the possibility to write the predicted profiles into a container hdf5 file. # In the extreme case when you give no argument ( parameters) :: # xrs_prediction : # The following canonical example will be run. # example :: # XRSprediction : # active : 1 # sample : # chem_formulas : ['C'] # list of strings of chemical sum formulas # concentrations : [1.0] # list of concentrations, should contain values between 0.0 and 1.0 # densities : [2.266] # list of densities of the constituents [g/cm^3] # angle_tth : 35.0 # scattering angle [deg] # sample_thickness : 0.1 # sample thickness/diameter in [cm] # angle_in : None # incident beam angle in [deg] relative to sample surface normal # angle_out : None # beam exit angle in [deg] relatice to sample surface normal # # (negative for transmission geometry) # shape : 'sphere' # keyword, can be 'slab' or 'sphere' # molar_masses : [12.0] # list of molar masses of all constituents # incident_beam : # i0_intensity : 1e13 # # number of incident photons [1/sec] # beam_height : 10.0 # in micron # beam_width : 20.0 # in micron # analyzer : # material : 'Si' # analyzer material (e.g. 'Si', 'Ge') # hkl : [6,6,0] # [hkl] indices of reflection used # mask_d : 60.0 # analyzer mask diameter in [mm] # bend_r : 1.0 # bending radius of the crystal [mm] # energy_resolution : 0.5 # energy resolution [eV] # diced : False # boolean (True or False) if a diced crystal is used or not (defalt is False) # thickness : 500.0 # thickness of the analyzer crystal # database_dir : installation_dir # compton_profiles : # eloss_range : np.arange(0.0,1000.0,0.1) # E0 : 9.7 # detector : # energy : 9.7 # analyzer energy [keV] # thickness : 500.0 # thickness of the active material [microns] # material : 'Si' # detector active material # thomson : # scattering_plane : 'vertical' # keyword to indicate scattering plane relative to lab frame ('vertical' or 'horizontal') # polarization : 0.99 # degree of polarization (close to 1.0 for undulator radiation) # saveaddress : # file_name : "myfile.hdf5" # path and filename # group_name : 'sample1' # hdf5 group name # """ # mydata = yamlData#["XRSprediction"] # if mydata is not None and ("active" in mydata ): # if mydata["active"]==0: # return # if mydata is not None: # try: # sample = mydata["sample"] # except: # sample = {} # chem_formulas = gvord(sample,"chem_formulas",["C"]) # concentrations = gvord(sample,"concentrations", [1.0]) # densities = gvord(sample,"densities", [2.266]) # angle_tth = gvord(sample,"angle_tth", 35.0) # sample_thickness = gvord(sample,"sample_thickness", 0.1) # angle_in = gvord(sample,"angle_in", None) # angle_out = gvord(sample,"angle_out", None) # shape = gvord(sample,"shape", 'sphere') # molar_masses = gvord(sample,"molar_masses", [12.0]) # sample_obj = xrs_prediction.sample(chem_formulas, concentrations, densities, angle_tth, sample_thickness, angle_in, angle_out, shape, molar_masses) # try: # incident_beam = mydata["incident_beam"] # except: # incident_beam = {} # i0_intensity = gvord(incident_beam,"i0_intensity", 1e13) # beam_height = gvord(incident_beam,"beam_height", 10.0) # beam_width = gvord(incident_beam,"beam_width", 20.0) # beam_obj = xrs_prediction.beam(i0_intensity, beam_height, beam_width, 0.0) # try: # analyzer = mydata["analyzer"] # except: # analyzer = {} # material = gvord(analyzer,"material", 'Si') # hkl = gvord(analyzer,"hkl", [6,6,0]) # mask_d = gvord(analyzer,"mask_d", 60.0) # bend_r = gvord(analyzer,"bend_r", 1.0) # energy_resolution = gvord(analyzer,"energy_resolution", 0.5) # diced = gvord(analyzer,"diced", False) # thickness = gvord(analyzer,"thickness", 500.0) # datadir_default = os.path.join( os.path.dirname( __file__ ) , "data" ) # database_dir = gvord(analyzer,"database_dir", datadir_default) # analyzer_obj = xrs_prediction.analyzer(material, hkl, mask_d, bend_r, energy_resolution, diced, thickness, database_dir) # try: # detector = mydata["detector"] # except: # detector = {} # energy = gvord(detector,"energy", 9.7) # thickness = gvord(detector,"thickness", 500.0) # material = gvord(detector,"material", 'Si') # detector_obj = xrs_prediction.detector(energy, thickness, material, [256,768]) # try: # compton_profile = mydata["compton_profile"] # except: # compton_profile = {} # eloss_range = gvord(compton_profile,"eloss_range", np.arange(0.0,1500.0,0.1)) # E0 = gvord(compton_profile,"E0", 9.7) # compton_profile_obj = xrs_prediction.compton_profiles(sample_obj, eloss_range, E0) # try: # thomson = mydata["thomson"] # except: # thomson = {} # scattering_plane = gvord(thomson,"scattering_plane", 'vertical') # polarization = gvord(thomson,"polarization", 0.99) # print('scatt plane>>> ', scattering_plane) # print('polarization>>> ', polarization) # thomson_obj = xrs_prediction.thomson(compton_profile_obj.get_energy_in_keV(), compton_profile_obj.get_E0(), compton_profile_obj.get_tth(), scattering_plane=scattering_plane, polarization=polarization ) # abs_cross_section_obj = xrs_prediction.absolute_cross_section(beam_obj, sample_obj, analyzer_obj, detector_obj, thomson_obj, compton_profile_obj) # from silx import sx # abs_cross_section_obj.plot_abs_cross_section() # try: # save_address = mydata['saveaddress'] # except: # save_address = {} # file_name = gvord(save_address, "file_name", "myfile.hdf5") # group_name = gvord(save_address, "group_name", 'sample1') # # safe file # abs_cross_section_obj.save_hdf5( file_name, group_name ) # def XRS_matrix_elements( yamlData ): # """ **XRS_matrix_elements** # This calculates transition matrix elements and plots them vs. q. # The following canonical example will be run. # example :: # XRS_matrix_elements : # active : 1 # atom : # Z : 47 # atomic number. # initial_n : 1 # initial state main quantum number. # initial_l : 0 # initial state orbital quantum number. # final_n : 2 # final state wave function. # final_l : 1 # final state orbital quantum number. # k : [1,3,5] # calculate dipole, octupole, and triacontadipole transition matrix elements. # plotting : 1 # saving : # ascii : # fname : # """ # mydata = yamlData # if mydata is not None and ("active" in mydata) : # if mydata["active"]==0: # return # if mydata is not None: # try: # data = mydata["atom"] # except: # data = {} # Z = gvord(data, "atom",47) # n_i = gvord(data, "initial_n", 3 ) # l_i = gvord(data, "initial_l", 0 ) # n_f = gvord(data, "final_n", 3 ) # l_f = gvord(data, "final_f", 1 ) # k = gvord(data, "k", [1,3,5] ) # R1 = xrs_prediction.radial_wave_function() # R1.load_from_sympy( Z, n_i, l_i ) # R2 = xrs_prediction.radial_wave_function() # R2.load_from_sympy( Z, n_f, l_f ) # Mel = xrs_prediction.matrix_element(R1, R2) # Mel.compute(k) # if mydata is not None: # if mydata["plotting"] == 1: # import matplotlib.pyplot as plt # plt.plot(Mel.q/0.5291, Mel.Mel**2) # plt.xlabel('q [A$^{-1}$]') # plt.ylabel('squared matrix elements') # plt.legend(['k = '+str(ii) for ii in k]) # plt.show() # if mydata is not None: # try: # data = mydata["saving"] # except: # data = {} # ascii = gvord(data, "ascii", False) # fname = gvord(data, "fname", None) # if ascii: # Mel.write_ascii() # else: # Mel.write_H5() def read_reader(dataadress): filename, groupname = split_hdf5_address(dataadress) reader = xrs_read.read_id20(None) reader.load_state_hdf5( filename, groupname) return reader, filename, groupname def superR_recreate_rois(mydata): """ This command extend the rois and creates an extrapolated foil scan The parameters are as following :: superR_recreate_rois : ### we have calculated the responses in responsefilename ### and we want to enlarge the scan by a margin of 3 times ### the original scan on the right and on the left ### ( so for a total of a 7 expansion factor ) responsefilename : "responses.h5:/fit" nex : 3 ## the old scan covered by the old rois old_scan_address : "../nonregressions/demo_imaging.hdf5:ROI_B/foil_scanXX/scans/Scan273/" ## where new rois and bnew scan are written target_filename : "newrois.h5:ROI_B_FIT8/" ## filter_rois=1 activates a filtering mechanism which discards ROIS according to a ## simple statistical criteria which is har coded in file reponse_percussionelle.py ## routine get_spots_list filter_rois : 1 # not necessary of old_scan_address contains also the ROI original_roi_path : None resynth_z_square : None """ allowed_keys = ["old_scan_address","original_roi_path","responsefilename","nex","target_filename","filter_rois","recenterings_refined","filter_path","resynth_z_square"] check_allowed_keys(mydata, allowed_keys) foil_scan_address = mydata["old_scan_address"] foil_filename ,foil_groupname = split_hdf5_address(foil_scan_address) tmp_groupname = foil_groupname newscanstarget = "" for i in range(2): pos = tmp_groupname.rfind("/") newscanstarget = tmp_groupname[pos:]+ newscanstarget tmp_groupname=tmp_groupname[:pos] if "original_roi_path" not in mydata or mydata["original_roi_path"] in ["None", None]: roisgroupname = tmp_groupname roi_filename = foil_filename else: roi_filename ,roisgroupname = split_hdf5_address(mydata["original_roi_path"]) responsefilename= mydata["responsefilename"] responsefilename, responsepath = split_hdf5_address( responsefilename) nex = mydata["nex"] target_filename , roisgroupname_target= split_hdf5_address( mydata["target_filename"]) newscanstarget = newscanstarget[1:] # if os.path.exists(target_filename): # sys.stdout.write("Error : file %s exists already. Remove it yourself\n"%target_filename) # sys.stderr.write("Error : file %s exists already. Remove it yourself\n"%target_filename) # sys.exit(1) if ("filter_rois" in mydata) : filter_rois = mydata["filter_rois"] else: filter_rois = 1 if ("resynth_z_square" in mydata and mydata["resynth_z_square"] != "None" ) : resynth_z_square = mydata["resynth_z_square"] else: resynth_z_square = None if "recenterings_refined" in mydata : recenterings_refined = mydata["recenterings_refined"] recenterings_filename, recenterings_groupname = split_hdf5_address( recenterings_refined ) h5f = h5py.File(recenterings_filename,"r") h5 = h5f[recenterings_groupname] recenterings= {} chiavi = filterRoiList(h5.keys(), prefix="") for c in chiavi: recenterings[int(c)]= h5[c][:] assert(recenterings[int(c)].shape == (2,)) h5f.close() else: recenterings= None filterMask=None if mydata is not None and ("filter_path" in mydata ) : filter_path = mydata["filter_path"] if filter_path is not None and len(filter_path): filename, dataname = split_hdf5_address(filter_path) h5f = h5py.File( filename,"r" ) filterMask = h5f[dataname][:] reponse_percussionelle.DOROIS(filename = foil_filename , groupname = foil_groupname, roi_filename = roi_filename, roisgroupname = roisgroupname, target_filename=target_filename, roisgroupname_target = roisgroupname_target , newscanstarget = newscanstarget, responsefilename = responsefilename, resynth_z_square = resynth_z_square, responsepath = responsepath, nex = nex, filter_rois=filter_rois, recenterings= recenterings , filterMask = filterMask) def superR_fit_responses(mydata): """ superR_fit_responses : foil_scan_address : "demo_foilroi.h5:/ROI_FOIL/foil_scan/scans/Scan273" nref : 5 # the number of subdivision per pixel dimension used to # represent the optical response function at higher resolution niter_optical : 100 # the number of iterations used in the optimisation of the optical # response beta_optical : 0.1 # The L1 norm factor in the regularisation # term for the optical functions pixel_dim : 6 # The pixel response function is represented with a # pixel_dim**2 array niter_pixel : 100 # The number of iterations in the pixel response optimisation # phase. A negative number stands for ISTA, positive for FISTA beta_pixel : 1000.0 # L1 factor for the pixel response regularisation ## The used trajectories are always written whith the calculated response ## They can be reloaded and used as initialization(and freezed with do_refine_trajectory : 0 ) ## Uncomment the following line if you want to reload a set of trajectories ## without this options trajectories are initialised from the spots drifts ## # reload_trajectories_file : "response.h5" ## may filter rois ( see in reponse_percussionelle.py ) if set to one filter_rois : 0 ###### ## The method first find an estimation of the foil scan trajectory on each roi ## then, based on this, obtain a fit of the optical response function ## assuming a flat pixel response. Finally the pixel response is optimised ## ## There is a final phase where a global optimisation ## is done in niter_global steps. ## ## Each step is composed of optical response fit, followed by a pixel response fit. ## If do_refine_trajectory is different from zero, the trajectory is reoptimised at each step ## niter_global : 20 ## if do_refine_trajectory=1 the start and end point of the trajectory are free ## if =2 then the start and end point are forced to a trajectory which is obtained ## from a reference scan : the foil scan may be short, then one can use the scan of ## an object to get another one : key *trajectory_reference_scan_address* ## do_refine_trajectory : 2 ## optional: only if do_refine_trajectory = 2 trajectory_reference_scansequence_address : "demo_newrois.h5:/ROI_FOIL/images/scans/" trajectory_threshold : 0.1 ## if the pixel response function is forced to be symmetrical simmetrizza : 1 ## where the found responses are written target_file : "demo_responses_bis.h5" """ allowed_keys = ["foil_scan_address","ref_scan_number","response_scan_address","nref","niter_optical","beta_optical","beta_pixel","niter_pixel","niter_global","pixel_dim","simmetrizza","do_refine_trajectory","target_file","trajectory_reference_scansequence_address","trajectory_threshold","reload_trajectories_file","filter_rois","fit_lines",] check_allowed_keys(mydata, allowed_keys) if "foil_scan_address" in mydata: foil_scan_address = mydata["foil_scan_address"] else: ref_scan_number = mydata["ref_scan_number"] foil_scan_address = mydata["response_scan_address"]+"/scans/Scan"+ ("%03d"% ref_scan_number) foil_filename ,foil_groupname = split_hdf5_address(foil_scan_address) nref = mydata["nref"] niter_optical = mydata["niter_optical"] beta_optical = mydata["beta_optical"] beta_pixel = mydata["beta_pixel"] niter_pixel = mydata["niter_pixel"] niter_global = mydata["niter_global"] pixel_dim = mydata["pixel_dim"] simmetrizza = mydata["simmetrizza"] do_refine_trajectory = mydata["do_refine_trajectory"] target_file = mydata["target_file"] target_file, target_groupname = split_hdf5_address( target_file ) # if os.path.exists(target_file): # sys.stdout.write("Error : file %s exists already. Remove it yourself\n"%target_file) # sys.stderr.write("Error : file %s exists already. Remove it yourself\n"%target_file) # sys.exit(1) if do_refine_trajectory==2 : ## optional: only if do_refine_trajectory = 2 trajectory_reference_scansequence_address = mydata["trajectory_reference_scansequence_address"] trajectory_reference_scansequence_filename , trajectory_reference_scansequence_groupname = split_hdf5_address(trajectory_reference_scansequence_address) trajectory_threshold = mydata["trajectory_threshold"] else: trajectory_reference_scansequence_filename , trajectory_reference_scansequence_groupname = None, None trajectory_threshold =0 if ("reload_trajectories_file" in mydata) : trajectory_file = mydata["reload_trajectories_file"] else: trajectory_file = None if ("filter_rois" in mydata ) : filter_rois = mydata["filter_rois"] else: filter_rois = 0 if ("fit_lines" in mydata) : fit_lines = mydata["fit_lines"] else: fit_lines = 0 reponse_percussionelle.DOFIT(filename=foil_filename, groupname=foil_groupname, nref=nref, niter_optical=niter_optical, beta_optical=beta_optical , beta_pixel=beta_pixel, niter_pixel = niter_pixel, niter_global = niter_global, pixel_dim=pixel_dim, simmetrizza=simmetrizza, do_refine_trajectory=do_refine_trajectory, target_file=target_file, target_groupname = target_groupname, trajectory_reference_scansequence_filename = trajectory_reference_scansequence_filename , trajectory_reference_scansequence_groupname = trajectory_reference_scansequence_groupname , trajectory_threshold = trajectory_threshold, trajectory_file = trajectory_file, filter_rois=filter_rois, fit_lines = fit_lines) swissknife_operations={ "help" : help, "create_rois" : create_rois, "create_rois_galaxies" : create_rois_galaxies, "loadscan_2Dimages_galaxies" : loadscan_2Dimages_galaxies, "loadscan_2Dimages_galaxies_foilscan" : loadscan_2Dimages_galaxies_foilscan, # "create_spectral_rois" : create_spectral_rois, # "load_scans" : load_scans, # "HFspectrum" : HFspectrum, "loadscan_2Dimages" : loadscan_2Dimages, # "volume_from_2Dimages" : volume_from_2Dimages, # "view_Volume_myavi" : view_Volume_myavi, "superR_scal_deltaXimages" : superR_scal_deltaXimages, "superR_scal_deltaXimages_Esynt" : superR_scal_deltaXimages_Esynt, "superR_fit_responses" : superR_fit_responses, "superR_recreate_rois" : superR_recreate_rois, "superR_getVolume" : superR_getVolume, "superR_getVolume_Esynt" : superR_getVolume_Esynt, "calculate_recenterings" : calculate_recenterings, "extract_spectra" : extract_spectra, # "sum_scans2maps" : sum_scans2maps, # "XRSprediction" : XRSprediction, # "Fourc_extraction" : Fourc_extraction, # "Hydra_extraction" : Hydra_extraction, # "XRS_matrix_elements" : XRS_matrix_elements } parallelised_operations = [ "loadscan_2Dimages" , "superR_scal_deltaXimages" , "superR_fit_responses" , "superR_recreate_rois" ] if __name__=="__main__": main() xrstools-0.15.0+git20210910+c147919d/XRStools/XRStools_c/000077500000000000000000000000001412732462000217465ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/XRStools_c/fitspectra.cc000066400000000000000000000076161412732462000244330ustar00rootroot00000000000000#include #include #include void spectra_roi_by_line_c( int Nsp, double *spettro_byline, double* error_sum, double* frequencies_sum, int Nenes, float* enes_data, float* denominator , int ny, int nx, float* mms, float* MASK, float mine, float de, float discard_threshold , float threshold_fraction, float hline, float slopeline, float DHoverDI, float deltaEref, int useref, int weight_by_response, float Xintercept , float CRX , float fNmiddle, float Xslope , int Nresp, double* response_line_intensity ) { { static int pass=0; if(!pass) { printf(" DENTRO spectra_roi_by_line_c \n" ); pass++; } } int mask_npix=0; for(int i=0; i< ny*nx; i++ ) { if(MASK[i]) mask_npix++; } for(int iE=0; iE0) { if(mm_npix>threshold_fraction*mask_npix) { continue; } } // one energy, one 2D image from the stack, one value for the denominator for(int iy=0; iy< ny ; iy++) { for(int ix=0; ix< nx ; ix++) { { static int pass=0; if(!pass) { printf(" DENTRO spectra_roi_by_line_c ny, nx %d %d\n" , ny, nx); pass++; } } if( MASK[ iy*nx+ix]==0 ) continue; { static int pass=0; if(!pass) { printf(" DENTRO spectra_roi_by_line_c MASK OK ny, nx %d %d\n" , ny, nx); pass++; } } // Assigning an energy to the pixel float H0 = iy- ( hline + ix * slopeline) ; // distance in pixels from the line float ii = H0 /DHoverDI ; // The above distance converted in energy steps float E = ene - ii *deltaEref ; // Corrected energy // calculating the longitudinal position along the line ( to weight with the projected response_line_intensity) float freq; if( useref && weight_by_response) { int posx_inref = (int) round( (ix-( (Xintercept-CRX+ fNmiddle*Xslope ) + ii * Xslope ) )) ; if (posx_inref>=0 and posx_inref< Nresp ) { freq = response_line_intensity[posx_inref]; // a weigthing factor ; } else { freq = 0; } }else { freq = 1.0; } { static int pass=0; if(!pass) { printf(" DENTRO spectra_roi_by_line_c freq %e\n" , freq); pass++; } } if(freq) { float fipos = (E-mine)/de ; // the position in pixel units of the contribution to the spectra array (which starts from mine) int ipos = (int)fipos ; // the integer part of fipos float f = fipos - ipos ; // the fractional residu of fipos // printf(" ,mm[iy*nx+ix] %e , freq %e , ipos %d , Nenes %d \n",mm[iy*nx+ix] , freq, ipos, Nenes ); if( ipos>0 && ipos < Nsp ) { // If I am within the range of the spectra frequencies_sum[ipos] += (1-f)* freq ; // I distribute the contribution : 100% if f=0, to ipos , with the weigth given by response spettro_byline [ipos] += (1-f)* mm[iy*nx+ix]; // Same thing : distributing intensity to spectro_.. frequencies_sum[ipos+1] += f*freq ; // Same thing as above, just 100% if f=1 because we are distributing to the upper pixel spettro_byline [ipos+1] += f* mm[iy*nx+ix] ; static double sum=0; sum+= mm[iy*nx+ix] ; printf(" SUM %e \n", sum); // Calculating the error by hoping that the final result be gaussian error_sum[ipos] += (1-f)*(1-f)* mm[iy*nx+ix] /deno ; error_sum[ipos+1] += f*f* mm[iy*nx+ix] /deno ; } } } } } } xrstools-0.15.0+git20210910+c147919d/XRStools/XRStools_c/fitspectra.h000066400000000000000000000011511412732462000242610ustar00rootroot00000000000000void spectra_roi_by_line_c( int Nsp, double *spettro_byline, double* error_sum, double* frequencies_sum, int Nenes, float* enes_data, float* denominator , int ny, int nx, float* mms, float* MASK, float mine, float de, float discard_threshold , float threshold_fraction, float hline, float slopeline, float DHoverDI, float deltaEref, int useref, int weight_by_response, float Xintercept , float CRX , float fNmiddle, float Xslope , int Nresp, double* response_line_intensity ) ; xrstools-0.15.0+git20210910+c147919d/XRStools/XRStools_c/fitspectra_cy.pyx000066400000000000000000000112341412732462000253500ustar00rootroot00000000000000# -*- coding: utf-8 -*- ################################################################################### # Fits, Elastic constants fits : Alessandro Mirone # European Synchrotron Radiation Facility ################################################################################### #distutils: extra_compile_args = -fopenmp # distutils: language = c++ import cython from cython.parallel cimport prange from cpython cimport bool cimport numpy from numpy cimport ndarray import math from libc.stdlib cimport free from libc.string cimport memcpy from libcpp.vector cimport vector from libc.stdio cimport printf cdef extern from "math.h": double fabs(float)nogil import numpy cdef extern from "fitspectra.h" : void spectra_roi_by_line_c( int Nsp, double *spettro_byline, double* error_sum, double* frequencies_sum, int Nenes, float* enes_data, float* denominator , int ny, int nx, float* mms, float* MASK, float mine, float de, float discard_threshold , float threshold_fraction, float hline, float slopeline, float DHoverDI, float deltaEref, int useref, int weight_by_response, float Xintercept , float CRX , float fNmiddle, float Xslope , int Nresp, double* response_line_intensity ) def spectra_roi_by_line( ndarray[numpy.float64_t, ndim = 1] spettro_byline, ndarray[numpy.float64_t, ndim = 1] error_sum , ndarray[numpy.float64_t, ndim = 1] frequencies_sum , ndarray[numpy.float32_t, ndim = 1] enes_data , ndarray[numpy.float32_t, ndim = 1] denominator , ndarray[numpy.float32_t, ndim = 3] mms , ndarray[numpy.float32_t, ndim = 2] MASK , float mine, float de, float discard_threshold, float threshold_fraction, float hline, float slopeline, float DHoverDI, float deltaEref, int useref, int weight_by_response, float Xintercept , float CRX , float fNmiddle, float Xslope , ndarray[numpy.float64_t, ndim = 1] response_line_intensity): cdef Nenes = enes_data.shape[0] cdef ny = mms.shape[1] cdef nx = mms.shape[2] cdef Nsp = spettro_byline.shape[0] cdef Nresp = response_line_intensity.shape[0] assert Nenes == mms.shape[0] assert Nenes == denominator.shape[0] assert MASK.shape[0] == ny assert MASK.shape[1] == nx assert Nsp == error_sum.shape[0] assert Nsp == frequencies_sum.shape[0] assert spettro_byline.flags["C_CONTIGUOUS"] assert error_sum.flags["C_CONTIGUOUS"] assert frequencies_sum.flags["C_CONTIGUOUS"] assert enes_data.flags["C_CONTIGUOUS"] assert mms.flags["C_CONTIGUOUS"] assert denominator.flags["C_CONTIGUOUS"] assert MASK.flags["C_CONTIGUOUS"] assert response_line_intensity.flags["C_CONTIGUOUS"] print(" CHIAMO SPPCCC ") spectra_roi_by_line_c( Nsp, &(spettro_byline[0]), &(error_sum[0]), &(frequencies_sum[0]), Nenes, &(enes_data[0]), &(denominator [0]), ny, nx, &(mms[0,0,0]), &(MASK[0,0]), mine, de, discard_threshold , threshold_fraction, hline, slopeline, DHoverDI, deltaEref, useref, weight_by_response, Xintercept , CRX , fNmiddle, Xslope , Nresp, &(response_line_intensity[0]) ) print(" CHIAMO SPPCCC ") return None xrstools-0.15.0+git20210910+c147919d/XRStools/XRStools_c/luts.cc000066400000000000000000000211241412732462000232440ustar00rootroot00000000000000#include #include #include #include #include // #include #include #define FLOAT_TO_INT(out,in) \ out=_mm_cvtss_si32(_mm_load_ss(&(in))); #define max(a,b) (((a)>(b))? (a):(b)) #define min(a,b) (((a)<(b))? (a):(b)) void lutprod( int n_1, float *lut1, int n_2, float *lut2, int na2, int nb2, int dim1, int dim2, float *reponse_pixel, int &n_result, float * &result, float * rois ) { std::vector res; int i1,j1; float y0,y1; float Y0,Y1; int iY0, iY1; float fiY0, fiY1; float f1,f2; // printf("qui\n"); // double SSUM = 0; for(int il1 =0; il1= dim1 || idx<0 || idx>= dim2 ) { printf(" idy, idx , dim1, dim2 %d %d %d %d\n", idy, idx , dim1, dim2); } Fatt += reponse_pixel[ idy*dim2 + idx ]*yfact ; } if ( fiX0-X0>1.0e-8) { int where ; float tmp; FLOAT_TO_INT(where , tmp=floor(X0)) ; Fatt += (fiX0-X0) * reponse_pixel[ idy*dim2 + where ] *yfact ; } if(iX1>=iX0) { if(X1-fiX1>1.0e-8) { int where = iX1; Fatt += (X1-fiX1) * reponse_pixel[ idy*dim2 + where ] *yfact; } } } Fatt /= (X1-X0)*(Y1-Y0) ; res.push_back( i1*na2+i2 ); res.push_back( j1*nb2+j2 ); res.push_back( f1*f2* Fatt ); // SSUM += f1*f2* Fatt ; // SSUM += Fatt ; } } } // printf("qua %e \n", SSUM); n_result = res.size()/3; result = new float [n_result*3]; memcpy(result, &(res[0]) , n_result*3*sizeof(float)); } void lutprod4reponse( int n_1, float *lut1, int n_2, float *lut2, int na1, int na2, int nb2, int dim1, int dim2, float *reponse_pixel, int dim_sol , float *solution, int &n_result, float * &result, int simmetrizza, float * rois ) { #define MATRIX(i,j,k,l) matrix[ ((( (i) )*na2 +(j) )*dim1 + (k) )*dim2 +(l) ] std::vector matrix( na1*na2* dim1*dim2 ,0.0 ) ; std::vector res; int i1,j1; float y0,y1; float Y0,Y1; int iY0, iY1; float fiY0, fiY1; float f1,f2; // int simmetrizza = 1; // printf("qui\n"); // double SSUM = 0; for(int il1 =0; il1= dim1 || idx<0 || idx>= dim2 ) { printf("CHE E STA ROBA ??? idy, idx , dim1, dim2 %d %d %d %d %e\n", idy, idx , dim1, dim2, yfact); } else { float term = solution[ j1*nb2+j2 ]* yfact ; MATRIX( i1,i2, idy,idx ) += term ; if(simmetrizza) { MATRIX( i1,i2, dim1-1 -idy,idx ) += term ; MATRIX( i1,i2, idy,dim1-1 -idx ) += term ; MATRIX( i1,i2, dim1-1 -idy,dim1-1 -idx ) += term ; MATRIX( i1,i2, idx,idy ) += term ; MATRIX( i1,i2, dim1-1 -idx,idy ) += term ; MATRIX( i1,i2, idx,dim1-1 -idy ) += term ; MATRIX( i1,i2, dim1-1 -idx,dim1-1 -idy ) += term ; } } // Fatt += reponse_pixel[ idy*dim2 + idx ]*yfact ; } if ( fiX0-X0>1.0e-8) { int where ; float tmp; FLOAT_TO_INT(where , tmp=floor(X0)) ; // Fatt += (fiX0-X0) * reponse_pixel[ idy*dim2 + where ] *yfact ; if ( idy<0 || idy>= dim1 || where<0 || where>= dim2 ) { printf("CHE E STA ROBA ??? idy, where , dim1, dim2 %d %d %d %d yfact %e\n", idy, where , dim1, dim2, yfact); } else { float term = solution[ j1*nb2+j2 ]* (fiX0-X0) * yfact ; MATRIX( i1,i2, idy,where) += term ; if(simmetrizza) { MATRIX( i1,i2, dim1-1 -idy,where ) += term ; MATRIX( i1,i2, idy,dim1-1 -where ) += term ; MATRIX( i1,i2, dim1-1 -idy,dim1-1 -where ) += term ; MATRIX( i1,i2, where,idy ) += term ; MATRIX( i1,i2, dim1-1 -where,idy ) += term ; MATRIX( i1,i2, where,dim1-1 -idy ) += term ; MATRIX( i1,i2, dim1-1 -where,dim1-1 -idy ) += term ; } } } if(iX1>=iX0) { if(X1-fiX1>1.0e-8) { int where = iX1; // Fatt += (X1-fiX1) * reponse_pixel[ idy*dim2 + where ] *yfact; if ( idy<0 || idy>= dim1 || where<0 || where>= dim2 ) { printf("CHE E STA ROBA idy, where , dim1, dim2 %d %d %d %d yfact %e\n", idy, where , dim1, dim2, yfact); } else { float term = solution[ j1*nb2+j2 ]* (X1-fiX1) * yfact ; MATRIX( i1,i2, idy,where) += term ; if(simmetrizza) { MATRIX( i1,i2, dim1-1 -idy,where ) += term ; MATRIX( i1,i2, idy,dim1-1 -where ) += term ; MATRIX( i1,i2, dim1-1 -idy,dim1-1 -where ) += term ; MATRIX( i1,i2, where,idy ) += term ; MATRIX( i1,i2, dim1-1 -where,idy ) += term ; MATRIX( i1,i2, where,dim1-1 -idy ) += term ; MATRIX( i1,i2, dim1-1 -where,dim1-1 -idy ) += term ; } } } } } } } } for(int i1 = 0; i1 #include // #include void lutprod( int n_1, float *lut1, int n_2, float *lut2, int na2, int nb2, int dim1, int dim2, float *reponse_pixel, int &n_result, float * &result, float * rois ); void lutprod4reponse( int n_1, float *lut1, int n_2, float *lut2, int na1, int na2, int nb2, int dim1, int dim2, float *reponse_pixel, int dim_sol , float *solution, int &n_result, float * &result, int simmetrizza, float * rois ); xrstools-0.15.0+git20210910+c147919d/XRStools/XRStools_c/luts_cy.pyx000066400000000000000000000071631412732462000242010ustar00rootroot00000000000000# -*- coding: utf-8 -*- ################################################################################### # Fits, Elastic constants fits : Alessandro Mirone # European Synchrotron Radiation Facility ################################################################################### #distutils: extra_compile_args = -fopenmp # distutils: language = c++ import cython from cython.parallel cimport prange from cpython cimport bool cimport numpy from numpy cimport ndarray import math from libc.stdlib cimport free from libc.string cimport memcpy from libcpp.vector cimport vector from libc.stdio cimport printf cdef extern from "math.h": double fabs(float)nogil import numpy cdef extern from "luts.h" : void lutprod( int n_1, float *lut1, int n_2, float *lut2, int na2, int nb2, int dim1, int dim2, float *reponse_pixel, int &n_result, float * &result, float * rois ) void lutprod4reponse( int n_1, float *lut1, int n_2, float *lut2, int na1, int na2, int nb2, int dim1, int dim2, float *reponse_pixel, int dim_sol , float *solution, int &n_result, float * &result, int simmetrizza, float * rois ) def get_product4reponse( ndarray[numpy.float32_t, ndim = 2] lut_1, ndarray[numpy.float32_t, ndim = 2] lut_2, int na1, int na2, int nb2 , ndarray[numpy.float32_t, ndim = 2]reponse_pixel, ndarray[numpy.float32_t, ndim = 2]solution, int simmetrizza, ndarray[numpy.float32_t, ndim = 2] rois): cdef int n_1 = lut_1.shape[0] cdef int n_2 = lut_2.shape[0] cdef int dim1 = reponse_pixel.shape[0] cdef int dim2 = reponse_pixel.shape[1] cdef int dim_sol = solution.shape[1] cdef int n_result = -1 cdef float *result = NULL assert lut_1.flags["C_CONTIGUOUS"] assert lut_2.flags["C_CONTIGUOUS"] assert reponse_pixel.flags["C_CONTIGUOUS"] assert solution.flags["C_CONTIGUOUS"] lutprod4reponse( n_1 , &lut_1[0,0],n_2 , &lut_2[0,0], na1, na2, nb2, dim1, dim2, &reponse_pixel[0,0], dim_sol, &solution[0,0], n_result, result, simmetrizza, &rois[0,0] ) cdef float[:,:] result_py = numpy.zeros( [n_result,3] , dtype=numpy.float32) memcpy( &(result_py[0,0]), result , n_result*3*sizeof(float) ) free(result) return result_py def get_product( ndarray[numpy.float32_t, ndim = 2] lut_1, ndarray[numpy.float32_t, ndim = 2] lut_2, int na2, int nb2 , ndarray[numpy.float32_t, ndim = 2]reponse_pixel, ndarray[numpy.float32_t, ndim = 2] rois): cdef int n_1 = lut_1.shape[0] cdef int n_2 = lut_2.shape[0] cdef int dim1 = reponse_pixel.shape[0] cdef int dim2 = reponse_pixel.shape[1] cdef int n_result = -1 cdef float *result = NULL assert lut_1.flags["C_CONTIGUOUS"] assert lut_2.flags["C_CONTIGUOUS"] assert reponse_pixel.flags["C_CONTIGUOUS"] lutprod( n_1 , &lut_1[0,0],n_2 , &lut_2[0,0], na2, nb2, dim1, dim2, &reponse_pixel[0,0], n_result, result, &rois[0,0] ) cdef float[:,:] result_py = numpy.zeros( [n_result,3] , dtype=numpy.float32) memcpy( &(result_py[0,0]), result , n_result*3*sizeof(float) ) free(result) return result_py xrstools-0.15.0+git20210910+c147919d/XRStools/XRStools_c/setup.py000066400000000000000000000026741412732462000234710ustar00rootroot00000000000000 from numpy.distutils.misc_util import Configuration import platform import os import numpy import sys def create_extension_config(name, extra_sources=None, can_use_openmp=False, language="c"): """ Util function to create numpy extension from the current pyFAI project. Prefer using numpy add_extension without it. """ include_dirs = [ numpy.get_include()] if can_use_openmp: extra_link_args = ['-fopenmp'] extra_compile_args = ['-fopenmp'] else: extra_link_args = [] extra_compile_args = [] sources = ["%s.pyx" % name] if extra_sources is not None: sources.extend(extra_sources) config = dict( name=name, sources=sources, include_dirs=include_dirs, language=language, extra_link_args=extra_link_args, extra_compile_args=extra_compile_args ) return config def configuration(parent_package='', top_path=None): config = Configuration('XRStools_c', parent_package, top_path) ext_modules = [ create_extension_config("luts_cy", extra_sources=["luts.cc"], language="c++"), create_extension_config("fitspectra_cy", extra_sources=["fitspectra.cc"],language="c++") ] for ext_config in ext_modules: print( ext_config) config.add_extension(**ext_config) return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration=configuration) xrstools-0.15.0+git20210910+c147919d/XRStools/__init__.py000066400000000000000000000002551412732462000220220ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function version = "" # version = "_alex" # version = "_unstable" xrstools-0.15.0+git20210910+c147919d/XRStools/bin/000077500000000000000000000000001412732462000204575ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_APS_example.py000066400000000000000000000032171412732462000240560ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from scipy import interpolate from .xrstools import xrs_read, theory, extraction from pylab import * from six.moves import range # try loading old Si data (this is more complicated since this is not id16 data) counters=[1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19] data = np.loadtxt('xrstools/things/Ba24Si100_rawdata_APS11.dat') tth = np.array(list(range(9,171,9))) # initiate data eloss = data[:,0] signals = data[:,1:] errors = np.sqrt(np.absolute(data[:,1:])) E0 = 7.912 # initiate id20 instance basi = xrs_read.read_id16('xrstools/things/licl_test_files/raman') basi.eloss = eloss basi.signals = np.absolute(signals) basi.errors = errors basi.E0 = E0 basi.tth = tth # initiate HFspectrum instance from the theory module hf = theory.HFspectrum(basi,['Ba24Si100'],[1.0],correctasym=[[0.0,0.0]]) # initiate instance of extraction class for background removal # (using the instance of the read_id20 and the HFspectrum class): extr = extraction.extraction(basi,hf) # apply energy dependent corrections to the experimental data (absorption, self-absorption, relativistic scattering cross section) extr.energycorrect(list(range(17)),10,2.3,0.1) # for a quick estimate of the core profile and if the edge is not too far into the compton profile: #extr.removeelastic(range(13,17),[0,50],[800,850],overwrite=True,stoploop=False) #extr.remquickval(range(13,17),[400,1000],[90,120],20) # for extraction of the valence profile: extr.removeelastic(list(range(13,17)),[0,50],[800,850],overwrite=True,stoploop=False) xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_SR_example.py000066400000000000000000000012621412732462000237550ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: superresolution.py import numpy as np from pylab import * from six.moves import range ion() from .xrstools import superresolution reload(superresolution) sr = superresolution.imageset() sr.loadhe3070('xrstools/things/he3070-zscan.mat') #sr.loadkimberlite('xrstools/things/kimberlite1.mat') #sr.loadkimberlite('xrstools/things/he3070-zscan_likesimo.mat') sr.estimate_xshifts() #sr.interpolate_xshift_images(2) sr.interpolate_xshift_images(4,whichimages=[0,1,2,4,5,6,7,8]) sr.plotSR() for ii in range(9): figure() sr.plotLR(ii) xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_SR_example_2.py000066400000000000000000000005721412732462000242010ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .xrstools import xrs_read, theory, extraction from pylab import * t = xrs_read.read_id20('/home/christoph/data/ch3914/orig/raman',energycolumn='energy',monitorcolumn='kap4dio') t.make_posscan_image(592,'sty','/home/christoph/data/ch3914/scratch/test.dat') xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_acetic_acid_example.py000066400000000000000000000011041412732462000256340ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .xrstools import xrs_read from pylab import * from six.moves import range ion() # C K-edge 180 C, 3 bars aaC180 = xrs_read.read_id20('/home/csahle/data/ch3898/raman',energycolumn='energy',monitorcolumn='kap4dio') aaC180.loadelastic([150]) aaC180.getlinrois([150],numrois = 3) aaC180.loadloop([135,143],4) aaC180.getrawdata() aaC180.getspectrum() aaC180.geteloss() for n in range(len(aaC180.signals[0,:])): plot(aaC180.eloss,aaC180.signals[:,n],'-') xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_calctools_example.py000066400000000000000000000044461412732462000254230ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .xrstools import calctools from pylab import * tmao = calctools.erkale('/home/christoph/programs/erkale-runs/tmao-6M/clusters_4A/results/cluster_mol','/trans_four-1.50.dat',0,980,20,stepformat=3) tmao.cut_rawspecs(500,600) tmao.broaden_lin() tmao.sum_specs() tmao.plot_spec() urea = calctools.erkale('/home/christoph/programs/erkale-runs/urea-6M/clusters_4A/results/cluster_mol','/trans_four-1.50.dat',0,980,20,stepformat=3) urea.cut_rawspecs(500,600) urea.sum_specs() urea.broaden_lin() water = calctools.erkale('/home/csahle/programs/erkale-runs/tip4p-water/clusters_6A/cluster_mol','/xrs/trans_four-1.50.dat',0,3000,50,stepformat=3) water.cut_rawspecs(500,600) water.broaden_lin() #params=[0.8, 8, 537.5, 550] water.sum_specs() water.norm_area(530,560) water.plot_spec() ion() plot(urea.energy,urea.sqw,tmao.energy,tmao.sqw) # TMAO 4M, only H2O oxygens tmao4M_OW = calctools.erkale('/home/csahle/programs/erkale-runs/tmao-4M/clusters_5A/cluster_mol','/xrs/trans_four-1.50.dat',80,980,20,stepformat=3) tmao4M_OW.cut_rawspecs(500,600) tmao4M_OW.broaden_lin() tmao4M_OW.sum_specs() tmao4M_OW.norm_area(530,560) tmao4M_OW.plot_spec() # TMAO 8M, only H2O oxygens tmao8M_OW = calctools.erkale('/home/csahle/programs/erkale-runs/tmao-8M/clusters_5A/cluster_mol','/xrs/trans_four-1.50.dat',160,1120,20,stepformat=3) tmao8M_OW.cut_rawspecs(500,600) tmao8M_OW.broaden_lin() tmao8M_OW.sum_specs() tmao8M_OW.norm_area(530,560) tmao8M_OW.plot_spec() # UREA 4M, only H2O oxygens urea4M_OW = calctools.erkale('/home/csahle/programs/erkale-runs/urea-4M/clusters_5A/cluster_mol','/xrs/trans_four-1.50.dat',80,1080,20,stepformat=3) urea4M_OW.cut_rawspecs(500,600) urea4M_OW.broaden_lin() urea4M_OW.sum_specs() urea4M_OW.norm_area(530,560) urea4M_OW.plot_spec() # UREA 8M, only H2O oxygens urea8M_OW = calctools.erkale('/home/csahle/programs/erkale-runs/urea-8M/clusters_4A/cluster_mol','/xrs/trans_four-1.50.dat',160,1080,20,stepformat=3) urea8M_OW.cut_rawspecs(500,600) urea8M_OW.broaden_lin() urea8M_OW.sum_specs() urea8M_OW.norm_area(530,560) urea8M_OW.plot_spec() plot(water.energy,water.sqw,tmao4M_OW.energy,tmao4M_OW.sqw,tmao8M_OW.energy,tmao8M_OW.sqw,urea4M_OW.energy,urea4M_OW.sqw,urea8M_OW.energy,urea8M_OW.sqw) xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_diamond_example.py000066400000000000000000000070441412732462000250500ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from scipy import interpolate from .xrstools import xrs_read, theory, extraction from .xrstools.helpers import * from pylab import * from six.moves import range ion() # try loading old Si data counters=[1, 2, 3, 4, 5, 6, 7, 8, 9] data = np.loadtxt('xrstools/things/diamond_data_E016keV_tth123.dat') inds = np.where(np.logical_and(data[:,0]>=6.0,data[:,0]<=7.5))[0] data[inds,1::] = 0 # remove some glitches in the data glitchindex1 = np.where(data[:,0]==1147.0999999999999)[0][0] data = np.delete(data, glitchindex1, 0) glitchindex2 = np.where(data[:,0]==397.10000000000002)[0][0] data = np.delete(data, glitchindex2, 0) # scale some data points inds = np.where(data[:,0]>1136.08) data[inds,5::] = data[inds,5::] + 0.0004 clf();plot(data[:,0],data[:,1::]) meantth = 133 # tth = np.array([meantth-13.0, meantth-6.5, meantth-6.5, meantth+0.0, meantth+0.0, meantth+7.5, meantth+7.5, meantth+14.0, meantth+14.0]) tth = np.array([115, 121, 121, 129, 129, 137, 137, 143, 143]) # initiate data eloss = data[:,0] signals = data[:,1:] errors = np.sqrt(np.absolute(data[:,1:])) E0 = 15.8 # reset the eloss scale inds = np.where(np.logical_and(eloss>=-10.0,eloss<=10.0))[0] for ii in range(len(data[0,1:])): cenom = np.trapz(signals[inds,ii]*eloss[inds],eloss[inds]) maxi = signals[inds,ii] == np.amax(signals[inds,ii]) #print cenom escale = eloss-eloss[maxi] signals[:,ii] = np.interp(eloss, escale, signals[:,ii]) #plot(eloss,signals) pz = e2pz(eloss/1e3+E0,E0,tth[0])[0] # initiate id20 instance (from a random Spec-file, since the init checks if the specified file actually exists) dia = xrs_read.read_id16('xrstools/things/licl_test_files/raman') dia.eloss = eloss dia.signals = np.absolute(signals) dia.errors = errors dia.E0 = E0 dia.tth = tth dia.cenom = np.zeros(len(tth))+15.8 # initiate cprofiles instance hf = theory.HFspectrum(dia,['C'],[1.0],correctasym=[[0.0]]) #plot(hf.eloss,hf.J[:,0],dia.eloss,dia.signals[:,0]) # initiate instance of extraction class for background removal: #reload(extraction) extr = extraction.extraction(dia,hf,prenormrange=[20,np.inf]) plot(extr.eloss,extr.signals[:,0],extr.eloss,extr.J[:,0]) extr.extractval_test(8,linrange1=[340,470],linrange2=[1500,2500]) extr.getallvalprof(8,smoothgval=50.0,stoploop=False) extr.remvalenceprof_test(list(range(9)),eoffset=20.0) ion() clf() plot(extr.eloss,extr.sqw[:,0],extr.eloss,extr.C[:,0]) # save some things for gnuplot thedata = np.zeros((len(extr.eloss),5)) thedata[:,0] = extr.eloss thedata[:,1] = extr.signals[:,8]-0.0002 thedata[:,2] = extr.C[:,8] thedata[:,3] = np.interp(extr.eloss,extr.eloss,extr.valence[:,8]) thedata[:,4] = np.interp(extr.eloss,extr.eloss,extr.valasymmetry[:,8]*8) np.savetxt('/home/csahle/Dropbox/tool_paper/figures/analysis/val_extraction_SCVA_det9.dat',thedata) thesqw = np.zeros((len(extr.eloss),10)) thesqw[:,0] = extr.eloss theval = np.zeros((len(extr.eloss),10)) theval[:,0] = extr.eloss thedata = np.zeros((len(extr.eloss),10)) thedata[:,0] = extr.eloss for ii in range(1,11): thesqw[:,ii] = extr.sqw[:,ii] theval[:,ii] = extr.valence[:,ii] thedata[:,ii] = extr.signals[:,ii]-extr.background[:,ii] np.savetxt('/home/csahle/Dropbox/tool_paper/figures/analysis/val_extraction_sqw_alldet.dat',thesqw) np.savetxt('/home/csahle/Dropbox/tool_paper/figures/analysis/val_extraction_val_alldet.dat',theval) np.savetxt('/home/csahle/Dropbox/tool_paper/figures/analysis/val_extraction_data_alldet.dat',thedata) xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_id20_h2so4_example.py000066400000000000000000000012061412732462000252040ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .xrstools import xrs_read, theory, extraction from pylab import * import numpy as np ion() h2so4 = xrs_read.read_id20('/home/christoph/data/ch3914/orig/raman',energycolumn='energy',monitorcolumn='kap4dio') #h2so4 = xrs_read.read_id20('xrstools/things/h2so4_test_files/raman',energycolumn='energy',monitorcolumn='kap4dio') h2so4.loadelastic([908,914]) h2so4.getzoomrois(908,numrois=12) h2so4.loadloopdirect([909,915],5) h2so4.getrawdata() h2so4.getspectrum() h2so4.geteloss() plot(h2so4.eloss,np.sum(h2so4.signals,axis=1)) xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_id20_test.py000066400000000000000000000023411412732462000235120ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .xrstools import xrs_read, theory, extraction from pylab import * from six.moves import range t = xrs_read.read_id20('/home/christoph/data/ch3898/raman',energycolumn='energy',monitorcolumn='kap4dio') t.loadelastic(259) t.getlinrois(259,numrois=3) # t.loadloop([210,221,232,243],3) t.loadscan([260],'edge1') t.loadscan([263],'edge2') t.loadscan([264],'edge3') t.loadscan([265],'edge4') t.loadscan([266],'edge5') t.loadscan([267],'edge6') t.loadscan([268],'edge7') t.loadlong([258]) t.getrawdata() t.getspectrum() t.geteloss() t.gettths(rhl=45.0,rhr=45.0,rhb=143.38,rvd=-45.0,rvu=87.66,rvb=121.88) # plot the experimental data for n in range(len(t.signals[0,:])): plot(t.eloss,t.signals[:,n],'-') show() #for n in range(len(t.signals[0,:])): # plot(t.energy,t.signals[:,n],[t.cenom[n],t.cenom[n]],[0,1])) from .xrstools import xrs_read, theory, extraction from pylab import * ion() t = xrs_read.read_id20('/home/csahle/data/ch3898/raman',energycolumn='energy',monitorcolumn='kap4dio') t.loadelastic(259) t.loadloop([265],4) t.loadlong(258) t.getlinrois(259,numrois=12) t.getrawdata() t.getspectrum() t.geteloss() xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_imaging_example.py000066400000000000000000000007211412732462000250430ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from pylab import * ion() from .xrstools import id20_imaging reload(id20_imaging) test = id20_imaging.imaging('xrstools/things/licl_test_files/raman') test.loadscan(90) test.getlinrois(90,numrois=1) test.image_from_line(90,'energy_cc') a = test.image_from_line(90,'energy_cc') from pylab import * ion() imshow(np.squeeze(a)) xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_licl_example.py000066400000000000000000000040131412732462000243510ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .xrstools import xrs_read, theory, extraction import pylab from six.moves import range # create an instance of the read_id16 class from the xrs_read module licl = xrs_read.read_id16('xrstools/things/licl_test_files/raman') # load a scan (Spec- and edf-file) into your class (here: an elastic scan) licl.loadelastic(90) # use the scan you loaded (No. 90) for finding ROIs automatically licl.getautorois(90) # other options to find ROIs are # getzoomedgerois(90,energyval) # getlinedgerois(90,energyval) # getzoomrois(90,numrois=9) # getlinrois(90) # load more scans, scan sequence of 2 loops (starting at scan No. 165 and 170) of 5 scans each licl.loadloop([165,170],5) #licl.loadscan([165,170],'edge1') # load a wide overview scan #licl.loadscan([166,171],'edge2') #licl.loadscan([167,172],'edge3') #licl.loadscan([168,173],'edge4') #licl.loadscan([169,174],'edge5') licl.loadlong(157) # apply the ROIs to all scans licl.getrawdata() licl.getspectrum() # find the center of mass of the elastic line (define energy loss scale) licl.geteloss() # set the scattering angle licl.gettths(35) # calculate the momentum transfers licl.getqvals() # plot the experimental data for n in range(len(licl.signals[0,:])): pylab.plot(licl.eloss,licl.signals[:,n],'.') pylab.axis([-50.0,620.0,0.0,0.005]) pylab.show() # create an instance of the HFspectrum class from the theory module hf = theory.HFspectrum(licl,['H2O'],[1.0],correctasym=[[0.0,0.0]]) # create an instance of the extraction class from the extraction # module (using the instances of the read_id20 and HFspectrum classes) extr = extraction.extraction(licl,hf) # remove a constant from the raw data and scale the data to the Hartree-Fock edge jump extr.removeconstpcore(list(range(9)),[527.0,534.5],[545.0,588.0],weights=[1,1],stoploop=False) # average over all 9 spectra extr.averageqs(list(range(9))) # plot the result pylab.plot(extr.eloss,extr.sqwav) pylab.show() xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_long_Si.py000066400000000000000000000054051412732462000233130ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from scipy import interpolate from .xrstools import xrs_read, theory, extraction from pylab import * from six.moves import range # try loading old Si data (this is more complicated since this is not id16 data) counters=[3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19] data = [] for counter in counters: try: nr = '%02d' % counter fn = 'xrstools/things/Si/fig_raw_si_' + nr + '.dat' data.append(np.loadtxt(fn)) except: print( 'Det ' + nr + ' not found!') tth = np.array([26, 32, 48, 63, 69, 79, 87, 96, 106, 115, 123, 135, 145, 157, 175])+8 # initiate data eloss = data[0][:,0] signals = np.zeros((len(eloss),len(counters))) errors = np.zeros(np.shape(signals)) E0 = 9.893 # np.zeros((len(counters),1))+9.893 # spline onto first detector for n in range(len(counters)): f = interpolate.interp1d(data[n][:,0],data[n][:,1], bounds_error=False, fill_value=0.0) signals[:,n] = f(eloss) f = interpolate.interp1d(data[n][:,0],data[n][:,2], bounds_error=False, fill_value=0.0) errors[:,n] = f(eloss) # initiate id20 instance (from a random Spec-file, since the init checks if the specified file actually exists) si = xrs_read.read_id16('xrstools/things/licl_test_files/raman') si.eloss = eloss si.signals = signals si.errors = errors si.E0 = E0 si.tth = tth #pylab.plot(si.eloss,si.signals,'.') #pylab.show() # initiate HFspectrum instance from the theory module for low-q extraction without core asymmetry correction: hf = theory.HFspectrum(si,['Si'],[1.0],correctasym=[[1.5]],correctasym_pertth=[0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.5, 0.5, 1.0, 1.0, 1.0, 1.5, 1.5, 1.5, 1.5]) # initiate instance of extraction class for background removal # (using the instance of the read_id20 and the HFspectrum class): extr = extraction.extraction(si,hf) # apply energy dependent corrections to the experimental data (absorption, self-absorption, relativistic scattering cross section) extr.energycorrect(list(range(15)),10,2.3,0.2) ion() plot(extr.eloss,extr.signals[:,14],extr.eloss,extr.J[:,14]) # example of removing a pearson function from low-q data (here, you have to play around with # the fitting regions quite a bit, and difficult already for whichq=2) extr.removecoreppearson2(0,[60,98],[105,350]) extr.removecoreppearson2(1,[80,98],[105,250]) # high q valence extraction extr.extractval(14,linrange1=[40,95],linrange2=[850,1200],mirror=True) extr.getallvalprof(14,smoothgval=5.0,stoploop=False) extr.remvalenceprof(14) # example of using the quick valence removal extr.remquickval(13,[700.0,1500.0],[98.0,115.0],10.0) plot(extr.eloss,extr.signals[:,0],extr.eloss,extr.J[:,0],extr.eloss,extr.C[:,0]) show() xrstools-0.15.0+git20210910+c147919d/XRStools/bin/run_p01_example.py000066400000000000000000000007211412732462000240300ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .xrstools import xrs_read reload(xrs_read) foo = xrs_read.read_p01('/home/christoph/data/p01jun13/') foo.loadelastic(268) foo.getautorois(268) #foo.getzoomrois(298) foo.loadedge([276])#137,138,143,145 #foo.getzoomrois(192,numrois=3) foo.getrawdata() foo.getspectrum() import pylab pylab.plot(foo.energy,foo.signals[:,0],'-') pylab.show() xrstools-0.15.0+git20210910+c147919d/XRStools/calctools.py000066400000000000000000000267031412732462000222540ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: stobe_analyze.py import os import numpy as np import array as arr from itertools import groupby from scipy.integrate import trapz from scipy.interpolate import interp1d from pylab import * from scipy import signal from scipy.ndimage import measurements import matplotlib.pyplot as plt from six.moves import range __metaclass__ = type # new style classes def gauss(x,x0,fwhm): # area-normalized gaussian sigma = fwhm/(2*np.sqrt(2*np.log(2))); y = np.exp(-(x-x0)**2/2/sigma**2)/sigma/np.sqrt(2*np.pi) return y def gauss_areanorm(x,x0,fwhm): """ area-normalized gaussian """ sigma = fwhm/(2.0*np.sqrt(2.0*np.log(2.0))) y = np.exp(-(x-x0)**2.0/2.0/sigma**2)/sigma/np.sqrt(2.0*np.pi) return y def convg(x,y,fwhm): """ Convolution with Gaussian """ dx = np.min(np.absolute(np.diff(x))) x2 = np.arange(np.min(x)-1.5*fwhm, np.max(x)+1.5*fwhm, dx) xg = np.arange(-np.floor(2.0*fwhm/dx)*dx, np.floor(2.0*fwhm/dx)*dx, dx) yg = gauss(xg,[0,fwhm]) yg = yg/np.sum(yg) y2 = spline2(x,y,x2) c = np.convolve(y2,yg, mode='full') n = int( np.floor(np.max(np.shape(xg))/2)) c = c[n:len(c)-n+1] # not sure about the +- 1 here f = interpolate.interp1d(x2,c) return f(x) def spline2(x,y,x2): """ Extrapolates the smaller and larger valuea as a constant """ xmin = np.min(x) xmax = np.max(x) imin = x == xmin imax = x == xmax f = interpolate.interp1d(x,y, bounds_error=False, fill_value=0.0) y2 = f(x2) i = np.where(x2xmax) y2[i] = y[imax] return y2 def readxas(filename): """ function output = readxas(filename)%[e,p,s,px,py,pz] = readxas(filename) % READSTF Load StoBe fort.11 (XAS output) data % % [E,P,S,PX,PY,PZ] = READXAS(FILENAME) % % E energy transfer [eV] % P dipole transition intensity % S r^2 transition intensity % PX dipole transition intensity along x % PY dipole transition intensity along y % PZ dipole transition intensity along z % % as line diagrams. % % T Pylkkanen @ 2011-10-17 """ # Open file f = open(filename,'r') lines = f.readlines() f.close() data = [] for line in lines[1:]: data.append([float(x) for x in line.replace('D', 'e').strip().split()]) data = np.array(data) data[:,0] = data[:,0]*27.211384565719481 # convert from a.u. to eV data[:,3] = 2.0/3.0*data[:,3]**2.0*e/27.211384565719481 # osc(x) data[:,4] = 2.0/3.0*data[:,4]**2.0*e/27.211384565719481 # osc(y) data[:,5] = 2.0/3.0*data[:,5]**2.0*e/27.211384565719481 # osc(z) return data def broaden_diagram(e,s,params=[1.0, 1.0, 537.5, 540.0],npoints=1000): """ function [e2,s2] = broaden_diagram2(e,s,params,npoints) % BROADEN_DIAGRAM2 Broaden a StoBe line diagram % % [ENE2,SQW2] = BROADEN_DIAGRAM2(ENE,SQW,PARAMS,NPOINTS) % % gives the broadened spectrum SQW2(ENE2) of the line-spectrum % SWQ(ENE). Each line is substituted with a Gaussian peak, % the FWHM of which is determined by PARAMS. ENE2 is a linear % scale of length NPOINTS (default 1000). % % PARAMS = [f_min f_max emin max] % % For ENE <= e_min, FWHM = f_min. % For ENE >= e_max, FWHM = f_min. % FWHM increases linearly from [f_min f_max] between [e_min e_max]. % % T Pylkkanen @ 2008-04-18 [17:37] """ f_min = params[0] f_max = params[1] e_min = params[2] e_max = params[3] e2 = np.linspace(np.min(e)-10.0,np.max(e)+10.0,npoints); s2 = np.zeros_like(e2) fwhm = np.zeros_like(e) # FWHM: Constant -- Linear -- Constant A = (f_max-f_min)/(e_max-e_min) B = f_min - A*e_min fwhm = A*e + B inds = e <= e_min fwhm[inds] = f_min inds = e >= e_max fwhm[inds] = f_max for i in range(len(s)): s2 += s[i]*gauss(e2,e[i],fwhm[i]) return e2, s2 def broaden_linear(spec,params=[0.8, 8, 537.5, 550],npoints=1000): evals = spec[:,0] sticks= spec[:,1] f_min = params[0] f_max = params[1] e_min = params[2] e_max = params[3] e2 = np.linspace(np.min(evals)-10.0,np.max(evals)+10.0,npoints) s2 = np.zeros(len(e2)) fwhm = np.zeros(len(evals)) # FWHM: Constant -- Linear -- Constant A = (f_max-f_min)/(e_max-e_min) B = f_min - A*e_min fwhm = A*evals + B fwhm[evals <= e_min] = f_min fwhm[evals >= e_max] = f_max for n in range(len(sticks)): s2 = s2 + sticks[n]*gauss(e2,evals[n],fwhm[n]) spectrum = np.zeros((len(e2),2)) spectrum[:,0] = e2 spectrum[:,1] = s2 return spectrum def load_stobe_specs(prefix,postfix,fromnumber,tonumber,step,stepformat=2): """ load a bunch of StoBe calculations, which filenames are made up of the prefix, postfix, and the counter in the between the prefix and postfix runs from 'fromnumber' to 'tonumber' in steps of 'step' (number of digits is 'stepformat') """ numbers = np.linspace(fromnumber,tonumber,(tonumber-fromnumber + step)//step) filenames = [] precision = '%0'+str(stepformat)+'d' for number in numbers: thenumber = precision % number thefilename = prefix+thenumber+postfix filenames.append(thefilename) specs = [] for filename in filenames: try: specs.append(readxas(filename)) except: print( 'found no file: ' + filename) return specs def load_erkale_spec(filename): spec = np.loadtxt(filename) return spec def load_erkale_specs(prefix,postfix,fromnumber,tonumber,step,stepformat=2): numbers = np.linspace(fromnumber,tonumber,(tonumber-fromnumber + step)//step) filenames = [] precision = '%0'+str(stepformat)+'d' for number in numbers: thenumber = precision % number thefilename = prefix+thenumber+postfix filenames.append(thefilename) specs = [] for filename in filenames: try: specs.append(load_erkale_spec(filename)) except: print( 'found no file: ' + filename) return specs def cut_spec(spec,emin=None,emax=None): if not emin: emin = spec[0,0] if not emax: emax = spec[-1,0] spec = spec[spec[:,0]>emin] spec = spec[spec[:,0]=emin,self.energy<=emax)) norm = np.trapz(self.signal[inds],self.energy[inds]) self.signal = self.signal/norm class erkale: """ class to analyze ERKALE results """ def __init__(self,prefix,postfix,fromnumber,tonumber,step,stepformat=2): self.energy = [] # array of final energy scale for all snapshots of this run self.sqw = [] # array of averaged and smoothed results self.rawspecs = load_erkale_specs(prefix,postfix,fromnumber,tonumber,step,stepformat) # list of all raw stick spectra self.broadened= [] self.norm = [] # results of normalization def cut_rawspecs(self,emin=None,emax=None): cutspecs = [] for spec in self.rawspecs: cutspecs.append(cut_spec(spec,emin,emax)) self.rawspecs = cutspecs def cut_broadspecs(self,emin=None,emax=None): cutspecs = [] for spec in self.broadened: cutspecs.append(cut_spec(spec,emin,emax)) self.broadened = cutspecs def broaden_lin(self,params=[0.8, 8, 537.5, 550],npoints=1000): for spec in self.rawspecs: self.broadened.append(broaden_linear(spec,params,npoints)) def sum_specs(self): self.energy = self.broadened[0][:,0] # first spectrum defines energy scale self.sqw = np.zeros(np.shape(self.energy)) for spec in self.broadened: f = interp1d(spec[:,0], spec[:,1],bounds_error=False, kind='cubic', fill_value=0.0) self.sqw += f(self.energy) def norm_area(self,emin=None,emax=None): if not emin: emin = self.energy[0] if not emax: emax = self.energy[-1] inds = np.where(np.logical_and(self.energy>=emin,self.energy<=emax))[0] self.sqw = self.sqw/np.trapz(self.sqw[inds],self.energy[inds]) def norm_max(self): pass def plot_spec(self): plt.plot(self.energy,self.sqw) plt.show() #### calculations # cut clusters from xyz files # cut clusters from gro files ################################### # reading function for cowan's code output def read_rcg_born(fname,q=9.2*0.529,egauss=0.2,elore=0.1): #% function [e,spectr]=read_rcg_born(fname,q); #% fname = filename (outg11 with plane wave born collision calculation) #% q = momentum transfer in at.units #% egauss = gaussian component of the resolution #% elore = lorentzian component of the resolution #% S. Huotari 2012 return e,spectr # function [e,spectr]=read_rcg_born(fname,q,egauss,elore); # fid=fopen(fname); # ei=[]; y=[]; # conti=1; # while conti & ~feof(fid), # s=fgetl(fid); # conti=isempty(findstr('k-values',s)); # end # conti=1; # while conti & ~feof(fid), # s=fgetl(fid); # conti=isempty(findstr('k-values',s)); # end # s=fgetl(fid); % empty string # conti=1;k=[]; # while conti, # s=fgetl(fid); # conti=length(str2num(s)); # k=[k str2num(s)]; # end # while ~feof(fid) # s=fgetl(fid); # if ~isempty(findstr('delta E',s)); # s=fgetl(fid); % empty line # s=fgetl(fid); disp(s); # sn=str2num(s); # ei=[ei sn(8)]; # s=fgetl(fid); % the text "0generalised.." # conti=1;ytmp=[]; # while conti, # s=fgetl(fid); # conti=length(str2num(s)); # ytmp=[ytmp str2num(s)]; # end # y=[y;ytmp]; # end # end # fclose(fid); # ei=ei/8.065-1.45; # %q=9.2*0.529; # [q,qi]=min((k-q).^2) # e=[min(ei)-2:0.01:max(ei)+2]'; # spectr=zeros(size(e)); # for ii=1:length(ei), # spectr=spectr+convg(e,lorentz2([y(ii,qi) elore ei(ii)],e),egauss); # end xrstools-0.15.0+git20210910+c147919d/XRStools/cowans/000077500000000000000000000000001412732462000212015ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/cowans/WORK/000077500000000000000000000000001412732462000217635ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/cowans/WORK/fe3_r1.dat000066400000000000000000000000001412732462000235220ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/docs/000077500000000000000000000000001412732462000206375ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/docs/Makefile000066400000000000000000000151621412732462000223040ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. 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The pseudo-XML files are in $(BUILDDIR)/pseudoxml." xrstools-0.15.0+git20210910+c147919d/XRStools/docs/conf.py000066400000000000000000000202501412732462000221350ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function # -*- coding: utf-8 -*- # # XRSTools documentation build configuration file, created by # sphinx-quickstart on Mon Nov 3 15:21:20 2014. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'XRSTools' copyright = u'2014, Christoph Sahle' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '0.0' # The full version, including alpha/beta/rc tags. release = '0.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'XRSToolsdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ('index', 'XRSTools.tex', u'XRSTools Documentation', u'Christoph Sahle', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'xrstools', u'XRSTools Documentation', [u'Christoph Sahle'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'XRSTools', u'XRSTools Documentation', u'Christoph Sahle', 'XRSTools', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False sys.path.insert(0,"/home/christoph/Dropbox/XRSTools") # extensions = ['numpydoc'] xrstools-0.15.0+git20210910+c147919d/XRStools/docs/help.txt000066400000000000000000000000001412732462000223160ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/docs/index.rst000066400000000000000000000007661412732462000225110ustar00rootroot00000000000000.. XRSTools documentation master file, created by sphinx-quickstart on Mon Nov 3 15:21:20 2014. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Welcome to XRSTools's documentation! ==================================== Contents: .. toctree:: :maxdepth: 4 .. automodule:: xrstools .. autoclass:: xrstools.xrs_read :members: Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` xrstools-0.15.0+git20210910+c147919d/XRStools/docs/user_manual.tex000066400000000000000000001163611412732462000237040ustar00rootroot00000000000000%% ****** Start of file template.aps ******% %% %% %% This file is part of the APS files in the REVTeX 4 distribution. %% Version 4.0 of REVTeX, August 2001 %% %% %% Copyright (c) 2001 The American Physical Society. %% %% See the REVTeX 4 README file for restrictions and more information. %% % % This is a template for producing manuscripts for use with REVTEX 4.0 % Copy this file to another name and then work on that file. % That way, you always have this original template file to use. % % Group addresses by affiliation; use superscriptaddress for long % author lists, or if there are many overlapping affiliations. % For Phys. Rev. appearance, change preprint to twocolumn. % Choose pra, prb, prc, prd, pre, prl, prstab, or rmp for journal % Add 'draft' option to mark overfull boxes with black boxes % Add 'showpacs' option to make PACS codes appear % Add 'showkeys' option to make keywords appear \documentclass[aps,prl,groupedaddress]{revtex4} %\documentclass[aps,prl,preprint,superscriptaddress]{revtex4} %\documentclass[aps,prl,twocolumn,groupedaddress]{revtex4} \usepackage{booktabs} \usepackage{hyperref} \usepackage{courier} \usepackage{listings} \usepackage{color} \lstdefinestyle{mybash}{ belowcaptionskip=1\baselineskip, breaklines=true, frame=L, xleftmargin=\parindent, language=bash, showstringspaces=false, basicstyle=\footnotesize\ttfamily, keywordstyle=\bfseries\color{green!40!black}, commentstyle=\itshape\color{purple!40!black}, identifierstyle=\color{blue}, stringstyle=\color{orange}, backgroundcolor=\color{grey} } % You should use BibTeX and apsrev.bst for references % Choosing a journal automatically selects the correct APS % BibTeX style file (bst file), so only uncomment the line % below if necessary. %\bibliographystyle{apsrev} \begin{document} % Use the \preprint command to place your local institutional report % number in the upper righthand corner of the title page in preprint mode. % Multiple \preprint commands are allowed. % Use the 'preprintnumbers' class option to override journal defaults % to display numbers if necessary %\preprint{} %Title of paper \title{XRSTools user manual} % repeat the \author .. \affiliation etc. as needed % \email, \thanks, \homepage, \altaffiliation all apply to the current % author. Explanatory text should go in the []'s, actual e-mail % address or url should go in the {}'s for \email and \homepage. % Please use the appropriate macro foreach each type of information % \affiliation command applies to all authors since the last % \affiliation command. The \affiliation command should follow the % other information % \affiliation can be followed by \email, \homepage, \thanks as well. \author{Ch.J.~Sahle} \email[]{christoph.sahle@esrf.fr} %\homepage[]{Your web page} %\thanks{} %\altaffiliation{} \affiliation{ESRF - The European Synchrotron Radiation Facility, Grenoble, France.} %Collaboration name if desired (requires use of superscriptaddress %option in \documentclass). \noaffiliation is required (may also be %used with the \author command). %\collaboration can be followed by \email, \homepage, \thanks as well. %\collaboration{} %\noaffiliation \date{\today} \begin{abstract} This is a general and more technical introduction to the Python package XRSTools, a collection of Python modules for non-resonant inelastic x-ray scattering. XRSTools, in its current state, is an experimental code but has now been used during and after a number of experiments to extract publication grade data. \end{abstract} % insert suggested PACS numbers in braces on next line \pacs{} % insert suggested keywords - APS authors don't need to do this %\keywords{} %\maketitle must follow title, authors, abstract, \pacs, and \keywords \maketitle \tableofcontents % body of paper here - Use proper section commands % References should be done using the \cite, \ref, and \label commands % Put \label in argument of \section for cross-referencing %\section{\label{}} %\subsection{} %\subsubsection{} \section{Introduction} Welcome to the XRSTools program package for non-resonant inelastic x-ray scattering. XRSTools is a collection of Python moldules (classes and functions) written to aid the planning and performing of synchrotron based NIXS experiments and analyze data from such experiments. Part of the functions used in the package are based on Matlab cade written at the Helixs-Group of the University of Helsinki, part of it is based on Matlab code written at the ESRF, and some of it is new. The aim of the code is to simplify and standardize experimental procedures, especially when it comes to the use of the new multi-analyzer beamlines equipped with 2D detectors. As this is written, the code is adapted solely to the ESRF beamline ID20, but it should be straightforward to extend it to support also data from other beamlines. In fact, there is an experimental version for the PetraIII beamline P01. The ideas and physics behind this code are summarized in \cite{XRSTools_paper}. If you used the package or functions from it, please cite this paper. Besides this paper on the package, there are a few other published items out there, to which I would refer anyone to who would like to dig deeper into inelastic x-ray scattering: 1. The book of W.~Sch\"ulke is probably the most complete reference guide on this subject \cite{schuelke2007book}. 2. A few review papers \cite{hamalainen2001,sinha2001}. 3. And the papers on which most of the 'background' subtraction by XRSTools is based on \cite{sternemann2008,huotari2012}. I would also like to point out the existing experimental endstations by citing their beamline papers \cite{Cai2004,Fister2006,Verbeni2009,Sokaras2012,hiraoka2013}. If you are not familiar with the Python programming language at all, maybe it is a good idea to have a look at some of the tutorials there are online (e.g. at \url{https://docs.python.org/2/tutorial/}). A book that has helped me a lot is this this one \cite{mlhetland}. To finish this introductory section, let's remind ourselves that this code is to be used at your own risk, it is, no doubt, not without bugs. If you find one, please let me know about it so we can try to fix it. So let's dig into the code. \section{Installation} XRSTools comes as a Python package, i.e.~as a \textit{.tar.gz} file called \textit{xrstools-.tar.gz}. Extract this file on your disk and change into the created directory: \lstset{language=bash} \begin{lstlisting}[frame=single] tar -xzf xrstools-.tar.gz cd xrstools- \end{lstlisting} For a global installation on your system type: \begin{lstlisting}[frame=single] sudo python setup.py install \end{lstlisting} a local installation into a directory \texttt{} can be made by \begin{lstlisting}[frame=single] python setup.py install --home= \end{lstlisting} You can find more information about how to install Python packages at \url{https://docs.python.org/2/install/}. If you do a local installation, do not forget to set your PYTHONPATH variable \begin{lstlisting}[frame=single] export PYTHONPATH=$PYTHONPATH:/path/to/XRSTools \end{lstlisting} or add it to your bashrc. \section{Planning} \subsection{using an input file} When planning XRS experiments, there are usually two things to consider: first, which momentum transfer(s) to measure at and, the second question is, if there will be enough counts from the sample such that it you can take statistically meaningful data in a reasonable amount of beam time. XRSTools has a module, called \texttt{xrs\_prediction}, to do that. Since we need quite a few variables for this prediction of intensities, \texttt{xrs\_prediction} can read an input file and returns a graph of the estimated cross section. An example of an input file is given in appendix (\ref{apx:planning}). The expected intensity is given by \begin{eqnarray} I = I_0 \frac{\mathrm{d}^2 \sigma}{\mathrm{d}\Omega \mathrm{d}\omega_2} \Delta\Omega \cdot \Delta\omega_2 \cdot \rho \cdot d \cdot a_{\mathrm{r/t}} \cdot R \cdot D . \label{eqn:abscounts} \end{eqnarray} Here, $I_0$ is the incident photon flux, $\frac{\mathrm{d}^2 \sigma}{\mathrm{d}\Omega \mathrm{d}\omega_2}$ is the DDSCS, $\Delta\Omega$ is the solid angle of detection, $\Delta\omega_2$ is the energy resolution, $\rho$ the number density of scatterers in the interaction volume, $d$ is the sample thickness, $a_{\mathrm{r/t}}$ is the sample absorption factor, and $R$ and $D$ are the finite reflectivity of the analyzer crystals and the detector efficiency, respectively. In XRSTools, each of the factors in equation (\ref{eqn:abscounts}) is a separate class in the xrs\_prediction module. Thus, there are six classes \begin{itemize} \item detector, \item analyzer, \item sample, \item polarization, \item x-ray beam, \item some output parameters, \end{itemize} each of which has a separate block in it's input file. Here is a summary of the variables to be set for each of these blocks. \subsubsection{Detector} The variables in the 'Detector' section are used to calculate the efficiency of the Detector at the energy used via: \begin{eqnarray} D = 1.0 - \exp(-d*\mu) , \end{eqnarray} where D is the efficiency of the detector, $d$ is the thickness of the active detector chip, and $\mu$ is the photoelectric absorption of the detector active material. \begin{ruledtabular} \begin{tabular}{lllll} variable & description & type & default & unit \\ \hline energy & analyzer energy & float & 9.7 & keV \\ thickness & detector chip thickness & float & 500.0 & microns \\ material & detector active material & string & 'Si' & - \\ pixel\_size & number of pixels (obsolete) & list & [256,768]& int \\ \end{tabular} \end{ruledtabular} \subsubsection{Analyzer} The analyzer class is meant to calculate the analyzer reflectivity for the used analyzer material and reflection. For an estimate of the analyzer reflectivity, the Takagi-Taupin equations\cite{tagaki1962,taupin1964,Vartanyants1993} are solved for a spherically bent crystal analyzers. We approximate $R$ in (\ref{eqn:abscounts}) by averaging over the full width at half maximum of the resulting reflectivity curve. Also $\Delta\Omega$ comes from the parameters in this section of the input file. \begin{ruledtabular} \begin{tabular}{lllll} variable & description & type & default & unit \\ \hline material & analyzer material & string & 'Si' & - \\ hkl & hkl of reflection used & list of ints & [6,6,0] & - \\ mask\_d & analyzer mask diameter & float & 60.0 & mm \\ bend\_r & analyzer curvature radius & float & 1.0 & meters \\ diced & keyword analyzer is diced (obsolete) & boolean & false & - \\ thickness & analyzer thickness & float & 500.0 & microns \\ energy\_resolution & approx. analyzer resolution & float & 0.5 & eV \\ database\_dir & path to reflectivity database & string & - & - \\ \end{tabular} \end{ruledtabular} \subsubsection{Sample} Whereas most parameters in the input file have default values, the sample part is the one section that has to be filled with parameters on the details of the sample in question. Here is a list for the necessary ones. Right now, the program assumes that the sample is spherical. A spherical sample is the ideal shape for the multi analyzer spectrometer at ID20. Future versions will also have options for flat samples (either in transmission or reflection geometry). Most of the input parameters used in this section of the input file are Python lists. The composition of the sample is passed by a list of chemical sum formulas (e.g. ['SiO2','H2O']), the relative concentration and densities of its constituents as lists in the same order as the sum formulas (e.g. [0.4, 0.6] and [ ... , ... ] for a sample made up of 40 \% SiO2 and 60 \% water). This, however, means that the program assumes the sample to also be a sort of emulsion of its constituents, each of which contribute separately to the total DDSCS. \begin{ruledtabular} \begin{tabular}{lllll} variable & description & type & default & unit \\ \hline chem\_formulas & chemical sum formulas & list of strings & - & - \\ concentrations & concentrations for each chem formula & list of ints & - & rel. units \\ densities & densities of constituents & list of ints & - & g/cm$^3$ \\ molar\_masses & molar masses of each constituent & list of ints & - & g/mol \\ angle\_tth & scattering angle [2Th] & float & - & degrees \\ sample\_thickness & sample thickness & float & - & cm \\ \end{tabular} \end{ruledtabular} \subsubsection{Polarization} Here, you can put some information about the Thomson part of the DDSCS, in the future (for simplicity) this could also move to the beamline specific paramters. \begin{ruledtabular} \begin{tabular}{lllll} variable & description & type & default & unit \\ \hline scattering\_plane & scattering plane used ('vertical' or 'horizontal') & keyword & 'vertical' & - \\ polarization & degree of polarization in your beam & float & 0.99 & \% \\ \end{tabular} \end{ruledtabular} \subsubsection{X-ray beam} Here is still some info on the incident beam. \begin{ruledtabular} \begin{tabular}{lllll} variable & description & type & default & unit \\ \hline i0\_intensity & number of incident photons & int & 1e13 & 1/second \\ beam\_heigh & beam size in the vertical direction & int & 10.0 & microns \\ beam\_width & beam size in the horizontal direction & int & 20.0 & microns \\ \end{tabular} \end{ruledtabular} \subsubsection{Output} Finally, there are some parameters to determine the output. \begin{ruledtabular} \begin{tabular}{lllll} variable & description & type & default & unit \\ \hline eloss\_range & energy loss range for the output & np.array & np.arange(0.0,1000.0,0.1) & eV \\ E0 & analyzer energy & int & 9.7 & keV \\ \end{tabular} \end{ruledtabular} \subsubsection{Outlook} As mentioned earlier, the samples are assumed to be spherical and inmissible. Rearrangement of some input/output paramters and internal variables should enable also different geometries and maybe also an easier-to-read input file. A GUI should also be straightforward to implement. \subsection{using the command line} All functions and classes used when calling the prediction routine via an input file are, of course, also accessible from the Python command line. \section{Getting started with the command line} Start by importing the XRSTools routines and some useful other modules like Pylab for plotting and Numpy for some other Matlab like behavior. \lstset{language=Python, breaklines=true} \begin{lstlisting}[frame=single] from xrstools import xrs_read, ... from pylab import * import numpy as np \end{lstlisting} I usually like to set the Pylab interactive mode to 'on'. This way, Pylab acts somewhat similar to Matlab plotting. \lstset{language=Python, breaklines=true} \begin{lstlisting}[frame=single] ion() \end{lstlisting} \section{Performing} During experiments, there are mainly two things that are important for the XRS user, the sample alignment and some fast online data analysis. XRSTools has some functions to make this easy. \subsection{Sample alignment} An elegant way to align samples contained in some complicated sample environment is to make use of the imaging properties of bent crystal analyzers in combination with a 2D detector (check out \cite{SHuotari2011} and \cite{XRSTools_paper} to learn more about the details). Start by importing the main XRSTools data reading routine and some useful other modules like Pylab for plotting and Numpy for some other Matlab like behavior. \lstset{language=Python, breaklines=true} \begin{lstlisting}[frame=single] from xrstools import xrs_read from pylab import * import numpy as np \end{lstlisting} Now create an instance of the read\_id20 class and give it some sensible name (name of the compound/sample, for simplicity, we will just call the variable 'image'). \begin{lstlisting}[frame=single] image = xrs_read.read_id20(absfilename,energycolumn='energy',monitorcolumn='kap4dio',edfName=None,single_image=True) \end{lstlisting} The input parameters for this class are explained in the following table. \begin{ruledtabular} \begin{tabular}{lllll} variable & description & type & default & unit \\ \hline absfilename & absolute path to the SPEC file & string & - & - \\ energycolumn & name of the energy counter in the SPEC file & string & 'energy' & - \\ monitorcolumn & name of the monitor counter in the SPEC file & string & 'kap4dio' & - \\ edfName & prefix of the EDF files & string & None & - \\ single\_image & keyword, if there is a single detector image & boolean & True & - \\ \end{tabular} \end{ruledtabular} To construct an image from an alignment scan (i.e. a sample translation scan), use the \textit{make\_posscan\_image} (for 'make position-scan image') function from the 'image' object you have just created. \begin{lstlisting}[frame=single] image.make_posscan_image(scannumber,motorname,filename=None) \end{lstlisting} \begin{ruledtabular} \begin{tabular}{lllll} \toprule variable & description & type & default & unit \\ \hline scannumber & scannumber from the alignment scan you want to image & string & - & - \\ motorname & name of the motor scanned ('stx', 'sty', 'stz') & string & - & - \\ filename & (optional) absolute path if image should be saved & string & None & - \\ \end{tabular} \end{ruledtabular} This will open a plot window showing a sum of all detector images taken during the translation scan. Using the zoom tool of the plotting window ('little magnifying glass' at the lower left corner of the plotting window) zoom into the spot you like (\textbf{screenshots of this procedure and link to 'setting the ROIs' section.}), go back to your command line and press enter. This will close the ROI window and open a new one with your reconstructed image. On the y-axis you will find the motor and range you scanned, on the x-axis of your plot you will have the pixels along the beam (remember that one pixel is 55 micron, which will give you the conversion into real distances). And here is how it looks like. \textbf{take examples from the acetic acid data also used in the paper... } %should maybe change the code, such that you can select as many ROIs as you want (save the ROIs as object in the class), such that the next time you want to plot the same thing or look at it from a different ROI, you do not have to re-select a ROI but just tell it which one of the ones you chose you took. If the 'whichanalyzer' keyword is missing, the procedure would be as usual (from scratch). \subsection{raw data, quick and dirty} Here is a minimal set of functions you will need for a 'quick and dirty' online data analysis. \begin{lstlisting}[frame=single] from xrstools import xrs_read from pylab import * import numpy as np ion() sample = xrs_read.read_id20(absfilename,energycolumn='energy',monitorcolumn='kap4dio',edfName=None,single_image=True) sample.loadelastic(scannumbers) sample.get_zoom_rois(scannumbers) sample.getrawdata() sample.loadscandirect(scannumbers,name) sample.getspectrum() sample.geteloss() \end{lstlisting} Let us go through these (and similar functions) in more detail and explain each of them and (most of) the parameters needed. The idea is that you 1.~import modules from the XRSTools package (and others from the standard library such as Numpy and Pylab), 2.~create an instance of the \textit{xrs\_read.read\_id20} class, and 3.~use the class's features to load SPEC- and EDF-files, set regions of interest (ROIs), integrate the detector images, and make simple plots of the data. \subsubsection{starting up} To start up, import some elastic line and create some regions of interest. If you have not imported the XRSTools module (and some other useful build in modules) now is a good time to do it. \begin{lstlisting}[frame=single] from xrstools import xrs_read from pylab import * import numpy as np \end{lstlisting} Let's also use Pylab's interactive mode (this makes Pylab behave a bit more like Matlab/Octave). \begin{lstlisting}[frame=single] ion() \end{lstlisting} As within the sample alignment, first create an object of the 'xrs\_read.read\_id20' class. \begin{lstlisting}[frame=single] sample = xrs_read.read_id20(absfilename,energycolumn='energy',monitorcolumn='kap4dio',edfName=None,single_image=True) \end{lstlisting} I chose the variable name 'sample', since the idea is to have one object of the \textit{read\_id20} class for each sample. Next, load some SPEC- and according EDF-files. \begin{lstlisting}[frame=single] sample.loadelastic(scannumbers,fromtofile=False) \end{lstlisting} Here is a description of the parameters this function takes. \begin{ruledtabular} \begin{tabular}{lllll} variable & description & type & default & unit \\ \hline scannumbers & number or list of number of scans to load & int or list of ints & - & - \\ fromtofile & keyword, if scan instance should be saved in a file (experimental) & boolean & False & - \\ \end{tabular} \end{ruledtabular} Now, we can use this elastic line to define some ROIs. \subsubsection{selecting the ROIs} XRSTool's xrs\_rois module provides a number of ways to define ROIs, so let's go through them one by one. In principle, you will have to follow the instructions printed out in the shell and on the plotting windows. \paragraph{Zoom ROIs} To define ROIs by using Matplotlib's built-in zoom function, use this. \begin{lstlisting}[frame=single] sample.get_zoom_rois(scannumbers) \end{lstlisting} \begin{itemize} \item activate the zoom function in the plot-window \item click the next button \item zoom into your first ROI \item click the next button \item repeat until you zoomed into the last ROI \item click the finish button \end{itemize} Look at the function's docstr for more help: \begin{lstlisting}[frame=single] help(sample.get_zoom_rois()) . \end{lstlisting} \paragraph{Line ROIs} To define ROIs by clicking two points, use this. \begin{lstlisting}[frame=single] sample.get_linear_rois(scannumbers) \end{lstlisting} \begin{itemize} \item activate the zoom function in the plot-window \item click the next button \item zoom into your first ROI \item click the next button \item repeat until you zoomed into the last ROI \item click the finish button \end{itemize} Look at the function's docstr for more help: \begin{lstlisting}[frame=single] help(sample.get_linear_rois()) . \end{lstlisting} \paragraph{Polygon ROIs} To define ROIs by clicking multiple points, use this. \begin{lstlisting}[frame=single] sample.get_plygon_rois(scannumbers) \end{lstlisting} \begin{itemize} \item blabla \end{itemize} Look at the function's docstr for more help: \begin{lstlisting}[frame=single] help(sample.get_polygon_rois()) . \end{lstlisting} \paragraph{Automatic ROIs} There are two ways of selecting automatic ROIs. One which uses the entire 6-detector image and one that goes through each detector. \begin{lstlisting}[frame=single] sample.get_auto_rois(scannumbers) \end{lstlisting} \begin{itemize} \item \end{itemize} Look at the function's docstr for more help: \begin{lstlisting}[frame=single] help(sample.get_auto_rois()) . \end{lstlisting} \paragraph{Saving and loading ROIs from files} Once you have defined some ROIs, it may be a good idea to save the ROIs into a text file so that you will not have to redefine them in a possible later session. To save ROIs into a file use \begin{lstlisting}[frame=single] sample.save_rois(filename) , \end{lstlisting} where 'filename' is a path and filename for your ROIs. \subsubsection{loading the data} Now, it is time to load some data. The general idea of the package is, that you measure different parts of your spectra in different scans. These could e.g. be frequent scans of the quasielastic peak, a rough overview over the whole range to estimate the valence background, and then your spectral region of interest. Also the absorption edge may be devided into several parts, not all of which may be desired with the same statistical accuracy. In the end, all of these scans should be added up, stitched together and shifted with respect to the elastic line energy. The most general function for loading data is: \begin{lstlisting}[frame=single] sample.loadscan(scannumbers,scantype='generic',fromtofile=False) \end{lstlisting} \begin{ruledtabular} \begin{tabular}{lllll} variable & description & type & default & unit \\ \hline scannumbers & number or list of number of scans to load & int or list of ints & - & - \\ fromtofile & keyword, if scan instance should be saved in a file (experimental) & boolean & False & - \\ \end{tabular} \end{ruledtabular} The idea is, that all scans that you load into your variable 'sample' will have an attribute 'scantype' by which the program knows which scans belong together (to add them up). There are a few 'scantypes' which are special in XRSTools. The first one is 'elastic' to define a scan as an elastic line scan. There is a shorthand version of the loadscan function to load elastic lines: \begin{lstlisting}[frame=single] sample.loadelastic(scannumbers,fromtofile=False) . \end{lstlisting} The second special scantype is 'long' which is used for overview scans. Also here, there is a shorthand version \begin{lstlisting}[frame=single] sample.loadlong(scannumbers,fromtofile=False) . \end{lstlisting} Since most XRS spectra are stiched together from several single scans (e.g. an elastic line scan, an overview scan, and several regions across some edge, or even different edges). \subsubsection{plotting} \section{a closer look} Right now, the most efficient way to measure XRS spectra is by splitting up the spectral regions around an absorption edge and weigh the different regions by counting time and energy increment. On top of these regions, we usually take elastic line scans frequently for energy calibration. Consider the oxygen K-edge of water as an example. For a decent background subtraction, we usually take a long overview scan on a rough energy grid that covers the whole range of the Compton/Valence excitation. The near edge, we usually devide in 3-5 regions, one (10-15 eV) before the edge, one that covers the pre- and main-edge region, one which covers the post-edge region, and one that covers the first big EXAFS-like oscillation around 555 eV energy loss. To easily add up data from different scans and analyzer crystals, we usually keep the same energy grid in all near-edge regions and weigh by chosing different counting times. To avoid problems when stitching different scan-regions together during the data analysis, it turns out the best practise is to let the monochromators make constant steps across the edge (i.e.~let one region start exactly one energy increment after the last region's end point) and then loop over the entire range of the edge. To clarify, a possible SPEC makro to do this could look like this: \lstset{language=bash} \begin{lstlisting}[frame=single] # define the elastic line energy E0 = 9.76 # elastic line scan umv energy E0-0.004 ascan energy E0-0.003 E0+0.003 60 0.1 # loop over the oxygen K-edge (constant energy step width, weigh by second/point) while (ii < 10, ii=1, ii++){ umv energy E0+0.515 umv energy E0+0.519 ascan energy E0+0.520 E0+0.532 60 5 ascan energy E0+0.5322 E0+0.542 49 10 ascan energy E0+0.5422 E0+0.5552 60 7 ascan energy E0+0.5554 E0+0.570 60 4 } # overview scan (covering the Compton/valence spectrum) umv energy E0-0.011 ascan energy E0-0.01 E0+0.60 610 2 \end{lstlisting} XRSTools 'knows' how to stich scans together is because during loading of scans, each scan is given a scan-type. A scan-type is just a string. However, there are a few special strings to describe scans that are treated specially. The special types are \textbf{elastic} and \textbf{long}. The default type is \textbf{generic}. For the above example, I would thus choose the following strings as type. \lstset{language=Python, breaklines=true} \begin{lstlisting}[frame=single] h2o = xrs_read.read_id20(absfilename,energycolumn='energy',monitorcolumn='kap4dio',edfName=None,single_image=True) h2o.loadelastic(14) # load elastic scan number 14 h2o.get_zoom_rois(14) # define some ROIs by zooming h2o.getrawdata() # integrate the elastic line scan data h2o.loadscandirect(15,'edge1') # load 1. part of the K-edge h2o.loadscandirect(16,'edge2') # load 2. part of the K-edge h2o.loadscandirect(17,'edge3') # load 3. part of the K-edge h2o.loadscandirect(17,'edge4') # load 4. part of the K-edge h2o.loadlongdirect(54) # load an overview scan h2o.getspectrum() # stitch all parts together h2o.geteloss() # find the center of mass of the elastic line \end{lstlisting} Note that \begin{lstlisting}[frame=single] h2o.loadelastic(14) \end{lstlisting} and \begin{lstlisting}[frame=single] h2o.loadlongdirect(54) \end{lstlisting} are just shorthand commands for \begin{lstlisting}[frame=single] h2o.loadscan(14,'elastic') \end{lstlisting} and \begin{lstlisting}[frame=single] h2o.loadscandirect(14,'long') \end{lstlisting} Instead of loading the individual parts of the spectrum separately, there is a function that can load an entire loop of spectra (such as in the example given above). \begin{lstlisting}[frame=single] h2o.loadloopirect([15],4) \end{lstlisting} For this to work, you have to just pass the starting numbers of all loops as a Python list (here, we only had one loop starting with scan number 14) and the number of scan within the loop (here 4). Notice that we are using two different types of functions for loading the spectra. In the case of the elastic line we used the \textit{loadelastic()}-function in case of the other scans we used the \textit{loadscandirect()}-function. Loading scans with functions including the word 'direct' merely tells the program to load both the SPEC- and EDF-file, then use existing ROIs to integrate the EDF-images, and subsequently delete the EDF-files from the memory. The only reason for this is that the EDF-files are somewhat large and quickly take up all of the memory and (in most cases) they are not needed after integration. \section{Analysis} With analysis, I mostly mean data reading as we have done before (see last chapters) and the subtraction of the backgound. \subsection{raw data} There are a few things worth mentioning that I did not cover in the on-line analysis part. scaling setting scattering angles \subsection{the theory module} here some information about the theory module, the HF compton profiles, etc. \subsection{background subtraction} The backgroung subtraction is done using the \textit{xrs\_extraction}-module. This is the most underdeveloped module of all the module in the XRSTools package. The reason is simply that it is also the most demanding to make up functions that work for most background subtraction problems that I can think of. \section{Imaging} This part of the program is purely experimental so far and thus somewhat a developer version. Functionality should be added in due time. \section{outlook} Here, I would like to keep a list of things to be done to improve the current version and things to add in a possible new version of the code. % If in two-column mode, this environment will change to single-column % format so that long equations can be displayed. Use % sparingly. %\begin{widetext} % put long equation here %\end{widetext} % figures should be put into the text as floats. % Use the graphics or graphicx packages (distributed with LaTeX2e) % and the \includegraphics macro defined in those packages. % See the LaTeX Graphics Companion by Michel Goosens, Sebastian Rahtz, % and Frank Mittelbach for instance. % % Here is an example of the general form of a figure: % Fill in the caption in the braces of the \caption{} command. Put the label % that you will use with \ref{} command in the braces of the \label{} command. % Use the figure* environment if the figure should span across the % entire page. There is no need to do explicit centering. % \begin{figure} % \includegraphics{}% % \caption{\label{}} % \end{figure} % Surround figure environment with turnpage environment for landscape % figure % \begin{turnpage} % \begin{figure} % \includegraphics{}% % \caption{\label{}} % \end{figure} % \end{turnpage} % tables should appear as floats within the text % % Here is an example of the general form of a table: % Fill in the caption in the braces of the \caption{} command. Put the label % that you will use with \ref{} command in the braces of the \label{} command. % Insert the column specifiers (l, r, c, d, etc.) in the empty braces of the % \begin{tabular}{} command. % The ruledtabular enviroment adds doubled rules to table and sets a % reasonable default table settings. % Use the table* environment to get a full-width table in two-column % Add \usepackage{longtable} and the longtable (or longtable*} % environment for nicely formatted long tables. Or use the the [H] % placement option to break a long table (with less control than % in longtable). % \begin{table}%[H] add [H] placement to break table across pages % \caption{\label{}} % \begin{ruledtabular} % \begin{tabular}{} % Lines of table here ending with \\ % \end{tabular} % \end{ruledtabular} % \end{table} % Surround table environment with turnpage environment for landscape % table % \begin{turnpage} % \begin{table} % \caption{\label{}} % \begin{ruledtabular} % \begin{tabular}{} % \end{tabular} % \end{ruledtabular} % \end{table} % \end{turnpage} % Specify following sections are appendices. Use \appendix* if there % only one appendix. \appendix \section{\texttt{xrs\_prediction} template}\label{apx:planning} \lstset{style=python} \begin{lstlisting}[frame=single] #### detector energy = 9.7 # working energy, same as analyzer energy (E0) thickness = 500.0 # thickness of analyzer active material (micron) material = 'Si' # analyzer active material pixel_size = [256,768] # number of pixels (height, width) #### analyzer material = 'Si' # analyzer material hkl = [6,6,0] # hkl-indices of reflection used mask_d = 60.0 # mask diameter (in mm) bend_r = 1.0 # analyzer crystal bending radius diced = False # boolean: False for bent crystals, True for diced crystals thickness = 500.0 # analyzer crystal thickness (micron) energy_resolution = 0.5 # expected overall resolution (eV) database_dir = '/dir/to/refl_database/' # directory to where already calculated # reflectivities are stored #### sample chem_formulas = ['FeC6N6','H2O'] # chemical formulae (python list) concentrations = [0.03,0.97] # concentrations (python list) densities = [1.9,1.0] # densities (python list) molar_masses = [211.9494,18.02] # molar masses of the sample's constituents, # needed for calculation of the number of # scatterers (python list) angle_tth = 122.0 # 2 theta scattering angle (degress) sample_thickness = 0.030 # sample thickness/diameter (cm) #### thomson scattering_plane = 'vertical' # keyword, either 'vertical' (no polarization # dependence) or 'horizontal' (polarization # dependence) polarization = 0.99 # degree of polarization of incident radiation #### beam i0_intensity = 1e13 # number of incident photons beam_height = 10.0 # beam dimension in the vertical (microns) beam_width = 20.0 # beam dimension in the horizontal (microns) #### compton_profiles eloss_range = np.arange(0.0,1000.0,0.1) # energy loss range for the calculation (eV) E0 = 9.7 # analyzer energy (keV) \end{lstlisting} % If you have acknowledgments, this puts in the proper section head. %\begin{acknowledgments} % put your acknowledgments here. %\end{acknowledgments} % Create the reference section using BibTeX: %\bibliography{basename of .bib file} \begin{thebibliography}{} \bibitem{XRSTools_paper} Ch.J.~Sahle et al. submitted to J.~Synchrotron Rad.~(2014). % reviews \bibitem{schuelke2007book} Sch\"ulke, W. (2007). Electron dynamics by inelastic X-ray scattering. Oxford Univ. Press. \bibitem{hamalainen2001} H\"am\"al\"ainen, K., \& Manninen, S. (2001). Resonant and non-resonant inelastic x-ray scattering. Journal of Physics: Condensed Matter, 13(34), 7539. \bibitem{sinha2001} Sinha, S. K. (2001). Theory of inelastic X-ray scattering from condensed matter. Journal of Physics: Condensed Matter, 13(34), 7511. \bibitem{mlhetland} Hetland, Magnus Lie. Beginning Python. Apress, 2005. % previous analysis codes \bibitem{sternemann2008} Sternemann, H., Sternemann, C., Seidler, G. T., Fister, T. T., Sakko, A., \& Tolan, M. (2008). An extraction algorithm for core-level excitations in non-resonant inelastic X-ray scattering spectra. Journal of synchrotron radiation, 15(2), 162-169. \bibitem{huotari2012} Huotari, S., Pylkk\"anen, T., Soininen, J. A., Kas, J. J., H\"am\"al\"ainen, K., \& Monaco, G. (2011). X-ray-Raman-scattering-based EXAFS beyond the dipole limit. Journal of synchrotron radiation, 19(1), 106-113. % beamline paper \bibitem{Cai2004} Sakurai, Y., Yamaoka, H., Kimura, H., Marechal, X. M., Ohtomo, K., Mochizuki, T., et al. (1995). Design of an elliptic multipole wiggler beamline for high‐energy inelastic scattering at the SPring‐8. Review of scientific instruments, 66(2), 1774-1776. \bibitem{Fister2006} Fister, T. T., Seidler, G. T., Wharton, L., Battle, A. R., Ellis, T. B., Cross, J. O., et al. (2006). Multielement spectrometer for efficient measurement of the momentum transfer dependence of inelastic x-ray scattering. Review of scientific instruments, 77(6), 063901-063901. \bibitem{Verbeni2009} Verbeni, R., Pylkk\"anen, T., Huotari, S., Simonelli, L., Vanko, G., Martel, K., et al. (2009). Multiple-element spectrometer for non-resonant inelastic X-ray spectroscopy of electronic excitations. Journal of synchrotron radiation, 16(4), 469-476. \bibitem{Sokaras2012} Sokaras, D., Nordlund, D., Weng, T. C., Mori, R. A., Velikov, P., Wenger, D., et al. (2012). A high resolution and large solid angle x-ray Raman spectroscopy end-station at the Stanford Synchrotron Radiation Lightsource. Review of Scientific Instruments, 83(4), 043112. \bibitem{hiraoka2013} Hiraoka, N., H. Fukui, H. Tanida, H. Toyokawa, Y. Q. Cai, and K. D. Tsuei. "An X-ray Raman spectrometer for EXAFS studies on minerals: bent Laue spectrometer with 20 keV X-rays." Journal of synchrotron radiation 20, no. 2 (2013): 266-271. % imaging \bibitem{SHuotari2011} Huotari, S., Pylkk\"anen, T., Verbeni, R., Monaco, G., \& H\"am\"al\"ainen, K. (2011). Direct tomography with chemical-bond contrast. Nature materials, 10(7), 489-493. \end{thebibliography} \end{document} % % ****** End of file template.aps ****** xrstools-0.15.0+git20210910+c147919d/XRStools/examples/000077500000000000000000000000001412732462000215255ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/examples/carbon.inp000066400000000000000000000034231412732462000235030ustar00rootroot00000000000000#### detector energy = 9.7 # working energy, same as analyzer energy (E0) thickness = 500.0 # thickness of analyzer active material (micron) material = 'Si' # analyzer active material pixel_size = [256,768] # number of pixels (height, width) #### analyzer material = 'Si' # analyzer material hkl = [6,6,0] # hkl-indices of reflection used mask_d = 100.0 # mask diameter (in mm) bend_r = 1.0 # analyzer crystal bending radius diced = False # boolean: False for bent crystals, True for diced crystals thickness = 500.0 # analyzer crystal thickness (micron) energy_resolution = 0.5 # expected overall resolution (eV) database_dir = '/home/christoph/sources/XRStools/data/chitables/' # directory to where already calculated reflectivities are stored #### sample chem_formulas = ['C'] # chemical formulae (python list) concentrations = [1.0] # concentrations (python list) densities = [2.2] # density of the sample's constituents (python list) molar_masses = [12.0] # molar masses of the sample's constituents, needed for calculation of the number of scatterers (python list) angle_tth = 45.0 # 2 theta scattering angle (degress) sample_thickness = 0.1 # sample thickness/diameter (cm) #### thomson scattering_plane = 'vertical' # keyword, either 'vertical' (no polarization dependence) or 'horizontal' (polarization dependence) polarization = 0.99 # degree of polarization of incident radiation #### beam i0_intensity = 1e14 # number of incident photons beam_height = 10.0 # beam dimension in vertical direction (microns) beam_width = 10.0 # beam dimension in horizontal direction (microns) #### compton_profiles eloss_range = np.arange(0.0,1200.0,0.1) # energy loss range for the calculation (eV) E0 = 9.7 # analyzer energy (keV) xrstools-0.15.0+git20210910+c147919d/XRStools/examples/nmf_example.py000066400000000000000000000005711412732462000243750ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/env python import numpy as np w1 = np.array([[1,2,3],[4,5,6]]) h1 = np.array([[1,2],[3,4],[5,6]]) w2 = np.array([[1,1,3],[4,5,6]]) h2 = np.array([[1,1],[3,4],[5,6]]) v = np.dot(w1,h1) (wo,ho) = nmf(v, w2, h2, 0.001, 10, 10) print( wo) print( ho) xrstools-0.15.0+git20210910+c147919d/XRStools/extraction.py000066400000000000000000002476421412732462000224600ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: extraction.py from .math_functions import * from .xrs_utilities import * import numpy as np import pylab import math from scipy import interpolate, signal, integrate, constants, optimize, ndimage import matplotlib.pyplot as plt from six.moves import range from six.moves import input class extraction: """ class for extraction of S(q,w) from an instance of the id20 class "data" and the predictthings class "theory" """ def __init__(self,data,theory,prenormrange=[5,np.inf]): self.data=data # the data self.eloss = data.eloss self.signals = data.signals self.errors = data.errors self.E0 = data.E0 self.tth = data.tth self.prenormrange = prenormrange # the theory self.J = theory.J self.C = theory.C self.V = theory.V self.qvals = theory.q self.formulas = theory.formulas self.concentrations = theory.concentrations # output self.background = np.zeros(np.shape(data.signals)) self.sqw = np.zeros(np.shape(data.signals)) self.pzscale = np.flipud(np.arange(-10,10,0.05)) # definition according to Huotari et al, JPCS 62 (2001) 2205 self.valencepz = np.zeros((len(self.pzscale),len(self.signals[0,:]))) self.valasymmetrypz = np.zeros((len(self.pzscale),len(self.signals[0,:]))) self.valence = np.zeros((len(self.eloss),len(self.signals[0,:]))) self.valasymmetry = np.zeros((len(self.eloss),len(self.signals[0,:]))) self.sqwav = np.zeros(np.shape(data.eloss)) self.sqwaverr = np.zeros(np.shape(data.eloss)) # some variables for averaging over analyzers/whole chambers self.avsignals = np.array([]) self.averrors = np.array([]) self.avC = np.array([]) self.avqvals = np.array([]) # rough normalization over range given by prenormrange for n in [ nn for nn in range(len(self.signals[0,:])) if ((self.signals[:,nn]).sum()>0) ]: # for n in range(len(self.signals[0,:])): HFnorm = np.trapz(self.J[:,n],self.eloss) inds = np.where(np.logical_and(self.eloss>=prenormrange[0],self.eloss<=prenormrange[1]))[0] EXPnorm = np.trapz(self.signals[inds,n],self.eloss[inds]) self.signals[:,n] = self.signals[:,n]/EXPnorm*HFnorm self.errors[:,n] = self.errors[:,n]/EXPnorm*HFnorm def areanorm(self,whichq,emin=None,emax=None): """ normalizes self.signals to area in between emin and emax, default values cover the whole self.eloss axis """ cols = [] if not isinstance(whichq,list): cols.append(whichq) else: cols = whichq if not emin: emin = self.eloss[0] if not emax: emax = self.eloss[-1] for col in cols: inds = np.where(np.logical_and(self.eloss>=emin,self.eloss<=emax)) self.signals[:,col] = self.signals[:,col]/np.trapz(self.signals[inds,col],self.eloss[inds]) def analyzerAverage(self,whichq,errorweighing=True): """ average signals over several analyzers before background subtraction, either with (default) or without error weighing whichq = either list of analyzer numbers to be averaged over or keywords ('VD','VB', ... ) to average over chambers """ analyzerNames = ['VD','VU','VB','HR','HL','HB'] if type(whichq) == str: if whichq.upper() == 'VD': columns = list(range(0,12)) elif whichq.upper() == 'VU': columns = list(range(12,24)) elif whichq.upper() == 'VB': columns = list(range(24,36)) elif whichq.upper() == 'HL': columns = list(range(36,48)) elif whichq.upper() == 'HR': columns = list(range(48,60)) elif whichq.upper() == 'HB': columns = list(range(60,72)) elif whichq not in analyzerNames: print( 'Unknown keyword ' + '\'' + whichq + '\'' + '! Try one of these: \'VD\', \'VU\', \'VB\', \'HR\', \'HL\', \'HB\'.') return else: if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq # build the matricies av = np.zeros((len(self.eloss),len(columns))) averr = np.zeros((len(self.eloss),len(columns))) avC = np.zeros((len(self.eloss),len(columns))) avqvals = np.zeros((len(self.eloss),len(columns))) for n in [ nn for nn in range(len(columns)) if ((self.signals[:,nn]).sum()>0) ]: # find data points with error = 0.0 and replace by 1.0 inds = np.where(self.errors[:,columns[n]] == 0.0)[0] for ind in inds: self.errors[ind,columns[n]] = 1.0 # arrange the desired columns into a matrix av[:,n] = self.signals[:,columns[n]] averr[:,n] = self.errors[:,columns[n]] avC[:,n] = self.C[:,columns[n]] avqvals[:,n] = self.qvals[:,columns[n]] # sum things up if errorweighing: self.avsignals = np.sum( av/averr**2.0 ,axis=1)/( np.sum(1.0/averr**2.0,axis=1)) self.averrors = np.sqrt( 1.0/np.sum(1.0/(averr)**2.0,axis=1) ) else: self.avsignals = np.sum(av,axis=1) self.averrors = np.sqrt(np.sum(np.absolute(averr)**2.0,axis=1)) # check this again self.avC = np.mean(avC,axis=1) self.avqvals = np.mean(avqvals,axis=1) def energycorrect(self,whichq,alpha,densities,samthickness): """ apply energy dependent corrections to the measured data based on scattering angles, sample material, ... whichq = single value or list of columns to apply the corrections to (index starts at zero) alpha = incident beam angle (relative to sample surface normal), need to be negative for transmission geometry density = single value (just one compound) or list of values (mixture of several compounds) samthickness = sample thickness in [cm] """ # make things iterable cols = [] if not isinstance(whichq,list): cols.append(whichq) else: cols = whichq denses = [] if not isinstance(densities,list): denses.append(densities) else: denses = densities # calculate beta (exit angle) from alpha and tth for all columns in whichq beta = [] if alpha >=0: # reflection geometry (check this again) for n in range(len(cols)): beta.append(np.absolute(180.0 - alpha - self.tth[cols[n]])) else: # transmission geometry (check this again) for n in range(len(cols)): beta.append(-1.0*(np.absolute(np.absolute(alpha) - self.tth[cols[n]]))) # absorption and self-absorption # mu_in and mu_out from log-log table mu_in, mu_out = mpr_compds(self.eloss/1e3+self.E0,self.formulas,self.concentrations,self.E0,denses) ac = np.zeros((len(self.eloss),len(cols))) for n in range(len(cols)): ac[:,n] = abscorr2(mu_in,mu_out,alpha,beta[n],samthickness) # cross section correction (use cf output from e2pz routine) pz = np.zeros((len(self.eloss),len(cols))) cf = np.zeros((len(self.eloss),len(cols))) for n in range(len(cols)): pz[:,n], cf[:,n] = e2pz(self.E0+self.eloss/1.0e3,self.E0,self.tth[n]) for col in cols: self.signals[:,col] = self.signals[:,col]*ac[:,n]*cf[:,n] self.errors[:,col] = self.errors[:,col]*ac[:,n]*cf[:,n] # normalize back to HF profiles (because we know those are on eV / [1/eV] scale (is this correct) HFnorm = np.trapz(self.J[:,col],self.eloss) inds = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] EXPnorm = np.trapz(self.signals[inds,col],self.eloss[inds]) self.signals[:,col] = self.signals[:,col]/EXPnorm*HFnorm self.errors[:,col] = self.errors[:,col]/EXPnorm*HFnorm def removeelastic(self,whichq,range1,range2,guess=None,stoploop=True,overwrite=False): """ subtract a pearson function before starting extraction procedure, e.g. for subtracting the elastic peak tail guess values: a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity a[4] = background """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq region1 = np.where(np.logical_and(self.eloss>=range1[0],self.eloss<=range1[1]))[0] region2 = np.where(np.logical_and(self.eloss>=range2[0],self.eloss<=range2[1]))[0] region = np.append(region1,region2) plt.ion() for col in columns: if not guess: guess = [] ind = self.signals[:,col].argmax(axis=0) guess.append(self.eloss[ind]) # max of signal (in range of prenorm from __init__) guess.append(1.0) # twice the position of the peason maximum guess.append(1.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(1e2) # Peak intensity guess.append(0.0) # background fitfct = lambda a: self.signals[region,col] - pearson7(self.eloss[region],a) res = optimize.leastsq(fitfct,guess) yres = pearson7(self.eloss,res[0]) plt.plot(self.eloss,self.signals[:,col],self.eloss,yres,self.eloss,self.signals[:,col]-yres) plt.legend(('data','pearson fit','data - pearson')) plt.draw() if stoploop: _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one if overwrite: self.signals[:,col] = self.signals[:,col] - yres plt.ioff() def removeconst(self,whichq,emin,emax,ewindow=100.0,stoploop=True): """ fits a constant as background in the range emin-emax and saves the constant in self.back and the background subtracted self.signals in self.sqw """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq plt.ion() for col in columns: inds = np.where(np.logical_and(self.eloss >= emin,self.eloss <= emax)) res = np.polyfit(self.eloss[inds],np.transpose(self.signals[inds,col]), 0) yres = np.polyval(res, self.eloss) plt.plot(self.eloss,self.signals[:,col],self.eloss,yres,self.eloss,self.signals[:,col]-yres) plt.legend(('signal','constant fit','signal - constant')) plt.title('Hit [enter] in the python shell to continue') plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.xlim(emin-ewindow,emax+ewindow) plt.autoscale(enable=True, axis='y', tight=False) plt.draw() self.background[:,col] = yres self.sqw[:,col] = self.signals[:,col] - yres if stoploop: _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one # close the figure to show the next one plt.ioff() def removeconstav(self,emin,emax,ewindow=100.0): """ fits a constant as background in the range emin-emax and saves the constant in self.back and the background subtracted self.signals in self.sqw """ if not np.any(self.avsignals): print( 'use averageAnalyzers first to create some averages') return plt.ion() plt.cla() inds = np.where(np.logical_and(self.eloss >= emin,self.eloss <= emax)) res = np.polyfit(self.eloss[inds],self.avsignals[inds], 0) yres = np.polyval(res, self.eloss) plt.plot(self.eloss,self.avsignals,self.eloss,yres,self.eloss,self.avsignals-yres) plt.legend(('signal','constant fit','signal - constant')) plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.xlim(emin-ewindow,emax+ewindow) plt.autoscale(enable=True, axis='y', tight=False) plt.draw() self.sqwav = self.avsignals - yres self.sqwaverr = self.averrors def removelinear(self,whichq,emin,emax,ewindow=100.0,stoploop=True): """ fits a linear function as background in the range emin-emax and saves the linear in self.back and the background subtracted self.signals in self.sqw """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq plt.ion() for col in columns: inds = np.where(np.logical_and(self.eloss >= emin,self.eloss <= emax)) res = np.polyfit(self.eloss[inds],np.transpose(self.signals[inds,col]), 1) yres = np.polyval(res, self.eloss) plt.plot(self.eloss,self.signals[:,col],self.eloss,yres,self.eloss,self.signals[:,col]-yres) plt.legend(('signal','linear fit','signal - linear')) plt.title('Hit [enter] in the python shell to continue') plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.grid(False) plt.xlim(emin-ewindow,emax+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() self.background[:,col] = yres self.sqw[:,col] = self.signals[:,col] - yres if stoploop: _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one plt.ioff() def removelinearav(self,emin,emax,ewindow=100.0): """ fits a linear function as background in the range emin-emax from averaged data and saves the result in self.sqwav and self.sqwerrav """ if not np.any(self.avsignals): print( 'use averageAnalyzers first to create some averages') return plt.ion() plt.cla() inds = np.where(np.logical_and(self.eloss >= emin,self.eloss <= emax)) res = np.polyfit(self.eloss[inds],self.avsignals[inds], 1) yres = np.polyval(res, self.eloss) plt.plot(self.eloss,self.avsignals,self.eloss,yres,self.eloss,self.avsignals-yres) plt.legend(('signal','linear fit','signal - linear')) plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.grid(False) plt.xlim(emin-ewindow,emax+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() self.sqwav = self.avsignals - yres self.sqwaverr = self.averrors def removeLinearAv(self,region1,region2=None,ewindow=100.0,scale=1, view=False): """ fits a linear function as background in the range emin-emax from averaged data and saves the result in self.sqwav and self.sqwerrav """ if not np.any(self.avsignals): print( 'use averageAnalyzers first to create some averages') return range1 = np.where(np.logical_and(self.eloss >= region1[0], self.eloss <= region1[1])) if region2: range2 = np.where(np.logical_and(self.eloss >= region2[0], self.eloss <= region2[1])) region = np.append(range1,range2) else: region = range1 res = np.polyfit(self.eloss[region],self.avsignals[region], 1) yres = np.polyval(res, self.eloss) newspec = (self.avsignals-yres)*scale newerrs = self.averrors*scale self.sqwav = newspec self.sqwaverr = newerrs self.yres = yres self.newspec = newspec if view: plt.ion() plt.cla() plt.plot(self.eloss,self.avsignals,self.eloss,yres,self.eloss,newspec,self.eloss,self.avC) plt.legend(('signal','linear fit','signal - linear','av. core Compton')) plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.grid(False) plt.xlim(region1[0]-ewindow,region1[-1]+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() input() def removepoly(self,whichq,emin,emax,polyorder=2.0,ewindow=100.0): """ fits a polynomial of order "polyorder" (default is quadratic) as background in the range emin-emax and saves the polynomial in self.back and the background subtracted self.signals in self.sqw """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq plt.ion() for col in columns: inds = np.where(np.logical_and(self.eloss >= emin,self.eloss <= emax)) res = np.polyfit(self.eloss[inds],np.transpose(self.signals[inds,col]), polyorder) yres = np.polyval(res, self.eloss) plt.plot(self.eloss,self.signals[:,col],self.eloss,yres,self.eloss,self.signals[:,col]-yres) plt.legend(('signal','poly fit','signal - poly')) plt.title('Hit [enter] in the python shell to continue') plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.xlim(emin-ewindow,emax+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() self.background[:,col] = yres self.sqw[:,col] = self.signals[:,col] - yres _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one plt.ioff() def removepolyav(self,polyregion,coreregion,weights=[1,1],scale=1.0,polyorder=2.0,ewindow=100.0,hfcoreshift=0.0): """ fits a polynomial of order "polyorder" (default is quadratic) as background in the range emin-emax to averaged signals, save it to self.sqwav and self.sqwerrav """ if not np.any(self.avsignals): print( 'use averageAnalyzers first to create some averages') return region1 = np.where(np.logical_and(self.eloss >= polyregion[0], self.eloss <= polyregion[1])) region2 = np.where(np.logical_and(self.eloss >= coreregion[0], self.eloss <= coreregion[1])) region = np.append(region1*weights[0],region2*weights[1]) # shift the HF core edge onset by hfcoreshift thecore = np.interp(self.eloss,self.eloss+hfcoreshift,self.avC) plt.ion() plt.cla() print( scale) newspec = (self.avsignals)*scale #inds = np.where(np.logical_and(self.eloss >= emin,self.eloss <= emax)) res = np.polyfit(self.eloss[region],newspec[region]-thecore[region], polyorder) yres = np.polyval(res, self.eloss) resspec = (newspec-yres) plt.plot(self.eloss,newspec,self.eloss,yres,self.eloss,resspec,self.eloss,thecore) plt.legend(('signal','poly fit','signal - poly (scaled)','av. C')) plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.xlim(polyregion[0]-ewindow,coreregion[1]+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() self.sqwav = resspec self.sqwaverr = self.averrors * scale def removepolyav1(self,polyregion1,polyregion2=None,polyorder=2.0,weights=[1,1]): """ """ if not np.any(self.avsignals): print( 'use averageAnalyzers first to create some averages') return region1 = np.where(np.logical_and(self.eloss >= polyregion1[0], self.eloss <= polyregion1[1]))[0] if polyregion2: region2 = np.where(np.logical_and(self.eloss >= polyregion2[0], self.eloss <= polyregion2[1]))[0] region = np.append(region1*weights[0],region2*weights[1]) else: region = region1 plt.ion() plt.cla() newspec = (self.avsignals) #inds = np.where(np.logical_and(self.eloss >= emin,self.eloss <= emax)) res = np.polyfit(self.eloss[region],newspec[region], polyorder) yres = np.polyval(res, self.eloss) resspec = (newspec-yres) plt.plot(self.eloss,newspec,self.eloss,yres,self.eloss,resspec) plt.legend(('signal','poly fit','signal - poly (scaled)')) plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.draw() self.sqwav = resspec self.sqwaverr = self.averrors def removePolyCoreAv(self,polyregion,coreregion,weights=[1,1],guess=[1.0,0.0,0.0],ewindow=100.0): """ fits a polynomial of order "polyorder" (default is quadratic) as background in the range emin-emax to averaged signals, save it to self.sqwav and self.sqwerrav """ if not np.any(self.avsignals): print( 'use averageAnalyzers first to create some averages') return plt.ion() plt.cla() region1 = np.where(np.logical_and(self.eloss >= polyregion[0], self.eloss <= polyregion[1])) region2 = np.where(np.logical_and(self.eloss >= coreregion[0], self.eloss <= coreregion[1])) region = np.append(region1*weights[0],region2*weights[1]) funct = lambda a: np.sum( (a[0]*self.avsignals[region] - self.avC[region] - np.polyval(a[1::],self.eloss[region]) )**2.0 ) res = optimize.minimize(funct,guess).x print( 'the fit results are: ', res) yres = np.polyval(res[1::], self.eloss) plt.plot(self.eloss,self.avC) plt.plot(self.eloss,self.avsignals*res[0],self.eloss,yres+self.avC,self.eloss,self.avsignals*res[0]-yres) plt.legend(('scaled signal','poly fit + core','scaled signal - poly')) plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.xlim(polyregion[0]-ewindow,polyregion[1]+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() self.sqwav = self.avsignals*res[0] - yres self.sqwaverr = self.averrors*res[0] def removePolyCoreAv2(self,polyregion,coreregion,weights=[1,1],guess=[1.0,0.0],ewindow=100.0,scale=1.0,hfcoreshift=0.0): """ fits a polynomial of order "polyorder" (default is quadratic) as background in the range emin-emax to averaged signals, save it to self.sqwav and self.sqwerrav """ if not np.any(self.avsignals): print( 'use averageAnalyzers first to create some averages') return plt.ion() plt.cla() region1 = np.where(np.logical_and(self.eloss >= polyregion[0], self.eloss <= polyregion[1])) region2 = np.where(np.logical_and(self.eloss >= coreregion[0], self.eloss <= coreregion[1])) region = np.append(region1*weights[0],region2*weights[1]) # try scaling data before the fit (so scale does not have to be a parameter in the fit) thespec = self.avsignals * scale # shift the HF core edge onset by hfcoreshift thecore = np.interp(self.eloss,self.eloss+hfcoreshift,self.avC) funct = lambda a: np.sum( (thespec[region] - thecore[region] - np.polyval(a,self.eloss[region]) )**2.0 ) res = optimize.minimize(funct,guess).x print( 'the fit results are: ', res) yres = np.polyval(res, self.eloss) plt.plot(self.eloss,thespec,self.eloss,yres+thecore,self.eloss,thespec-yres,self.eloss,thecore) plt.legend(('scaled signal','poly fit + core','scaled signal - poly','core profile')) plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.xlim(polyregion[0]-ewindow,polyregion[1]+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() self.sqwav = thespec - yres self.sqwaverr = self.averrors*scale def removeconstpcore(self,whichq,constregion,coreregion,weights=[5,1],guess=[0.0, 1.0],ewindow=100.0,stoploop=True): """ fit a const to the preedge and scale data to postedge matches the theory profiles fminconv: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq region1 = np.where(np.logical_and(self.eloss >= constregion[0], self.eloss <= constregion[1])) region2 = np.where(np.logical_and(self.eloss >= coreregion[0], self.eloss <= coreregion[1])) region = np.append(region1*weights[0],region2*weights[1]) plt.ion() for col in columns: # first scale data to same area as core profile in region2 corenorm = np.trapz(self.C[region2,col],self.eloss[region2]) self.signals[:,col] = self.signals[:,col]/np.trapz(self.signals[region2,col],self.eloss[region2])*corenorm #fitfct = lambda a: a[1]*self.signals[region,col] - (np.polyval([a[0]],self.eloss[region])+self.C[region,col]) #res = optimize.leastsq(fitfct,guess)[0] #yres = np.polyval([res[0]],self.eloss) c1 = lambda a: -np.sum((a[1]*self.signals[region2,col] - (np.polyval([a[0]],self.eloss[region2])+self.C[region2,col]))**2.0) # post edge should oscillate around HF core profile fitfct = lambda a: np.sum( (a[1]*self.signals[region1,col] - (np.polyval([a[0]],self.eloss[region1])+self.C[region1,col])) ) cons = [c1] #, c2, c3, c4, c5, c6 res = optimize.fmin_cobyla(fitfct,guess,cons,maxfun=10000) yres = np.polyval([res[0]],self.eloss) plt.plot(self.eloss,self.signals[:,col]*res[1],self.eloss,yres+self.C[:,col],self.eloss,self.signals[:,col]*res[1]-yres,self.eloss,self.C[:,col]) plt.legend(('data','fit','data - constant','core profile')) plt.title('Hit [enter] in the python shell to continue') plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.xlim(constregion[0]-ewindow,coreregion[1]+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() # save some data for paper-plot #thedata = np.zeros((len(self.eloss),5)) #thedata[:,0] = self.eloss #thedata[:,1] = self.signals[:,col]*res[1] #thedata[:,2] = yres+self.C[:,col] #thedata[:,3] = self.signals[:,col]*res[1]-yres #thedata[:,4] = self.C[:,col] #filename = '/home/csahle/Dropbox/tool_paper/figures/analysis/licl_linpcore_background_det' + '%s' % col + '.dat' #np.savetxt(filename,thedata) self.background[:,col] = yres self.sqw[:,col] = res[1]*self.signals[:,col] - yres if stoploop: _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one plt.ioff() def removelinpcore(self,whichq,linregion,coreregion,weights=[5,1],guess=[0.0, 0.0, 1.0],ewindow=100.0,stoploop=True): """ fit a linear to the preedge and scale data so postedge matches the theory profiles fminconv: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq region1 = np.where(np.logical_and(self.eloss >= linregion[0], self.eloss <= linregion[1])) region2 = np.where(np.logical_and(self.eloss >= coreregion[0], self.eloss <= coreregion[1])) region = np.append(region1*weights[0],region2*weights[1]) plt.ion() for col in columns: # first scale data to same area as core profile in region2 corenorm = np.trapz(self.C[region2,col],self.eloss[region2]) self.signals[:,col] = self.signals[:,col]/np.trapz(self.signals[region2,col],self.eloss[region2])*corenorm # then try a minimization #fitfct = lambda a: (a[2]*self.signals[region,col] - (np.polyval(a[0:2],self.eloss[region])+self.C[region,col])) #res = optimize.leastsq(fitfct,guess)[0] #yres = np.polyval(res[0:2],self.eloss) c1 = lambda a: -np.sum((a[2]*self.signals[region2,col] - (np.polyval(a[0:2],self.eloss[region2])+self.C[region2,col]))**2.0) # post edge should oscillate around HF core profile c2 = lambda a: a[2] # scaling should not be negative fitfct = lambda a: np.sum( (a[2]*self.signals[region1,col] - (np.polyval(a[0:2],self.eloss[region1])+self.C[region1,col])) ) cons = [c1,c2] #, c2, c3, c4, c5, c6 res = optimize.fmin_cobyla(fitfct,guess,cons) yres = np.polyval(res[0:2],self.eloss) plt.plot(self.eloss,res[2]*self.signals[:,col],self.eloss,yres+self.C[:,col],self.eloss,res[2]*self.signals[:,col]-yres,self.eloss,self.C[:,col]) plt.legend(('data','fit','data - linear','core profile')) plt.title('Hit [enter] in the python shell to continue') plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.xlim(linregion[0]-ewindow,coreregion[1]+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() self.background[:,col] = yres self.sqw[:,col] = res[2]*self.signals[:,col] - yres if stoploop: _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one plt.ioff() def removelinpcoreav(self,linregion,coreregion,weights=[5,1],guess=[0.0, 0.0, 1.0],ewindow=100.0): """ fit a linear to the preedge and scale data so postedge matches the theory profiles fminconv: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize """ if not np.any(self.avsignals): print( 'use averageAnalyzers first to create some averages') return plt.ion() plt.cla() region1 = np.where(np.logical_and(self.eloss >= linregion[0], self.eloss <= linregion[1])) region2 = np.where(np.logical_and(self.eloss >= coreregion[0], self.eloss <= coreregion[1])) region = np.append(region1*weights[0],region2*weights[1]) # remove a constant from before the edge linguess = guess[0:2] fitfct = lambda a: self.avsignals[region1] - np.polyval(a,self.eloss[region1]) res1 = optimize.leastsq(fitfct,linguess)[0] back = np.polyval(res1,self.eloss) newspec = self.avsignals - back # scale results in a way that it oscillates around the core profile coreguess = guess[2] fitfct = lambda a: (a*newspec[region2] - self.avC[region2]) res2 = optimize.leastsq(fitfct,coreguess)[0] # first scale data to same area as core profile in region2 #corenorm = np.trapz(self.avC[region2],self.eloss[region2]) #self.signalsav = self.avsignals/np.trapz(self.avsignals[region2],self.eloss[region2])*corenorm #c1 = lambda a: -np.sum((a[2]*self.avsignals[region2] - (np.polyval(a[0:2],self.eloss[region2])+self.avC[region2]))**2.0) # post edge should oscillate around HF core profile #c2 = lambda a: a[2] # scaling should not be negative #fitfct = lambda a: np.sum( (a[2]*self.avsignals[region] - (np.polyval(a[0:2],self.eloss[region])+self.avC[region]) )**2.0 ) #cons = [c1,c2] #, c2, c3, c4, c5, c6 #res = optimize.fmin_cobyla(fitfct,guess,cons) #yres = np.polyval(res[0:2],self.eloss) plt.plot(self.eloss,res2*self.avsignals,self.eloss,self.avsignals,self.eloss,back+self.avC,self.eloss,res2*newspec,self.eloss,self.avC) plt.legend(('scaled data','data','fit','data - linear','core profile')) plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.xlim(linregion[0]-ewindow,coreregion[1]+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() self.sqwav = res2*newspec self.sqwaverr = self.averrors*res2 def removeLinCoreAv(self,linregion,coreregion,weights=[5,1],guess=[0.0, 0.0, 1.0],ewindow=100.0): """ fit a linear to the preedge and scale data so postedge matches the theory profiles fminconv: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize """ if not np.any(self.avsignals): print( 'use averageAnalyzers first to create some averages') return plt.ion() plt.cla() region1 = np.where(np.logical_and(self.eloss >= linregion[0], self.eloss <= linregion[1])) region2 = np.where(np.logical_and(self.eloss >= coreregion[0], self.eloss <= coreregion[1])) region = np.append(region1*weights[0],region2*weights[1]) # remove a constant from before the edge fitfct = lambda a: np.sum( (a[2]*self.avsignals[region] - (a[0]*self.eloss[region] + a[1] + self.avC[region]) )**2.0) res1 = optimize.minimize(fitfct,guess).x back = np.polyval(res1[0:2],self.eloss) print( 'the result of the fit is: ', res1) newspec = res1[2]*self.avsignals - back plt.plot(self.eloss,res1[2]*self.avsignals,self.eloss,self.avsignals,self.eloss,back+self.avC,self.eloss,newspec,self.eloss,self.avC) plt.legend(('scaled data','data','fit','data - linear','core profile')) plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.xlim(linregion[0]-ewindow,coreregion[1]+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() self.sqwav = res1[2]*newspec self.sqwaverr = self.averrors*res1[2] def removepearson(self,whichq,emin,emax,guess=None,stoploop=True): """ guess values: a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity a[4] = background """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq region = np.where(np.logical_and(self.eloss>=emin,self.eloss<=emax))[0] guessregion = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] plt.ion() for col in columns: if not guess: guess = [] ind = np.where( self.signals[guessregion,col] == np.max(self.signals[guessregion,col]) )[0][0] guess.append(self.eloss[ind]) # max of signal (in range of prenorm from __init__) guess.append(guess[0]*2.0) # twice the position of the peason maximum guess.append(1000.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(1.0) # Peak intensity guess.append(0.0) # background fitfct = lambda a: self.signals[region,col] - pearson7(self.eloss[region],a) res = optimize.leastsq(fitfct,guess) yres = pearson7(self.eloss,res[0]) plt.plot(self.eloss,self.signals[:,col],self.eloss,yres,self.eloss,self.signals[:,col]-yres) plt.legend(('data','pearson fit','data - pearson')) plt.draw() self.background[:,col] = yres self.sqw[:,col] = self.signals[:,col] - yres if stoploop: _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one plt.ioff() def removepearson2(self,whichq,emin,emax,guess=None,stoploop=True): """ guess values: a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity a[4] = background """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq region = np.where(np.logical_and(self.eloss>=emin,self.eloss<=emax))[0] guessregion = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] plt.ion() for col in columns: if not guess: guess = [] ind = np.where( self.signals[guessregion,col] == np.max(self.signals[guessregion,col]) )[0][0] guess.append(self.eloss[ind]) # max of signal (in range of prenorm from __init__) guess.append(guess[0]*2.0) # twice the position of the peason maximum guess.append(1000.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(1.0) # Peak intensity guess.append(0.0) # background print( guess) popt, pcov = optimize.curve_fit(pearson7_forcurvefit, self.eloss[region], self.signals[region,col],p0=guess) yres = pearson7(self.eloss,popt) plt.plot(self.eloss,self.signals[:,col],self.eloss,yres,self.eloss,self.signals[:,col]-yres) plt.legend(('data','pearson fit','data - pearson')) plt.draw() self.background[:,col] = yres self.sqw[:,col] = self.signals[:,col] - yres if stoploop: _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one plt.ioff() def removePearsonAv(self,region1,region2=None,guess=None,scale=1.0): """ weights must be integers! guess values: pearson (always zero background): a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity linear: a[4] = linear slope a[5] = linear background/offset data: a[6] = scaling factor """ range1 = np.where(np.logical_and(self.eloss >= region1[0], self.eloss <= region1[1])) if region2: range2 = np.where(np.logical_and(self.eloss >= region2[0], self.eloss <= region2[1])) region = np.append(range1,range2) else: region = range1 guessregion = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] plt.ion() plt.cla() if not guess: guess = [] ind = self.avsignals[guessregion].argmax(axis=0) # find index of maximum of signal in "prenormrange" (defalt [5,inf]) guess.append(self.eloss[guessregion][ind]) # max of signal (in range of prenorm from __init__) guess.append(guess[0]*1.0) # once the position of the peason maximum guess.append(1.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(self.avsignals[guessregion][ind]) # Peak intensity guess.append(0.0) # linear slope guess.append(0.0) # linear background # fit a pearson to the whole region res1 = optimize.curve_fit(pearson7_linear_forcurvefit, self.eloss[region], self.avsignals[region],p0=guess)[0] yres1 = pearson7_zeroback(self.eloss,res1[0:4]) + np.polyval(res1[4:6],self.eloss) print( 'the fitting results are: ', res1) newspec = (self.avsignals-yres1)*scale plt.plot(self.eloss,self.avsignals,self.eloss,yres1,self.eloss,newspec,self.eloss,self.avC) plt.legend(('data','pearson fit','data - pearson')) plt.draw() self.sqwav = newspec self.sqwaverr = self.averrors * scale def removePearsonAv2(self,region1,region2=None,weights=[1,1],guess=None,ewindow=100.0,scale=1.0,hfcoreshift=0.0): """ weights must be integers! guess values: pearson (always zero background): a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity linear: a[4] = linear slope a[5] = linear background/offset data: a[6] = scaling factor """ range1 = np.where(np.logical_and(self.eloss >= region1[0], self.eloss <= region1[1])) if region2: range2 = np.where(np.logical_and(self.eloss >= region2[0], self.eloss <= region2[1])) region = np.append(range1*weights[0],range2*weights[1]) else: region = range1 guessregion = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] plt.ion() plt.cla() if not guess: guess = [] ind = self.avsignals[guessregion].argmax(axis=0) # find index of maximum of signal in "prenormrange" (defalt [5,inf]) guess.append(self.eloss[guessregion][ind]) # max of signal (in range of prenorm from __init__) guess.append(guess[0]*1.0) # once the position of the peason maximum guess.append(1.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(self.avsignals[guessregion][ind]) # Peak intensity guess.append(0.0) # linear slope guess.append(0.0) # linear background # try scaling data before the fit (so scale does not have to be a parameter in the fit) thespec = self.avsignals * scale # shift the HF core edge onset by hfcoreshift thecore = np.interp(self.eloss,self.eloss+hfcoreshift,self.avC) # fit a pearson to the whole region fitfct = lambda a: np.sum( (thespec[region] - pearson7_zeroback(self.eloss[region],a[0:4]) - np.polyval(a[4:6],self.eloss[region]) - thecore[region])**2.0 ) res = optimize.minimize(fitfct,guess).x #res1 = optimize.curve_fit(pearson7_linear_forcurvefit, self.eloss[region], thespec[region],p0=guess)[0] #yres1 = pearson7_zeroback(self.eloss,res1[0:4]) + np.polyval(res1[4:6],self.eloss) print( 'the fitting results are: ', res) yres = pearson7_zeroback(self.eloss,res[0:4]) + np.polyval(res[4:6],self.eloss) plt.plot(self.eloss,thespec,self.eloss,yres,self.eloss,thespec-yres,self.eloss,thecore) plt.legend(('data','pearson fit','data - pearson','core')) plt.draw() self.sqwav = thespec-yres self.sqwaverr = self.averrors * scale def removecoreppearson(self,whichq,pearsonrange,postrange,weights=[2,1],guess=None,stoploop=True): """ weights must be integers! guess values: pearson (always zero background): a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity linear: a[4] = linear slope a[5] = linear background/offset data: a[6] = scaling factor """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq region1 = np.where(np.logical_and(self.eloss >= pearsonrange[0], self.eloss <= pearsonrange[1])) region2 = np.where(np.logical_and(self.eloss >= postrange[0], self.eloss <= postrange[1])) region = np.append(region1*weights[0],region2*weights[1]) guessregion = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] print( len(self.eloss[region])) plt.ion() for col in columns: if not guess: guess = [] ind = self.signals[guessregion,col].argmax(axis=0) # find index of maximum of signal in "prenormrange" (defalt [5,inf]) guess.append(self.eloss[guessregion][ind]) # max of signal (in range of prenorm from __init__) guess.append(guess[0]*1.0) # once the position of the peason maximum guess.append(1.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(self.signals[guessregion,col][ind]) # Peak intensity guess.append(0.0) # linear slope guess.append(0.0) # linear background guess.append(1.0) # scaling factor for exp. data # some sensible boundary conditions for the fit: c1 = lambda a: a[1]*np.absolute(2e2 - a[1]) # FWHM should not be bigger than 200 eV and positive c2 = lambda a: a[2] # shape should not be negative c3 = lambda a: a[3] # peak intensity should not be negative c4 = lambda a: np.absolute(5e-1 - a[4]) # slope for linear background should be small c5 = lambda a: a[3] - a[5] # offset for linear should be smaller than maximum of pearson c6 = lambda a: a[6]*np.absolute(1e10 - a[6]) # scaling factor for the data should not be negative c7 = lambda a: np.sum( (a[5]*self.signals[region2,col] - pearson7_zeroback(self.eloss[region2],a[0:5]) - self.C[region2,col])**2.0 ) fitfct = lambda a: np.sum( (a[6]*self.signals[region,col] - pearson7_zeroback(self.eloss[region],a[0:4]) - np.polyval(a[4:6],self.eloss[region]) - self.C[region,col])**2.0 ) cons = [c7] #[c1, c2, c3, c4, c5, c6, c7] res = optimize.fmin_cobyla(fitfct,guess,cons) print( res) yres = pearson7_zeroback(self.eloss,res[0:4]) + np.polyval(res[4:6],self.eloss) + self.C[:,col] plt.plot(self.eloss,self.signals[:,col]*res[6],self.eloss,yres,self.eloss,self.signals[:,col]*res[6]-yres,self.eloss,self.C[:,col]) plt.legend(('scaled data','pearson + linear + core','data - (pearson + linear +core)','core')) plt.draw() self.background[:,col] = yres self.sqw[:,col] = self.signals[:,col] - yres if stoploop: _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one plt.ioff() def removeCorePearsonAv(self,fitrange,guess=None): """ weights must be integers! guess values: pearson (always zero background): a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity linear: a[4] = linear slope a[5] = linear background/offset data: a[6] = scaling factor """ inds = np.where(np.logical_and(self.eloss >= fitrange[0], self.eloss <= fitrange[1])) plt.ion() plt.cla() if not guess: guess = [] ind = self.avsignals[inds].argmax(axis=0) # find index of maximum of signal in "prenormrange" (defalt [5,inf]) guess.append(self.eloss[inds][ind]) # max of signal (in range of prenorm from __init__) guess.append(guess[0]*1.0) # once the position of the peason maximum guess.append(1.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(self.avsignals[inds][ind]) # Peak intensity guess.append(0.0) # linear slope guess.append(0.0) # linear background guess.append(1.0) # scaling factor for exp. data fitfct = lambda a: a[6]*self.avsignals[inds] - (pearson7_zeroback(self.eloss[inds],a[0:4]) + np.polyval(a[4:6],self.eloss[inds]) + self.avC[inds]) res = optimize.leastsq(fitfct,guess)[0] yres = pearson7_zeroback(self.eloss,res[0:4]) + np.polyval(res[4:6],self.eloss) print( 'the current fit-parameters are: ' + str(res) + ', try using these as guess parameters in a more refined fit!') plt.plot(self.eloss,self.avsignals*res[6],self.eloss,yres+self.avC,self.eloss,self.avsignals*res[6]-yres,self.eloss,self.avC) plt.legend(('scaled data','pearson + linear + core','data - (pearson + linear +core)','core')) plt.draw() self.sqwav = self.avsignals*res[6] - yres self.sqwaverr = self.averrors*res[6] def removeCorePearsonAv2(self,pearsonrange,corerange,weights=[2,1],guess=None): """ weights must be integers! guess values: pearson (always zero background): a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity linear: a[4] = linear slope a[5] = linear background/offset data: a[6] = scaling factor """ region1 = np.where(np.logical_and(self.eloss >= pearsonrange[0], self.eloss <= pearsonrange[1])) region2 = np.where(np.logical_and(self.eloss >= corerange[0], self.eloss <= corerange[1])) region = np.append(region1*weights[0],region2*weights[1]) guessregion = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] plt.ion() plt.cla() # scale the data approximately to get a better match in edge jumb btw. data and compton profiles #try: #theojump = np.abs(self.avC[region2[0][0]] - self.avC[region1[0][-1]]) #datajump = np.abs(self.avsignals[region2[0][0]] - self.avsignals[region1[0][-1]]) #prescaling = datajump/theojump #except: # prescaling = 1.0 #self.avsignals *= prescaling if not guess: guess = [] ind = self.avsignals[guessregion].argmax(axis=0) # find index of maximum of signal in "prenormrange" (defalt [5,inf]) guess.append(self.eloss[guessregion][ind]) # max of signal (in range of prenorm from __init__) guess.append(guess[0]*1.0) # once the position of the peason maximum guess.append(1.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(self.avsignals[guessregion][ind]) # Peak intensity guess.append(0.0) # linear slope guess.append(0.0) # linear background guess.append(1.0) # scaling factor for exp. data # some sensible boundary conditions for the fit: c1 = lambda a: a[1]*np.absolute(2e2 - a[1]) # FWHM should not be bigger than 200 eV and positive c2 = lambda a: a[2] # shape should not be negative c3 = lambda a: a[3] # peak intensity should not be negative c4 = lambda a: np.absolute(5e-1 - a[4]) # slope for linear background should be small c5 = lambda a: a[3] - a[5] # offset for linear should be smaller than maximum of pearson c6 = lambda a: a[6] # scaling factor for the data should not be negative c7 = lambda a: np.sum( (a[6]*self.avsignals[region2] - pearson7_zeroback(self.eloss[region2],a[0:4]) - self.avC[region2] - np.polyval(a[4:6],self.eloss[region2]))**2.0 ) fitfct = lambda a: np.sum( (a[6]*self.avsignals[region1] - pearson7_zeroback(self.eloss[region1],a[0:4]) - np.polyval(a[4:6],self.eloss[region1]) - self.avC[region1])**2.0 ) + np.sum( (a[6]*self.avsignals[region2] - pearson7_zeroback(self.eloss[region2],a[0:4]) - np.polyval(a[4:6],self.eloss[region2]) - self.avC[region2])**2.0 ) cons = []#[c1, c2, c3, c4, c5, c6, c7] res = optimize.minimize(fitfct,guess) yres = pearson7_zeroback(self.eloss,res[0:4]) + np.polyval(res[4:6],self.eloss) plt.plot(self.eloss,self.avsignals*res[6],self.eloss,yres+self.avC,self.eloss,self.avsignals*res[6]-yres,self.eloss,self.avC) plt.legend(('scaled data','pearson + linear + core','data - (pearson + linear)','core')) plt.draw() self.sqwav = self.avsignals*res[6] - yres self.sqwaverr = self.averrors*res[6] def removeCorePearsonAv3(self,pearsonrange,corerange,weights=[1,1],guess=None): """ weights must be integers! guess values: pearson (always zero background): a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity linear: a[4] = linear slope a[5] = linear background/offset data: a[6] = scaling factor """ region1 = np.where(np.logical_and(self.eloss >= pearsonrange[0], self.eloss <= pearsonrange[1])) region2 = np.where(np.logical_and(self.eloss >= corerange[0], self.eloss <= corerange[1])) region = np.append(region1*weights[0],region2*weights[1]) guessregion = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] plt.ion() plt.cla() if not guess: guess = [] ind = self.avsignals[guessregion].argmax(axis=0) # find index of maximum of signal in "prenormrange" (defalt [5,inf]) guess.append(self.eloss[guessregion][ind]) # max of signal (in range of prenorm from __init__) guess.append(guess[0]*1.0) # once the position of the peason maximum guess.append(1.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(self.avsignals[guessregion][ind]) # Peak intensity guess.append(0.0) # linear slope guess.append(0.0) # linear background # fit a pearson to the whole region res1 = optimize.curve_fit(pearson7_linear_forcurvefit, self.eloss[region], self.avsignals[region],p0=guess)[0] yres1 = pearson7_zeroback(self.eloss,res1[0:4]) + np.polyval(res1[4:6],self.eloss) plt.plot(self.eloss,self.avsignals,self.eloss,yres1+self.avC,self.eloss,self.avsignals-yres1,self.eloss,self.avC) # estimate a scaling factor from area in the coreregion scale = np.trapz(self.avC[region2],self.eloss[region2])/np.abs(np.trapz(self.avsignals[region2]-yres1[region2],self.eloss[region2])) print( 'trapz of core is ',np.trapz(self.avC[region2],self.eloss[region2])) print( 'trapz of data is ',np.trapz(self.avsignals[region2]-yres1[region2],self.eloss[region2])) print( 'scale is ', scale) newspec = self.avsignals * scale newerrs = self.averrors * scale # fit scaled averaged data again (fit scaling with this too) guess = res1 guess = np.append(guess,1.0) # scaling factor for exp. data res2 = optimize.curve_fit(pearson7_linear_scaling_forcurvefit, self.eloss[region], newspec[region],p0=guess)[0] yres2 = pearson7_zeroback(self.eloss,res2[0:4]) + np.polyval(res2[4:6],self.eloss) print( res2) plt.figure() plt.plot(self.eloss,newspec*res2[6],self.eloss,yres2+self.avC,self.eloss,newspec*res2[6]-yres2,self.eloss,self.avC) plt.legend(('scaled data','pearson + linear + core','scaled data - (pearson + linear)','core')) plt.draw() self.sqwav = newspec*res2[6] - yres2 self.sqwaverr = newerrs*res2[6] def removecoreppearson2(self,whichq,pearsonrange,postrange,guess=None,stoploop=True): """ guess values: pearson (always zero background): a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity linear: a[4] = linear slope a[5] = linear background/offset data: a[6] = scaling factor """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq region1 = np.where(np.logical_and(self.eloss >= pearsonrange[0], self.eloss <= pearsonrange[1]))[0] region2 = np.where(np.logical_and(self.eloss >= postrange[0], self.eloss <= postrange[1]))[0] region = np.append(region1,region2) guessregion = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] plt.ion() for col in columns: if not guess: guess = [] ind = self.signals[guessregion,col].argmax(axis=0) # find index of maximum of signal in "prenormrange" (defalt [5,inf]) guess.append(self.eloss[guessregion][ind]) # max of signal (in range of prenorm from __init__) guess.append(guess[0]*1.0) # once the position of the peason maximum guess.append(1.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(self.signals[guessregion,col][ind]) # Peak intensity guess.append(0.0) # ax guess.append(0.0) # b guess.append(1.0) # scaling factor for exp. data print( guess) # boundary conditions for the fit: let the post-edge region oscilate around the HF core profile c1 = lambda a: np.sum( (a[6]*self.signals[region2,col] - pearson7_zeroback(self.eloss[region2],a[0:4]) - self.C[region2,col])**2.0 ) c2 = lambda a: a[3] c3 = lambda a: a[1] c4 = lambda a: a[5] #fitfct = lambda a: np.sum( (a[5]*self.signals[region1,col] - pearson7_zeroback(self.eloss[region1],a[0:4]) - self.C[region1,col])**2.0 ) fitfct = lambda a: np.sum( ( a[6]*self.signals[region,col] - pearson7_zeroback(self.eloss[region],a[0:4]) - np.polyval(a[4:6],self.eloss[region]) - self.C[region,col])**2.0 ) cons = [c2,c3,c4] #, c2, c3, c4, c5, c6 res = optimize.fmin_cobyla(fitfct,guess,cons) #res = optimize.fmin(fitfct,guess) yres = pearson7_zeroback(self.eloss,res[0:4]) plt.plot(self.eloss,res[6]*self.signals[:,col], self.eloss,pearson7_zeroback(self.eloss[:],res[0:4]), self.eloss,np.polyval(res[4:6],self.eloss[:]) ) #plt.plot(self.eloss,self.signals[:,col]*res[6],self.eloss,yres,self.eloss,self.signals[:,col]*res[6]-yres ,self.eloss,self.C[:,col]) #plt.legend(('scaled data','pearson','data - pearson','core')) plt.draw() self.background[:,col] = yres self.sqw[:,col] = self.signals[:,col] - yres if stoploop: _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one plt.ioff() def removecoreppearson3(self,whichq,pearsonrange,postrange,guess=None,stoploop=True): """ guess values: pearson (always zero background): a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity a[4] = pearson offset linear: a[5] = linear slope a[6] = linear background/offset data: a[7] = scaling factor """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq region1 = np.where(np.logical_and(self.eloss >= pearsonrange[0], self.eloss <= pearsonrange[1]))[0] region2 = np.where(np.logical_and(self.eloss >= postrange[0], self.eloss <= postrange[1]))[0] guessregion = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] plt.ion() for col in columns: if not guess: guess = [] ind = self.signals[guessregion,col].argmax(axis=0) # find index of maximum of signal in "prenormrange" (defalt [5,inf]) guess.append(self.eloss[guessregion][ind]) # max of signal (in range of prenorm from __init__) guess.append(guess[0]*1.0) # once the position of the peason maximum guess.append(1.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(self.signals[guessregion,col][ind]) # Peak intensity guess.append(0.0) # pearson offset guess.append(0.0) # scaling factor for exp. data guess.append(0.0) guess.append(1.0) print( 'guessing start values: ', guess) # formulate some contraints: def constr1(a): return a[1] # let FWHM be positive def constr2(a): return a[3] # let the peak intensity be positive def constr3(a): return a[7] # scaling should be positive # initial fit: fitfct = lambda a: np.sum( (a[5]*self.signals[region1,col] - pearson7_zeroback(self.eloss[region1],a[0:5]))**2.0 ) res = optimize.fmin_cobyla(fitfct,guess,[constr1, constr2, constr3]) # try again and again to optimize until post-edge region oscilates around the HF core profile etol = 1e-4 while np.sum( (res[5]*self.signals[region2,col] - pearson7_zeroback(self.eloss[region2],res[0:5]) - self.C[region2,col])**2.0 )>etol: res = optimize.fmin_cobyla(fitfct,res,[constr1, constr2, constr3]) yres = pearson7_zeroback(self.eloss,res[0:5]) if stoploop: plt.plot(self.eloss,self.signals[:,col]*res[7],self.eloss,yres,self.eloss,self.signals[:,col]*res[7]-yres ,self.eloss,self.C[:,col]) plt.legend(('scaled data','pearson','data - pearson','core')) plt.draw() _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one self.background[:,col] = yres self.sqw[:,col] = self.signals[:,col] - yres def remquickval(self,whichq,corefitrange,interpolrange,convwidth,stoploop=True): """ quick and dirty way of valence profile extraction from a single spectrum. works if the edge rides on the tail of the valence profile at high q. the HF core profile is fitted to the spectrum in the 'corefitrange' and the resulting valence profile is cut out and interpolated over in the 'interpolrange'. finally the valence profile is smoothed by convolution with a gaussian of FWHM 'convwidth' and subtracted from the original data such that the resulting S(q,w) oscillates around the HF core profile. """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq fitrange = np.where(np.logical_and(self.eloss >= corefitrange[0], self.eloss <= corefitrange[1]))[0] interprange1 = np.where(self.eloss<=interpolrange[0])[0] interprange2 = np.where(self.eloss>=interpolrange[1])[0] interprange = np.append(interprange1,interprange2) plt.ion() for col in columns: fitfct = lambda a: np.sum((self.signals[fitrange,col] - a*self.C[fitrange,col])**2.0) constr = lambda a: a # scaling factor for the HF core profile should not be negative res = optimize.fmin_cobyla(fitfct,[1.0],cons=constr)[0] # subtract the HF core compton profile and interpolate through the edge f = interpolate.interp1d(self.eloss[interprange],self.signals[interprange,col]-res*self.C[interprange,col], bounds_error=False, fill_value=0.0) valdata = f(self.eloss) valdata = convg(self.eloss,valdata,convwidth) subdata = self.signals[:,col] - valdata plt.plot(self.eloss,self.signals[:,col],self.eloss,self.C[:,col]*res,self.eloss,valdata,self.eloss,subdata) plt.legend(('data','scaled core compton','estimated valence','extracted data')) plt.draw() self.valence[:,col] = valdata self.sqw[:,col] = subdata/res # scale the extracted data back to fit the HF core profile if stoploop: _ = input("Press [enter] to continue.") # wait for input from the user plt.close() # close the figure to show the next one plt.ioff() def extractval(self,whichq,mirror=False,linrange1=None,linrange2=None): """ extracts a valence profile from q-value(s) given in whichq by first fitting the core HF profile to the data at places linrange1 and linrange2 (one, two, or no ranges can be given), then subtracting the HF profile from the data. the resulting valence profile in the near-edge region can be replaced by a pearson function (default) or by mirroring the negative side of the valence profile (mirror=True). if mirror is set to False, also the asymmetry is fitted. """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq # set pz scale of first given q-val as 'master' grid absolutepz = e2pz(self.eloss/1e3+self.E0,self.E0,self.tth[columns[0]])[0] plt.cla() plt.ion() for col in columns: # set the pz scale for each q pz = e2pz(self.eloss/1e3+self.E0,self.E0,self.tth[col])[0] if linrange1 and linrange2: range1 = np.where(np.logical_and(self.eloss>=linrange1[0],self.eloss<=linrange1[1]))[0] range2 = np.where(np.logical_and(self.eloss>=linrange2[0],self.eloss<=linrange2[1]))[0] linrange = np.append(range1,range2) elif linrange1: linrange = np.where(np.logical_and(self.eloss>=linrange1[0],self.eloss<=linrange1[1]))[0] else: linrange = np.where(0.1*self.C[:,col] > self.V[:,col])[0] # simple minimization to subtract a linear from the data to fit ontop of the Compton profile: fitfct = lambda a: (self.signals[linrange,col] - np.polyval([a[0],a[1]],self.eloss[linrange]) ) - self.J[linrange,col] res = optimize.leastsq(fitfct,[0.0,0.0])[0] # raw valence (when later extracted from the data, also a linear shoud be accounted for) val = self.signals[:,col] - np.polyval(res,self.eloss) - self.C[:,col] if mirror: # just replace the edgepart of the valence profile by the other half of the profile mirrorval = np.append(val[pz<=0.0],np.flipud(val[pz<=0])) mirrorpz = np.append(pz[pz<=0],np.flipud(pz[pz<=0]*-1)) order = np.argsort(mirrorpz) f = interpolate.interp1d(mirrorpz[order],mirrorval[order],bounds_error=False, fill_value=0.0) extractedval = f(absolutepz) plt.plot(absolutepz,val,absolutepz,extractedval) plt.legend(['exp. S(q,w) - HF core profile','mirrored extracted valence profile']) plt.xlabel('pz [a.u.]') plt.ylabel('S(q,w) [1/eV]') plt.draw() _ = input("Press [enter] to continue.") # wait for input from the user plt.close() self.valence[:,col] = extractedval self.valasymmetry = np.zeros_like(self.valence) else: # fit pearson to replace near edge part print ('select a point above which the valence profile should be replaced by a Pearson function') plt.plot(absolutepz,val) plt.ylim(np.amin(val)-np.amin(val)*1.1,val[absolutepz.flat[np.abs(absolutepz - 0.0).argmin()]]*1.5) xyval = pylab.ginput(1)[0] edgeregion = np.where(pz < xyval[0])[0] start_param = [pz[val==np.amax(val[edgeregion])][0], 4.0, 1.0, np.amax(val[edgeregion]), 0.0 ] fitfct = lambda a: val[edgeregion] - pearson7(pz[edgeregion],a) param = optimize.leastsq(fitfct,start_param)[0] # param = optimize.curve_fit(pearson7_forcurvefit, pz[theregion], val[theregion],p0=start_param)[0] pearson = pearson7(pz,param) val[pz>xyval[0]] = pearson[pz>xyval[0]] extractedval = val; plt.close() # try fitting the valence asymmetry print( 'trying to extract valence asymmetry!') pzp = absolutepz[absolutepz >= 0.0] pzm = absolutepz[absolutepz < 0.0] jvalp = extractedval[absolutepz >=0.0 ] f = interpolate.UnivariateSpline(-pzm,extractedval[absolutepz<0.0]) jvalm = f(pzp) fitfct = lambda a: jvalp-jvalm - a[0]*(np.tanh(pzp/a[1])*np.exp(-(pzp/np.absolute(a[2]))**4.0)) res = optimize.leastsq(fitfct,[0.0,1.0,1.0])[0] asym = (res[0]*(np.tanh(pz/res[1])*np.exp(-(pz/np.absolute(res[2]))**4.0)))/2.0 plt.plot(absolutepz,extractedval,absolutepz,asym,absolutepz,extractedval+asym) plt.legend(['extracted valence profile','fitted valence asymmetry','asymmetry corrected valence profile']) plt.draw() self.valencepz[:,col] = extractedval-asym self.valasymmetrypz[:,col] = asym self.pzscale = absolutepz plt.ioff() def extractval_test(self,whichq,mirror=False,linrange1=None,linrange2=None): """ extracts a valence profile from q-value(s) given in whichq by first fitting the core HF profile to the data at places linrange1 and linrange2 (one, two, or no ranges can be given), then subtracting the HF profile from the data. the resulting valence profile in the near-edge region can be replaced by a pearson function (default) or by mirroring the negative side of the valence profile (mirror=True). if mirror is set to False, also the asymmetry is fitted. """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq plt.cla() plt.ion() for col in columns: # set the pz scale for each q pz_dmy = e2pz(self.eloss/1e3+self.E0,self.E0,self.tth[col])[0] # find the regions in which to fit a linear function if linrange1 and linrange2: range1 = np.where(np.logical_and(self.eloss>=linrange1[0],self.eloss<=linrange1[1]))[0] range2 = np.where(np.logical_and(self.eloss>=linrange2[0],self.eloss<=linrange2[1]))[0] linrange = np.append(range1,range2) elif linrange1: linrange = np.where(np.logical_and(self.eloss>=linrange1[0],self.eloss<=linrange1[1]))[0] else: linrange = np.where(0.1*self.C[:,col] > self.V[:,col])[0] # simple minimization to subtract a linear from the data to fit ontop of the HF Compton profile: fitfct = lambda a: (self.signals[linrange,col] - np.polyval([a[0],a[1]],self.eloss[linrange]) ) - a[2]*self.J[linrange,col] res = optimize.leastsq(fitfct,[0.0,0.0,1.0])[0] self.background[:,col] = np.polyval(res[0:2],self.eloss) # save the linear # raw valence (when later extracted from the data, also a linear shoud be accounted for) val = self.signals[:,col] - np.polyval(res[0:2],self.eloss) - res[2]*self.C[:,col] #plt.plot(self.eloss,self.signals[:,col],self.eloss,self.C[:,col]*res[2]+np.polyval(res[0:2],self.eloss)) if mirror: # just replace the edgepart of the valence profile by the other half of the profile mirrorval = np.append(val[pz_dmy<=0.0],np.flipud(val[pz_dmy<=0])) mirrorpz = np.append(pz_dmy[pz_dmy<=0],np.flipud(pz_dmy[pz_dmy<=0]*-1)) order = np.argsort(mirrorpz) f = interpolate.interp1d(mirrorpz[order],mirrorval[order],bounds_error=False, fill_value=0.0) extractedval = f(pz_dmy) plt.plot(pz_dmy,val,pz_dmy,extractedval) plt.legend(['exp. S(q,w) - HF core profile','mirrored extracted valence profile']) plt.xlabel('pz [a.u.]') plt.ylabel('S(q,w) [1/eV]') plt.draw() _ = input("Press [enter] to continue.") # wait for input from the user plt.close() self.valence[:,col] = extractedval self.valasymmetry = np.zeros_like(self.valence) else: # fit pearson to replace near edge part print ('select a point above which the valence profile should be replaced by a Pearson function') plt.plot(pz_dmy,val) plt.ylim((np.amin(val[pz_dmy<2.0])*0.9,np.amax(val[pz_dmy<2.0])*1.1)) # np.amin(val)-np.amin(val)*1.1,val[pz_dmy.flat[np.abs(pz_dmy - 0.0).argmin()]]*1.5) xyval = pylab.ginput(1)[0] edgeregion = np.where(pz_dmy < xyval[0])[0] start_param = [pz_dmy[val==np.amax(val[edgeregion])][0], 4.0, 1.0, np.amax(val[edgeregion]), 0.0 ] fitfct = lambda a: val[edgeregion] - pearson7(pz_dmy[edgeregion],a) param = optimize.leastsq(fitfct,start_param)[0] # param = optimize.curve_fit(pearson7_forcurvefit, pz_dmy[theregion], val[theregion],p0=start_param)[0] pearson = pearson7(pz_dmy,param) val[pz_dmy>xyval[0]] = pearson[pz_dmy>xyval[0]] extractedval = val; plt.close() # try fitting the valence asymmetry print( 'trying to extract valence asymmetry!') pzp = pz_dmy[pz_dmy >= 0.0] pzm = pz_dmy[pz_dmy < 0.0] jvalp = extractedval[pz_dmy >=0.0 ] f = interpolate.interp1d(-pzm,extractedval[pz_dmy<0.0],bounds_error=False, fill_value=0.0) jvalm = f(pzp) fitfct = lambda a: jvalp-jvalm - a[0]*(np.tanh(pzp/a[1])*np.exp(-(pzp/np.absolute(a[2]))**4.0)) res = optimize.leastsq(fitfct,[0.0,1.0,1.0])[0] print( res) asym = -(res[0]*(np.tanh(pz_dmy/res[1])*np.exp(-(pz_dmy/np.absolute(res[2]))**4.0)))/2.0 plt.plot(pz_dmy,extractedval,pz_dmy,asym,pz_dmy,extractedval+asym) #plt.plot(pz_dmy[pz_dmy<0],extractedval[pz_dmy<0]+asym[pz_dmy<0],-pz_dmy[pz_dmy>=0],extractedval[pz_dmy>=0]+asym[pz_dmy>=0]) plt.legend(['extracted valence profile','fitted valence asymmetry','asymmetry corrected valence profile']) plt.draw() #self.valence[:,col] = extractedval-asym #self.valasymmetry[:,col] = asym #print pz_dmy[0], pz_dmy[-1], absolutepz[0],absolutepz[-1] f = interpolate.interp1d(np.flipud(pz_dmy),np.flipud(extractedval-asym), kind='cubic',bounds_error=False, fill_value=0.0) #f = interpolate.UnivariateSpline(pz_dmy,extractedval-asym,k=3,s=10) absval = f(np.flipud(self.pzscale)) self.valencepz[:,col] = np.flipud(absval) f = interpolate.interp1d(np.flipud(pz_dmy),np.flipud(asym), kind='cubic',bounds_error=False, fill_value=0.0) absasym = f(np.flipud(self.pzscale)) self.valasymmetrypz[:,col] = np.flipud(absasym) #print absval #plt.cla() #plt.plot(absolutepz,absval) #plt.cla() #plt.plot(absolutepz,np.interp(absolutepz,pz_dmy,extractedval-asym),pz_dmy,extractedval-asym) #break #self.pzscale[:,col] = absolutepz #pz_dmy plt.ioff() def getallvalprof(self,whichq,smoothgval=0.0,stoploop=True): """ takes the extracted valence profile extracted from q-value whichq and transforms them onto the other q-values whichq = column from which the valence profile was extracted smoothgval = FWHM used for gaussian smoothing (default is 0.0, i.e. no smoothing) stoploop = boolean, plots each result if set to True """ newenergy = np.zeros((len(self.pzscale),len(self.tth))) newvalence = np.zeros((len(self.eloss),len(self.tth))) newasym = np.zeros((len(self.eloss),len(self.tth))) plt.ion() for n in range(len(self.tth)): newenergy[:,n] = (pz2e1(self.E0,self.pzscale,self.tth[n]) - self.E0)*1e3 # each energy scale in [eV] f = interpolate.interp1d(newenergy[:,n],self.valencepz[:,whichq],bounds_error=False, fill_value=0.0) newvalence[:,n] = f(self.eloss)/self.qvals[:,n] f = interpolate.interp1d(newenergy[:,n],self.valasymmetrypz[:,whichq],bounds_error=False, fill_value=0.0) newasym[:,n] = f(self.eloss)/self.qvals[:,n] plt.plot(self.eloss,newvalence[:,n],self.eloss,newasym[:,n]) plt.draw() if stoploop: _ = input("Press [enter] to continue.") # wait for input from the user plt.close() if smoothgval > 0.0: for n in range(len(self.tth)): self.valence[:,n] = convg(self.eloss,newvalence[:,n],smoothgval) + newasym[:,n] self.valasymmetry = newasym else: self.valence = newvalence + newasym self.valasymmetry = newasym plt.ioff() def remvalenceprof(self,whichq,eoffset=0.0): """ removes extracted valence profile from q-values given in list whichq by fitting the scaled valence profile plus a linear function plus the HF core compton profile to the data """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq inds = np.where(self.eloss>=self.prenormrange[0])[0] plt.ion() for col in columns: f = interpolate.interp1d(self.eloss+eoffset,self.valence[:,col],bounds_error=False, fill_value=0.0) #self.valence[:,col] = f(self.eloss) thevalence = f(self.eloss) fitfct = lambda a: self.signals[inds,col]-self.C[inds,col]-a[0]*thevalence[inds]-np.polyval([a[1],a[2]],self.eloss[inds]) res = optimize.leastsq(fitfct,[6.0,0.0,0.0])[0] plt.plot(self.eloss,self.signals[:,col]) plt.plot(self.eloss,self.C[:,col]+res[0]*thevalence) plt.plot(self.eloss,np.polyval(res[1:3],self.eloss)) plt.plot(self.eloss,self.signals[:,col]-res[0]*thevalence-np.polyval([res[1],res[2]],self.eloss),self.eloss,self.C[:,col]) plt.draw() self.sqw = self.signals[:,col]-res[0]*thevalence-np.polyval([res[1],res[2]],self.eloss) self.valence[:,col] = thevalence plt.ioff() def remvalenceprof_test(self,whichq,eoffset=0.0): """ removes extracted valence profile from q-values given in list whichq by fitting the scaled valence profile plus a linear function plus the HF core compton profile to the data """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq inds = np.where(self.eloss>=self.prenormrange[0])[0] plt.ion() for col in columns: f = interpolate.interp1d(self.eloss+eoffset,self.valence[:,col],bounds_error=False, fill_value=0.0) #self.valence[:,col] = f(self.eloss) thevalence = f(self.eloss) fitfct = lambda a: self.signals[inds,col]-self.C[inds,col]-a[0]*np.interp(self.eloss,self.eloss+a[1],thevalence)[inds]-np.polyval([a[2],a[3]],self.eloss[inds]) res = optimize.leastsq(fitfct,[1.0,0.0,0.0,0.0])[0] plt.plot(self.eloss,self.signals[:,col]) plt.plot(self.eloss,self.C[:,col]+res[0]*np.interp(self.eloss,self.eloss+res[1],thevalence)+np.polyval(res[2:4],self.eloss)) plt.plot(self.eloss,np.polyval(res[2:4],self.eloss)) plt.plot(self.eloss,self.signals[:,col]-res[0]*np.interp(self.eloss,self.eloss+res[1],thevalence)-np.polyval([res[2],res[3]],self.eloss),self.eloss,self.C[:,col]) plt.draw() self.sqw[:,col] = self.signals[:,col]-res[0]*np.interp(self.eloss,self.eloss+res[1],thevalence)-np.polyval([res[2],res[3]],self.eloss) self.valence[:,col] = res[0]*np.interp(self.eloss,self.eloss+res[1],thevalence) # reassign shifted and scaled valence profile #self.valasymmetry *= res[0] # reassign scaled valence asymmetry for plotting self.background[:,col] = np.polyval(res[2:4],self.eloss) # save some ascii files for paper thedata = np.zeros((len(self.eloss),6)) thedata[:,0] = self.eloss thedata[:,1] = self.signals[:,col] thedata[:,2] = res[0]*np.interp(self.eloss,self.eloss+res[1],thevalence)+np.polyval(res[2:4],self.eloss) thedata[:,3] = np.polyval(res[2:4],self.eloss) thedata[:,4] = self.signals[:,col]-res[0]*np.interp(self.eloss,self.eloss+res[1],thevalence)-np.polyval([res[2],res[3]],self.eloss) thedata[:,5] = self.C[:,col] filename = '/home/christoph/Dropbox/tool_paper/figures/analysis/val_extraction_det' + '%s' % col + '.dat' #np.savetxt(filename,thedata) plt.ioff() def averageqs(self,whichq,errorweighing=True): """ averages S(q,w) over the q-values given whichq = list of q-values over which to average (index starts at zero) errorweighing = boolean, weighs sum by errors if set to True """ if not isinstance(whichq,list): columns = [] columns.append(whichq) else: columns = whichq # build the matricies av = np.zeros((len(self.eloss),len(whichq))) averr = np.zeros((len(self.eloss),len(whichq))) for n in range(len(columns)): # find data points with error = 0.0 and replace by 1.0 inds = np.where(self.errors[:,columns[n]] == 0.0)[0] for ind in inds: self.errors[ind,columns[n]] = 1.0 # arrange the desired columns into a matrix av[:,n] = self.sqw[:,columns[n]] averr[:,n] = self.errors[:,columns[n]] # sum things up if errorweighing: self.sqwav = np.sum( av/averr**2.0 ,axis=1)/( np.sum(1.0/averr**2.0,axis=1)) self.sqwaverr = np.sqrt( 1.0/np.sum(1.0/(averr)**2.0,axis=1) ) else: self.sqwav = np.sum(av,axis=1) self.sqwaverr = np.sqrt(np.sum(np.absolute(self.errors)**2.0,axis=1)) # check this again def save_state_hdf5(self, filename, groupname, comment ="" ): import h5py h5 = h5py.File(filename,"a") if( groupname in list(h5.keys()) ): del h5[groupname] h5.require_group(groupname) h5group = h5[groupname] for key in ["eloss" ,"sqwav" ,"sqwaverr" ,"avsignals","avC" ,"yres" ,"newspec" ]: if hasattr( self, key ) : data= getattr(self,key) h5group[key] = data h5group["comment"] = comment h5.flush() h5.close() def savetxtsqwav(self,filename, emin=None, emax=None, normrange=None): """ save the S(q,w) into a filename (energy loss, sqw, error), save only part of the spectrum, if emin and emax are given, normalize to area using np.trapz if normrange is given and a list of length 2 """ if emin and emax: inds = np.where(np.logical_and(self.eloss>=emin,self.eloss<=emax))[0] data = np.zeros((len(inds),3)) data[:,0] = self.eloss[inds] data[:,1] = self.sqwav[inds] data[:,2] = self.sqwaverr[inds] else: data = np.zeros((len(self.eloss),3)) data[:,0] = self.eloss data[:,1] = self.sqwav data[:,2] = self.sqwaverr if normrange: assert type(normrange) is list and len(normrange) is 2, "normrange has to be a list of length two!" inds = np.where(np.logical_and(data[:,0]>=normrange[0],data[:,0]<=normrange[1]))[0] norm = trapz(data[inds,1],data[inds,0]) data[:,1] /= norm data[:,2] /= norm np.savetxt(filename,data) xrstools-0.15.0+git20210910+c147919d/XRStools/fit_spectra.py000066400000000000000000000505121412732462000225670ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import scipy import math import sys from six.moves import range from six.moves import zip import pickle if(sys.argv[0][-12:]!="sphinx-build"): from XRStools.XRStools_c import fitspectra_cy def Fista(solution , problem, niter, niterLip): # print " SHAPE " , solution.shape dim = solution.shape[0] err = 0.0 beta=problem.beta grad , err=problem. calculate_grad( solution ) Lip = math.sqrt( np.linalg.norm(grad) ) grad = grad/ Lip print( "CALCULATING LIPSCHITZ FACTOR ") for i in range(niterLip): grad2,err2 = problem.calculate_grad(grad, quadratic_only=1) Lip = math.sqrt( np.linalg.norm(grad2) ) grad = grad2/ Lip print( "LIP ", Lip) Lip = Lip*1.05 t=1.0 y = solution x_old = solution errList=[] for i in range(niter): grad, err = problem.calculate_grad(y) print( "err " , err) errList.append(err) y = y-grad/Lip y=np.maximum(y-beta/Lip,0) return y,errList errList=[] for iter in range(abs(niter)): grad, err = problem.calculate_grad(y) errList.append(err) solution = y + grad/Lip solution = np.maximum(solution-beta/Lip, 0) tnew = ( 1+math.sqrt(1.0+4*t*t) )/2 y[:] = solution +(t-1)/tnew *( solution - x_old ) t = tnew if niter<0: t=1 x_old[:] = solution if iter%1 ==0: # sys.stdout.write(" "*60+"\r"+("FISTA iter %d errore est %e mod_grad est %e" % ( iter, err, grad.std()) )) sys.stdout.write(("FISTA iter %d errore est %e mod_grad est %e\n" % ( iter, err, grad.std()) )) sys.stdout.flush() print( " ") return solution, errList def calculate_grad(problem, spettro, quadratic_only=0): err = problem.DD_scal/2 grad = -problem.i2f.dot( problem.SD_scal_flat ) err = err +np.dot( spettro, grad ) tmp = problem.f2i.dot(spettro) tmp = np.reshape(tmp,[-1, problem.SS_scal.shape[0]] ) tmp = np.tensordot( tmp, problem.SS_scal, axes=[[-1],[0]] ) tmp = np.reshape(tmp,[-1] ) add = problem.i2f.dot(tmp) err = err + np.dot(add, spettro )/2 if not quadratic_only: grad[:]+=add else: grad = add err=err + problem.beta*( np.abs(spettro) ).sum() return grad, err def fitta( DD_scal, SS_scal, SD_scal, energie_spettro, energie_for_SD , spettro , beta, niter, niterLip ): de = energie_spettro[1]-energie_spettro[0] e_min = energie_spettro[0] e_max = energie_spettro[-1] energie_for_SD_flat = np.array(energie_for_SD.flat) SD_scal_flat = np.array(SD_scal.flat) sparse_elements = [] for j in range(len(energie_for_SD_flat)): eneSD = energie_for_SD_flat[j] assert(eneSD>e_min ) assert(eneSD0 and fpos0: spettro_byscal[i] = spettro_byscal[i]/byscal_sum[i] def fit_spectra_roi( ref, datas, user_de , beta, niter, niterLip , SInfo , discard_threshold = 0 , threshold_fraction = 0 ): ## reference scan energy ( the analyser energies, in an array_ if ref is not None: enes_ref = ref.zscale DE_ref = (enes_ref[-1]-enes_ref[0]) ME_ref = (enes_ref[-1]+enes_ref[0])/2 if hasattr( ref, "incidentE" ): incidentE = ref.incidentE if incidentE is not None: SHIFT = incidentE*1000 - ME_ref print( " SHIFT " , SHIFT) else: SHIFT = 0 deltaEref = (enes_ref[-1]-enes_ref[0])/(len(enes_ref)-1) enes_ref = ME_ref+SHIFT - enes_ref # da aggiunger # (ME_ref+SHIFT) is nothing more than incident energy for reference scan MASK = ref.mask CRY = (MASK.shape[0]-1)/2.0 ## The intercepts refer the positions of the center of the ROI CRX = (MASK.shape[1]-1)/2.0 ## Here the center coordinates fNmiddle = ( len(enes_ref) - 1 )/2.0 + SHIFT/deltaEref ## this is the reference step's number for which the analyser energy = incidentE else: MASK = SInfo["mask"] CRY = (MASK.shape[0]-1)/2.0 ## The intercepts refer the positions of the center of the ROI CRX = (MASK.shape[1]-1)/2.0 ## The intercepts refer the positions of the center of the ROI # fNmiddle = ( len(enes_ref)-1.0 )/2.0 fNmiddle = None SHIFT=0 count=0 for data in datas: count+=1 enes_data = data.zscale # print " ENES DATA ", enes_data mymine = enes_data.min() mymaxe = enes_data.max() myde = (mymaxe-mymine)/(len(enes_data)-1) if count==1: mine = mymine maxe = mymaxe de = myde else: mine = min(mine, mymine ) maxe = max(maxe, mymaxe ) de = min(de, myde ) if user_de !=0: de = max(user_de,de) if ref is not None: mine = mine+enes_ref.min()-de maxe = maxe+enes_ref.max()+de else: mine = mine+ (-CRY/ abs( SInfo["zrate"]))*SInfo["estep"] - de maxe = maxe+ ( CRY/ abs( SInfo["zrate"]))*SInfo["estep"] + de nsteps = int( round((maxe-mine)/de) ) maxe = mine + (nsteps)*de # print maxe,mine,de, (maxe-mine)/de energie_spettro = np.linspace(mine -mine, maxe-mine, num=nsteps+1 , endpoint=True) spettro = np.zeros(nsteps+1,"d") ############################################################### spettro_byline = np.zeros(nsteps+1,"d") ## where intensity will be accumulated error_sum = np.zeros(nsteps+1,"d") ## where the N of occurencies will be accumulated ## needed to do the average frequencies_sum = np.zeros(nsteps+1,"d") ## to keep statisthics if ref is not None: hline =ref.line_infos.line [0] ## the height of the line slopeline = ref.line_infos.line [1] ## the slope of the line response_line_intensity = ref.line_infos.optical_response.sum(axis=0) # This is used to do some kind of weigthing # The reponse might be stronger at the middle of the line and weaker # at the left and right extrema # The response function is a 2D image , with this instruction we # project it over the line abscissa ## Yintercept and Xintercept are always subtracted by CRY and CRX respectively because in reponsepercussionelle.py ## they are the coordinate of the moving center of the response function hline += (ref.line_infos.Yintercept-CRY) + fNmiddle*ref.line_infos.Yslope - ((ref.line_infos.Xintercept-CRX) + fNmiddle*ref.line_infos.Xslope ) *slopeline ## This isthe line height ## in the middle of the reference scan ## We count energy from this middle DHoverDI = ref.line_infos.Yslope - ref.line_infos.Xslope *slopeline ## at the begginiing of the scan. ## The line start from a shiftx = ref.line_infos.Xintercept and this ## cross-talk with the slopeline ## DHoverDI is the height variation at each energy step, it keeps into account also ## the slope else: DHoverDI = SInfo["zrate"] ## when given in input zrate must correspond to: Yslope - Xslope *slopeline slopeline = SInfo["slope"] deltaEref = SInfo["estep"] hline = CRY #### - CRX*slopeline for data in datas: ## here data is a stack of 2D images for a given ROI enes_data = data.zscale ## this is supposed to be the analyser_energy denominator = data.denominator ## This is the factor applied for renormalisation ## it will be used to properly account for statisthical error mms = data.mm # P_enes=[] # P_mms=[] # P_sp_e=[] # P_sp_s=[] # P_MASK=MASK usecython=1 if usecython: if ref is not None: useref=1 weight_by_response = ref.line_infos.weight_by_response Xintercept = ref.line_infos.Xintercept # CRX # fNmiddle Xslope =ref.line_infos.Xslope response_line_intensity=response_line_intensity else: useref=0 weight_by_response =0 Xintercept =0.0 CRX =0.0 fNmiddle=0.0 Xslope =0.0 response_line_intensity=np.array([0.0,0.0],"d") print( " CHIAMO FITSP") print (" enes_data " , enes_data.dtype) print (" denominator " , denominator.dtype) print (" mms " , mms.dtype) print (" MASK " , MASK.dtype) # spettro_byline = spettro_byline.astype("d") ne pas faire ca enligne de l'argument # car spettro_byline est valeur de retour, ainsi que frequencies_sum = frequencies_sum.astype("d") error_sum = error_sum.astype("d") fitspectra_cy.spectra_roi_by_line(spettro_byline, error_sum, frequencies_sum, enes_data.astype("f"), denominator.astype("f") , mms.astype("f"), MASK.astype("f") , mine, de, discard_threshold , threshold_fraction, hline, slopeline, DHoverDI, deltaEref, useref, weight_by_response, Xintercept , CRX , fNmiddle, Xslope , response_line_intensity.astype("d") ) print( " in uscita da spectra_roi_by_line " , spettro_byline.sum() ) else: mask_npix = MASK.sum() for iE , (ene , mm, deno) in enumerate(zip(enes_data,mms, denominator)): if discard_threshold : mm_npix=( np.less( discard_threshold ,mm ) ).sum() print (" DISCARD ", discard_threshold, threshold_fraction , mask_npix, mm_npix) if mm_npix > mask_npix*threshold_fraction: continue ## one energy, one 2D image from the stack, one value for the denominator # P_enes.append(ene) # P_mms.append(mm) # P_sp_e.append( ene -(np.arange(mm.shape[0]) -hline)/DHoverDI * deltaEref ) # P_sp_s.append( mm[:,int(CRX)] ) for iy in range(mm.shape[0]): for ix in range(mm.shape[1]): # slow loop in python. You know why it is orribly slow. if not MASK[iy,ix]: ## Good. Mask is already taken into account ## But needs to be passed . Issue to be followed continue ## Assigning an energy to the pixel H0 = iy- ( hline + ix * slopeline) ii = H0 /DHoverDI E = ene - ii *deltaEref # + DE_ref*0 ## calculating the longitudinal position along the line ( to weight with the projected response_line_intensity) if ref is not None and ref.line_infos.weight_by_response: posx_inref = int( round( (ix-( (ref.line_infos.Xintercept-CRX+ fNmiddle*ref.line_infos.Xslope ) + ii * ref.line_infos.Xslope ) ))) if posx_inref>=0 and posx_inref< len(response_line_intensity): freq = response_line_intensity[posx_inref] # a weigthing factor else: freq = None else: freq = 1.0 if freq is not None: fipos = (E-mine)/de # the position in pixel units of the contribution to the spectra array (which starts from mine) ipos = int(fipos) # the integer part of fipos f = fipos - ipos # the fractional residu of fipos if ipos>0 and ipos < nsteps: # If I am withing the range of the spectra frequencies_sum[ipos] += (1-f)* freq # I distribute the contribution : 100% if f=0, to ipos , with the weigth given by response spettro_byline [ipos] += (1-f)* mm[iy,ix] # Same thing : distributing intensity to spectro_.. frequencies_sum[ipos+1] += f*freq # Same thing as above, just 100% if f=1 because we are distributing to the upper pixel spettro_byline [ipos+1] += f* mm[iy,ix] ## Calculating the error by hoping that the final result be gaussian error_sum[ipos] += (1-f)*(1-f)* mm[iy,ix] /deno # error_sum[ipos+1] += f*f* mm[iy,ix] /deno # # ff = open("/tmp/sp.p","wb") # todump = [P_enes, P_mms, P_sp_e, P_sp_s, P_MASK] # pickle.dump( todump , ff) for i in range(len(spettro_byline)): if spettro_byline [i] >0: spettro_byline [i] = spettro_byline [i] /frequencies_sum[i] error_sum[ i ] = math.sqrt(error_sum[ i ])/frequencies_sum[i] #################################################################### if niter: len_data = 0 for data in datas: len_data += len(data.zscale) len_ref = len(enes_ref) SS_scal = np.zeros([len_ref, len_ref],"d") S_L1 = np.zeros([len_ref],"d") SD_scal = np.zeros([len_ref, len_data],"d") DD_scal = 0.0 for data in datas: DD_scal += ( data.mm* data.mm ).sum() mm = ref.mm for i in range(len_ref): S_L1[i] = np.abs(mm[i]).sum() for j in range(len_ref): SS_scal[i,j] = ( mm[i] * mm[j] ).sum() count=0 for data in datas: for image in data.mm: for i in range(len_ref): SD_scal[ i , count] = ( mm[i] * image ).sum() count+=1 energies_sample = np.zeros( len_data , "d") len_data = 0 for data in datas: energies_sample[len_data:len_data+len(data.zscale)]=data.zscale len_data += len(data.zscale) energie_for_SD = enes_ref[:,None] + (energies_sample-mine) # spettro_byscal = np.zeros_like(energie_spettro) # byscal_sum = np.zeros_like(energie_spettro) #do_spettro_byscal(energie_spettro, spettro_byscal, SD_scal, energie_for_SD, S_L1 ) ##solution, errList = fitta( DD_scal, SS_scal, SD_scal, energie_spettro, energie_for_SD , spettro , beta, niter, niterLip ) ii, solution, errList = fitta( DD_scal, SS_scal, SD_scal, energie_spettro, energie_for_SD , spettro , beta, niter, niterLip ) ii.shape = energie_for_SD.shape # print mm.shape # print ii.T.shape sintesi = np.tensordot(ii.T , mm , axes = [ (-1),(0) ] ) len_data = 0 else: sintesi , solution, errList = None, None, None # return sintesi, energie_spettro, spettro_byline, solution, spettro_byscal, errList return sintesi, energie_spettro+mine, spettro_byline, error_sum, solution, errList xrstools-0.15.0+git20210910+c147919d/XRStools/fitmap.py000066400000000000000000000416521412732462000215510ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/env python # embedding_in_qt4.py --- Simple Qt4 application embedding matplotlib canvases # # Copyright (C) 2005 Florent Rougon # 2006 Darren Dale # # This file is an example program for matplotlib. It may be used and # modified with no restriction; raw copies as well as modified versions # may be distributed without limitation. from __future__ import unicode_literals import sys import os import random import PyQt4 from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.backends import qt_compat from matplotlib.colors import LogNorm import matplotlib.patches import math import h5py from six.moves import range use_pyside = qt_compat.QT_API == qt_compat.QT_API_PYSIDE from PyQt4 import QtGui, QtCore from numpy import arange, sin, pi from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.figure import Figure import numpy as np import glob progname = os.path.basename(sys.argv[0]) progversion = "0.1" class MyMplCanvas(FigureCanvas): """Ultimately, this is a QWidget (as well as a FigureCanvasAgg, etc.).""" def __init__(self, parent=None, width=5, height=4, dpi=100): fig = Figure(figsize=(width, height), dpi=dpi) self.fig=fig self.axes = fig.add_subplot(111) # We want the axes cleared every time plot() is called self.axes.hold(False) # FigureCanvas.__init__(self, fig) self.setParent(parent) self.compute_initial_figure() FigureCanvas.setSizePolicy(self, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Expanding) FigureCanvas.updateGeometry(self) self.cidpress = fig.canvas.mpl_connect( 'button_press_event', self.on_press) self.cidmotion = fig.canvas.mpl_connect( 'motion_notify_event', self.on_motion) def compute_initial_figure(self): pass def on_press(self, event): pass def on_motion(self, event): pass class Plot2D(MyMplCanvas): """2D Plot with interactions.""" def change_action(self, i): print( "change action ", i) print( self.combobox.currentText()) self.Mask_annotate.changeAction( i) for t in self.Points_annotate: t.changeAction( i) def renorm(self): mask = self.Mask_annotate.mask vmax = (self.img[mask]).max() print( " DA RINORMALIZZARE ", vmax) self.im.set_norm(LogNorm(vmax = vmax)) self.draw() def write_all(self): error = np.array(self.error) merr = error.max() self.Mask_annotate.update_mask() error[True - self.Mask_annotate.mask ] = merr*1.0e10 file = h5py.File("fitinit.h5","w") file["error"] = error file["data"] = self.img curve_group = file.require_group("curves") for i,t in enumerate(self.Points_annotate): rects = t.confirmed_rects points=[p for r,p in rects.items()] if len(p): curve_group[str(i)] = np.array(points) rects = self.Mask_annotate.confirmed_rects masks = [ r for m,r in rects.items() ] file["masks"] = np.array(masks) file.close() def read_all(self): file = h5py.File("fitinit.h5","r") masks = file["masks"][:] for x0,x1,y0,y1 in masks: rect = matplotlib.patches.Rectangle((x0,y0),(x1-x0), (y1-y0), facecolor='None', edgecolor='green') self.Mask_annotate.ax.add_patch(rect) self.Mask_annotate.confirmed_rects[ rect ] = [ x0,x1,y0,y1] self.Mask_annotate.update_mask() curve_group = file["curves"] for i in range(5): points = curve_group[str(i)][:] anno = self.Points_annotate[i] for x0,y0 in points: print( " riaggiungo in " , x0,y0) rect = matplotlib.patches.Circle(( x0,y0 ), radius=1) anno.confirmed_rects[ rect ] = [ x0,y0] anno.ax.add_patch(rect ) anno.fig.canvas.draw() def compute_initial_figure(self): file_list = glob.glob("scan_*.txt") file_list.sort( key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) img=[] error = [] for f in file_list: sc = np.loadtxt( f ) img.append( sc[:,-2] ) error.append( sc[:,-1] ) print( len(img[-1])) img = np.array(img ).T error = np.array(error ).T self.img = img self.error = error self.im = self.axes.imshow(img, norm=LogNorm(), #vmax=1000), aspect='auto', origin='lower', interpolation='nearest') # print " IMIMIMIM ", im.norm # print dir(im) self.Mask_annotate = Annotate(self.fig, self.axes,0) self.Points_annotate = [] for i in range(5): self.Points_annotate.append( Annotate(self.fig, self.axes,i+1) ) def on_press(self, event): 'on button press we will see if the mouse is over us and store some data' pass # print " ##################################### " # print dir(event) # print " BUTTON ", event.button, event.xdata, event.ydata def on_motion(self, event): if event.xdata is not None: ix,iy = int(round(event.xdata)), int(round(event.ydata)) self.label.setText( "X=%10d\nY=%10d\nZ=%10.5e " %(ix,iy , self.img[iy,ix]) ) pass # print " ##################################### " # print " BUTTON move", event.button, event.xdata, event.ydata class MyStaticMplCanvas(MyMplCanvas): """Simple canvas with a sine plot.""" def compute_initial_figure(self): t = arange(0.0, 3.0, 0.01) s = sin(2*pi*t) self.axes.plot(t, s) def readjust( x0,x1,y0,y1 ) : tx0 = math.ceil(min(x0,x1)+0.5 )-0.5 tx1 = math.floor(max(x0,x1)+0.5)-0.5 ty0 = math.ceil(min(y0,y1)+0.5)-0.5 ty1 = math.floor(max(y0,y1)+0.5 )-0.5 return tx0,tx1,ty0,ty1 class MyDynamicMplCanvas(MyMplCanvas): """A canvas that updates itself every second with a new plot.""" def __init__(self, *args, **kwargs): MyMplCanvas.__init__(self, *args, **kwargs) timer = QtCore.QTimer(self) timer.timeout.connect(self.update_figure) timer.start(1000) def compute_initial_figure(self): self.axes.plot([0, 1, 2, 3], [1, 2, 0, 4], 'r') def update_figure(self): # Build a list of 4 random integers between 0 and 10 (both inclusive) l = [random.randint(0, 10) for i in range(4)] self.axes.plot([0, 1, 2, 3], l, 'r') self.draw() class Annotate(object): def __init__(self, fig, axes, myaction): self.fig = fig self.ax = axes self.rect = matplotlib.patches.Rectangle((0,0), 1, 1, facecolor='None', edgecolor='green') self.x0 = None self.y0 = None self.x1 = None self.y1 = None self.ax.add_patch(self.rect) self.ax.figure.canvas.mpl_connect('button_press_event', self.on_press) self.fig.canvas.mpl_connect('button_press_event', self.on_press) self.fig.canvas.mpl_connect('button_release_event', self.on_release) self.fig.canvas.mpl_connect('motion_notify_event', self.on_motion) self.pressed = False self.confirmed_rects = {} self.LX0,self.LX1 = self.ax.get_xlim() self.LY0,self.LY1 = self.ax.get_ylim() self.LX0 = math.ceil(self.LX0 +0.5 )-0.5 self.LX1 = math.floor(self.LX1+0.5)-0.5 self.LY0 = math.ceil(self.LY0+0.5)-0.5 self.LY1 = math.floor(self.LY1 +0.5 )-0.5 self.mask = np.zeros([ self.LY1-self.LY0 , self.LX1-self.LX0 ], dtype = bool) self.currentAction = 0 self.myaction=myaction def changeAction(self,i): self.currentAction = i isactive=False if self.currentAction==self.myaction: isactive=True for rect in self.confirmed_rects: if not isactive: rect.set_facecolor('red') rect.set_edgecolor('blue') else: rect.set_facecolor('blue') rect.set_edgecolor('red') self.fig.canvas.draw() def update_mask(self): if self.myaction==0: self.mask[:]= True for rect, (x0,x1,y0,y1) in self.confirmed_rects.items(): self.mask[ int(math.ceil(y0)):int(math.ceil(y1)) , int(math.ceil(x0)):int(math.ceil(x1)) ] = False def on_press(self, event): if self.currentAction!=self.myaction : return # print 'press' if event.xdata is None: return print( " BUTTON ", event.button) if event.button==3: for rect in list(self.confirmed_rects): contains, attrd = rect.contains( event ) print( contains, attrd) if contains: print( " RIMUOVO " , self.confirmed_rects[rect]) del self.confirmed_rects[rect] rect.remove() self.fig.canvas.draw() if self.myaction==0: self.update_mask() return self.x0 = event.xdata self.y0 = event.ydata self.x1 = event.xdata self.y1 = event.ydata self.oldx=None self.oldy=None if self.myaction==0: self.rect.set_width(self.x1 - self.x0) self.rect.set_height(self.y1 - self.y0) self.rect.set_xy((self.x0, self.y0)) self.rect.set_linestyle('dashed') self.fig.canvas.draw() self.pressed = True else: if event.button==1: print( " VORREI AGGIUNGER UN PUNTO ") self.rect = matplotlib.patches.Circle(( self.x0,self.y0 ), radius=1) self.confirmed_rects[ self.rect ] = [ self.x0,self.y0] self.ax.add_patch(self.rect ) self.fig.canvas.draw() elif event.button==2: print( "oppure iniziare a spostarlo se bottone di mezzo ") self.rect=None self.pressed=False for rect in list(self.confirmed_rects): contains, attrd = rect.contains( event ) if contains: self.rect = rect self.pressed=True def on_motion(self,event): if self.currentAction!=self.myaction : return if not self.pressed : return if event.xdata is None: return self.oldx=self.x1 self.oldy=self.y1 self.x1 = event.xdata self.y1 = event.ydata print( self.x1) print( self.y1) # pc = self.ax.transData.transform(np.vstack([[self.x1],[self.y1]]).T) # print pc.T if self.myaction ==0: self.rect.set_width(self.x1 - self.x0) self.rect.set_height(self.y1 - self.y0) self.rect.set_xy((self.x0, self.y0)) self.rect.set_linestyle('dashed') self.fig.canvas.draw() else: if event.button==2: print( " provo aspostare ", self.rect, " su ", (self.x1, self.y1)) self.rect.center = (self.x1, self.y1) self.confirmed_rects[self.rect] = (self.x1, self.y1) self.fig.canvas.draw() def on_release(self, event): if self.myaction !=0: return if self.currentAction!=self.myaction : return self.pressed = False print( 'release') if event.xdata is not None: self.x1 = event.xdata self.y1 = event.ydata elif self.oldx is not None: X0,X1 = self.ax.get_xlim() Y0,Y1 = self.ax.get_ylim() if abs(self.oldx-self.x1 )> abs(X1-self.x1 ): self.x1 = X1 elif abs(self.oldx-self.x1 )> abs(X0-self.x1 ): self.x1 = X0 if abs(self.oldy-self.y1 )> abs(Y1-self.y1 ): self.y1 = Y1 elif abs(self.oldy-self.y1 )> abs(Y0-self.y1 ): self.y1 = Y0 if self.x0 is None: return self.x0,self.x1,self.y0,self.y1 = readjust( self.x0,self.x1,self.y0,self.y1 ) print( " READJUSTED ", self.x0,self.x1,self.y0,self.y1) self.rect.set_width(self.x1 - self.x0) self.rect.set_height(self.y1 - self.y0) self.rect.set_xy((self.x0, self.y0)) self.rect.set_linestyle('solid') if self.x0==self.x1 or self.y0==self.y1: self.rect.remove() ret_val = [ None]*4 else: ret_val = [ self.x0,self.x1,self.y0,self.y1] self.confirmed_rects[ self.rect ] = [ self.x0,self.x1,self.y0,self.y1] self.fig.canvas.draw() self.rect = matplotlib.patches.Rectangle((0,0), 1, 1, facecolor='None', edgecolor='green') self.x0 = None self.y0 = None self.x1 = None self.y1 = None self.ax.add_patch(self.rect) self.update_mask() return ret_val class ApplicationWindow(QtGui.QMainWindow): def __init__(self): QtGui.QMainWindow.__init__(self) self.setAttribute(QtCore.Qt.WA_DeleteOnClose) self.setWindowTitle("application main window") self.file_menu = QtGui.QMenu('&File', self) self.file_menu.addAction('&Quit', self.fileQuit, QtCore.Qt.CTRL + QtCore.Qt.Key_Q) self.menuBar().addMenu(self.file_menu) self.help_menu = QtGui.QMenu('&Help', self) self.menuBar().addSeparator() self.menuBar().addMenu(self.help_menu) self.help_menu.addAction('&About', self.about) self.main_widget = QtGui.QWidget(self) hlayout = QtGui.QHBoxLayout(self.main_widget) self.panel_widget = QtGui.QWidget(self) self.plot_widget = QtGui.QWidget(self) hlayout.addWidget(self.panel_widget) hlayout.addWidget(self.plot_widget ) self.vl_cmds = QtGui.QVBoxLayout(self.panel_widget) self.label = QtGui.QLabel("pippo") self.vl_cmds.addWidget(self.label) if 0: l = QtGui.QVBoxLayout(self.main_widget) sc = MyStaticMplCanvas(self.main_widget, width=5, height=4, dpi=100) dc = MyDynamicMplCanvas(self.main_widget, width=5, height=4, dpi=100) l.addWidget(sc) l.addWidget(dc) if 1: l = QtGui.QVBoxLayout(self.plot_widget) # sc = MyStaticMplCanvas(self.plot_widget, width=5, height=4, dpi=100) # dc = MyDynamicMplCanvas(self.plot_widget, width=5, height=4, dpi=100) sc = Plot2D(self.plot_widget, width=5, height=4, dpi=100) sc.label = self.label l.addWidget(sc) # l.addWidget(dc) but = QtGui.QPushButton('Renorm', self) but.clicked.connect(sc.renorm) self.vl_cmds.addWidget(but) combobox = QtGui.QComboBox( self) combobox.insertItems( 0, ["Work On Masks","Work On N 0","Work On N 1","Work On N 2","Work On N 3","Work On N 4"] ) # comboBox.connect(comboBox, QtCore.SIGNAL("activated(int)"), self.textChangedFilter) combobox.activated.connect(sc.change_action ) sc.combobox = combobox self.vl_cmds.addWidget(combobox) write_button = QtGui.QPushButton('Write', self) write_button.clicked.connect(sc.write_all) self.vl_cmds.addWidget(write_button) read_button = QtGui.QPushButton('Read', self) read_button.clicked.connect(sc.read_all) self.vl_cmds.addWidget(read_button) self.main_widget.setFocus() self.setCentralWidget(self.main_widget) self.statusBar().showMessage("All hail matplotlib!", 2000) def fileQuit(self): self.close() def closeEvent(self, ce): self.fileQuit() def about(self): QtGui.QMessageBox.about(self, "About", """embedding_in_qt4.py example Copyright 2005 Florent Rougon, 2006 Darren Dale This program is a simple example of a Qt4 application embedding matplotlib canvases. It may be used and modified with no restriction; raw copies as well as modified versions may be distributed without limitation.""" ) qApp = QtGui.QApplication(sys.argv) aw = ApplicationWindow() aw.setWindowTitle("%s" % progname) aw.show() sys.exit(qApp.exec_()) #qApp.exec_() xrstools-0.15.0+git20210910+c147919d/XRStools/hdf5_dialog.py000066400000000000000000000034061412732462000224310ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] class hdf5dialog(Qt.QDialog): def __init__(self, parent=None): super(hdf5dialog , self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "hdf5dialog.ui" ), self) def hdf5dialog(): filename = Qt.QFileDialog.getOpenFileName(None,'Open hdf5 file with rois',filter="hdf5 (*h5)\nall files ( * )" ) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) if len(filename): import PyMca5.PyMcaGui.HDF5Widget as HDF5Widget storage=[None] def mySlot(ddict): name = ddict["name"] storage[0]=name # browse self.__hdf5Dialog = hdf5dialog() self.__hdf5Dialog.setWindowTitle('Select a Group containing roi_definitions by a double click') self.__hdf5Dialog.mainLayout = self.__hdf5Dialog.verticalLayout_2 fileModel = HDF5Widget.FileModel() fileView = HDF5Widget.HDF5Widget(fileModel) hdf5File = fileModel.openFile(filename) shiftsDataset = None fileView.sigHDF5WidgetSignal.connect(mySlot) self.__hdf5Dialog.mainLayout.addWidget(fileView) self.__hdf5Dialog.resize(400, 200) ret = self.__hdf5Dialog.exec_() print( ret) hdf5File.close() if ret: return filename +":" + name else: return filename +":" + name else: return filename xrstools-0.15.0+git20210910+c147919d/XRStools/helpers.py000066400000000000000000000777121412732462000217410ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range from six.moves import input #!/usr/bin/python # Filename: extraction.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import os import math import numpy as np import array as arr import matplotlib.pyplot as plt import pickle from matplotlib.widgets import Cursor from itertools import groupby from scipy.integrate import trapz from scipy import interpolate, signal, integrate, constants, optimize from re import findall from scipy.ndimage import measurements from scipy.optimize import leastsq, fmin from scipy.interpolate import Rbf, RectBivariateSpline from scipy.integrate import odeint #fcomp = 1/(-i*lex)*(-2*((abb0*(abb8 + abb7*sgbeta*t) + abb1) + i*y0) *(y(1) + i*y(2)) + c1*(1 + (y(1) + i*y(2)).^2)); #print mpr_compds([1,2,3],['SiO2','CO2'],[0.5,0.5],9.86,[1 1]) # xrs_read class scan: """ this is a container class for inelastic x-ray scattering scans with 2D detectors each scan is an instance of this class, the scans of an experiment can then be grouped and ordered by this class's attributes scantype, starting energy, etc. """ def __init__(self,mats,num,en,monoa,moni,counts,mots,data,scantype='generic'): # rawdata self.edfmats = np.array(mats) self.number = num self.scantype = scantype self.energy = np.array(en) self.monoangle= np.array(monoa) self.monitor = np.array(moni) # some things maybe not imediately necessary self.counters = np.array(counts) self.motors = mots self.specdata = np.array(data) # data (to be filled after defining rois) self.eloss = [] self.signals = [] self.errors = [] self.signals_orig = [] # keep a copy of uninterpolated data # would like to keep uninterpolated signals/eloss/errors, too def applyrois(self,rois,scaling=None): """ sums up each 2D matrix of a scan over the indices given in rois, i.e. turns the 3D matrix of a scan (stack of 2D detector images) into a matrix of size (len(energy),number of rois) rois are a list of tuples """ data = np.zeros((len(self.edfmats),len(rois))) for n in range(len(rois)): # each roi (default is 9) for m in range(len(self.edfmats)): # each energy point along the scan for l in range(len(rois[n])): # each pixel on the detector data[m,n] += self.edfmats[m,rois[n][l][1],rois[n][l][0]] self.signals = np.array(data) self.signals_orig = np.array(data) if np.any(scaling): assert len(scaling) == len(rois) # make sure, there is one scaling factor for each roi for ii in range(len(rois)): self.signals[:,ii] *= scaling[ii] self.signals_orig[:,ii] *= scaling[ii] def applyrois_old(self,rois): """ sums up each 2D matrix of a scan over the indices given in rois, i.e. turns the 3D matrix of a scan (stack of 2D detector images) into a matrix of size (len(energy),number of rois) rois are a list of tuples this seems a bit faster than the old version """ data = np.zeros((len(self.edfmats),len(rois))) roixinds = [] roiyinds = [] for r in range(len(rois)): for n in range(len(rois[r])): roixinds.append(rois[r][n][0]) roiyinds.append(rois[r][n][1]) data[:,r] = np.sum(np.sum(self.edfmats[:,np.amin(roiyinds):np.amax(roiyinds)+1,np.amin(roixinds):np.amax(roixinds)+1],axis=1),axis=1) self.signals = data def gettype(self): return self.scantype def getnumber(self): return self.number def getshape(self): if not np.any(self.signals): print( 'please apply the ROIs first.') return else: return np.shape(self.signals) def getnumofrois(self): if not self.signals.any(): print( 'please apply the ROIs first.') return else: return np.shape(self.signals)[1] class scangroup: """ container class for scans with the same 'scantype' attribute each group of scans will summed into an instance of this class different goups of scans will then be stitched together based on their type, starting energy, etc. """ def __init__(self,energy,signals,errors,grouptype='generic'): self.energy = energy self.eloss = [] self.signals = signals self.errors = errors self.grouptype = grouptype self.signals_orig = signals # keep a copy of uninterpolated data def gettype(self): return self.grouptype def getmeanenergy(self): return np.mean(self.energy) def getestart(self): return self.energy[0] def geteend(self): return self.energy[-1] def getmeanegridspacing(self): return np.mean(np.diff(self.energy)) def getmaxediff(self): return (self.energy[-1]-self.energy[0]) # superresolution/imaging ################################# # ROIs ################################# class rois: """ a class to define ROIs ROIs are saved in a container (self.rois) """ def __init__(self,scans,scannumbers): self.scandata = scans # dictionary with instances of the onescan class if not isinstance(scannumbers,list): thescannumbers = [] thescannumbers.append(scannumbers) else: thescannumbers = scannumbers self.scannums = thescannumbers self.DET_PIXEL_NUM = 256 self.rois = container() # single variable that holds all relevant info about the defined rois self.rois.inds = [] # list of lists of touples (for each ROI) self.rois.xinds = [] # list of lists of x-indices (for each ROI) self.rois.yinds = [] # list of lists of y-indices (for each ROI) self.rois.roiNumber = 0 # number of ROIs defined self.rois.kind = [] # what kind of ROI (auto, zoom, linear) self.rois.height = [] # list of hights (in pixels, for each ROI) self.rois.width = [] # list of widths (in pixels, for each ROI) def preparemats(self): """ sums and squeezes all edf-files of a scan/all scans into one matrix """ # take shape of the first edf-matrices dim = np.shape(self.scandata['Scan%03d' % self.scannums[0]].edfmats) edfmatrices = np.zeros((1,dim[1],dim[2])) for number in self.scannums: scanname = 'Scan%03d' % number edfmatrices = np.append(edfmatrices,self.scandata[scanname].edfmats,axis=0) return sumx(edfmatrices) def prepareedgemats(self,index): """ difference between two summed and squeezed edf-files of a scan from below and above energyval """ # take shape of the first edf-matrices dim = np.shape(self.scandata['Scan%03d' % self.scannums[0]].edfmats) abovemats = np.zeros((1,dim[1],dim[2])) belowmats = np.zeros((1,dim[1],dim[2])) for number in self.scannums: scanname = 'Scan%03d' % number abovemats = np.append(abovemats,self.scandata[scanname].edfmats[index:,:,:],axis=0) belowmats = np.append(belowmats,self.scandata[scanname].edfmats[:index,:,:],axis=0) return np.absolute(np.squeeze(np.sum(abovemats,axis=0))-np.squeeze(np.sum(belowmats,axis=0))) def getlinrois(self,numrois,logscaling=True,height=5,colormap='jet'): """ define ROIs by clicking two points """ # clear all existing rois self.deleterois() plt.clf() fig = plt.figure(figsize=(8, 6)) ax = fig.add_subplot(111, axisbg='#FFFFCC') thematrix = self.preparemats() plt.title('Click start and endpoints of the linear ROIs you whish.',fontsize=14) if logscaling: theimage = plt.imshow(np.log(np.absolute(thematrix))) else: theimage = plt.imshow(thematrix) # Choose a color palette theimage.set_cmap(colormap) plt.colorbar() therois = [] # list of lists of tuples with (x,y) coordinates theroisx = [] # list of lists of x-indices theroisy = [] # list of lists of y-indices cursor = Cursor(ax, useblit=True, color='red', linewidth=1 ) for m in range(numrois): plt.clf() if m>0: if therois[m-1]: for index in therois[m-1]: thematrix[index[1],index[0]] = np.amax(thematrix) print ("chose two points as endpoints for linear ROI No. %s" % (m+1)) if logscaling: theimage = plt.imshow(np.log(np.absolute(thematrix))) else: theimage = plt.imshow(thematrix) theimage.set_cmap(colormap) plt.draw() endpoints = [] endpts = plt.ginput(2) for point in endpts: point = [round(element) for element in point] endpoints.append(np.array(point)) roix = np.arange(endpoints[0][0],endpoints[1][0]) roiy = [round(num) for num in np.polyval(np.polyfit([endpoints[0][0],endpoints[1][0]],[endpoints[0][1],endpoints[1][1]],1),roix)] theheight = np.arange(-height,height) eachroi = [] eachroix = [] eachroiy = [] for n in range(len(roix)): for ind in theheight: eachroi.append((roix[n],roiy[n]+ind)) eachroix.append(roix[n]) eachroiy.append(roiy[n]+ind) therois.append(eachroi) theroisx.append(eachroix) theroisy.append(eachroiy) self.rois.height.append(len(theheight)) # save the hight of each roi (e.g. for imaging) self.rois.width.append(endpoints[1][0]-endpoints[0][0]) # save the width of each roi del endpoints del eachroi del eachroix del eachroiy self.rois.kind = 'linear' self.rois.inds = therois self.rois.xinds = theroisx self.rois.yinds = theroisy self.rois.roiNumber = numrois def getzoomrois(self,numrois,logscaling=True,colormap='jet'): """ define ROIs by zooming in on plot """ # clear all existing rois self.deleterois() plt.clf() thematrix = self.preparemats() limmax = np.shape(thematrix)[1] therois = [] # list of lists of tuples with (x,y) coordinates theroisx = [] # list of lists of x-indices theroisy = [] # list of lists of y-indices plt.title('Zoom around ROI, change to shell, and press Return') if logscaling: theimage = plt.imshow(np.log(np.absolute(thematrix))) else: theimage = plt.imshow(thematrix) theimage.set_cmap(colormap) plt.ion() for m in range(numrois): plt.clf() if m>0: if therois[m-1]: for index in therois[m-1]: thematrix[index[0],index[1]] = np.amax(thematrix) plt.title('Zoom around ROI, change to shell, and press Return') if logscaling: theimage = plt.imshow(np.log(np.absolute(thematrix))) else: theimage = plt.imshow(thematrix) theimage.set_cmap(colormap) plt.draw() thestring = 'Zoom around ROI No. %s and press enter to continue.' % (m+1) wait = input(thestring) limits = np.floor(plt.axis()) inds = limits < 1 limits[inds] = 1 inds = limits > limmax limits[inds] = limmax print( 'selected limits are: ', limits) T = np.zeros(np.shape(thematrix)) T[limits[3]:limits[2],:] = 1 t = np.zeros(np.shape(thematrix)) t[:,limits[0]:limits[1]] = 1 indsx,indsy = np.where(t*T == 1) eachroi = [] eachroix = [] eachroiy = [] for n in range(len(indsx)): eachroi.append((indsy[n],indsx[n])) eachroix.append(indsx[n]) eachroiy.append(indsy[n]) therois.append(eachroi) theroisx.append(eachroix) theroisy.append(eachroiy) plt.show() self.rois.kind = 'zoom' self.rois.inds = therois self.rois.xinds = theroisx self.rois.yinds = theroisy self.rois.roiNumber = numrois def getzoomrois_frommatrix(self,matrix,numrois,logscaling=True,colormap='jet'): """ define ROIs by zooming in on plot """ # clear all existing rois self.deleterois() plt.clf() thematrix = matrix limmax = np.shape(thematrix)[1] therois = [] plt.title('Zoom around ROI, change to shell, and press Return') if logscaling: theimage = plt.imshow(np.log(np.absolute(thematrix))) else: theimage = plt.imshow(thematrix) theimage.set_cmap(colormap) plt.ion() for m in range(numrois): plt.title('Zoom around ROI, change to shell, and press Return') if logscaling: theimage = plt.imshow(np.log(np.absolute(thematrix))) else: theimage = plt.imshow(thematrix) theimage.set_cmap(colormap) plt.ion() thestring = 'Zoom around ROI No. %s and press enter to continue.' % (m+1) wait = input(thestring) limits = np.floor(plt.axis()) plt.axis([0.0,self.DET_PIXEL_NUM,self.DET_PIXEL_NUM,0.0]) inds = limits < 1 limits[inds] = 1 inds = limits > limmax limits[inds] = limmax print( 'selected limits are: ', limits) T = np.zeros(np.shape(thematrix)) T[limits[3]:limits[2],:] = 1 t = np.zeros(np.shape(thematrix)) t[:,limits[0]:limits[1]] = 1 indsx,indsy = np.where(t*T == 1) eachroi = [] for n in range(len(indsx)): eachroi.append((indsy[n],indsx[n])) therois.append(eachroi) plt.show() self.rois.kind = 'zoom' self.rois.inds = therois self.rois.roiNumber = numrois def getlinedgerois(self,index,numrois=9,logscaling=True,height=5,colormap='jet'): """ define ROIs by clicking two points in difference picture """ # clear all existing rois self.deleterois() plt.clf() thematrix = self.prepareedgemats(index) # change this to use an index if logscaling: theimage = plt.imshow(np.log(np.absolute(thematrix))) else: theimage = plt.imshow(thematrix) # Choose a color palette theimage.set_cmap(colormap) plt.colorbar() therois = [] for m in range(numrois): print ("chose two points as endpoints for linear ROI No. %s" % (m+1)) endpoints = [] endpts = plt.ginput(2) for point in endpts: point = [round(element) for element in point] endpoints.append(np.array(point)) roix = np.arange(endpoints[0][0],endpoints[1][0]) roiy = [round(num) for num in np.polyval(np.polyfit([endpoints[0][0],endpoints[1][0]],[endpoints[0][1],endpoints[1][1]],1),roix)] del endpoints theheight = np.arange(-height,height) eachroi = [] for n in range(len(roix)): for ind in theheight: eachroi.append((roix[n],roiy[n]+ind)) therois.append(eachroi) del eachroi plt.show() self.rois.kind = 'linear' self.rois.inds = therois self.rois.roiNumber = numrois def getzoomedgerois(self,index,numrois=9,logscaling=True,colormap='jet'): """ define ROIs by zooming in on plot """ # clear all existing rois self.deleterois() plt.clf() thematrix = self.prepareedgemats(index) limmax = np.shape(thematrix)[1] therois = [] title('Zoom around ROI, change to shell, and press Return') if logscaling: theimage = plt.imshow(np.log(np.absolute(thematrix))) else: theimage = plt.imshow(thematrix) theimage.set_cmap(colormap) plt.ion() for m in range(numrois): plt.title('Zoom around ROI, change to shell, and press Return') if logscaling: theimage = plt.imshow(np.log(np.absolute(thematrix))) else: theimage = plt.imshow(thematrix) theimage.set_cmap(colormap) plt.ion() thestring = 'Zoom around ROI No. %s and press enter to continue.' % (m+1) wait = input(thestring) limits = np.floor(axis()) plt.axis([0.0,self.DET_PIXEL_NUM,self.DET_PIXEL_NUM,0.0]) inds = limits < 1 limits[inds] = 1 inds = limits > limmax limits[inds] = limmax print( 'selected limits are: ', limits) T = np.zeros(np.shape(thematrix)) T[limits[3]:limits[2],:] = 1 t = np.zeros(np.shape(thematrix)) t[:,limits[0]:limits[1]] = 1 indsx,indsy = np.where(t*T == 1) eachroi = [] for n in range(len(indsx)): eachroi.append((indsy[n],indsx[n])) therois.append(eachroi) plt.show() self.rois.kind = 'zoom' self.rois.inds = therois self.rois.roiNumber = numrois def getautorois(self,kernel_size=5,colormap='jet',thresholdfrac=100): """ define ROIs by choosing a threshold through a slider bar on the plot window """ # clear all existing rois self.deleterois() plt.clf() thematrix = self.preparemats() # the starting matrix to plot plt.title('Crank the threshold and close the plotting window, when you are satisfied.',fontsize=14) ax = plt.subplot(111) plt.subplots_adjust(left=0.05, bottom=0.2) thres0 = 0 # initial threshold value theimage = plt.imshow(np.log(np.absolute(thematrix))) theimage.set_cmap(colormap) plt.colorbar() thresxcolor = 'lightgoldenrodyellow' thresxamp = plt.axes([0.2, 0.10, 0.55, 0.03], axisbg=thresxcolor) sthres = plt.Slider(thresxamp, 'Threshold', 0.0, np.floor(np.amax(thematrix)/thresholdfrac), valinit=thres0) def update(val): thres = sthres.val newmatrix = signal.medfilt2d(thematrix, kernel_size=kernel_size) belowthres_indices = newmatrix < thres newmatrix[belowthres_indices] = 0 labelarray,numfoundrois = measurements.label(newmatrix) print( str(numfoundrois) + ' ROIs found!' ) # organize rois therois = [] for n in range(numfoundrois): rawindices = np.nonzero(labelarray == n+1) eachroi = [] for m in range(len(rawindices[0])): eachroi.append((rawindices[1][m],rawindices[0][m])) therois.append(eachroi) self.rois.inds = therois self.rois.roiNumber = numfoundrois theimage.set_data(newmatrix) plt.draw() sthres.on_changed(update) plt.show() self.rois.kind = 'auto' def getautorois_eachdet(self,kernel_size=5,colormap='jet',thresholdfrac=100): """ autoroi, detector for detector for ID20 (6 detector setup) so that different thresholds can be choosen for the different detectors define ROIs by choosing a threshold through a slider bar on the plot window """ # clear all existing rois self.deleterois() wholematrix = np.array(self.preparemats()) # full 6-detector image imagelims = [[0,256,0,256],[0,256,256,512],[0,256,512,768],[256,512,0,256],[256,512,256,512],[256,512,512,768]] for n in range(len(imagelims)): plt.clf() # thematrix now is a single detector image (256x256 pixels) thematrix = wholematrix[imagelims[n][0]:imagelims[n][1],imagelims[n][2]:imagelims[n][3]] plt.title('Crank the threshold and close the plotting window, when you are satisfied.',fontsize=14) ax = plt.subplot(111) plt.subplots_adjust(left=0.05, bottom=0.2) thres0 = 0 # initial threshold value theimage = plt.imshow(np.log(np.absolute(thematrix))) theimage.set_cmap(colormap) plt.colorbar() thresxcolor = 'lightgoldenrodyellow' thresxamp = plt.axes([0.2, 0.10, 0.55, 0.03], axisbg=thresxcolor) sthres = plt.Slider(thresxamp, 'Threshold', 0.0, np.floor(np.amax(thematrix)/thresholdfrac), valinit=thres0) # using the "container" class to pass the results of the rois from the nested function roi_result = container() def update(val): thres = sthres.val newmatrix = signal.medfilt2d(thematrix, kernel_size=kernel_size) belowthres_indices = newmatrix < thres newmatrix[belowthres_indices] = 0 labelarray,numfoundrois = measurements.label(newmatrix) print( str(numfoundrois) + ' ROIs found!' ) # organize rois therois = [] for n in range(numfoundrois): rawindices = np.nonzero(labelarray == n+1) eachroi = [] for m in range(len(rawindices[0])): eachroi.append((rawindices[1][m],rawindices[0][m])) therois.append(eachroi) theimage.set_data(newmatrix) plt.draw() roi_result.therois = therois roi_result.numfoundrois = numfoundrois sthres.on_changed(update) plt.show() thestring = 'Press enter to continue.' wait = input(thestring) self.rois.inds.extend(roi_result.therois) self.rois.roiNumber += roi_result.numfoundrois self.rois.kind = 'auto' def saverois_old(self,filename): """ save the ROIs in file with name filename """ f = open(filename, 'wb') theobject = [self.rois, self.roinums, self.roikind] pickle.dump(theobject, f,protocol=-1) f.close() def loadrois_old(self,filename): """ load ROIs from file with name filename """ f = open(filename,'rb') theobject = pickle.load(f) f.close() self.rois = theobject[0] self.roinums = theobject[1] self.roikind = theobject[2] def saverois(self,filename): """ save the ROIs in file with name filename """ f = open(filename, 'wb') pickle.dump(self.rois, f,protocol=-1) f.close() def loadrois(self,filename): """ load ROIs from file with name filename """ f = open(filename,'rb') self.rois = pickle.load(f) f.close() def deleterois_old(self): """ delete the existing ROIs """ self.rois = [] self.roinums = [] self.roikind = [] def deleterois(self): """ delete existing ROIs (same as at initialization) """ self.rois = container() # single variable that holds all relevant info about the defined rois self.rois.inds = [] # list of lists of touples (for each ROI) self.rois.xinds = [] # list of lists of x-indices (for each ROI) self.rois.yinds = [] # list of lists of y-indices (for each ROI) self.rois.roiNumber = 0 # number of ROIs defined self.rois.kind = [] # what kind of ROI (auto, zoom, linear) self.rois.height = [] # list of hights (in pixels, for each ROI) self.rois.width = [] # list of widths (in pixels, for each ROI) def plotrois(self): """ returns a plot of the ROI shapes """ plt.figure() thematrix = np.zeros((self.DET_PIXEL_NUM,self.DET_PIXEL_NUM)) for roi in self.rois: for index in roi: thematrix[index[1],index[0]] = 1 theimage = plt.imshow(thematrix) plt.show() class container: """ random container class to hold values """ def __init__(self): pass ######################################################################### # # trash # # def createrois(self,scannumbers): # """ # Creates a Rois object from information from this class and scannumbers. # INPUT: # scannumbers = integer or list of integers of scannumbers from which should be used to define the ROIs. # """ # rois_object = xrs_rois.rois(self.scans,scannumbers) # return rois_object # # def getautorois(self,scannumbers,kernel_size=5,colormap='jet',threshfrac=100): # """ # Define ROIs automatically using median filtering and a variable threshold. # INPUT: # scannumbers = # kernel_size = # colormap = # threshfrac = # (This function should be deprecated once the new style of ROI definitions work properly) # """ # rois_object = self.createrois(scannumbers) # rois_object.getautorois(kernel_size,colormap,thresholdfrac=threshfrac) # self.rois = rois_object.rois # # add the new xrs_rois's functions and classes # new_obj = xrs_rois.roi_object() # new_obj.indices = self.rois.inds # #print np.shape(self.rois.inds), np.shape(new_obj.indices) # self.roi_obj = new_obj # # def getautorois_eachdet(self,scannumbers,kernel_size=5,colormap='jet',threshfrac=100): # """ # define ROIs automatically one detector at a time using median filtering and a variable threshold # (This function should be deprecated once the new style of ROI definitions work properly) # """ # rois_object = self.createrois(scannumbers) # rois_object.getautorois_eachdet(kernel_size,colormap,thresholdfrac=threshfrac) # self.rois = rois_object.rois # # add the new xrs_rois's functions and classes # new_obj = xrs_rois.roi_object() # new_obj.indices = rois_object.rois.inds # self.roi_obj = new_obj # # def getlinrois(self,scannumbers,numrois=72,logscaling=True,height=5,colormap='jet'): # """ # define ROIs by clicking two points # (This function should be deprecated once the new style of ROI definitions work properly) # """ # rois_object = self.createrois(scannumbers) # rois_object.getlinrois(numrois,logscaling,height,colormap) # self.rois = rois_object.rois # # add the new xrs_rois's functions and classes # new_obj = xrs_rois.roi_object() # new_obj.indices = self.rois.inds # self.roi_obj = new_obj # def getzoomrois(self,scannumbers,numrois=72,logscaling=True,colormap='jet'): # """ # define ROIs by zooming in on a region of interest # (This function should be deprecated once the new style of ROI definitions work properly) # """ # rois_object = self.createrois(scannumbers) # rois_object.getzoomrois(numrois,logscaling,colormap) # self.rois = rois_object.rois # # add the new xrs_rois's functions and classes # new_obj = xrs_rois.roi_object() # new_obj.indices = rois_object.rois.inds # self.roi_obj = new_obj # # def getlinedgerois(self,scannumbers,energyval,numrois=72,logscaling=True,height=5,colormap='jet'): # """ # define ROIs by clicking two points on a matrix, which is a difference of edf-files above and # below an energy value # (This function should be deprecated once the new style of ROI definitions work properly) # """ # if not isinstance(scannumbers,list): # scannums = [] # scannums.append(scannumbers) # else: # scannums = scannumbers # # search for correct index in the first of the given scans # scanname = 'Scan%03d' % scannums[0] # index = self.scans[scanname].energy.flat[np.abs(self.scans[scanname].energy - energyval).argmin()] # rois_object = self.createrois(scannumbers) # rois_object.getlinedgerois(index,numrois,logscaling,height,colormap) # self.rois = rois_object.rois # # add the new xrs_rois's functions and classes # new_obj = xrs_rois.roi_object() # new_obj.indices = rois_object.rois.inds # self.roi_obj = new_obj # # def getzoomedgerois(self,scannumbers,energyval,numrois=72,logscaling=True,colormap='jet'): # """ # define ROIs by zooming in on a matrix, which is a difference of edf-files above and # below an energy value # (This function should be deprecated once the new style of ROI definitions work properly) # """ # if not isinstance(scannumbers,list): # scannums = [] # scannums.append(scannumbers) # else: # scannums = scannumbers # # search for correct index in the first of the given scans # scanname = 'Scan%03d' % scannums[0] # index = self.scans[scanname].energy.flat[np.abs(self.scans[scanname].energy - energyval).argmin()] # rois_object = self.createrois(scannumbers) # rois_object.getlinedgerois(index,numrois,logscaling,colormap) # self.rois = rois_object.rois # # add the new xrs_rois's functions and classes # new_obj = xrs_rois.roi_object() # new_obj.indices = rois_object.rois.inds # self.roi_obj = new_obj # # def saverois(self,filename): # """ # saves the rois into an file using pickle # filename = absolute path to file and filename # """ # import pickle # f = open(filename, 'wb') # pickle.dump(self.rois, f,protocol=-1) # f.close() xrstools-0.15.0+git20210910+c147919d/XRStools/id20_imaging.py000066400000000000000000000423541412732462000225220ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: id20_imaging.py import numpy as np import matplotlib.pyplot as plt from scipy import io from itertools import groupby from scipy.interpolate import Rbf, RectBivariateSpline from scipy.optimize import leastsq, fmin from helpers import * from .xrs_read import rois from six.moves import range class imaging: """ a class to make images from id20 data, it will use some of the methods the read_id20 class has also, maybe it would make sense to use read_id20 as a superclass for this one and just add the imaging functionality """ def __init__(self,absfilename,energycolumn='energy_cc',monitorcolumn='monitor'): self.scans = {} # dictionary of scans that are loaded if not os.path.isfile(absfilename): raise Exception('IOError! No such file, please check filename.') self.path = os.path.split(absfilename)[0] + '/' self.filename = os.path.split(absfilename)[1] self.scannumbers = [] self.EDF_PREFIX = 'edf/' self.EDF_POSTFIX = '.edf' self.DET_PIXEL_NUM = 256 self.TTH_OFFSETS = np.array([-13.0,-6.5,-6.5,0.0,0.0,6.5,6.5,13.0,13.0]) # which column in the SPEC file to be used for the energy and monitor self.encolumn = energycolumn.lower() self.monicolumn = monitorcolumn.lower() # ROIs: only rectangular rois should be possible (line or zoom ROIs) self.rois = [] # the stored images self.images = {} # dictionary of images, which will be stored under their scan name def readscan(self,scannumber): """ returns the data, motors, counter-names, and edf-files from the calss's specfile of "scannumber" should use PyMca's routines to read the Spec- and edf-files """ fn = self.path + self.filename # loading spec file print ("parsing edf- and SPEC-files of scan No. %s" % scannumber) data, motors, counters = specread(fn,scannumber) # loading according edf files edfmats = np.array(np.zeros((len(counters['ccdno']),self.DET_PIXEL_NUM,self.DET_PIXEL_NUM))) for m in range(len(counters['ccdno'])): ccdnumber = counters['ccdno'][m]+1 edfname = self.path + self.EDF_PREFIX + self.filename + '_' + "%04d" % ccdnumber + self.EDF_POSTFIX edfmats[m,:,:] = edfread(edfname) self.scannumbers.extend([scannumber]) # keep track of the scnanumbers return data, motors, counters, edfmats def loadscan(self,scannumbers,scantype='generic'): """ loads the files belonging to scan No "scannumber" and puts it into an instance of the container-class scan. the default scantype is 'generic', later the scans will be grouped (then added) based on the scantype """ # make sure scannumbers are iterable (list) if not isinstance(scannumbers,list): scannums = [] scannums.append(scannumbers) else: scannums = scannumbers for number in scannums: scanname = 'Scan%03d' % number data, motors, counters, edfmats = self.readscan(number) # can assign some things here already (even if maybe redundant) monitor = counters[self.monicolumn] # energy related columns cannot be assigned now, since scans here could also be position scans (like z-scans) monoangle = None #counters['pmonoa'] energy = None #counters[self.encolumn] # create an instance of the "scan" class for every scan onescan = scan(edfmats,number,energy,monoangle,monitor,counters,motors,data,scantype) # assign one dictionary entry to each scan self.scans[scanname] = onescan def createrois(self,scannumbers): """ create rois object from this class and scannumbers """ rois_object = rois(self.scans,scannumbers) return rois_object def getlinrois(self,scannumbers,numrois=9,logscaling=True,height=5,colormap='jet'): """ define ROIs by clicking two points """ rois_object = self.createrois(scannumbers) rois_object.getlinrois(numrois,logscaling,height,colormap) # asign the entire rois object to self.rois to also have access to the roikind attribute e.g. self.rois = rois_object def getzoomrois(self,scannumbers,numrois=9,logscaling=True,colormap='jet'): """ define ROIs by zooming in on a region of interest """ rois_object = self.createrois(scannumbers) rois_object.getzoomrois(numrois,logscaling,colormap) # asign the entire rois object to self.rois to also have access to the roikind attribute e.g. self.rois = rois_object def image_from_line(self,scannumber,scanmotor,whichroi=[0]): """ create images from a point focus, either along a sample position (scanmotor = 'sx', 'sz', ...) or along energy (scanmotor = 'energy_cc', 'nrj', ...) """ scanname = 'Scan%03d' % scannumber # make mask out of rois (this can be deleted once the ROIs are always handled as masks of zeros and ones) maskrois = [] for roi in self.rois.rois: mask = np.zeros((self.DET_PIXEL_NUM,self.DET_PIXEL_NUM)) for pixel in roi: mask[pixel[1],pixel[0]] = 1 maskrois.append(mask) roisalongscan = [] # list of rectangular matricies (shaped like the rois) for each roi (len(scan)*width*height matrix) lineimages = [] # list of images from lines (len(scan)*width) for n in whichroi: # for each roi inds = np.nonzero(maskrois[n]) roialongscan = [] lineimage = [] for edfmat in self.scans[scanname].edfmats: # each edf file of a given scan roialongscan.append(np.reshape(edfmat[inds],(self.rois.roiheight[n],self.rois.roiwidth[n]))) # the whole roi lineimage.append(np.sum( np.reshape(edfmat[inds],(self.rois.roiheight[n],self.rois.roiwidth[n])),axis=0)) # summed over height roisalongscan.append(roialongscan) lineimages.append(np.squeeze(lineimage)) print( np.shape(roisalongscan), np.shape(lineimages)) plt.imshow(lineimages[0]) class imageset: """ class to make SR-images from list of LR-images """ def __init__(self): self.list_of_images = [] self.xshifts = [] self.yshifts = [] self.shifts = [] self.srimage = [] self.srxscale = [] self.sryscale = [] self.refimagenum = [] def estimate_xshifts(self,whichimage=None): if not whichimage: ind = 0 # first image in list_of_images is the reference image else: ind = whichimage origx = self.list_of_images[ind].xscale origy = self.list_of_images[ind].yscale origim = self.list_of_images[ind].matrix xshifts = [] for image in self.list_of_images: newx = image.xscale newy = image.yscale newim = image.matrix xshifts.append(estimate_xshift(origx,origy,origim,newx,newy,newim)) self.refimagenum = ind self.xshifts = xshifts def estimate_yshifts(self,whichimage=None): if not whichimage: ind = 0 # first image in list_of_images is the reference image else: ind = whichimage origx = self.list_of_images[ind].xscale origy = self.list_of_images[ind].yscale origim = self.list_of_images[ind].matrix yshifts = [] for image in self.list_of_images: newx = image.xscale newy = image.yscale newim = image.matrix yshifts.append(estimate_yshift(origx,origy,origim,newx,newy,newim)) self.refimagenum = ind self.yshifts = yshifts def estimate_shifts(self,whichimage=None): if not whichimage: ind = 0 # first image in list_of_images is the reference image else: ind = whichimage origx = self.list_of_images[ind].xscale origy = self.list_of_images[ind].yscale origim = self.list_of_images[ind].matrix shifts = [] for image in self.list_of_images: newx = image.xscale newy = image.yscale newim = image.matrix shifts.append(estimate_shift(origx,origy,origim,newx,newy,newim)) self.refimagenum = ind self.shifts = shifts def interpolate_xshift_images(self,scaling,whichimages=None): if not whichimages: inds = list(range(len(self.list_of_images))) elif not isinstance(whichimages,list): inds = [] inds.append(whichimages) else: inds = whichimages newim = np.zeros((len(inds),np.shape(self.list_of_images[inds[0]].matrix)[0]*scaling,np.shape(self.list_of_images[inds[0]].matrix)[1])) newx = np.linspace(self.list_of_images[inds[0]].xscale[0]-self.xshifts[inds[0]],self.list_of_images[inds[0]].xscale[-1]-self.xshifts[inds[0]],len(self.list_of_images[inds[0]].xscale)*scaling) newy = self.list_of_images[inds[0]].yscale for n in range(len(inds)): print( self.xshifts[inds[n]]) oldim = self.list_of_images[inds[n]].matrix oldx = self.list_of_images[inds[n]].xscale-self.xshifts[inds[n]] oldy = self.list_of_images[inds[n]].yscale newim[n,:,:] = interpolate_image(oldx,oldy,oldim,newx,newy) self.srimage = np.sum(newim,axis=0) self.srxscale = newx self.sryscale = newy def interpolate_yshift_images(self,scaling,whichimages=None): if not whichimages: inds = list(range(len(self.list_of_images))) elif not isinstance(whichimages,list): inds = [] inds.append(whichimages) else: inds = whichimages newim = np.zeros((len(inds),np.shape(self.list_of_images[inds[0]].matrix)[0]*scaling,np.shape(self.list_of_images[inds[0]].matrix)[1])) newx = self.list_of_images[0].xscale newy = np.linspace(self.list_of_images[inds[0]].yscale[0]-self.yshifts[inds[0]],self.list_of_images[inds[0]].yscale[-1]-self.yshifts[inds[0]],len(self.list_of_images[inds[0]].yscale)*scaling) for n in range(len(inds)): oldim = self.list_of_images[inds[n]].matrix oldx = self.list_of_images[inds[n]].xscale oldy = self.list_of_images[inds[n]].yscale-self.yshifts[inds[n]] newim[n,:,:] = interpolate_image(oldx,oldy,oldim,newx,newy) self.srimage = np.sum(newim,axis=0) self.srxscale = newx self.sryscale = newy def interpolate_shift_images(self,scaling,whichimages=None): if not whichimages: inds = list(range(len(self.list_of_images))) elif not isinstance(whichimages,list): inds = [] inds.append(whichimages) else: inds = whichimages if len(scaling)<2: scaling = [scaling, scaling] print( inds, self.list_of_images[inds[0]].xscale[0], self.shifts[inds[0]], self.list_of_images[inds[0]].xscale[-1]) newim = np.zeros((len(self.list_of_images),np.shape(self.list_of_images[inds[0]].matrix)[0]*scaling[0],np.shape(self.list_of_images[inds[0]].matrix)[1]*scaling[1])) newx = np.linspace(self.list_of_images[inds[0]].xscale[0]-self.shifts[inds[0]][0],self.list_of_images[inds[0]].xscale[-1]-self.shifts[inds[0]][0],len(self.list_of_images[inds[0]].xscale)*scaling[0]) newy = np.linspace(self.list_of_images[inds[0]].yscale[0]-self.shifts[inds[0]][1],self.list_of_images[inds[0]].yscale[-1]-self.shifts[inds[0]][1],len(self.list_of_images[inds[0]].yscale)*scaling[1]) for n in range(len(inds)): oldim = self.list_of_images[inds[n]].matrix oldx = self.list_of_images[inds[n]].xscale-self.shifts[inds[n]][0] oldy = self.list_of_images[inds[n]].yscale-self.shifts[inds[n]][1] newim[n,:,:] = interpolate_image(oldx,oldy,oldim,newx,newy) self.srimage = np.sum(newim,axis=0) self.srxscale = newx self.sryscale = newy def plotSR(self): X,Y = pylab.meshgrid(self.srxscale,self.sryscale) pylab.pcolor(X,Y,np.transpose(self.srimage)) pylab.show(block=False) def plotLR(self,whichimage): if not isinstance(whichimage,list): inds = [] inds.append(whichimage) else: inds = list(whichimage) for ind in inds: X,Y = pylab.meshgrid(self.list_of_images[ind].xscale,self.list_of_images[ind].yscale) pylab.figure(ind) pylab.pcolor(X,Y,np.transpose(self.list_of_images[ind].matrix)) pylab.show(block=False) def save(self): pass def load(self): pass def loadkimberlite(self,matfilename): data_dict = io.loadmat(matfilename) sorted_keys = sorted(data_dict.keys(), key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) sy = data_dict['sy'][0] allsx = [] for key in sorted_keys[3:12]: allsx.append(data_dict[key][0]) allmats = [] for key in sorted_keys[13:]: allmats.append(data_dict[key]) alllengths = [] for sx in allsx: alllengths.append(len(sx)) ind = np.where(alllengths == np.max(alllengths))[0][0] # spline everything onto longest sx-scale for n in range(len(allmats)): print( np.shape(allsx[n]), np.shape(sy), np.shape(allmats[n])) ip = RectBivariateSpline(allsx[n],sy,allmats[n]) allmats[n] = ip(allsx[ind],sy) allsx[n] = allsx[ind] allimages = [] for n in range(len(allmats)): allimages.append(image(allmats[n],allsx[n],sy)) self.list_of_images = allimages def loadhe3070(self,matfilename): data_dict = io.loadmat(matfilename) sy = data_dict['det'][0][0]['sy'][0][0][0] allsx = [] allmats = [] for n in range(9): allsx.append(np.reshape(data_dict['det'][0][n]['sx'][0][0]-data_dict['det'][0][n]['sx'][0][0][0],len(data_dict['det'][0][n]['sx'][0][0],))) allmats.append(data_dict['det'][0][n]['img'][0][0]) alllengths = [] for sx in allsx: alllengths.append(len(sx)) ind = np.where(alllengths == np.max(alllengths))[0][0] for n in range(len(allmats)): print( np.shape(allsx[n]), np.shape(sy), np.shape(np.transpose(allmats[n]))) ip = RectBivariateSpline(allsx[n],sy,np.transpose(allmats[n])) allmats[n] = ip(allsx[ind],sy) allsx[n] = allsx[ind] allimages = [] for n in range(len(allmats)): allimages.append(image(allmats[n],allsx[n],sy)) self.list_of_images = allimages class image: """ container class to hold info of a single LR-image to be put togther in a SR-image by the imageset class """ def __init__(self,matrix,xscale,yscale): self.name = [] self.matrix = matrix self.xscale = xscale self.yscale = yscale self.tth = [] def plotimage(self): pass def shiftx(self): pass def shifty(self): pass def save(self): pass def load(self): pass def interpolate_image(oldx,oldy,oldIM,newx,newy): """ 2d interpolation """ interp = RectBivariateSpline(oldx,oldy,oldIM) return interp(newx,newy) def estimate_xshift(x1,y1,im1,x2,y2,im2): """ estimate shift in x-direction only by stepwise shifting im2 by precision and thus minimising the sum of the difference between im1 and im2 """ funct = lambda a: np.sum((interpolate_image(x1,y1,im1,x1,y1) - interpolate_image(x2,y2,im2,x2+a,y2))**2.0) res = leastsq(funct,0.0) return res[0] def estimate_yshift(x1,y1,im1,x2,y2,im2): """ estimate shift in x-direction only by stepwise shifting im2 by precision and thus minimising the sum of the difference between im1 and im2 """ funct = lambda a: np.sum((interpolate_image(x1,y1,im1,x1,y1) - interpolate_image(x2,y2,im2,x2,y2+a))**2.0) res = leastsq(funct,0.0) return res[0] def estimate_shift(x1,y1,im1,x2,y2,im2): """ estimate shift in x-direction only by stepwise shifting im2 by precision and thus minimising the sum of the difference between im1 and im2 """ funct = lambda a: np.sum((interpolate_image(x1,y1,im1,x1,y1) - interpolate_image(x2,y2,im2,x2+a[0],y2+a[1]))**2.0) res = fmin(funct,[0.0,0.0],disp=0) return res class LRimage: """ container class to hold info of a single LR-image to be put togther in a SR-image by the imageset class """ def __init__(self,matrix,xscale,yscale): self.name = [] self.matrix = matrix self.xscale = xscale self.yscale = yscale self.tth = [] def plotimage(self): pass def shiftx(self): pass def shifty(self): pass def save(self): pass def load(self): pass xrstools-0.15.0+git20210910+c147919d/XRStools/installation_dir.py000066400000000000000000000001101412732462000236100ustar00rootroot00000000000000import os installation_dir = os.path.dirname(os.path.abspath(__file__)) xrstools-0.15.0+git20210910+c147919d/XRStools/ixs_offDiagonal.py000066400000000000000000000462371412732462000233710ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range from collections import Iterable #!/usr/bin/python # Filename: ixs_offDiagonal.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" #from helpers import * from . import xrs_rois, xrs_scans, xrs_utilities, math_functions, xrs_fileIO, roifinder_and_gui import h5py from numpy import array import scipy.io import traceback import sys import os import numpy as np import array as arr import pickle from itertools import groupby from scipy.integrate import trapz from scipy.interpolate import interp1d from pylab import * from scipy import signal from scipy.ndimage import measurements from itertools import groupby import matplotlib.pyplot as plt import warnings # try to import the fast PyMCA parsers try: import PyMca5.PyMcaIO.EdfFile as EdfIO import PyMca5.PyMcaIO.specfilewrapper as SpecIO use_PyMca = True except: use_PyMca = False print( " >>>>>>>> use_PyMca " , use_PyMca) __metaclass__ = type # new style classes def print_citation_message(): """Prints plea for citing the XRStools article when using this software. """ print (' ') print (' ############################# Welcome to XRStools #############################') print (' # If you are using this software, please cite the following work: #') print (' # Ch.J. Sahle, A. Mirone, J. Niskanen, J. Inkinen, M. Krisch, and S. Huotari: #') print (' # "Planning, performing, and analyzing X-ray Raman scattering experiments." #') print (' # Journal of Synchrotron Radiation 22, No. 2 (2015): 400-409. #') print (' ###############################################################################') print (' ') class offDiagonal: """ **offDiagonal** Class for reading scans from off-diagonal IXS experiments on the high-resolution setup at ID20. Arguments: ---------- absFilename (string): Absolute path and filename of the SPEC-file. scanMotor (string): Mnemonic of the motor that is scanned. monitorName (string): Mnemonic of the counter used for normalization. edfName (string): EDF-file base file name (default is None, i.e. same as SPEC-file). armLength (float): Legth (in m) of the spectrometer arm used (either 1.0 or 2.0). """ def __init__( self, path, SPECfname='fourc', EDFprefix='/edf/', EDFname='fourc_', \ EDFpostfix='.edf', en_column='sry', moni_column='izero' ): self.path = path self.SPECfname = SPECfname if not os.path.isfile(os.path.join(path, SPECfname)): raise Exception('IOError! No such file or directory.') self.EDFprefix = EDFprefix self.EDFname = EDFname self.EDFpostfix = EDFpostfix self.en_column = en_column.lower() self.moni_column = moni_column.lower() self.scans = {} self.scan_numbers = [] self.eloss = np.array([]) self.energy = np.array([]) self.signals = np.array([]) self.errors = np.array([]) self.q_values = [] self.groups = {} self.cenom = [] self.E0 = [] self.tth = [] self.resolution = [] self.comp_factor = None self.cenom_dict = {} self.raw_signals = {} self.raw_errors = {} self.TTH_OFFSETS1 = np.array([5.0, 0.0, -5.0, 5.0, 0.0, -5.0, 5.0, 0.0, -5.0, 5.0, 0.0, -5.0]) self.TTH_OFFSETS2 = np.array([-9.71, -9.75, -9.71, -3.24, -3.25, -3.24, 3.24, 3.25, 3.24, 9.71, 9.75, 9.71]) self.PIXEL_SIZE = 0.055 # pixel size in mm self.roi_obj = None # specific to off-diagonal experiments self.scanMatrix = np.array([]) self.offDiaDataSets = [] print_citation_message() def SumDirect(self, scan_numbers): """ **SumDirect** Creates a summed 2D image of a given scan or list of scans. Args: scan_numbers (int or list): Scan number or list of scan numbers to be added up. Returns: A 2D np.array of the same size as the detector with the summed image. """ # make sure scannumbers are iterable (list) numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers im_sum = None en_column = None # uses first column in SPEC file as scanned motor for number in numbers: scan = xrs_scans.Scan() print(" SONO IN SumDirect number " , number, " en_column " , en_column, " moni_column " , self.moni_column, " scan_type " , None, " scaling " , None ) scan.load(self.path, self.SPECfname, self.EDFprefix, self.EDFname, self.EDFpostfix, number, \ direct=False, roi_obj=None, scaling=None, scan_type='generic', \ en_column=en_column, moni_column=self.moni_column) print( " IN SumDirect la shape est ", scan.edfmats.shape ) if im_sum is None: im_sum = np.zeros(scan.edfmats[0].shape ,"f") im_sum[:] += scan.edfmats.sum(axis=0) return im_sum def set_roiObj(self,roiobj): """ **set_roiObj** Assigns an object of the roi_obj class to this class. """ self.roi_obj = roiobj def load_scan( self, scan_numbers, scan_type='generic', direct=True, scaling=None, method='sum' ): """ **load_scan** Load a single or multiple scans. Note: When composing a spectrum later, scans of the same 'scan_type' will be averaged over. Scans with scan type 'elastic' or 'long' in their names are recognized and will be treated specially. Args: scan_numbers (int or list): Integer or iterable of scan numbers to be loaded. scan_type (str): String describing the scan to be loaded (e.g. 'edge1' or 'K-edge'). direct (boolean): Flag, 'True' if the EDF-files should be deleted after loading/integrating the scan. """ # make sure scannumbers are iterable (list) numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers # make sure there is a cenom_dict available if direct=True AND method='sum' if direct and method=='pixel' and not self.cenom_dict: print('Please run the get_compensation_factor method first for pixel-wise compensation.') return # go throught list of scan_numbers and load scans for number in numbers: # create a name for each scan scan_name = 'Scan%03d' % number # create an instance of the Scan class scan = xrs_scans.Scan() # load the scan scan.load( self.path, self.SPECfname, self.EDFprefix, self.EDFname, self.EDFpostfix, number, \ direct=direct, roi_obj=self.roi_obj, scaling=scaling, scan_type=scan_type, \ en_column=self.en_column, moni_column=self.moni_column, method=method, cenom_dict=self.cenom_dict ) # add it to the scans dict self.scans[scan_name] = scan # add the number to the list of scan numbers if not number in self.scan_numbers: self.scan_numbers.extend([number]) def save_state_hdf5( self, file_name, group_name, comment="" , overwriteFile = True, overwriteGroup=True): if overwriteFile: h5 = h5py.File(file_name,"w") else: h5 = h5py.File(file_name,"a") if overwriteGroup: if group_name in h5: del h5[group_name] h5group = h5.require_group(group_name) h5group["scan_numbers"] = self.scan_numbers for scan_name, scan in self.scans.items(): h5group_scan = h5.require_group(group_name+"/"+scan_name) scan.save_hdf5( h5group_scan) h5group_scan["offdia_energy"] = scan.offdia_energy h5group_scan["RCmonitor"] = scan.RCmonitor h5group["comment"] = comment for i,ds in enumerate(self.offDiaDataSets): h5group_ds = h5group.require_group("offDiaDataSets" ) nm = "DS%04d"%i h5group_ds_ii = h5group_ds.require_group( nm) ds.save_hdf5(h5group_ds_ii) h5.flush() h5.close() def load_state_hdf5( self, file_name, group_name ): h5 = h5py.File(file_name,"r") h5group = h5.require_group(group_name) self.scan_numbers = h5group["scan_numbers"][()] self.scans = {} for key in h5group: if str(key)[:4] == "Scan": scan_name = str(key) # for scan_name, scan in self.scans.items(): h5group_scan = h5group[h5group_scan] scan = xrs_scans.Scan() scan.load_hdf5( h5group_scan) scan.offdia_energy = h5group_scan["offdia_energy"] [()] scan.RCmonitor = h5group_scan["RCmonitor"] [()] self.scans[scan_name] = scan if "offDiaDataSets" in h5group: self.offDiaDataSets = [] ds_group = h5group[ "offDiaDataSets" ] keys = [nm for nm in ds_group] keys.sort(key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) for k in keys: dataset = xrs_scans.offDiaDataSet() dataset.load_hdf5( ds_group[k] ) self.offDiaDataSets .append(dataset) h5.flush() h5.close() def loadRockingCurve(self, scan_numbers, en_colum='energy', RCmoni='alirixs', direct=False, scaling=None, method='sum', scan_type='RC', storeInsets = False): """ **loadRockingCurve** Load one or more rocking curves. Arguments: ---------- scanNumbers (int or list of ints): Scan number or list of scan numbers of rocking curve scans to be loaded. scanType (string): String describing the scan for later automatic stitching/interpolation. Few special types exist: elastic, long. energyCoor (list): Indices to find the energy during the scan based on the SPEC-file header. direct (boolean): Keyword if EDF-files should be deleted or kept (default). """ # make sure scanNumbers are iterable (list numbers = [] if not isinstance( scan_numbers , Iterable) : numbers.append(scan_numbers) else: numbers = scan_numbers for number in numbers: # create a name for each scan scan_name = 'Scan%03d' % number # create an instance of the Scan class scan = xrs_scans.Scan() # load the scan scan.load( self.path, self.SPECfname, self.EDFprefix, self.EDFname, self.EDFpostfix, number, \ direct=direct, roi_obj=self.roi_obj, scaling=scaling, scan_type=scan_type, \ en_column=self.en_column, moni_column=self.moni_column, method=method, cenom_dict=self.cenom_dict , storeInsets = storeInsets) scan.offdia_energy = scan.motors[en_colum] scan.RCmonitor = scan.counters[RCmoni] self.scans[scan_name] = scan if not number in self.scan_numbers: self.scan_numbers.extend([number]) def stitchRockingCurves(self,RCmoni='kaprixs',I0moni='izero',addColumns = 0): # , CORREZIONE = True): """ **stitchRockingCurves** Go through all rocking curves and stitch them together to a 3D matrix. """ CORREZIONE = True RcScans = xrs_scans.findScans_bytype(self.scans,"RC" ) sorted_RcScans = sorted(RcScans,key=lambda x:x.offdia_energy) energy_points = sorted(list(set([scan.offdia_energy for scan in sorted_RcScans]))) dim1 = len(energy_points) dim2 = int(sum(list(set([scan.signals.shape[0] for scan in sorted_RcScans])))+addColumns) # dirty fix for 2 scans of the same length for ii in range(len(self.roi_obj.red_rois)): dataset = xrs_scans.offDiaDataSet() dataset.ROI_number = ii dataset.energy = energy_points moniMatrix = np.zeros((dim1,dim2)) motorMatrix = np.zeros((dim1,dim2)) signalMatrix = np.zeros((dim1,dim2)) I0Matrix = np.zeros((dim1,dim2)) errorMatrix = np.zeros((dim1,dim2)) for jj in range(len(energy_points)): moniCol = np.array([]) motorCol = np.array([]) signalCol = np.array([]) I0Col = np.array([]) for scan in sorted_RcScans: if scan.offdia_energy == energy_points[jj]: if CORREZIONE : ordine = np.searchsorted(motorCol,scan.energy) moniCol = np.insert(moniCol , ordine ,scan.counters[RCmoni]) motorCol = np.insert(motorCol , ordine ,scan.energy) signalCol = np.insert(signalCol, ordine ,scan.signals[:,ii]) I0Col = np.insert(I0Col , ordine ,scan.counters[I0moni]) else: print( " ORDINE = " , np.searchsorted(moniCol,scan.counters[RCmoni])) moniCol = np.insert(moniCol,np.searchsorted(moniCol,scan.counters[RCmoni]),scan.counters[RCmoni]) print( " ORDINE = " , np.searchsorted(motorCol,scan.energy)) motorCol = np.insert(motorCol,np.searchsorted(motorCol,scan.energy),scan.energy) print( " ORDINE SIGNAL= " , np.searchsorted(signalCol,scan.signals[:,ii]) ) print( scan.signals[:,ii] ) signalCol = np.insert(signalCol,np.searchsorted(signalCol,scan.signals[:,ii]),scan.signals[:,ii]) I0Col = np.insert(I0Col,np.searchsorted(I0Col,scan.counters[I0moni]),scan.counters[I0moni]) # print( " ordre for Signal " , np.searchsorted(signalCol,scan.signals[:,ii]) ) # print( " ordre for I0Col" , np.searchsorted(I0Col,scan.counters[I0moni]) ) # print( " aggiungo moniCol " , len( moniCol) ) # print( " aggiungo signalCol " , len( signalCol) ) # print( " aggiungo motorCol " , len(motorCol ) ) # print( " aggiungo I0Col " , len( I0Col) ) # print( " aggiungo signalCol " , len( signalCol) ) moniMatrix[jj,:] = moniCol signalMatrix[jj,:] = signalCol motorMatrix[jj,:] = motorCol I0Matrix[jj,:] = I0Col errorMatrix[jj,:] = np.sqrt(signalCol) dataset.signalMatrix = signalMatrix dataset.motorMatrix = motorMatrix dataset.RCmonitor = moniMatrix dataset.I0Matrix = I0Matrix dataset.errorMatrix = errorMatrix self.offDiaDataSets.append(dataset) def getrawdata(self): """ **getrawdata** Iterates through all instances of the scan class and calls it's applyrois method to sum up over all rois. """ if not np.any(self.roi_obj.indices): print( 'Please define some ROIs first.') return for scan in self.scans: if len(self.scans[scan].edfmats): print ("integrating "+scan) self.scans[scan].applyrois(self.roi_obj.indices) def getrawdata_pixelwise(self): """ Goes through all instances of the scan class and calls it's applyrois_pw method to extract intensities for all pixels in each ROI. """ if not np.any(self.roi_obj.indices): print( 'Please define some ROIs first.') return for scan in self.scans: if len(self.scans[scan].edfmats): print ("integrating pixelwise "+scan) self.scans[scan].applyrois_pw(self.roi_obj.indices) def SumDirect(self,scannumbers): """ **SumDirect** """ sum = None for number in scannumbers: data, motors, counters, edfmats = self.readscan(number) if sum is None: sum = np.zeros(edfmats[0].shape ,"f") sum[:] += edfmats.sum(axis=0) return sum def deletescan(self,scannumbers): """ **deletescan** Deletes scans from the class. INPUT: scannumbers = integer or list of integers (SPEC scan numbers) to delete """ numbers = [] if not isinstance(scannumbers,list): numbers.append(scannumbers) else: numbers = scannumbers for number in numbers: scanname = 'Scan%03d' % number del(self.scans[scanname]) self.scannumbers.remove(number) def save_raw_data(self,filename): data = np.zeros((len(self.eloss),len(self.signals[0,:]))) data[:,0] = self.eloss data[:,1::] = self.signals np.savetxt(filename,data) xrstools-0.15.0+git20210910+c147919d/XRStools/localfilesdialog.py000066400000000000000000000046321412732462000235630ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function # from PyQt4 import Qt from silx.gui import qt as Qt import os from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] import PyMca5.PyMcaIO.specfile as specfile # this will use the ui module to load # the structure specified in localfilesdialog.ui class localfilesdialog(Qt.QDialog): def __init__(self, user_input, parent=None): super(localfilesdialog , self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "localfilesdialog.ui" ), self) self.BrowseSpec_pushButton.clicked.connect(self.__onBrowseSpec) self.BrowseImage_pushButton.clicked.connect(self.__onBrowseFile) self.SpecFileName_lineEdit.textChanged.connect(self.__onChangeSpec) self.ScanNumber_spinBox.setMaximum(-1) if "sf" in user_input: self.SpecFileName_lineEdit.setText( user_input["sf"] ) if "fn" in user_input: self.FileName_lineEdit.setText( user_input["fn"] ) def __onBrowseSpec(self): filename = Qt.QFileDialog.getOpenFileName() if isinstance(filename, tuple): filename = filename[0] if filename is not None: filename=str(filename) self.SpecFileName_lineEdit.setText(filename) def __onChangeSpec(self): filename = str(self.SpecFileName_lineEdit.text()) print( filename) try: s=specfile.Specfile(filename) except: s=None if s is not None: ns = len(s) self.ScanNumber_spinBox.setMinimum(0) self.ScanNumber_spinBox.setMaximum(ns) else: self.ScanNumber_spinBox.setMaximum(-1) # s[450].alllabels() # s[450]["ccdno"] # s[424].datacol("ccdno") # s[425].datacol("ccdno") # ls # history def __onBrowseFile(self): filename = Qt.QFileDialog.getOpenFileName() if isinstance(filename, tuple): filename = filename[0] self.FileName_lineEdit.setText(filename) if __name__=="__main__": app=Qt.QApplication([]) w = localfilesdialog() w.show() app.exec_() xrstools-0.15.0+git20210910+c147919d/XRStools/match.py000066400000000000000000000123241412732462000213570ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from six.moves import range from six.moves import zip def match(distorted,refgrid, coors, targets, possibili): for target in set(tuple(coors))-set(tuple(targets)): new_targets=targets+[tuple(target)] if validate( distorted,refgrid, coors, new_targets): if len(new_targets)==len(distorted): print( new_targets) possibili.append( new_targets ) else: match(distorted,refgrid, coors, new_targets, possibili) def validate(distorted,refgrid, coors, new_targets) : C=new_targets[-1] targets= new_targets[:-1] nt=len(targets) tC=distorted[nt] for i in range(nt): A=targets[i] tA=distorted[i] for j in range(i+1,nt): B=targets[j] tB=distorted[j] v1 = [ C[0]-A[0] , C[1]-A[1] ] v2 = [ C[0]-B[0] , C[1]-B[1] ] vv=abs(np.array( [ C[0]-A[0] , C[1]-A[1], C[0]-B[0] , C[1]-B[1]])) Area = v1[0]*v2[1]-v1[1]*v2[0] dmax= vv.max() if abs(Area)>= dmax*dmax/2 : v1 = [ tC[0]-tA[0] , tC[1]-tA[1] ] v2 = [ tC[0]-tB[0] , tC[1]-tB[1] ] tArea = v1[0]*v2[1]-v1[1]*v2[0] if Area*tArea<0: return False return True def merit( distorted,refgrid, coors, choice): result=0.0 dic={} minx=1.0e20 maxx=-1.0e20 for (k,l),b in zip(choice,distorted): dic[(int(k), int(l))]=b minx=min(minx, b[0]) maxx=max(maxx, b[0]) D = maxx-minx print( dic) NX=np.max(np.array(coors)[:,0])+1 NY=np.max(np.array(coors)[:,1])+1 for i in range(NX-1): for j in range(NY-1): try: a1=dic[(i,j)] presenti=1 except: presenti=0 if presenti: result=result - D/NX * i*a1[0]*2 result=result - D/NX * j*a1[1]*2 for i in range(NX-1): for j in range(NY-1): try: a1=dic[(i,j)] a2=dic[(i+1,j+1)] b1=dic[(i+1,j)] b2=dic[(i,j+1)] presenti=1 except: presenti=0 if presenti: d1=a1-a2 d2=b1-b2 c1=(a1+a2)/2.0 c2=(b1+b2)/2.0 D1=np.sqrt((d1*d1).sum()) D2=np.sqrt((d2*d2).sum()) DC = ((c1-c2)*(c1-c2)).sum() DD=(D1-D2)*(D1-D2) result=result+ (DC+DD)-((d1*d1).sum()+(d1*d1).sum())/2.0 for i in range(NX-1): for j in range(NY-1): try: a1=dic[(i,j)] b1=dic[(i+1,j)] b2=dic[(i-1,j)] presenti=1 except: presenti=0 if presenti: v1=b2-a1 v2=b1-a1 Area = v1[0]*v2[1]-v1[1]*v2[0] result=result+ abs(Area) for i in range(NX-1): for j in range(NY-1): try: a1=dic[(i,j)] b1=dic[(i,j+1)] b2=dic[(i,j-1)] presenti=1 except: presenti=0 if presenti: v1=b2-a1 v2=b1-a1 Area = v1[0]*v2[1]-v1[1]*v2[0] result=result+ abs(Area) return result def register(distorted): refgrid=np.zeros([4,3,2],"f") refgrid[:,:,1] = np.arange(3) refgrid[:,:,0] = np.reshape(np.arange(4),[4,1]) coors=np.reshape(refgrid,[-1,2]) targets=[] possibili=[] match(distorted,refgrid, [tuple(tok) for tok in coors], targets, possibili) print( possibili) best=1.0e30 coors=coors.astype("i") for choice in possibili: m=merit( distorted , refgrid ,[tuple(tok.tolist()) for tok in coors], choice) if m', connectionstyle = 'arc3,rad=0')) pylab.show() xrstools-0.15.0+git20210910+c147919d/XRStools/math_functions.py000066400000000000000000000165431412732462000233130ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: math_functions.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group and contains a collection of useful # mathematical functions. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import numpy as np def flat2DGaussian(xxx_todo_changeme, amplitude, xo, yo, sigma_x, sigma_y, offset): """ **flat2DGaussian** Returns a flattened 2D Gaussian profile. Args: ----- (xx,yy) (tuple): tuple containing x-y-meshgrid for 2D object to be fitted. amplitude (float): amplitude for Gaussian. xo (float): x-offset for Gaussian. yo (float): y-offset for Gaussian. sigma_x (float): FWHM in x-direction. sigma_y (float): FWHM in y-direction. offset (float): Background/offset. Returns: -------- g (np.array): flattened array of a 2D Gaussian profile. """ (xx, yy) = xxx_todo_changeme g = offset + amplitude*np.exp( - (((xx-xo)**2/(2.0*sigma_x**2)) + ((yy-yo)**2/(2.0*sigma_y**2)))) return g.ravel() def gauss1(x,x0,fwhm): """ area-normalized gaussian x = x-axis vector x0 = position fwhm = full width at half maximum """ sigma = fwhm/(2*np.sqrt(2*np.log(2))); y = np.exp(-(x-x0)**2/2/sigma**2)/sigma/np.sqrt(2*np.pi) return y def gauss_forcurvefit(x,amp,x0,fwhm): """ area-normalized gaussian x = x-axis vector amp = amplitude x0 = position fwhm = full width at half maximum """ sigma = fwhm/(2*np.sqrt(2*np.log(2))) y = amp*np.exp(-(x-x0)**2/2/sigma**2)/sigma/np.sqrt(2*np.pi) return y def two_gauss_forcurvefit(x, amp1,x01,fwhm1, amp2,x02,fwhm2): sigma1 = fwhm1/(2.0*np.sqrt(2.0*np.log(2.0))) sigma2 = fwhm2/(2.0*np.sqrt(2.0*np.log(2.0))) y = amp1*np.exp(-(x-x01)**2.0/2.0/sigma1**2)/sigma1/np.sqrt(2.0*np.pi) + amp2*np.exp(-(x-x02)**2.0/2.0/sigma2**2)/sigma2/np.sqrt(2.0*np.pi) return y def gauss_areanorm(x,x0,fwhm): """ area-normalized gaussian x = x-axis vector x0 = position fwhm = full width at half maximum """ sigma = fwhm/(2.0*np.sqrt(2.0*np.log(2.0))) y = np.exp(-(x-x0)**2.0/2.0/sigma**2)/sigma/np.sqrt(2.0*np.pi) return y def constant(x,a): """ returns a constant x = x-axis a = value of the constant returns vector with same length as x with entries a """ x = np.array(x) y = np.zeros(len(x)) + a return y def linear(x,a): """ returns a linear function y = ax + b x = x-axis a = list of parameters, a[0] = slope, a[1] = y-offset it is better to use numpy's polyval """ x = np.array(x) y = a[0]*x + a[1] return y def lorentz(x,a): """ % LORENTZ The Lorentzian function % % y = lorentz(x,a) % x = x-vector % a[0] = peak position % a[1] = FWHM % a[2] = Imax (if not defined, Imax = 1) """ y = a[2]*(((x-a[0])/(a[1]/2.0))**2.0+1.0)**(-1.0) return y def pearson7(x,a): """ returns a pearson function a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity a[4] = Background """ x = np.array(x) y = a[3] * (1.0+(2.0**(1.0/a[2])-1.0) * (2.0*(x-a[0])/a[1])**2.0)**(-a[2])+a[4] return y def pearson7_forcurvefit(x,a0,a1,a2,a3,a4): """ special version of person7 for use in constrained/bound minimizations returns a pearson function a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity a[4] = Background """ x = np.array(x) y = a3 * (1.0+(2.0**(1.0/a2)-1.0) * (2.0*(x-a0)/a1)**2.0)**(-a2)+a4 return y def pearson7_zeroback(x,a): """ returns a pearson function but without y-offset a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity """ x = np.array(x) y = a[3] * (1.0+(2.0**(1.0/a[2])-1.0) * (2.0*(x-a[0])/a[1])**2.0)**(-a[2]) return y def pearson7_linear(x,a): """ returns a pearson function but without y-offset a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity a[4] = ax b[5] = +b """ x = np.array(x) y = a[3] * (1.0+(2.0**(1.0/a[2])-1.0) * (2.0*(x-a[0])/a[1])**2.0)**(-a[2]) + a[4]*x + a[5] return y def pearson7_linear_forcurvefit(x,a0,a1,a2,a3,a4,a5): """ returns a pearson function but without y-offset a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity a[4] = ax b[5] = +b """ x = np.array(x) y = ( a3 * (1.0+(2.0**(1.0/a2)-1.0) * (2.0*(x-a0)/a1)**2.0)**(-a2) + a4*x + a5 ) return y def pearson7_linear_scaling_forcurvefit(x,a0,a1,a2,a3,a4,a5,a6): """ returns a pearson function but without y-offset a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity a[4] = ax b[5] = +b """ x = np.array(x) y = a6*( a3 * (1.0+(2.0**(1.0/a2)-1.0) * (2.0*(x-a0)/a1)**2.0)**(-a2) + a4*x + a5 ) return y def gauss(x,a): """ returns a gaussian with peak value normalized to unity a[0] = peak position a[1] = Full Width at Half Maximum a[2] = intensity """ y = a[2]*np.exp(-np.log(2.0)*((x-a[0])/a[1]*2.0)**2.0) return y def gauss2D(M,): np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2) def arctan_forcurvefit(x, a0, a1, a2, a3): """ **arctan_forcurvefit** Returns an arctan function. Args: x (array): Numpy array for the x-axis. a0 (float): amplitude a1 (float): squeezing factor a2 (float): x-offset a3 (float): y-offset """ return a0*np.arctan(a1*(x-a2) )+a3 xrstools-0.15.0+git20210910+c147919d/XRStools/minimiser.py000066400000000000000000000462501412732462000222640ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #/*########################################################################## # Copyright (C) 2004-2012 European Synchrotron Radiation Facility # # PPM : Alessandro Mirone. # GPU Cuda ( OCL is in progress.. ) Dimitris Karkoulis # Qt : Interface : Vorobeva Anastasiya Vorobyeva Veronika # nvorobeva@hotmail.fr vorobyevav@yahoo.com # and Alessandro Mirone # European Synchrotron Radiation Facility, Grenoble,France # # # PPM is developed at # the ESRF by the SciSoft group. # PPM CUDA is developed by Dimitris Karkoulis, financed by: # LinkSCEEM-2 (INFRA-2010-1.2.3) Work Package 12 project # (grant number RI-261600) # # This toolkit is free software; you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by the Free # Software Foundation; either version 2 of the License, or (at your option) # any later version. # # PPM is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the GNU General Public License for more # details. # # You should have received a copy of the GNU General Public License along with # PPM; if not, write to the Free Software Foundation, Inc., 59 Temple Place, # Suite 330, Boston, MA 02111-1307, USA. # # PPM follows the dual licensing model of Trolltech's Qt and Riverbank's PyQt # and cannot be used as a free plugin for a non-free program. # # Please contact the ESRF industrial unit (industry@esrf.fr) if this license # is a problem for you. #############################################################################*/ ############################################################ # Alessandro Mirone # April 2001 # ESRF from math import exp import math from six.moves import map from six.moves import range def difforzero(a): if(a<0): return 0 else: return a ISPARALLEL=0 ISMASTER=1 try: import mpi ISPARALLEL=1 if(mpi.rank==0): ISMASTER=1 else: ISMASTER=0 except: pass from numpy import * import random import os import glob import re from PyMca5.PyMcaMath.fitting import Gefit colpo=[10] try: import beansgui beans_OK=1 except: beans_OK=0 def update( var, x): var.value=(var.max+var.min)*0.5 + (var.max-var.min)*math.sin(x)*0.5 def getx( var): x=( 2*var.value -var.min - var.max)/(var.max-var.min) return math.asin(x) # def update( var, x): # var.value=( (1-tanh(x))*var.min+(1+tanh(x))*var.max)/2.0 # def getx( var): # x=( 2*var.value -var.min - var.max)/(var.max-var.min) # # print "x ", x # if (x==1): return 40 # if(x==-1): return -40 # if(x>1 or x<-1): # print var.value," ", var.min," ", var.max # raise " out of range" # x=0.5*log( (1+x) /(1-x) ) # return x def generateverticesNormal(X,dx): pointlist=[array(X)] for i in range(0,len(X)): nX=array(X) nX[i]=nX[i]+dx pointlist.append(nX) return array(pointlist) def generateverticesRandomNormal(X,dx, centershiftFact=0): pointlist=[array(X)] add=0 centershift=0 if( ISPARALLEL==0 or mpi.rank==0): centershift=centershiftFact*dx*array([ (-0.5+random.random()) for i in range(0, len(X)) ] ) if( ISPARALLEL ): centershift =mpi.bcast(centershift ,0 ) for i in range(0,len(X)): if( ISPARALLEL==0 or mpi.rank==0): add=dx[i]*(-0.5+random.random())+centershift[i] if( ISPARALLEL ): add =mpi.bcast(add ,0 ) pointlist[0][i]+=add for i in range(0,len(X)): nX=array(X) if( ISPARALLEL==0 or mpi.rank==0): add=dx[i]*(-0.5+random.random())+centershift[i] if( ISPARALLEL ): add =mpi.bcast(add ,0 ) nX[i]=nX[i]+add pointlist.append(nX) return array(pointlist) class minimiser: extrapotfactor=10.0 stoppa=[0,0] # emergency stop, delayed stop history=[] def __init__(self, EcartObject, VariableList): self.EcartObject=EcartObject self.VariableList=[] for tok in VariableList: if(isinstance(tok,list)): self.VariableList = self.VariableList + tok else: if tok.minlimit): extrapot=extrapot+(exp(abs(x[i]))-exp(limit)) # print " EXTRAPOT__ " , extrapot*self.extrapotfactor if( beans_OK): if( beansgui.BLock is not None): beansgui.BLock.acquire() error = self.EcartObject.error() if( beans_OK): if( beansgui.BLock is not None): beansgui.BLock.release() # print " error ", error return error +extrapot*self.extrapotfactor def amoeba(self, ftol, arret=None): X=[] for var in self.VariableList: X.append(getx(var) ) x=array(X) p=generateverticesNormal(X, 0.1) y=list(map(self.calculateerror,p)) return self.amoebaspecial(ftol,p,y, arret=arret) def amoebaAnnealed(self, ftol, temperature_fn, max_refusedcount=100, centershiftFact=0.0, arret=None, max_isthesame=10, expandby_if_isthesame=3.0, shrinkby_if_accepted =0.7, triggerby_if_accepted=1, proportionality_whenlower=2.5, maxium_ratio_max_min_whenlower=33.33, callbackIT=None, callbackLocal=None, maxiters=100000 ): count=0 minimiser.history=[] history=self.history ############################################### # If EcartObject has the method setitercounter # then set it if(hasattr(self.EcartObject, 'setitercounter' ) ): self.EcartObject.setitercounter(0) ############################################### # initialization of x variable. # x maps [-inf,+inf] to the limited interval # that has been assigned to the given variable # X contains the x for every variable. # self.history.append(str(list(map(par,self.VariableList)))) X=[] for var in self.VariableList: X.append(getx(var) ) p=generateverticesNormal(X, 0.1) y=list(map(self.calculateerror,p)) self.history[-1]=self.history[-1 ]+ str([" ERROR ", y[0] ]) if( ISPARALLEL==0 or mpi.rank==0): fperformance=open("performances","a") fperformance.write("At ITERATION %d, ERROR %e \n" % (0, y[0] )) fperformance.close() if( hasattr(self.EcartObject,"writeminimum")): self.EcartObject.writeminimum( 0 , y[0] ) res=self.amoebaspecial(ftol,p,y, arret=arret, callbackIT=callbackIT) if( callbackLocal is not None): callbackLocal() X=res[0] Y=res[1] Yold=Y Xmin=X Ymin=res[1] lower=1 Dpar=ones(len(X))*0.1 refusedcount=0 isthesamecount=0 while(refusedcount 1): Dpar=proportionality_whenlower*abs(res[0]-Xold) Dpar=Dpar+max(Dpar)/maxium_ratio_max_min_whenlower else: if(isthesame): Dpar*=expandby_if_isthesame else: mask=less(Dpar,triggerby_if_accepted) Dpar=Dpar*mask+(1-mask)*triggerby_if_accepted Dpar*=shrinkby_if_accepted Xold=X*1.0 mask=less(Dpar,10.0) Dpar=Dpar*mask+(1-mask)*10 # ---print " X " , X # ---print " Xold " , Xold p=generateverticesRandomNormal(X, Dpar, centershiftFact) # ---print p y=list(map(self.calculateerror,p)) res=self.amoebaspecial(ftol,p,y, arret=arret, callbackIT=callbackIT) if( callbackLocal is not None): callbackLocal() self.history.append(str(list(map(par,self.VariableList)))) self.history[-1]= self.history[-1]+ str([" ERROR ", res[1] ]) ############################################### # If EcartObject has the method getitercounter # then use it getitercounter if(hasattr(self.EcartObject,'getitercounter') ): if( ISPARALLEL==0 or mpi.rank==0): fperformance=open("performances","a") fperformance.write("At ITERATION %d, ERROR %e \n" % (self.EcartObject.getitercounter(), res[1] )) fperformance.close() if( hasattr(self.EcartObject,"writeminimum")): self.EcartObject.writeminimum( self.EcartObject.getitercounter(), res[1] ) # ---print "********************************************" # ---print " newY = ", res[1] # ---print " Y = ", Y # ---print " Ymin= ", Ymin if(abs(res[1]-Y)/abs(res[1]+Y)<4*ftol): isthesame=1 isthesamecount=isthesamecount+1 accepted=0 lower=0 X=res[0]*1 Y=res[1] else: isthesame=0 if(res[1]30): accepted = 0 prob=exp(-espo) if( ISPARALLEL==0 or mpi.rank==0): probcomp=random.random() if( ISPARALLEL ): probcomp =mpi.bcast( probcomp,0 ) if(probcompy[ihighest]): isecondhighest=ihighest ihighest=i elif(y[i]>y[isecondhighest]): isecondhighest=i if( y[i]y[ilowest]): break iter=iter+1 if(iter>maxiter): return (p[ilowest]*1.0,y[ilowest]) ptry= 2*center- p[ihighest] # print ptry ytry=self.calculateerror(ptry) if( callbackIT is not None): callbackIT() if(ytry< y[ilowest]): ptry2= 2*ptry-center # print ptry2 ytry2= self.calculateerror(ptry2) if( callbackIT is not None): callbackIT() if(ytry2 < y[ilowest] ): p[ihighest]=ptry2 y[ihighest]=ytry2 else: p[ihighest]=ptry y[ihighest]=ytry elif( ytry>=y[isecondhighest]): if(ytry=self.depth): self.vardepthstart = self.depth self.varcounter=0 self.varlastdepth=-1 result = self.varcounter if(result==0): self.vardepthstart=self.depth if( self.depth > self.varlastdepth): self.varcounter=self.varcounter+1 self.varlastdepth=self.depth # print " for ", self, " DependentVariable.VarCounter has been called, result is ", result # print " depth is ", self.depth return result def __init__(self,stringa , dictio={}): self.varcounter=0 self.vardepthstart=0 self.varlastdepth=0 ##################################################################################3 posold=0 while(1): pos = stringa[posold:].find("par(") if pos==-1: break pos2= stringa[posold+pos:].find(")") pos2=pos2+pos key = stringa[posold+pos+4:posold+pos2] if key in dictio: stringa1=stringa[:posold+pos+4]+"self."+key+")" stringa=stringa1+stringa[posold+pos2+1:] posold=len(stringa1) setattr(self,key,dictio[key]) else: msg= " KEY "+ key+ "\n STRINGA " +stringa+"\n error processing dependent variable string " raise Exception(msg) # for key in dictio.keys(): # while(1): # res=re.search("par *\( *%s *\)"%key, stringa ) # if(res is not None): # stringa=stringa[0:res.start()]+"par(self.%s)"%key + stringa[res.end():] # setattr(self,key,dictio[key]) # else: # break self.getvalue=self.getvalue self.formula=stringa # def __repr__(self): # res="DependentVariable ->getvalue() = %e"% self.getvalue() # return res def par(a): if( hasattr(a,'getvalue' )): return a.getvalue() else: return a class wrapperForFit: def __init__(self, fit, variable_list): self.fit=fit self.variable_list=variable_list def get_angles(self): res=[] for v in self.variable_list: ss=(v.max+v.min)*0.5 sd=(v.max-v.min)*0.5 if sd==0: angle=0 else: angle = arcsin((v.value-ss)/sd) res.append(angle) return res def apply_angles(self,angles): for i in range(len(self.variable_list)): v=self.variable_list[i] x=angles[i] ss=(v.max+v.min)*0.5 sd=(v.max-v.min)*0.5 v.value = ss+sd*sin(x) def theory(self, paras , xs=None, ): self.apply_angles(paras) colpo[0]=colpo[0]+1 self.fit.error() return self.fit.GetResForGeFit() def getData(self): return self.fit.GetDataForGeFit() if __name__=='__main__': class scarto: def __init__(self, a,b,c): self.a=a self.b=b self.c=c def error(self): # print par(self.a) # print dir(self.a) res= 1+par(self.a)*par(self.a)+par(self.b)*par(self.b)+par(self.c)*par(self.c) # print "error=", res return res a=Variable(2.,5.,-6.) b=Variable(7.,9.,-10.) c=Variable(7.,10.,-11.) ecob=scarto(a,b,c) miniob=minimiser(ecob,[a,b,c]) (p,y)=miniob.amoeba(0.000000001) miniob.amoebaAnnealed( 0.001, lambda x: 1000.0/(1.0+x*0.3), 100, arret=1.0e-12, maxiters=10) # ---print p # ---print y class scartoF2: def __init__(self, a,b): self.a=a self.b=b def error(self): x1=par(self.a) x2=par(self.b) a=x2-x1*x1 res= 100*a*a + (1-x1)*(1-x1) # ---print "error=", res, " itc= ", self.itc self.itc=self.itc+1 return res def setitercounter(self, n): self.itc=n def getitercounter(self): return self.itc for ini in [ (1001,1001), (1001,-999), (-999,-999), (-999,1001), (1443,1), (1,1443),(1.2,1)] : a=Variable(ini[0],-2000.048,2000.048) b=Variable(ini[1],-2000.048,2000.048) tominimise=scartoF2(a,b) miniob=minimiser(tominimise,[a,b]) os.system("rm performances") miniob.amoebaAnnealed( 0.001, lambda x: 1000.0/(1.0+x*0.3), 100, arret=1.0e-12) s=open("performances","r").read() x1=par(a) x2=par(b) dist=(x1-1)*(x1-1)+(x2-1)*(x2-1) dist=sqrt(dist) open("resocontoRosF2d","a").write( "##### ini=(%f %f)\n%s// finale= %e x1= %e x2=%e dist=%e\n" % ( ini[0],ini[1], s, tominimise.error(), x1,x2,dist) ) xrstools-0.15.0+git20210910+c147919d/XRStools/myMaskImageWidget.py000066400000000000000000000140261412732462000236340ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from PyMca5.PyMcaGui import MaskImageWidget as sole_MaskImageWidget import PyMca5.PyMcaGui.MaskImageWidget print ( PyMca5.PyMcaGui.MaskImageWidget .__file__) # from PyQt4 import Qt, QtCore from silx.gui import qt as Qt from silx.gui import qt as QtCore import numpy import string from PyMca5.PyMcaGraph.Plot import Plot from six.moves import range # from PyMca5.PyMcaGraph.backends.OpenGLBackend import OpenGLBackend # Plot.defaultBackend = OpenGLBackend # Plot.defaultBackend = OpenGLBackend import silx.gui.plot.MaskToolsWidget as MTW class MaskImageWidget(sole_MaskImageWidget.MaskImageWidget): # class MaskImageWidget(MTW.ImageMask ): changeTagOn=False def _graphSignal(self, ddict, ownsignal = None): if self.changeTagOn: if self.__selectionMask is not None and ddict['event']=="mouseClicked" and ddict['button']=="middle" : x,y = int(ddict["x"]), int(ddict["y"]) y, x = sole_MaskImageWidget.convertToRowAndColumn(x , y, self.__imageData.shape, xScale=self._xScale, yScale=self._yScale, safe=True) id_target = self.__selectionMask[y,x] if id_target and id_target!= self._nRoi: mask_target = (self.__selectionMask == id_target ) mask_swap = (self.__selectionMask== self._nRoi) self.__selectionMask [mask_target] = self._nRoi self.__selectionMask [mask_swap] = id_target emitsignal = True if emitsignal: self.plotImage(update = False) self._emitMaskChangedSignal() return super(MaskImageWidget, self)._graphSignal(ddict, ownsignal) # sole_MaskImageWidget.MaskImageWidget._graphSignal(self, ddict, ownsignal) def dragEnterEvent(self,event): print( dir(event.mimeData())) print( list(event.mimeData().formats())) print( event.mimeData().text()) model = Qt.QStandardItemModel() model.dropMimeData(event.mimeData(), QtCore.Qt.CopyAction, 0,0, Qt.QModelIndex()) print( model) if event.mimeData().hasFormat('application/x-qabstractitemmodeldatalist'): event.acceptProposedAction() bytearray = event.mimeData().data('application/x-qabstractitemmodeldatalist') data_items = decode_data(bytearray) print( data_items) if event.mimeData().hasFormat("text/plain"): print( " OK ") event.acceptProposedAction() def dropEvent(self, e): localpos = self.graph.getWidgetHandle().mapFromGlobal( Qt.QCursor().pos() ) x,y = localpos.x(), localpos.y() x,y = self.graph.pixelToData(x,y ) print( "POSITION ",x,y) mask = self.getSelectionMask() Ct = mask[int(y), int(x)] print( " VALORE MASCHERA ", Ct) if Ct: print( str(e.mimeData().text())) Cc = int( str(e.mimeData().text())) # bytearray = e.mimeData().data('application/x-qabstractitemmodeldatalist') # data = decode_data(bytearray) # print data # Cc = (2-data[0])*4+data[1] + 1 # print Cc zonet = (mask==Ct) zonec = (mask==Cc) mask[zonet]=Cc mask[zonec]=Ct self.setSelectionMask(mask) self.annotateSpots() def annotateSpots(self, a_ids = None, offset=None): self.graph.clearMarkers() mask = self.getSelectionMask().astype("i") nspots = mask.max() for i in range(1,nspots+1): m = (mask==i).astype("f") msum=m.sum() if msum: ny,nx = m.shape px= (m.sum(axis=0)*numpy.arange(nx)).sum()/msum py= (m.sum(axis=1)*numpy.arange(ny)).sum()/msum print( " ################################## ", px, py) extra_info = "" if a_ids is not None: if offset is None: extra_info = "(N%d)"% (a_ids.index(i) +1) else: extra_info = "(N%d,ROI%02d )"% ( a_ids.index(i) +1 , offset+i-1) if hasattr( self.graph, "insertMarker") : res=self.graph.insertMarker( px, py, "legend"+str(i)+extra_info, "%d"%(i)+extra_info, color='black', selectable=False, draggable=False, searchFeature=True, xytext = (-20, 0), textcoords = 'offset points', ha = 'right', va = 'bottom', bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.4), arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0')) else: res=self.graph.addMarker( px, py, "legend"+str(i)+extra_info, "%d"%(i)+extra_info, color='black', selectable=False, draggable=False, symbol="+") def decode_data( bytearray): data = [] item = {} ds = QtCore.QDataStream(bytearray) while not ds.atEnd(): row = ds.readInt32() column = ds.readInt32() return row, column print( row, column) map_items = ds.readInt32() for i in range(map_items): key = ds.readInt32() value = QtCore.QVariant() ds >> value item[QtCore.Qt.ItemDataRole(key)] = value data.append(item) return data xrstools-0.15.0+git20210910+c147919d/XRStools/nmf.py000066400000000000000000000055771412732462000210570ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function # NMF by alternative non-negative least squares using projected gradients # Author: Chih-Jen Lin, National Taiwan University # Python/numpy translation: Anthony Di Franco import numpy as np from numpy.linalg import norm from time import time from sys import stdout from six.moves import range def nmf(V,Winit,Hinit,tol,timelimit,maxiter): """ (W,H) = nmf(V,Winit,Hinit,tol,timelimit,maxiter) W,H: output solution Winit,Hinit: initial solution tol: tolerance for a relative stopping condition timelimit, maxiter: limit of time and iterations """ W = Winit H = Hinit initt = time() gradW = np.dot(W, np.dot(H, H.T)) - np.dot(V, H.T) gradH = np.dot(np.dot(W.T, W), H) - np.dot(W.T, V) initgrad = norm(r_[gradW, gradH.T]) print( 'Init gradient norm %f' % initgrad ) tolW = max(0.001,tol)*initgrad tolH = tolW for iter in range(1,maxiter): # stopping condition projnorm = norm(r_[gradW[logical_or(gradW<0, W>0)],gradH[logical_or(gradH<0, H>0)]]) if projnorm < tol*initgrad or time() - initt > timelimit: break (W, gradW, iterW) = nlssubprob(V.T,H.T,W.T,tolW,1000) W = W.T gradW = gradW.T if iterW==1: tolW = 0.1 * tolW (H,gradH,iterH) = nlssubprob(V,W,H,tolH,1000) if iterH==1: tolH = 0.1 * tolH if iter % 10 == 0: stdout.write('.') print( '\nIter = %d Final proj-grad norm %f' % (iter, projnorm)) return (W,H) def nlssubprob(V,W,Hinit,tol,maxiter): """ H, grad: output solution and gradient iter: #iterations used V, W: constant matrices Hinit: initial solution tol: stopping tolerance maxiter: limit of iterations """ H = Hinit WtV = np.dot(W.T, V) WtW = np.dot(W.T, W) alpha = 1 beta = 0.1 for iter in range(1, maxiter): grad = np.dot(WtW, H) - WtV projgrad = norm(grad[np.logical_or(grad < 0, H >0)]) if projgrad < tol: break # search step size for inner_iter in range(1,20): Hn = H - alpha*grad Hn = np.where(Hn > 0, Hn, 0) d = Hn-H gradd = np.sum(grad * d) dQd = np.sum(dot(WtW,d) * d) suff_decr = 0.99*gradd + 0.5*dQd < 0 if inner_iter == 1: decr_alpha = not suff_decr Hp = H if decr_alpha: if suff_decr: H = Hn break else: alpha = alpha * beta else: if not suff_decr or (Hp == Hn).all(): H = Hp break else: alpha = alpha/beta Hp = Hn if iter == maxiter: print( 'Max iter in nlssubprob') return (H, grad, iter) xrstools-0.15.0+git20210910+c147919d/XRStools/ramanWidget/000077500000000000000000000000001412732462000221515ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/ramanWidget/MainWindow.py000066400000000000000000001177361412732462000246160ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function try: from PyQt4 import Qt, QtCore, QtGui except: from PyQt5 import Qt, QtCore, QtGui from silx.gui import qt as Qt from silx.gui import qt as QtCore from silx.gui import qt as QtGui import h5py # from .. import ui from .subsetTable import subsetTable from .scansTable import scansTable from .acquisition import acquisition from .edgesTable import edgesTable from .source import source from .. import roiSelectionWidget from ..roiNmaSelectionGui import roiNmaSelectionWidget import numpy as np from XRStools import xrs_read, xrs_rois, xrs_extraction import os import PyMca5.PyMcaIO.specfilewrapper as SpecIO from silx.gui.plot.PlotWindow import Plot1D , Plot2D from collections import OrderedDict as odict from scipy import optimize import sys import traceback from six import StringIO import yaml Resolver = yaml.resolver.Resolver import re from six import u Resolver.add_implicit_resolver( u'tag:yaml.org,2002:float', re.compile(u(r"""^(?:[-+]?(?:[0-9][0-9_]*)(\.[0-9_]*)?(?:[eE][-+]?[0-9]+)? |\.[0-9_]+(?:[eE][-+][0-9]+)? |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\.[0-9_]* |[-+]?\.(?:inf|Inf|INF) |\.(?:nan|NaN|NAN))$"""), re.X), list(u'-+0123456789.')) import yaml import yaml.resolver DEBUG=0 DEBUG2=0 import pickle from XRStools.installation_dir import installation_dir import os my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] if my_relativ_path[0]=="/": my_relativ_path = my_relativ_path[1:] class MainWindow(Qt.QMainWindow) : def __init__(self, parent=None): super( MainWindow, self).__init__(parent) print( installation_dir ) print( "resources" ) print( my_relativ_path ) print(os.path.join( installation_dir,"resources" , my_relativ_path , "MainWindow.ui" ) ) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path , "MainWindow.ui" ), self) sys.excepthook = excepthook self.tabWidget.clear() self.subsettable = subsetTable() self.scanstable = scansTable() self.acquisition = acquisition() self.source = source() self.edges = edgesTable() rsw = roiSelectionWidget.mainwindow() self.rsw = rsw self.tabWidget.addTab( rsw ,"Spatial ROIS") sprsw = roiNmaSelectionWidget.mainwindow() rsw.user_input_signal.connect(sprsw.update_user_input) sprsw.user_input_signal.connect(self.source.update_user_input) self.sprsw = sprsw self.tabWidget.addTab( sprsw ,"Spectral ROIS") self.tabWidget.addTab( self.source ,"experiment") sca = QtGui.QScrollArea() sca.setWidgetResizable(True) sca.setWidget(self.subsettable) self.tabWidget.addTab( sca ,"analyzers subsets") sca = QtGui.QScrollArea() sca.setWidgetResizable(True) sca.setWidget(self.scanstable) self.tabWidget.addTab( sca ,"scans selection") self.tabWidget.addTab( self.edges ,"edges") self.tabWidget.addTab( self.acquisition ,"loading") self.acquisition.pushButton.clicked.connect(self.acquire) self.acquisition.pushButton_ixstools.clicked.connect(self.acquire_ixstools) self.acquisition.pushButton_saveAnalysis.clicked.connect(self.saveAnalysis) self.lw = None self.lw_ex = None self.plots=[] self.plots_conf = {} self.emitter2plotC={} self.actionSave_Configuration.triggered.connect(self.saveConfiguration) self.actionLoad_Configuration.triggered.connect(self.loadConfiguration) def loadConfiguration(self): self.loadConfigurationOption(None) def loadConfigurationOption(self, option=None): if not option: filename = QtGui.QFileDialog.getOpenFileName(None, "select", ) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) if len(filename)==0: return else: filename = option d = yaml.load(open(filename,"r"), yaml.Loader) selected_scans_d = d["selected_scans"] selected_scans = [] if selected_scans_d is not None: for name, scan in selected_scans_d.items(): selected_scans.append( [name]+scan) self.setScansSelection( selected_scans ) selected_subsets_d = d["selected_subsets"] selected_subsets = [] if selected_subsets_d is not None: for name, subset in selected_subsets_d.items(): selected_subsets.append( [subset[0],name]+subset[1:]) self.setSubsetsSelection( selected_subsets ) selected_acquisition_d = d["selected_acquisition"] names = [ "method", "refscan", "include_elastic", "output_prefix"] selected_acquisition = [ selected_acquisition_d[name] for name in names ] self.setLoadingSelection(selected_acquisition) selected_experiment_d = d["selected_experiment"] names = [ "specfile_name" , "roifile_address" ] selected_experiment = [ selected_experiment_d[name] for name in names ] self.setExperimentSelection(selected_experiment) selected_edges_d = d["selected_edges"] formula, edges = selected_edges_d["formula"] , selected_edges_d["edges"] self.setEdgesSelection( formula , edges ) if "plots" in d: self.plots_conf = d["plots"] self.consume_plots_definitions() def consume_plots_definitions(self): if self.plots_conf is None: return for plotC in self.plots: name = plotC.name if name in self.plots_conf: defs = self.plots_conf[name] names = ["hfcore_shift" , "pea_center", "pea_width","pea_shape","pea_height","lin_back0", "lin_back1", "hf_factor"] inputs = plotC.inputs for i,n in zip(inputs, names): if defs[n] is not None: i.setText("%e"%defs[n]) D = defs roisDefs = odict() for rn in [ "range1","range2", "Output", "Norm" ]: roisDefs[rn] = odict([["from", D[rn][0]] ,["to",D[rn][1]],["type", "energy"] ]) plotC.plot.getCurvesRoiDockWidget().setRois(roisDefs) del self.plots_conf[name] def saveConfiguration(self): filename = QtGui.QFileDialog.getSaveFileName(None, "select", ) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) if len(filename)==0: return selected_scans = self.getScansSelection() selected_subsets = self.getSubsetsSelection() selected_acquisition = self.getLoadingSelection() specfile_name , roifile_address = self.getExperimentSelection() selected_edges = self.getEdgesSelection( ) # [[ "H2O",1.0]] , {'O':['K'] } ) print( " DEVO SALVARE " ) print ( selected_scans ) print ( selected_subsets ) print ( selected_acquisition ) print ( specfile_name , roifile_address ) for plotC in self.plots: print( " per plotC ", plotC.name) print ( [ tok.text() for tok in plotC.inputs ] ) print( plotC.plot.getCurvesRoiDockWidget().getRois() ) # @@@@@@@@@@ààà quando si ricarica se c'e' un plotC di nome corrispondente inizializzare # @@@@@@@@@@@@ se no si tiene da parte e si consuma quando si fa un'acquire ## recuperare anche output prefix # yaml.load( file o stringa,yaml.Loader) ## fare per batch senza grafica file = open(filename,"w") file.write("selected_scans :\n" ) for scan in selected_scans: name, nums = scan[0], scan[1:] file.write( " %s : %s\n"%( name , str(list(nums)) ) ) file.write("selected_subsets :\n" ) for subset in selected_subsets: scal, name, nums = subset[0], subset[1], subset[2:] file.write(" %s : %s\n"%( name , str([scal] + list(nums)) ) ) file.write("selected_acquisition :\n" ) acqui = selected_acquisition names = [ "method", "refscan", "include_elastic", "output_prefix"] vals = acqui[0], acqui[1], acqui[2], acqui[3] for n,v in zip(names, vals ) : file.write(" %s : %s\n"%( n,v) ) file.write("selected_experiment :\n" ) names = [ "specfile_name" , "roifile_address" ] vals = specfile_name , roifile_address for n,v in zip(names, vals ) : file.write(" %s : %s\n"%( n,v) ) file.write("selected_edges :\n" ) formula, edges = selected_edges file.write(" %s : %s\n"%( " formula", str(formula)) ) file.write(" %s : %s\n"%( " edges", str(edges)) ) file.write("plots :\n" ) for plotC in self.plots: print( " per plotC ", plotC.name) file.write(" %s :\n"%plotC.name ) vals = [ tok.text() for tok in plotC.inputs ] names = ["hfcore_shift" , "pea_center", "pea_width","pea_shape","pea_height","lin_back0", "lin_back1", "hf_factor"] for n,v in zip(names, vals ) : file.write(" %s : %s\n"%( n,v) ) dizio_rois = plotC.plot.getCurvesRoiDockWidget().getRois() for kroi, val in dizio_rois.items(): file.write(" %s : [%e,%e]\n"% (kroi, val.getFrom(), val.getTo() ) ) file.close() def acquire_ixstools(self): print(" =============== LOADING============ ") selected_acquisition = self.getLoadingSelection() prefix = str(self.acquisition.lineEdit_outputPrefix.text()) plottables={} file = h5py.File( prefix+".h5", "r" ) for key in file["plottables"]: plottables[key] = {} for k in ["x","y", "errors"]: plottables[key][k] = file["plottables"][key][k][()] for p in self.plots: self.tabWidget.removeTab( self.tabWidget.indexOf(p)) p.deleteLater() del p toreport = {} for plotC in self.plots: vals = [ tok.text() for tok in plotC.inputs ] dizio_rois = plotC.plot.getCurvesRoiDockWidget().getRois() toreport[plotC.name]=[vals, dizio_rois ] self.plots=[] for iplot,name in enumerate(plottables): x,y = plottables[name]["x"] , plottables[name]["y"] y = y[~np.isnan(x)] x = x[~np.isnan(x)] plotC = plotContainer( x*1000.0, y , name, None, None, None ) plotC.controller=self self.plots.append(plotC) self.tabWidget.addTab(plotC, name) if name in toreport: vals, dizio = toreport[name] for tok,v in zip(plotC.inputs, vals): tok.setText(v) plotC.plot.getCurvesRoiDockWidget().setRois(dizio) self.consume_plots_definitions() def acquire(self): print(" =============== LOADING============ ") selected_scans = self.getScansSelection() selected_subsets = self.getSubsetsSelection() selected_acquisition = self.getLoadingSelection() if not DEBUG: specfile_name , roifile_address = self.getExperimentSelection() formulas, edges = self.getEdgesSelection() if len(edges)!=1: raise Exception(" So far only one edge can be processed") element = list(edges.keys())[0] if len(edges[element])!=1: raise Exception(" So far only one edge can be processed") else: edge = edges[element][0] forms=[] weights=[] for f,ww in formulas: forms.append(f) weights.append(float(ww) ) self.lw, roinums = integrate( specfile_name, roifile_address, selected_scans, selected_subsets, selected_acquisition ) for p in self.plots: self.tabWidget.removeTab( self.tabWidget.indexOf(p)) p.deleteLater() del p toreport = {} for plotC in self.plots: vals = [ tok.text() for tok in plotC.inputs ] dizio_rois = plotC.plot.getCurvesRoiDockWidget().getRois() toreport[plotC.name]=[vals, dizio_rois ] self.plots=[] self.lw_ex_s = [] for iplot,subset in enumerate(selected_subsets): if not DEBUG: scal = subset[0] name = subset[1] nums = subset[2:] nums = [ i for i in range(len(roinums)) if roinums[i] in nums ] lw_ex = xrs_extraction.edge_extraction( self.lw,forms,weights,{element:[edge]}) lw_ex.analyzerAverage(nums, errorweighing=False) y = np.maximum(1.0e-10 , lw_ex.avsignals) print(" INITIALISATION y, ", y) plotC = plotContainer(lw_ex.eloss, y, name, lw_ex, element, edge ) plotC.controller=self self.plots.append(plotC) self.tabWidget.addTab(plotC, name) if name in toreport: vals, dizio = toreport[name] for tok,v in zip(plotC.inputs, vals): tok.setText(v) plotC.plot.getCurvesRoiDockWidget().setRois(dizio) self.consume_plots_definitions() else: scal = subset[0] name = subset[1] nums = subset[2:] saved = np.load("debug%d.npy"%iplot) eloss, y = saved plotC = plotContainer(eloss, y, name) self.plots.append(plotC) self.tabWidget.addTab(plotC, name) def saveAnalysis(self): prefix = str(self.acquisition.lineEdit_outputPrefix.text()) for C in self.plots: C.saveAnalysis(prefix) def setScansSelection(self, selection): self.scanstable.set_selection( selection ) def getScansSelection(self): res,mini,maxi = self.scanstable.get_selection() return res def setSubsetsSelection(self, selection): self.subsettable.set_selection( selection ) def getSubsetsSelection(self): return self.subsettable.get_selection() def setLoadingSelection(self, selection): if selection is not None: self.acquisition.set_selection( selection ) def getLoadingSelection(self): return self.acquisition.get_selection() def setExperimentSelection(self, selection): self.source.set_selection( selection ) def getExperimentSelection(self): return self.source.get_selection() def setEdgesSelection(self, formulas, edges): self.edges.set_selection( formulas, edges ) def getEdgesSelection(self): return self.edges.get_selection() def integrate( specfile_name, roifile_address, selected_scans, selected_subsets, selected_acquisition ) : print(" SETTING PATH TO LW", specfile_name) assert( os.path.exists(specfile_name ) ) if not os.path.isdir(specfile_name): specfile_dir = os.path.dirname(specfile_name) print(" DEBUG lw creo ", specfile_dir) lw = xrs_read.Hydra(specfile_dir) print("LOADING ROIS") myroi = xrs_rois.load_rois_fromh5_address(roifile_address) print("DEBUG roi ", roifile_address ) ; lw.set_roiObj(myroi) print(" TRIMMING KEYS") print ( list(myroi.red_rois.keys() ) ) roinums = [ int(''.join(filter(str.isdigit, str(key) ))) for key in myroi.red_rois.keys() ] roinums.sort() #### subsets = self.getSubsetsSelection() subsets = selected_subsets new_subsets=[] scaling = np.zeros(72) for ss in subsets: first = ss[:2] last = ss[2:] nl = [ i for i in range(len(roinums)) if roinums[i] in last ] scal,name = first scaling[nl] = scal if len(nl): nuovo = first +nl print(" APPENDING SET ") print( nuovo) new_subsets.append( nuovo ) else: print( " NIENTE PER ") print( first) method, ref_scan, keep_elastic, output_prefix = selected_acquisition print(" CALCULATING COMPENSATION", ref_scan, method) print("DEBUG compensation factor ", ref_scan, method ) lw.get_compensation_factor(ref_scan, method=method ) print(" --------------------------------------------------------") print(lw.cenom_dict.keys()) print("=====================================================") print(" LOADING GROUPS OF SCANS ") for scan in selected_scans: scan_ns = scan[1:] scan_name = scan[0] print("DEBUG LOADING ", scan_name, scan_ns, method) lw.load_scan( scan_ns, method=method, direct=True, scan_type=scan_name) # scaling = scaling) print(" DEBUG get spe ", method, keep_elastic) lw.get_spectrum_new( method=method , include_elastic=keep_elastic) print(" SET detector angles") specfile = SpecIO.Specfile( specfile_name ) scan = specfile.select(str(ref_scan)) rvd = scan.motorpos("RVD" ) rvu = scan.motorpos("RVU" ) rvb = scan.motorpos("RVB" ) rhr = scan.motorpos("RHR" ) rhl = scan.motorpos("RHL" ) rhb = scan.motorpos("RHB" ) lw.get_tths(rvd=rvd, rvu=rvu, rvb=rvb, rhr=rhr, rhl=rhl, rhb=rhb, order=[0, 1, 2, 3, 4, 5]) return lw, roinums class MyPlot1D(Plot1D): def __init__(self, parent=None): super(MyPlot1D, self).__init__(parent) # , backend = "gl") # self.shint = 400 # self.setSizePolicy( Qt.QSizePolicy.Fixed, Qt.QSizePolicy.Fixed ) # ou maximum # def sizeHint(self ) : # return Qt.QSize( self.shint, self.shint) # def setSizeHint(self, val ) : # self.shint = val # self.updateGeometry() # @ui.UILoadable class plotContainer(QtGui.QWidget) : def __init__(self, eloss, y, name, lw_ex, element, edge, parent=None): super( plotContainer, self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "plotContainer.ui" ), self) self.plot = MyPlot1D() self.layout().addWidget(self.plot) plot = self.plot plot.getFitAction().setVisible(True) plot.setVisible(True) plot.getFitAction().setVisible(True) y = np.maximum(1.0e-3 , y) plot.addCurve(x=eloss, y=y, legend = name, replace="True") plot.setYAxisLogarithmic(True) self.name = name self.lw_ex = lw_ex E0,E1 = eloss.min(), eloss.max() roisDefs = odict( [ ["ICR", odict([["from",E0-0.1*(E1-E0) ],["to", E1+0.3*(E1-E0) ],["type","energy"]])], ["range1", odict([["from",E0+0.1*(E1-E0) ],["to", E0+0.3*(E1-E0) ],["type","energy"]])], ["range2", odict([["from",E0+0.6*(E1-E0)],["to",E0+0.8*(E1-E0)],["type","energy"]])], ["Output", odict([["from",E0+0.1*(E1-E0)],["to",E0+0.9*(E1-E0)],["type","energy"]])], ["Norm", odict([["from",E0+0.6*(E1-E0)],["to",E0+0.7*(E1-E0)],["type","energy"]])] ] ) plot.getCurvesRoiDockWidget().setRois(roisDefs) plot.getCurvesRoiDockWidget().setVisible(True) ## plot.getCurvesRoiDockWidget().roiWidget.showAllMarkers(True) # plot.getCurvesRoiDockWidget().roiWidget._isInit = True plot.getCurvesRoiDockWidget().sigROISignal.connect(self.on_rois_changed) self.pushButton_guess.clicked.connect(self.do_guess) self.pushButton_fit.clicked.connect(self.do_fit) self.pushButton_plot.clicked.connect(self.plotta) self.eloss = eloss self.y = y self.inputs = [self.lineEdit_hfshift,self.lineEdit_A0,self.lineEdit_A1,self.lineEdit_A2,self.lineEdit_A3,self.lineEdit_A4,self.lineEdit_A5,self.lineEdit_A6] self.element = element self.edge = edge self.pushButton_save.clicked.connect(self.saveAnalysis_local) def checkInputs(self): for tok in self.inputs: if len( str( tok.text()))==0: tok.setText("0") for tok in self.inputs: try: tmp = float( str(tok.text())) except: tok.setText("ERROR") def do_guess(self): roisDef = self.plot.getCurvesRoiDockWidget().getRois() range1 =[ roisDef["range1"].getFrom() , roisDef["range1"].getTo() ] range2 =[ roisDef["range2"].getFrom() , roisDef["range2"].getTo() ] print( self.plot.getCurvesRoiDockWidget().getRois()) # define fitting ranges region1 = np.where(np.logical_and(self.eloss >= range1[0], self.eloss <= range1[1])) print(" REGION 1 " , region1) region2 = np.where(np.logical_and(self.eloss >= range2[0], self.eloss <= range2[1])) print(" REGION 2 " , region2) # region = np.append(region1*weights[0],region2*weights[1]) region = np.append(region1,region2) # find indices for guessing start values from HF J_total # print(" QUI FITTO ") # fitfct = lambda a: np.sum( (self.y[region] - pearson7_zeroback(self.eloss,a)[region] - # np.polyval(a[4:6],self.eloss[region]) )**2.0 ) fact = self.y[region].max() # fitfct = functor2minim( self.eloss[region1], self.y[region1]/fact ) # guess1 = optimize.minimize(fitfct, [1.0,1.0,1.0,1.0 ], method='SLSQP').x # guess1 = optimize.minimize(fitfct, [1.0,1.0,1.0,1.0 ], method='SLSQP').x fitfct = functorObjectV( self.y[region1]/fact , self.eloss[region1], 0 ) bndsa = [ -np.inf]+ [ 0 for tmp in range(3)] bndsb = [ np.inf for tmp in range(4)] guess = [1.0,1.0,1.0,1.0 ] guess_alt = [] for t in self.inputs[1:5]: if len(str(t.text()))==0: break else: guess_alt.append(float( str(t.text()) )) else: guess = guess_alt guess[3]/=fact soluzione = optimize.least_squares(fitfct,guess , method='trf', bounds=[bndsa,bndsb] ) # constraints=cons) guess1 = soluzione.x guess1[3]*=fact print(" IL RISULTATO DEL FIT EST ", guess1) guess=list(guess1)+[0.0,0.0,1.0] self.lineEdit_A0.setText(str("%e"%guess[0])) self.lineEdit_A1.setText(str("%e"%guess[1])) self.lineEdit_A2.setText(str("%e"%guess[2])) self.lineEdit_A3.setText(str("%e"%guess[3])) self.lineEdit_A4.setText(str("%e"%guess[4])) self.lineEdit_A5.setText(str("%e"%guess[5])) self.lineEdit_A6.setText(str("%e"%guess[6])) # fit = fitfct.funct( guess , self.eloss ) fit = fitfct.funct( guess , self.eloss ) self.plot.addCurve(x=self.eloss, y=fit, legend = "guess", replace=False) self.guess=guess def do_fit(self): roisDef = self.plot.getCurvesRoiDockWidget().getRois() self.checkInputs() HFcore_shift = float(self.inputs[0].text()) guess = [ float(tok.text()) for tok in self.inputs ][1:] result = removeCorePearson(guess, self.eloss, self.y, roisDef,HFcore_shift, self.lw_ex, self.element, self.edge) guess = result["x"] self.lineEdit_A0.setText(str("%e"%guess[0])) self.lineEdit_A1.setText(str("%e"%guess[1])) self.lineEdit_A2.setText(str("%e"%guess[2])) self.lineEdit_A3.setText(str("%e"%guess[3])) self.lineEdit_A4.setText(str("%e"%guess[4])) self.lineEdit_A5.setText(str("%e"%guess[5])) self.lineEdit_A6.setText(str("%e"%guess[6])) self.plotta() def saveAnalysis_local(self): roisDef = self.plot.getCurvesRoiDockWidget().getRois() prefix = str(self.controller.acquisition.lineEdit_outputPrefix.text()) self.saveAnalysis_nogui( prefix, roisDef) def saveAnalysis(self, prefix): roisDef = self.plot.getCurvesRoiDockWidget().getRois() self.saveAnalysis_nogui( prefix, roisDef) def saveAnalysis_nogui(self, prefix, roisDef): y_tot=self.y eloss = self.eloss this_plot_def = roisDef fitfct_result = self.plotta(doplot=True) sqwav = (y_tot - fitfct_result.peapol) if self.lw_ex is not None: sqwaverr = self.lw_ex.averrors else: sqwaverr = np.ones_like( sqwav) allfit = fitfct_result.fit rangeOutput =[ this_plot_def["Output"].getFrom() , this_plot_def["Output"].getTo() ] rangeNorm =[ this_plot_def["Norm"].getFrom() , this_plot_def["Norm"].getTo() ] regionOutput = np.where(np.logical_and(eloss >= rangeOutput[0], eloss <= rangeOutput[1])) data = np.zeros((len(regionOutput[0]),3),"d") data[:,0] = eloss[regionOutput] data[:,1] = sqwav[regionOutput] data[:,2] = sqwaverr[regionOutput] regionNorm = np.where(np.logical_and(data[:,0] >= rangeNorm[0], data[:,0] <= rangeNorm[1])) if len(regionNorm): norm = np.trapz(data[regionNorm,1],data[regionNorm,0]) print(" NORM ", norm) data[:,1] /= norm data[:,2] /= norm np.savetxt(prefix+"_"+self.name+".txt",data) data = np.zeros((len(eloss),4),"d") data[:,0] = eloss data[:,1] = y_tot data[:,2] = allfit data[:,3] = fitfct_result.peapol # np.savetxt(prefix+"_"+self.name+"_all.txt",data) return def plotta(self, doplot=True): roisDef = self.plot.getCurvesRoiDockWidget().getRois() self.checkInputs() HFcore_shift = float(self.inputs[0].text()) guess = [ float(tok.text()) for tok in self.inputs ][1:] if self.lw_ex is not None: HF_core = np.interp(self.eloss,self.eloss+HFcore_shift,self.lw_ex.av_C[self.element][self.edge]) else: HF_core = hf_core_surrogate( self.eloss,HFcore_shift ) fitfct_result = functorObjectV( self.y, self.eloss, HF_core ) fitfct_result(guess) if doplot: self.plot.addCurve(x=self.eloss, y=fitfct_result.fit, legend = "Fit(Pearson+linear+CoreHF)", replace=False) self.plot.addCurve(x=self.eloss, y=fitfct_result.hf_fit, legend = "Core HF", replace=False) self.plot.addCurve(x=self.eloss, y=fitfct_result.peapol, legend = "pea+pol", replace=False) return fitfct_result def on_rois_changed(self, message): print(" ROIS changed ", message, self.sender()) class functor2minim: def __init__(self, eloss, y): self.eloss=eloss self.y = y print(" ELOSS ===================================================================================") print( eloss) def funct(self, a,eloss): pear = pearson7_zeroback(eloss,a) poly = np.polyval(a[4:6],eloss ) tot = pear+poly return tot def __call__(self, a): tot = self.funct( a, self.eloss) diff = self.y-tot res = (diff*diff/self.y).sum() print(" RETURN ", res) return res def hf_core_surrogate( eloss,HFcore_shift ): x = np.maximum(0.001, eloss) / HFcore_shift return np.less(0.0, eloss-HFcore_shift).astype(np.float32) / ( x*x*x) def pearson7_zeroback(x,a): """ returns a pearson function but without y-offset a[0] = Peak position a[1] = FWHM a[2] = Shape, 1 = Lorentzian, infinite = Gaussian a[3] = Peak intensity """ x = np.array(x) y = a[3] * (1.0+(2.0**(1.0/a[2])-1.0) * (2.0*(x-a[0])/a[1])**2.0)**(-a[2]) return y class myObject(object): pass class functorObject: def __init__(self, y, eloss, hfcore): self.y = y self.eloss = eloss self.hfcore = hfcore def __call__(self, x): pea = pearson7_zeroback(self.eloss,x[0:4]) pol = np.polyval(x[4:6],self.eloss) hf = self.hfcore*x[6] self.hf_fit = hf ## self.fit = pea+pol+hf ## self.peapol=pea+pol diff = self.y-self.fit res = (diff*diff/self.y).sum() # np.save( "Oeloss",self.eloss) # np.save( "Oy",self.y) # np.save( "Ohf",hf) # np.save( "Of" , +pea+pol+hf ) print(" RETURN ", res) return res class functorObjectV: def __init__(self, y, eloss, hfcore): self.y = y self.eloss = eloss self.hfcore = hfcore def funct(self, a,eloss): pear = pearson7_zeroback(eloss,a) poly = np.polyval(a[4:6],eloss ) tot = pear+poly return tot def __call__(self, x): pea = pearson7_zeroback(self.eloss,x[0:4]) if len(x)==7: pol = np.polyval(x[4:6],self.eloss) hf = self.hfcore*x[6] else: pol=0 hf=0 self.hf_fit = hf ## self.fit = pea+pol+hf ## self.peapol = pea+pol diff = self.y-self.fit res = diff/np.sqrt(self.y) # np.save( "Oeloss",self.eloss) # np.save( "Oy",self.y) # np.save( "Ohf",hf) # np.save( "Of" , +pea+pol+hf ) return res def removeCorePearson(guess, eloss, y_tot, roisDef,HFcore_shift, lw_ex, element, edge): range1 =[ roisDef["range1"].getFrom() , roisDef["range1"].getTo() ] range2 =[ roisDef["range2"].getFrom() , roisDef["range2"].getTo() ] region1 = np.where(np.logical_and(eloss >= range1[0], eloss <= range1[1])) region2 = np.where(np.logical_and(eloss >= range2[0], eloss <= range2[1])) region = np.append(region1,region2) if lw_ex is not None: HF_core = np.interp(eloss,eloss+HFcore_shift,lw_ex.av_C[element][edge]) else: HF_core = hf_core_surrogate( eloss,HFcore_shift ) fact = y_tot[region].max() guess[3] /=fact guess[4] /=fact guess[5] /=fact guess[6] /=fact y=y_tot/fact y_reg1 = y[region1] eloss_reg1 = eloss[region1] HF_core_reg1 = HF_core[region1] y_reg2 = y[region2] eloss_reg2 = eloss[region2] HF_core_reg2 = HF_core[region2] y_reg = y[region] eloss_reg = eloss[region] HF_core_reg = HF_core[region] fitfct = functorObjectV( y_reg, eloss_reg, HF_core_reg ) bndsa = [ -np.inf]+ [ 0 for tmp in range(6)] bndsa[5]=-np.inf bndsb = [ np.inf for tmp in range(7)] soluzione = optimize.least_squares(fitfct, guess, method='trf', bounds=[bndsa,bndsb] ) # constraints=cons) guess = soluzione.x print ( "RISULTATO ", soluzione) guess[3] *=fact guess[4] *=fact guess[5] *=fact guess[6] *=fact fitfct_result = functorObjectV( y_tot, eloss, HF_core ) fitfct_result(guess) result = {} result["x"] = guess return result def batch( filename): d = yaml.load(open(filename,"r"), yaml.Loader) selected_scans_d = d["selected_scans"] selected_scans = [] for name, scan in selected_scans_d.items(): selected_scans.append( [name]+scan) selected_subsets_d = d["selected_subsets"] selected_subsets = [] for name, subset in selected_subsets_d.items(): selected_subsets.append( [subset[0],name]+subset[1:]) selected_acquisition_d = d["selected_acquisition"] if selected_acquisition_d["output_prefix"] is None: selected_acquisition_d["output_prefix"] = "" names = [ "method", "refscan", "include_elastic", "output_prefix"] selected_acquisition = [ selected_acquisition_d[name] for name in names ] selected_experiment_d = d["selected_experiment"] names = [ "specfile_name" , "roifile_address" ] selected_experiment = [ selected_experiment_d[name] for name in names ] lw, roinums = integrate( selected_experiment[0], selected_experiment[1] , selected_scans, selected_subsets, selected_acquisition ) selected_edges_d = d["selected_edges"] formula, edges = selected_edges_d["formula"] , selected_edges_d["edges"] if len(edges)!=1: raise Exception(" So far only one edge can be processed") element = list(edges.keys())[0] if len(edges[element])!=1: raise Exception(" So far only one edge can be processed") else: edge = edges[element][0] forms=[] weights=[] for f,ww in formula: forms.append(f) weights.append(float(ww) ) if "plots" in d: plots_def = d["plots"] else: plots_def = {} for iplot,subset in enumerate(selected_subsets): scal = subset[0] name = subset[1] nums = subset[2:] if name not in plots_def: continue this_plot_def = plots_def [name] nums = [ i for i in range(len(roinums)) if roinums[i] in nums ] lw_ex = xrs_extraction.edge_extraction( lw,forms,weights,{element:edge}) lw_ex.analyzerAverage(nums, errorweighing=False) eloss = lw_ex.eloss range1 =[ this_plot_def["range1"][0] , this_plot_def["range1"][1] ] range2 =[ this_plot_def["range2"][0] , this_plot_def["range2"][1] ] region1 = np.where(np.logical_and(eloss >= range1[0], eloss <= range1[1])) region2 = np.where(np.logical_and(eloss >= range2[0], eloss <= range2[1])) region = np.append(region1,region2) HFcore_shift = this_plot_def["hfcore_shift"] HF_core = np.interp(eloss,eloss+HFcore_shift,lw_ex.av_C[element][edge]) guess = [ this_plot_def[k] for k in ["pea_center","pea_width","pea_shape","pea_height","lin_back0","lin_back1","hf_factor"] ] y_tot = np.maximum(1.0e-10 , lw_ex.avsignals) fact = y_tot[region].max() guess[3] /=fact guess[4] /=fact guess[5] /=fact guess[6] /=fact y=y_tot/fact y_reg1 = y[region1] eloss_reg1 = eloss[region1] HF_core_reg1 = HF_core[region1] y_reg2 = y[region2] eloss_reg2 = eloss[region2] HF_core_reg2 = HF_core[region2] y_reg = y[region] eloss_reg = eloss[region] HF_core_reg = HF_core[region] fitfct = functorObjectV( y_reg, eloss_reg, HF_core_reg ) bndsa = [ -np.inf]+ [ 0 for tmp in range(6)] bndsa[5]=-np.inf bndsb = [ np.inf for tmp in range(7)] soluzione = optimize.least_squares(fitfct, guess, method='trf', bounds=[bndsa,bndsb] ) # constraints=cons) guess = soluzione.x print ( "RISULTATO ", soluzione) guess[3] *=fact guess[4] *=fact guess[5] *=fact guess[6] *=fact y=y*fact fitfct_result = functorObjectV( y, eloss, HF_core ) fitfct_result(guess) all4plot = (y ) fit4plot = (fitfct_result.fit) sqwav = (y - fitfct_result.peapol) sqwaverr = lw_ex.averrors rangeOutput =[ this_plot_def["Output"][0] , this_plot_def["Output"][1] ] rangeNorm =[ this_plot_def["Norm"][0] , this_plot_def["Norm"][1] ] regionOutput = np.where(np.logical_and(eloss >= rangeOutput[0], eloss <= rangeOutput[1])) data = np.zeros((len(regionOutput[0]),3),"d") data[:,0] = eloss[regionOutput] data[:,1] = sqwav[regionOutput] data[:,2] = sqwaverr[regionOutput] regionNorm = np.where(np.logical_and(data[:,0] >= rangeNorm[0], data[:,0] <= rangeNorm[1])) if len(regionNorm): norm = np.trapz(data[regionNorm,1],data[regionNorm,0]) print(" NOOOOOOOOOOOOOOOOOOOOOOOOOOOORM ", norm) data[:,1] /= norm data[:,2] /= norm np.savetxt(selected_acquisition_d["output_prefix"]+"_"+name+".txt",data) data = np.zeros((len(eloss),3),"d") data[:,0] = eloss data[:,1] = all4plot data[:,2] = fit4plot # np.savetxt(selected_acquisition_d["output_prefix"]+"_"+name+"_all",data) separator = '-' * 80 def excepthook(type, value, tracebackobj): tbinfofile = StringIO() traceback.print_tb(tracebackobj, None, tbinfofile) # traceback.print_exc() # traceback.print_stack( tracebackobj , file=tbinfofile) tbinfofile.seek(0) tbinfo = tbinfofile.read() errmsg = '%s: %s' % (str(type), str(value)) sections = [separator, errmsg, separator, tbinfo] msg = '\n'.join(sections) msgBox = Qt.QMessageBox(None) msgBox.setText("An exception Occurred") msgBox.setInformativeText(msg) msgBox.setStandardButtons( Qt. QMessageBox.Ok) msgBox.setDefaultButton( Qt.QMessageBox.Ok) ret = msgBox.exec_() return def main(manageQApp = True) : if len(sys.argv) > 1: batch(sys.argv[1]) else: if manageQApp: app=Qt.QApplication([]) w = MainWindow() w.show() if manageQApp: app.exec_() else: return w if __name__ =="__main__": if len(sys.argv) > 1: batch(sys.argv[1]) else: lowq = list(range(24)) lowq.extend(range(36,60)) medq = list(range(24,36)) highq = list(range(60,72)) app=Qt.QApplication([]) w = MainWindow() w.setScansSelection( [ ["elastic" , 611,615,619,623 ] , [ "ok0", 628], [ 'ok1', 612,616,620,624 ], [ "ok2", 613,617,621,625], [ "ok3", 614,618,622,626] ] ) w.setSubsetsSelection( [[3.14, "lowq" ]+lowq , [ 3.14, 'middleq']+ medq , [3.14, "highq"]+ highq ]) w.setLoadingSelection( ["sum",611, True, "pippo" ] ) w.setExperimentSelection([ "/data/id20/inhouse/data/run5_17/run7_ihr/hydra", "Aspectral_myroi.h5:/datas/ROI" ]) w.setEdgesSelection( [[ "H2O",1.0]] , {'O':['K'] } ) w.show() app.exec_() print(" SELECTION ") print( w.getScansSelection()) print( w.getSubsetsSelection()) print( w.getLoadingSelection()) print( w.getExperimentSelection()) print( w.getEdgesSelection()) # recordmydesktop --v_quality 20 --s_quality 10 --fps 10 --overwrite --device plughw:0,0 -o timefinder-v4-screencast.ogv # ffmpeg -i terza -ss 0 -t 23 -acodec copy -vcodec copy terza_a.ogv # ffmpeg -f concat -safe 0 -i mylist.txt -c copy del.ogv # alex@debian:~/src/XRStools/doc$ more ~/mylist.txt # file 'prima.ogv' # file 'seconda.ogv' # file 'terza_a.ogv' # file 'terza_b.ogv' # avconf -i a.ogv a.webm # per prender solo il video # avconv -i /olddisk/home/alex/spectralRoiSelector.ogv -map 0:0 nnmf.webm # conversione # ffmpe -i a.ogv -f webm a.webm # ffmpeg -i tds2el2_1.ogv -f webm -crf 2 tds2el2_1.webm # ffmpeg -i TUT1.ogv -vcodec copy -af "volume=enable='between(t,33.6,35.2)':volume=0" test.ogv xrstools-0.15.0+git20210910+c147919d/XRStools/ramanWidget/__init__.py000066400000000000000000000003111412732462000242550ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function # from .scansTable import * # from .subsetTable import * # from .MainWindow import * xrstools-0.15.0+git20210910+c147919d/XRStools/ramanWidget/acquisition.py000066400000000000000000000061611412732462000250570ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from silx.gui import qt as Qt from silx.gui import qt as QtCore from silx.gui import qt as QtGui # from .. import ui import collections from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] if my_relativ_path[0]=="/": my_relativ_path = my_relativ_path[1:] # @ui.UILoadable class acquisition(QtGui.QWidget) : def __init__(self, parent=None): super( acquisition , self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path , "acquisition.ui" ), self) self.checkBox_includeElastic.setChecked(True) extraActions = collections.OrderedDict([("Browse",self.browse)] ) self.lineEdit_outputPrefix.contextMenuEvent = Functor_contextMenuEvent(self.lineEdit_outputPrefix , extraActions = extraActions ) def set_selection( self, selection ): if selection[3] is None: return self.comboBox_method.setCurrentIndex(self.comboBox_method.findText( selection[0]) ) self.spinBox_scan.setValue( selection[1] ) self.checkBox_includeElastic.setChecked( selection[2] ) self.lineEdit_outputPrefix.setText(selection[3]) def get_selection( self ): res = [ str(self.comboBox_method.currentText()) , self.spinBox_scan.value() , self.checkBox_includeElastic.isChecked() , str(self.lineEdit_outputPrefix.text()) ] return res def browse(self): filename = self.lineEdit_outputPrefix.text() if len(filename): filename = QtGui.QFileDialog.getSaveFileName(None, "select", filename) else: filename = QtGui.QFileDialog.getSaveFileName(None, "select", ) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) if len(filename): self.lineEdit_outputPrefix.setText(filename) if __name__ =="__main__": app=Qt.QApplication([]) w = acquisition() w.set_selection( ["pixel" ,611 ] ) w.show() app.exec_() print(" SELECTION =") print(w.get_selection()) class Functor_contextMenuEvent: def __init__(self, ql, extraActions): self.ql=ql self.extraActions = extraActions def __call__(self, event): if not hasattr(self.ql,"createStandardContextMenu"): self.mem=[] else: menu = self.ql.createStandardContextMenu() self.mem=[] menu.insertSeparator(menu.actions()[0]) menu.insertSeparator(menu.actions()[0]) for na, aa in list(self.extraActions.items())[::-1]: testAction = Qt.QAction( na ,None); self.mem.append(testAction) menu.insertAction( menu.actions()[0] , testAction) testAction.triggered.connect( aa ) menu.exec_(event.globalPos()); del menu xrstools-0.15.0+git20210910+c147919d/XRStools/ramanWidget/edgesTable.py000066400000000000000000000212371412732462000245670ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from silx.gui import qt as Qt from silx.gui import qt as QtCore from silx.gui import qt as QtGui import os # from .. import ui from .. import xrs_utilities import collections from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] if my_relativ_path[0]=="/": my_relativ_path = my_relativ_path[1:] deletion_mode = 1 # @ui.UILoadable class edgesTable(QtGui.QWidget) : def __init__(self, parent=None): super( edgesTable, self).__init__(parent) print(my_relativ_path) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "edgesTable.ui" ), self) self.gridLayout.setHorizontalSpacing(0) self.abstract=[] self.abstract_edges=[] self.render_abstract() def clear_layout(self): for l in self.bottoni_info.keys(): self.gridLayout_edges.removeWidget(l) if deletion_mode: l.hide() l.setParent(None) else: l.deleteLater() del l for l in self.abstract_edges: print( " RIMUOVO =====================", l[0].text()) for t in l: self.gridLayout_edges.removeWidget(t) if deletion_mode: t.hide() t.setParent(None) else: t.deleteLater() del t for l in self.abstract: for t in l: self.gridLayout.removeWidget(t) if deletion_mode: t.hide() t.setParent(None) else: t.deleteLater() del t def get_abstractN(self): res=[] for line in self.abstract: if len(line): aline = [ (str(line[0].text())) , str(line[1].text()) ] res.append(aline) res_edges = {} for line in self.abstract_edges: if len(line): dl = collections.OrderedDict() el = str(line[0].text())[:-1] ## removing ":" res_edges[el]=dl for tok in line[1:]: dl[ tok.text ] = tok.isChecked() return res, res_edges def render_abstract(self): self.bottoni_info={} for irow,line in enumerate(self.abstract): for icol,tok in enumerate(line): self.gridLayout.addWidget(tok ,irow,icol) tok.show() qb = QtGui.QPushButton("+line",None) self.bottoni_info[ qb ]="addrow" qb.clicked.connect(self.modifica) self.gridLayout.addWidget(qb,len(self.abstract), 0) self.plusLine = qb qb = QtGui.QPushButton("-line",None) self.bottoni_info[ qb ]="removerow" qb.clicked.connect(self.modifica) self.gridLayout.addWidget(qb,len(self.abstract), 1) for irow,line in enumerate(self.abstract_edges): for icol,tok in enumerate(line): self.gridLayout_edges.addWidget(tok ,irow,icol) tok.show() def vLine(self): line = QtGui.QFrame() line.setGeometry(Qt.QRect()) line.setFrameShape(QtGui.QFrame.VLine); line.setFrameShadow(QtGui.QFrame.Sunken); return line def set_abstractN(self,abst_stoichio, elements_edges ): self.clear_layout() self.abstract=[] self.abstract_edges=[] for line in abst_stoichio: if len(line): qlf = QtGui.QLineEdit() qlf.setToolTip("Weight") qlf.setText(str( line[1])) qle = QtGui.QLineEdit() qle.setToolTip("Formula") qle.setText( str(line[0])) qle.editingFinished.connect(self.onFormulaChange) al=[qle,qlf] # for ipos,val in enumerate(line[2:]): # cb = MyCheckbox(str(ipos ) ) # cb.setChecked(val>0) # al.append(cb) self.abstract.append(al) for el, edglist in elements_edges.items(): print("========================= " , el,) print("========================= " , edglist) ql = QtGui.QLabel(str(el)+":") f = Qt.QFont ( "Arial", 20, Qt.QFont.Bold); ql.setFont( f); al=[ql] for edg in edglist: cb = MyCheckbox(edg) cb.setChecked( edglist[edg]>0 ) al.append( cb ) self.abstract_edges.append(al) self.render_abstract() def modifica(self): print( " RICEVUTO DA ", self.bottoni_info[self.sender()]) if self.bottoni_info[self.sender()] == "addrow": abstr_stoichio, edges = self.get_abstractN() abstr_stoichio.append([ "formula here",1.0] ) self.set_abstractN( abstr_stoichio, edges ) return if self.bottoni_info[self.sender()] == "removerow": abstr_stoichio, edges = self.get_abstractN() abstr_stoichio = abstr_stoichio[:-1] self.set_abstractN(abstr_stoichio, edges ) return def onFormulaChange(self): # abstN, edges = self.get_abstractN() abstN, edges = self.get_selection() print(" abstN, edges " , abstN, edges ) self.set_selection( abstN, edges ) pass def set_selection(self, stoichio_arg, edges_arg): abst_stoichio=[] elements_edges = {} for formu,f in stoichio_arg: print( formu, f) abst_stoichio.append( [ formu,f ] ) elements,weights = xrs_utilities.parseformula( formu) for el in elements: if xrs_utilities.element(el) is None: break else: for el in elements: if not (el in elements_edges) : elements_edges[el] = collections.OrderedDict() z=xrs_utilities.element(el) if z<= 35: edges = ['pz', 'total', 'K', 'L1', 'L23', 'M1', 'M23', 'M45', 'N1', 'N23' ] else: edges = ['pz', 'total', 'K', 'L1', 'L2', 'L3', 'M1', 'M2', 'M3', 'M4', 'M5', 'N1', 'N2', 'N3', 'N4', 'N5', 'N6', 'N7', 'O1', 'O2', 'O3', 'O4', 'O5', 'P1', 'P2', 'P3'] for ed in edges: elements_edges[el][ed]=0 for el in elements_edges: if el in edges_arg: elist = edges_arg[el] for ed in elist: if ed in elements_edges[el]: elements_edges[el][ed] = 1 print(" CHIAMO SET ABSTRACT ", abst_stoichio, elements_edges ) self.set_abstractN(abst_stoichio, elements_edges ) def get_selection(self): abstN, edges = self.get_abstractN() stoichio_arg=abstN edges_arg = {} print ( " EDGES ", edges) for atom, dictio in edges.items(): l=[] for ed,val in dictio.items(): if val: l.append(ed) if len(l): edges_arg[atom]=l return stoichio_arg, edges_arg class MyCheckbox(QtGui.QWidget): def __init__(self, text): Qt.QWidget.__init__(self, None) self.setLayout(QtGui. QVBoxLayout() ) self.cb = QtGui.QCheckBox(self) self.layout().addWidget(self.cb) self.layout().setContentsMargins(2,0,2,0) self.layout().addWidget(QtGui.QLabel(text)) self.text = text sp = Qt.QSizePolicy() sp.setVerticalPolicy(Qt.QSizePolicy.Fixed) #, Qt.QSizePolicy.Expanding sp.setVerticalStretch(0) self.setSizePolicy(sp) def setChecked(self,val): self.cb.setChecked(val) def isChecked(self): return self.cb.isChecked() if __name__ =="__main__": app=Qt.QApplication([]) w = edgesTable() # w.set_selection( [[1.0, "H2O"] ] , {'O':['K'] } ) w.set_selection( [[ "H2O",1.0],["Formula Here",1.0]] , {'O':['K'] } ) w.show() app.exec_() print(" SELECTION ") print( w.get_selection()) xrstools-0.15.0+git20210910+c147919d/XRStools/ramanWidget/hdf5dialog.py000066400000000000000000000471211412732462000245360ustar00rootroot00000000000000#!/usr/bin/env python # coding: utf-8 # /*########################################################################## # # Copyright (c) 2016-2017 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # ###########################################################################*/ """Qt Hdf5 widget examples """ import logging import sys import tempfile import numpy import h5py # from .. import ui from XRStools.installation_dir import installation_dir import os my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] logging.basicConfig() _logger = logging.getLogger("customHdf5TreeModel") """Module logger""" from silx.gui import qt import silx.gui.hdf5 from silx.gui.data.DataViewerFrame import DataViewerFrame from silx.gui.widgets.ThreadPoolPushButton import ThreadPoolPushButton from silx.gui.hdf5.Hdf5TreeModel import Hdf5TreeModel # class Hdf5TreeModel(originalH5model): # f2close=[] # def appendFile(self, filename): # try: # h5file = silx_io.open(filename) # f2close = f2close+[h5file] # self.insertH5pyObject(h5file, row=-1) # except IOError: # print ("File '%s' can't be read.", filename) # raise # def chiudi(self): # for obj in self.f2close: # obj.close() def readme_to_dict(README): res={} sl=README.split("\n") for l in sl: ll=l.split(":") if len(ll)>1: nm = ll[0] doc="" for t in ll[1:]: doc=doc+t+":" res[nm.strip()]=doc return res class CustomTooltips(qt.QIdentityProxyModel): """Custom the tooltip of the model by composition. It is a very stable way to custom it cause it uses the Qt API. Then it will not change according to the version of Silx. But it is not well integrated if you only want to add custom fields to the default tooltips. """ def data(self, index, role=qt.Qt.DisplayRole): if role == qt.Qt.ToolTipRole: # Reach information from the node sourceIndex = self.mapToSource(index) sourceModel = self.sourceModel() originalTooltip = sourceModel.data(sourceIndex, qt.Qt.ToolTipRole) originalH5pyObject = sourceModel.data(sourceIndex, Hdf5TreeModel.H5PY_OBJECT_ROLE) # We can filter according to the column if sourceIndex.column() == Hdf5TreeModel.TYPE_COLUMN: return super(CustomTooltips, self).data(index, role) # Let's create our own tooltips template = u"""
Original
{original}
Parent name
{parent_name}
Name
{name}
Power of 2
{pow_of_2}
Help
{README}
""" try: README = originalH5pyObject.parent["README"].value except: README = "NA" try: data = originalH5pyObject[()] if data.size <= 10: result = data ** 2 else: result = "..." except Exception: result = "NA" name=originalH5pyObject.name ipos = name.rfind("/") if ipos!=-1: name=name[ipos+1:] print(" README ", README ) print (" name ", name) if name =="README": pass else: dico = readme_to_dict(README) print(" dico " , dico) if name in dico: README = dico[name] else: README = "" info = dict( original=originalTooltip, parent_name=originalH5pyObject.parent.name, name=originalH5pyObject.name, pow_of_2=str(result), README = README ) return template.format(**info) elif ( not self.RO and role == qt.Qt.BackgroundRole): s=self.mled.toPlainText() sl =s.split("\n") sourceIndex = self.mapToSource(index) sourceModel = self.sourceModel() myn = sourceModel.data(sourceIndex, Hdf5TreeModel.H5PY_OBJECT_ROLE ) sl = self.selected_h5_objects if(myn in sl): return qt.QColor(255, 0, 0) return super(CustomTooltips, self).data(index, role) _file_cache = {} def get_hdf5_with_all_types(): global _file_cache ID = "alltypes" if ID in _file_cache: return _file_cache[ID].name tmp = tempfile.NamedTemporaryFile(prefix=ID + "_", suffix=".h5", delete=True) tmp.file.close() h5 = h5py.File(tmp.name, "w") g = h5.create_group("arrays") g.create_dataset("scalar", data=10) g.create_dataset("list", data=numpy.arange(10)) base_image = numpy.arange(10**2).reshape(10, 10) images = [base_image, base_image.T, base_image.size - 1 - base_image, base_image.size - 1 - base_image.T] dtype = images[0].dtype data = numpy.empty((10 * 10, 10, 10), dtype=dtype) for i in range(10 * 10): data[i] = images[i % 4] data.shape = 10, 10, 10, 10 g.create_dataset("image", data=data[0, 0]) g.create_dataset("cube", data=data[0]) g.create_dataset("hypercube", data=data) g = h5.create_group("dtypes") g.create_dataset("int32", data=numpy.int32(10)) g.create_dataset("int64", data=numpy.int64(10)) g.create_dataset("float32", data=numpy.float32(10)) g.create_dataset("float64", data=numpy.float64(10)) g.create_dataset("string_", data=numpy.string_("Hi!")) # g.create_dataset("string0",data=numpy.string0("Hi!\x00")) g.create_dataset("bool", data=True) g.create_dataset("bool2", data=False) h5.close() _file_cache[ID] = tmp return tmp.name # @ui.UILoadable class hdf5dialog(qt.QDialog): """ This window show an example of use of a Hdf5TreeView. The tree is initialized with a list of filenames. A panel allow to play with internal property configuration of the widget, and a text screen allow to display events. """ f2close = None def __del__(self): if self.f2close is not None: self.f2close.close() def __init__(self,filenames=None, storage=None, parent=None, groupname = "",RO=True): super(hdf5dialog , self).__init__(parent) Qt.loadUi( os.path.join( installation_dir ,"resources" , my_relativ_path , "hdf5dialog.ui" ), self) self.RO=RO # def closeEvent(self,event): # if self.hdf5File is not None: # self.hdf5File.close() # event.accept() # qt.QMainWindow.__init__(self) self.storage=storage self.setWindowTitle("Silx HDF5 widget example") self.__asyncload = False self.__treeview = silx.gui.hdf5.Hdf5TreeView(self) """Silx HDF5 TreeView""" self.__sourceModel = self.__treeview.model() """Store the source model""" self.__text = qt.QTextEdit(self) """Widget displaying information""" self.__dataViewer = DataViewerFrame(self) vSpliter = qt.QSplitter(qt.Qt.Vertical) vSpliter.addWidget(self.__dataViewer) vSpliter.addWidget(self.__text) vSpliter.setSizes([10, 0]) spliter = qt.QSplitter(self) spliter.addWidget(self.__treeview) spliter.addWidget(vSpliter) spliter.setStretchFactor(1, 1) main_panel = qt.QWidget(self) layout = qt.QVBoxLayout() layout.addWidget(spliter) layout.addWidget(self.createTreeViewConfigurationPanel(self, self.__treeview)) layout.setStretchFactor(spliter, 1) main_panel.setLayout(layout) # self.setLayout( qt.QVBoxLayout()) self.mainLayout = self.verticalLayout_2 self.mainLayout.addWidget(main_panel) hl = qt.QWidget() hl.setLayout(qt.QHBoxLayout()) self.varname = qt.QLineEdit("Set_here_variable_name") hl.layout().addWidget(self.varname) self.verticalLayout.addWidget(hl) if not RO: button = qt.QPushButton("Add Selected to the list") button.clicked.connect(self.__AddToList) self.verticalLayout.layout().addWidget(button) button = qt.QPushButton("Remove Selected from the list") button.clicked.connect(self.__RemoveFromList) self.verticalLayout.layout().addWidget(button) hl = qt.QWidget() hl.setLayout(qt.QHBoxLayout()) button = qt.QPushButton("Exit Cancelling Selecteds") button.clicked.connect(self.__WriteCancel) hl.layout().addWidget(button) # button = qt.QPushButton("Exit Keeping Selecteds") # button.clicked.connect(self.__WriteKeep) # hl.layout().addWidget(button) button = qt.QPushButton("Discard") button.clicked.connect(self.reject) hl.layout().addWidget(button) self.verticalLayout.addWidget(hl) self.buttonBox.clear() self.mled = qt.QTextEdit() self.selected_h5_objects = [] self.mled.setReadOnly(True) self.verticalLayout.layout().addWidget(self.mled) # self.layout().addWidget(main_panel) # append all files to the tree self.filenames = filenames for file_name in filenames: f = h5py.File(file_name,"r") self.f2close = f # self.__treeview.findHdf5TreeModel().appendFile(file_name) self.__treeview.findHdf5TreeModel().insertH5pyObject(f) self.__treeview.activated.connect(self.displayData) self.__treeview.doubleClicked.connect(self.toConsole) self.__treeview.clicked.connect(self.toConsole1) self.__useCustomLabel() ## ce qui est ci dessous ca va marcher pour le premier fichier # si non il faudra chercher le fichier # h5 = self.__treeview.model().data(indice, Hdf5TreeModel.H5PY_OBJECT_ROLE) if groupname !="": if 0 and hasattr( self.__treeview, "setSelectedH5Node" ): print(" DDDDDDDDDDDDDDDDDDDDISPONIBILE PUOI CANCELLARE CODICE PER SELECTION\n"*1, f[groupname], self.__treeview.__class__.__module__) self.__treeview.setSelectedH5Node(f[groupname] ) else: indice = self.__treeview.model().index(0,0,qt.QModelIndex()) # parce que on va au premier fichier self.__treeview.expand(indice) groupname.replace("//","/") gn_l = groupname.split("/") i=None for tn in gn_l: if tn=="": continue nl = [ ] for k in range( self.__treeview.model().rowCount(indice) ): ind_tmp = self.__treeview.model().index(k,0,indice) nl.append( str(self.__treeview.model().data(ind_tmp) ) ) if tn not in nl: break i = nl.index( tn ) indice = self.__treeview.model().index(i,0,indice) print (self.__treeview.model().rowCount(indice)) self.__treeview.expand(indice) # h5=h5[tn] if i is not None: self.__treeview.setCurrentIndex(indice) def __AddToList(self): s=self.mled.toPlainText() sl =s.split("\n") selecteds = list(self.__treeview.selectedH5Nodes()) ns = "" for sel in selecteds: if str(sel.name) not in sl: ns = ns+ sel.name +"\n" self.selected_h5_objects.append(sel.h5py_object) s=str(s)+ ns self.mled.setPlainText(s) def __RemoveFromList(self): s=self.mled.toPlainText() sl =s.split("\n") selecteds = list(self.__treeview.selectedH5Nodes()) for sel in selecteds: if str(sel.name) in sl: i = sl.index( str(sel.name) ) del sl[i] self.selected_h5_objects.remove(sel.h5py_object) s="" for t in sl: s=str(s)+ t+"\n" self.mled.setPlainText(s) def __WriteCancel(self): msgBox = qt.QMessageBox () msgBox.setText("You are going change the hdf5 file"); msgBox.setInformativeText(" Do you want to proceed?"); msgBox.setStandardButtons(qt.QMessageBox.Ok | qt.QMessageBox.Cancel); msgBox.setDefaultButton(qt.QMessageBox.Cancel); ret = msgBox.exec_(); res = (ret==qt.QMessageBox.Ok) if res: s = self.mled.toPlainText() sl = s.split("\n") self.storage[0]=sl # target = h5py.File(self.filenames[0],"a") # for l in sl: # if not len(l) : continue # if not subcontained(l,sl ): # print( " CANCELLO :", l) # del target[l] # target.flush() # target.close() # target = None self.accept() else: pass def __WriteKeep(self): self.reject() def toConsole1(self, index): originalH5pyObject = self.__treeview.model().data(index, Hdf5TreeModel.H5PY_OBJECT_ROLE) self.storage[0] = originalH5pyObject.name def toConsole(self, index): # sourceIndex = self.mapToSource(index) # sourceModel = self.sourceModel() originalH5pyObject = self.__treeview.model().data(index, Hdf5TreeModel.H5PY_OBJECT_ROLE) self.storage[0] = originalH5pyObject.name # originalH5pyObject = sourceModel.data(sourceIndex, Hdf5TreeModel.H5PY_OBJECT_ROLE) vn = str(self.varname.text()) if len(vn)==0: vn="grabbed" self.consoleAction (originalH5pyObject, vn) def consoleAction(self,h5group, vn ): from . import ShadokWidget from . import Wizard_safe_module groupname = h5group.name h5 = h5group.file runit, oggetto = Wizard_safe_module.make_obj( h5group ,h5, groupname ) if runit is not None: runit(oggetto) update = {vn:oggetto} if runit is None: ShadokWidget.ShadokWidget.LaunchConsole() ShadokWidget.ShadokWidget.my_console[0].updateNamespace(update) ShadokWidget.ShadokWidget.my_console[0].showMessage("# %s is now in namespace "% list(update.keys())[0]) def displayData(self): """Called to update the dataviewer with the selected data. """ selected = list(self.__treeview.selectedH5Nodes()) if len(selected) == 1: # Update the viewer for a single selection data = selected[0] # data is a hdf5.H5Node object # data.h5py_object is a Group/Dataset object (from h5py, spech5, fabioh5) # The dataviewer can display both self.__dataViewer.setData(data) def __fileCreated(self, filename): if self.__asyncload: self.__treeview.findHdf5TreeModel().insertFileAsync(filename) else: self.__treeview.findHdf5TreeModel().insertFile(filename) def __hdf5ComboChanged(self, index): function = self.__hdf5Combo.itemData(index) self.__createHdf5Button.setCallable(function) def __edfComboChanged(self, index): function = self.__edfCombo.itemData(index) self.__createEdfButton.setCallable(function) def __useCustomLabel(self): customModel = CustomTooltips(self.__treeview) customModel.RO = self.RO customModel.setSourceModel(self.__sourceModel) if not self.RO : customModel.mled = self.mled customModel.selected_h5_objects = self.selected_h5_objects self.__treeview.setModel(customModel) def __useOriginalModel(self): self.__treeview.setModel(self.__sourceModel) def createTreeViewConfigurationPanel(self, parent, treeview): """Create a configuration panel to allow to play with widget states""" panel = qt.QWidget(parent) panel.setLayout(qt.QHBoxLayout()) # content = qt.QGroupBox("Create HDF5", panel) # content.setLayout(qt.QVBoxLayout()) # panel.layout().addWidget(content) # combo = qt.QComboBox() # combo.addItem("Containing all types", get_hdf5_with_all_types) # combo.activated.connect(self.__hdf5ComboChanged) # content.layout().addWidget(combo) # button = ThreadPoolPushButton(content, text="Create") # button.setCallable(combo.itemData(combo.currentIndex())) # button.succeeded.connect(self.__fileCreated) # content.layout().addWidget(button) # self.__hdf5Combo = combo # self.__createHdf5Button = button # content.layout().addStretch(1) self.varname.hide() if 0: self.varname = qt.QLineEdit("Set_here_variable_name") panel.layout().addWidget(self.varname) button = qt.QPushButton("Delete") button.clicked.connect(self.__Cancel) panel.layout().addWidget(button) # option = qt.QGroupBox("Custom model", panel) # option.setLayout(qt.QVBoxLayout()) # panel.layout().addWidget(option) # button = qt.QPushButton("Original model") # button.clicked.connect(self.__useOriginalModel) # option.layout().addWidget(button) # button = qt.QPushButton("Custom tooltips by composition") # button.clicked.connect(self.__useCustomLabel) # option.layout().addWidget(button) # option.layout().addStretch(1) panel.layout().addStretch(1) return panel def main(filenames): """ :param filenames: list of file paths """ app = qt.QApplication([]) sys.excepthook = qt.exceptionHandler window = Hdf5TreeViewExample(filenames) window.show() result = app.exec_() # remove ending warnings relative to QTimer app.deleteLater() sys.exit(result) if __name__ == "__main__": main(sys.argv[1:]) xrstools-0.15.0+git20210910+c147919d/XRStools/ramanWidget/ramanWidget.py000066400000000000000000000020371412732462000247670ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from PyQt4 import Qt, QtCore, QtGui # from .. import ui from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] # @ui.UILoadable class scansTable(QtGui.QWidget) : def __init__(self, parent=None): super( scansTable , self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "scansTable.ui" ), self) qb = QtGui.QPushButton("+") qb.setFixedWidth(20) sb = QtGui.QSpinBox( ) sb.setMinimum(1) sb.setMaximum(1000) self.gridLayout.addWidget(sb ,0,0) self.gridLayout.addWidget(qb,0,1) self.gridLayout.addWidget(QtGui.QPushButton("-"),0,2) if __name__ =="__main__": app=Qt.QApplication([]) w = scansTable() w.show() app.exec_() xrstools-0.15.0+git20210910+c147919d/XRStools/ramanWidget/scansTable.py000066400000000000000000000132611412732462000246050ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from silx.gui import qt as Qt from silx.gui import qt as QtCore from silx.gui import qt as QtGui from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] if my_relativ_path[0]=="/": my_relativ_path = my_relativ_path[1:] deletion_mode = 1 # @ui.UILoadable class scansTable(QtGui.QWidget) : def __init__(self, parent=None): super( scansTable, self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "scansTable.ui" ), self) self.abstract=[] self.render_abstract() def clear_layout(self): for t in list(self.bottoni_info.keys()): print( " levo ", t) self.gridLayout.removeWidget(t) if deletion_mode: t.hide() t.setParent(None) else: t.deleteLater() del t # self. bottoni_info={} for l in self.abstract: for t in l: self.gridLayout.removeWidget(t) if deletion_mode: t.hide() t.setParent(None) else: t.deleteLater() del t self.abstract=[] def get_abstractN(self): res=[] done = False if len(self.abstract): minS = 100000 maxS=-1 for line in self.abstract: if len(line): aline = [ str(line[1].text()) ] for t in line[3:]: done= True aline.append( t.value() ) if t.value()> maxS: maxS = t.value() if t.value()< minS: minS = t.value() res.append(aline) if not done : minS = 1 maxS= 100000 return res, minS, maxS def render_abstract(self): self.bottoni_info={} maxlen=0 for l in self.abstract: if len(l)> maxlen: maxlen = len(l) for irow,line in enumerate(self.abstract): for icol,tok in enumerate(line): self.gridLayout.addWidget(tok ,irow,icol) tok.show() qb = QtGui.QPushButton("+",None) qb.setFixedWidth(20) self.bottoni_info[ qb ]=+1, irow qb.clicked.connect(self.modifica) self.gridLayout.addWidget(qb,irow, maxlen) qb = QtGui.QPushButton("-",None) qb.setFixedWidth(20) self.bottoni_info[ qb ]=-1, irow qb.clicked.connect(self.modifica) self.gridLayout.addWidget(qb,irow, maxlen+1) qb = QtGui.QPushButton("+line",None) self.plusLine = qb # qb.setFixedWidth(20) self.bottoni_info[ qb ]="addrow" qb.clicked.connect(self.modifica) self.gridLayout.addWidget(qb,len(self.abstract), 0) qb = QtGui.QPushButton("-line",None) # qb.setFixedWidth(20) self.bottoni_info[ qb ]="removerow" qb.clicked.connect(self.modifica) self.gridLayout.addWidget(qb,len(self.abstract), 1) print ( " aggiunto ", qb) line = self.vLine() self.gridLayout.addWidget(line ,len(self.abstract), 2) self.bottoni_info[ line ]=0 def vLine(self): line = QtGui.QFrame() line.setGeometry(Qt.QRect()) line.setFrameShape(QtGui.QFrame.VLine); line.setFrameShadow(QtGui.QFrame.Sunken); return line def set_abstractN(self, aN): self.clear_layout() self.abstract=[] for line in aN: if len(line): ql = QtGui.QLabel("") qle = QtGui.QLineEdit() qle.setText( line[0]) al=[ql,qle, self.vLine() ] for N in line[1:]: sb = QtGui.QSpinBox( ) sb.setMinimum(1) sb.setMaximum(100000) sb.setValue(N) al.append(sb) self.abstract.append(al) self.render_abstract() def modifica(self): print( " RICEVUTO DA ", self.bottoni_info[self.sender()]) if self.bottoni_info[self.sender()] == "addrow": resN, minS, maxS = self.get_abstractN() self.set_abstractN(resN+[["name here"]]) return if self.bottoni_info[self.sender()] == "removerow": resN, minS, maxS = self.get_abstractN() self.set_abstractN(resN[:-1]) return todo, irow = self.bottoni_info[self.sender()] if todo>0 : res, minS, maxS = self.get_abstractN() res[irow].append(minS) self.set_abstractN(res) else: res, minS, maxS = self.get_abstractN() if(len(res[irow])>2): del res[irow][-1] self.set_abstractN(res) def set_selection(self, abst): return self.set_abstractN(abst) def get_selection(self): return self.get_abstractN() if __name__ =="__main__": app=Qt.QApplication([]) w = scansTable() w.set_selection( [["elastic" , 611,615,619,623 ] , [ 'ok1', 612,616,620,624 ] ] ) w.show() app.exec_() print(" SELECTION =") print(w.get_selection()) xrstools-0.15.0+git20210910+c147919d/XRStools/ramanWidget/setup.py000066400000000000000000000004761412732462000236720ustar00rootroot00000000000000 import os from numpy.distutils.misc_util import Configuration def configuration(parent_package='', top_path=None): config = Configuration('ramanWidget', parent_package, top_path) return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration=configuration) xrstools-0.15.0+git20210910+c147919d/XRStools/ramanWidget/source.py000066400000000000000000000142451412732462000240310ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from silx.gui import qt as Qt from silx.gui import qt as QtCore from silx.gui import qt as QtGui import collections from . import hdf5dialog import os from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] if my_relativ_path[0]=="/": my_relativ_path = my_relativ_path[1:] class Functor_contextMenuEvent: def __init__(self, ql, extraActions): self.ql=ql self.extraActions = extraActions def __call__(self, event): if not hasattr(self.ql,"createStandardContextMenu"): self.mem=[] else: menu = self.ql.createStandardContextMenu() self.mem=[] menu.insertSeparator(menu.actions()[0]) menu.insertSeparator(menu.actions()[0]) for na, aa in list(self.extraActions.items())[::-1]: testAction = Qt.QAction( na ,None); self.mem.append(testAction) menu.insertAction( menu.actions()[0] , testAction) testAction.triggered.connect( aa ) menu.exec_(event.globalPos()); del menu # @ui.UILoadable class source(QtGui.QWidget) : def __init__(self, parent=None): super( source, self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "source.ui" ), self) extraActions = collections.OrderedDict([("Browse",self.browse),("pymcaview" ,self.pymcaview)] ) self.lineEdit_specfileName.contextMenuEvent = Functor_contextMenuEvent(self.lineEdit_specfileName , extraActions = extraActions ) extraActions = collections.OrderedDict([("Browse",self.browse_hdf5),("pymcaview" ,self.pymcaview)] ) self.lineEdit_roiAddress.contextMenuEvent = Functor_contextMenuEvent(self.lineEdit_roiAddress , extraActions = extraActions ) self.load_user_input = {} def update_user_input(self, uinput): self.load_user_input.update(uinput) if "sf" in self.load_user_input : self.lineEdit_specfileName.setText(self.load_user_input["sf"]) if "roi_spectral" in self.load_user_input : self.lineEdit_roiAddress.setText(self.load_user_input["roi_spectral"]) def browse(self): filename = self.lineEdit_specfileName.text() if len(filename): filename = QtGui.QFileDialog.getOpenFileName(None, "select", filename) else: filename = QtGui.QFileDialog.getOpenFileName(None, "select", ) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) if len(filename): self.lineEdit_specfileName.setText(filename) def pymcaview(self): filename = str(self.lineEdit_specfileName.text()) os.system("pymca -f '%s' &"%filename) def browse_hdf5(self): filename = str(self.lineEdit_roiAddress.text()) fn, dg = hdf5_filedialog(filename, 1) if fn is not None: filename=str(fn) if dg is not None: filename = filename +":"+str(dg) self.lineEdit_roiAddress.setText(filename) def set_selection(self, selection): self.lineEdit_specfileName.setText(str(selection[0])) self.lineEdit_roiAddress.setText(str(selection[1])) def get_selection(self): selection = [] selection.append(str(self.lineEdit_specfileName.text())) selection.append(str(self.lineEdit_roiAddress.text())) return selection def split_hdf5_address(dataadress): pos = dataadress.rfind(":") if ( pos==-1): return None filename, groupname = dataadress[:pos], dataadress[pos+1:] return filename, groupname def hdf5_filedialog(hint, fileme=1, myaction = None, modal=1): sphint= split_hdf5_address(hint) groupname="/" if sphint is not None and len(sphint): hint , groupname = sphint if fileme in [1]: if len(hint): # QtGui.QApplication.instance().processEvents() # dialog = QWS.QFileDialog(parent) # dialog.setFileMode(QWS.QFileDialog.ExistingFile) # dialog.setNameFilter("hdf5 (*h5)\nall files ( * )" ) # dialog.selectFile(hint) # dialog.show() # if (dialog.exec()): # filename = dialog.selectedFiles() filename = QtGui.QFileDialog.getOpenFileName( None,'Open hdf5 file and then choose groupname', hint,filter="hdf5 (*h5)\nall files ( * )" ) else: filename = QtGui.QFileDialog.getOpenFileName(None,'Open hdf5 file and then choose groupname',filter="hdf5 (*h5)\nall files ( * )" ) else: if len(hint): filename = QtGui.QFileDialog.getSaveFileName(None,'Open hdf5 file and then choose groupname', hint,filter="hdf5 (*h5)\nall files ( * )" ) else: filename = QtGui.QFileDialog.getSaveFileName(None,'Open hdf5 file and then choose groupname',filter="hdf5 (*h5)\nall files ( * )" ) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) if len(filename) : ret =None if os.path.exists(filename): storage=[None] window = hdf5dialog.hdf5dialog(filenames = [filename], storage = storage, groupname = groupname) window.setModal(True) ret = window.exec_() if ret: name = storage[0] return filename, name else: # return filename , None return None , None else: return None,None if __name__ =="__main__": app=Qt.QApplication([]) w = source() w.show() w.set_selection([ "/data/id20/inhouse/data/run5_17/run7_ihr/hydra", "/mntdirect/_scisoft/users/mirone/WORKS/Christoph/XRStoolsSuperResolution/sandbox/spectral_myroi.h5" ]) app.exec_() print("===== SELECTION =============== " ) print(w.get_selection()) xrstools-0.15.0+git20210910+c147919d/XRStools/ramanWidget/subsetTable.py000066400000000000000000000123611412732462000250030ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #from PyQt4 import Qt, QtCore, QtGui import os from silx.gui import qt as Qt from silx.gui import qt as QtCore from silx.gui import qt as QtGui # from .. import ui from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] if my_relativ_path[0]=="/": my_relativ_path = my_relativ_path[1:] # @ui.UILoadable class subsetTable(QtGui.QWidget) : def __init__(self, parent=None): super( subsetTable, self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "subsetTable.ui" ), self) self.gridLayout.setHorizontalSpacing(0) self.abstract=[] self.render_abstract() def clear_layout(self): for t in list(self.bottoni_info.keys()): print( " levo ", t) self.gridLayout.removeWidget(t) t.hide() t.setParent(None) del t for l in self.abstract: for t in l: self.gridLayout.removeWidget(t) t.hide() t.setParent(None) del t def get_abstractN(self): res=[] for line in self.abstract: if len(line): aline = [ (str(line[0].text())) , str(line[1].text()) ] for t in line[3:]: aline.append( t.isChecked() ) res.append(aline) return res def render_abstract(self): self.bottoni_info={} maxlen=0 for irow,line in enumerate(self.abstract): for icol,tok in enumerate(line): self.gridLayout.addWidget(tok ,irow,icol) tok.show() qb = QtGui.QPushButton("+line",None) self.plusLine = qb # qb.setFixedWidth(20) self.bottoni_info[ qb ]="addrow" qb.clicked.connect(self.modifica) self.gridLayout.addWidget(qb,len(self.abstract), 0) qb = QtGui.QPushButton("-line",None) # qb.setFixedWidth(20) self.bottoni_info[ qb ]="removerow" qb.clicked.connect(self.modifica) self.gridLayout.addWidget(qb,len(self.abstract), 1) print ( " aggiunto ", qb) line = self.vLine() self.gridLayout.addWidget(line ,len(self.abstract), 2) self.bottoni_info[ line ]=0 def vLine(self): line = QtGui.QFrame() line.setGeometry(Qt.QRect()) line.setFrameShape(QtGui.QFrame.VLine); line.setFrameShadow(QtGui.QFrame.Sunken); return line def set_abstractN(self, aN): self.clear_layout() self.abstract=[] for line in aN: if len(line): qlf = QtGui.QLineEdit() qlf.setToolTip("Scaling") qlf.setText(str( line[0])) qle = QtGui.QLineEdit() qle.setToolTip("SubSet Name") qle.setText( str(line[1])) al=[qlf,qle, self.vLine() ] for ipos,val in enumerate(line[2:]): cb = MyCheckbox(str(ipos//12 +1)+"-"+str(ipos%12+1) ) cb.setChecked(val>0) al.append(cb) self.abstract.append(al) self.render_abstract() def modifica(self): print( " RICEVUTO DA ", self.bottoni_info[self.sender()]) if self.bottoni_info[self.sender()] == "addrow": abstrN = self.get_abstractN() abstrN.append([ 1.0, "name here"]+[False]*72 ) self.set_abstractN(abstrN) return if self.bottoni_info[self.sender()] == "removerow": abstrN = self.get_abstractN()[:-1] print ( " ECCO abstr ", abstrN) self.set_abstractN(abstrN) return def set_selection(self, sele_arg): sele=[] for line in sele_arg: l=[line[0] , line[1] ]+[0]*72 for t in line[2:]: l[2+t]=True sele.append(l) self.set_abstractN(sele) def get_selection(self): sele = self.get_abstractN() sele_arg=[] for line in sele: l=[float(line[0]),line[1] ] for pos, t in enumerate(line[2:]): if t: l.append(pos) sele_arg.append(l) return sele_arg class MyCheckbox(QtGui.QWidget): def __init__(self, text): Qt.QWidget.__init__(self, None) self.setLayout(QtGui. QVBoxLayout() ) self.cb = QtGui.QCheckBox(self) self.layout().addWidget(self.cb) self.layout().setContentsMargins(2,0,2,0) self.layout().addWidget(QtGui.QLabel(text)) def setChecked(self,val): self.cb.setChecked(val) def isChecked(self): return self.cb.isChecked() if __name__ =="__main__": app=Qt.QApplication([]) w = subsetTable() w.set_selection( [[2.0, "lowq" , 0,1,2,3 ] , [ 2.0, 'middleq', 12,13,14,15 ] ] ) w.show() app.exec_() print(" SELECTION ") print( w.get_selection()) xrstools-0.15.0+git20210910+c147919d/XRStools/reponse_percussionelle.py000066400000000000000000001532761412732462000250660ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import math import scipy import scipy.stats import scipy.ndimage.filters as filt from scipy.ndimage import gaussian_filter import sys import pickle import os from scipy.optimize import minimize from six.moves import filter from six.moves import range from six.moves import zip scipymin=1 from . import minimiser import h5py if(sys.argv[0][-12:]!="sphinx-build"): from .XRStools_c import luts_cy try: from mpi4py import MPI myrank = MPI.COMM_WORLD.Get_rank() nprocs = MPI.COMM_WORLD.Get_size() procnm = MPI.Get_processor_name() comm = MPI.COMM_WORLD print( "MPI LOADED , nprocs = ", nprocs) except: nprocs=1 myrank = 0 print( "MPI NOT LOADED ") global indent indent = "" def filterRoiList(l): return [t for t in l if t not in [ "motorDict"] ] def get_LUT_1d( na ,nb, cp, cb , nref): # ecco un caso dove bisogna considerare che il pixel va da -0.5 a 0.5 res=[] # print na, nb, cp, cb , nref for i in range(na): ## qui sotto si pensa che lo zero e' all' inizio del pixel X0 = (cb+0.5) +(i-(cp+0.5))*nref X1 = (cb+0.5) +(i+1-(cp+0.5))*nref I0 = int(math.floor(X0)) I1 = int(math.ceil(X1)) for j in range(I0,I1): x0 = float(max(j,X0)) x1 = float(min(j+1,X1)) if j>=0 and j1.0e-10 : facts[:] += (fiY0-Y0) * reponse_pixel[int(math.floor(Y0))] if(iY1>=iY0): if(Y1-fiY1>1.0e-10): facts[:] += (Y1-fiY1) * reponse_pixel[iY1] facts[:] /= (Y1-Y0) # media. Il fattore area c' e' gia in f1, f2 for i2,j2,f2, x0,x1 in lut_2: # print j2 X0 = dim2*x0 X1 = dim2*x1 iX0 = int(math. ceil(X0)) iX1 = int(math.floor(X1)) Fatt = (facts[ iX0:iX1]).sum(axis=0) fiX0 = min( iX0, X1 ) fiX1 = max( iX1, X0 ) if ( fiX0-X0>1.0e-10): Fatt += (fiX0-X0) * facts[int(math.floor(X0))] if(iX1>=iX0): if(X1-fiX1>1.0e-10): Fatt += (X1-fiX1) * facts[iX1] Fatt /= (X1-X0) # media. Il fattore area c' e' gia in f1, f2 if Fatt>1.1: print( Fatt, " Fatt ") print( facts, Y0, Y1, iY0, iY1) print( " X0,X1 " , X0,X1) print( " iX0, iX1 " ,iX0, iX1) print( "(facts[ iX0:iX1]).sum(axis=0) " , (facts[ iX0:iX1]).sum(axis=0)) print( " (fiX0-X0) * facts[int(math.floor(X0))] " ,(fiX0-X0) * facts[int(math.floor(X0))]) print( " (X1-fiX1) * facts[iX1] " ,(X1-fiX1) * facts[iX1]) raise newel = [ i1*na2+i2 , j1*nb2+j2 , f1*f2* Fatt ] res.append(newel) return res def get_product4reponse(lut_1,lut_2, na2, nb2 , reponse_pixel, solution): res=[] dim1,dim2 = reponse_pixel.shape repnseq = np.arange(reponse_pixel.size) repnseq.shape = reponse_pixel.shape for i1,j1,f1, y0,y1 in lut_1: Y0 = dim1*y0 Y1 = dim1*y1 iY0 = int(math.ceil(Y0)) iY1 = int(math.floor(Y1)) facts = (reponse_pixel[ iY0:iY1]).sum(axis=0) recolte = np.array(repnseq[ iY0:iY1] ) factrec = np.ones( repnseq[ iY0:iY1].shape ) fiY0 = min( iY0, Y1 ) fiY1 = max( iY1, Y0 ) if fiY0-Y0>1.0e-10 : where = int(math.floor(Y0)) recolte = np.concatenate( [recolte , [repnseq[where]] ] ) factrec = np.concatenate( [factrec , [ [ (fiY0-Y0) ] * repnseq.shape[1]] ] ) if(iY1 > Y0): if( Y1-fiY1 > 1.0e-10 ): recolte = np.concatenate( [ [repnseq[iY1]], recolte ] ) factrec = np.concatenate( [ [[(Y1-fiY1)]*repnseq.shape[1]], factrec ] ) ### facts[:] /= (Y1-Y0) # media. Il fattore area c' e' gia in f1, f2 factrec[:] /= (Y1-Y0) # *dim1 # print " =========== " # TERM = factrec.sum(axis=0) # print factrec.sum(axis=0) for i2,j2,f2, x0,x1 in lut_2: X0 = dim2*x0 X1 = dim2*x1 iX0 = int(math. ceil(X0)) iX1 = int(math.floor(X1)) recolteX = np.array(recolte [ :, iX0:iX1] ) factrecX = np.array(factrec [ :, iX0:iX1] ) # print " PEZZZO " , TERM[ iX0:iX1] fiX0 = min( iX0, X1 ) fiX1 = max( iX1, X0 ) if(fiX0-X0>1.0e-10): where = int(math.floor(X0)) recolteX = np.concatenate( [ recolte[:, where:where+1] , recolteX] , axis=1 ) factrecX = np.concatenate( [ (fiX0-X0) * factrec [:, where:where+1] , factrecX] , axis=1 ) # print " PEZZZO " , (fiX0-X0) * TERM[ where:where+1] if(iX1>=iX0): if(X1-fiX1>1.0e-10): recolteX = np.concatenate( [ recolteX , recolte[:, iX1:iX1+1] ] , axis=1 ) factrecX = np.concatenate( [ factrecX , (X1-fiX1) * factrec[ :, iX1:iX1+1 ] ] , axis=1 ) # print " PEZZZO " , (X1-fiX1) * TERM[ iX1:iX1+1] factrecX /= (X1-X0) # *dim2 # media. Il fattore area c' e' gia in f1, f2 # print " ---> " , factrecX.sum() if ( factrecX.sum()>1.1 ) : print( X0,X1,Y0,Y1) print( iX0,iX1,iY0,iY1) print( fiX0,fiX1,fiY0,fiY1) raise ## factrecX[:] = factrecX/factrecX.sum() for J,F in zip( recolteX.flatten() , factrecX.flatten() ): # print j1, nb2, j2 newel = [ i1*na2+i2 , J , f1*f2* solution[j1,j2]*F ] # newel = [ i1*na2+i2 , j1*nb2+j2 , f1*f2, Fatt ] res.append(newel) return res def get_LUT( mat_a,mat_b, center_pic, nref, reponse_pixel, doproduct = 1, soluzione=None, ROI = None): na1, na2 = mat_a.shape nb1, nb2 = mat_b.shape if ROI is None: ROI = np.ones_like(mat_a) ROI = ROI.astype("f") center_b = np.array( [ (nb1-1)/2.0, (nb2-1)/2.0 ] ) lut_1 = get_LUT_1d( na1 ,nb1 , center_pic[0] ,center_b[0] , nref) lut_2 = get_LUT_1d( na2 ,nb2 , center_pic[1] ,center_b[1] , nref) if doproduct: if doproduct ==1: # LUT = get_product(lut_1,lut_2, na2, nb2 , reponse_pixel) # print " cy_product ", np.array(LUT).sum() #print np.array(lut_1,"f").shape #print np.array(lut_2,"f").shape # print " SHAPE " # print np.array(reponse_pixel,"f").shape, np.array(lut_1,"f").shape, np.array(lut_2,"f").shape, na2, nb2 , len( np.array(lut_1,"f") ), len( np.array(lut_2,"f") ) if len(lut_1)==0 or len(lut_2)==0: return None tmp = luts_cy.get_product( np.array(lut_1,"f"), np.array(lut_2,"f"), na2, nb2 , np.array(reponse_pixel,"f"), ROI) LUT = np.array(tmp) #print LUT.sum() else : #print soluzione.shape #print mat_a.shape #print " ------------------ " #print np.array(lut_2)[:,1].max() #print np.array(lut_1)[:,1].max() #print soluzione.shape if 0: LUT= get_product4reponse( lut_1, lut_2, na2, nb2 , reponse_pixel, soluzione) else: global SYMM_RESPO tmp = luts_cy.get_product4reponse(np.array(lut_1,"f"),np.array(lut_2,"f"), na1, na2, nb2 , np.array(reponse_pixel,"f"), np.array(soluzione,"f"), SYMM_RESPO, ROI) LUT = np.array(tmp) return LUT else: return lut_1, lut_2 def calculate_grad(grad,data ,solution , s2d, d2s, solution_shape , parallel = 0 , beta=0.1 ) : nsol, ndata = d2s.shape proj = s2d.dot(solution) if data is not None: err = data - proj else: err = -proj fid = np.dot(err,err) grad[:] = d2s.dot(err) #print solution_shape if parallel and nprocs>1: grad_res = np.zeros_like(grad) comm.Allreduce([ grad , MPI.FLOAT ], [grad_res, MPI.FLOAT ], op=MPI.SUM) grad[:]= grad_res a = np.array( [fid] , "f" ) a_r = np.array( [0] , "f" ) comm.Allreduce([ a , MPI.FLOAT ], [a_r, MPI.FLOAT ], op=MPI.SUM) fid = a_r[0] sol_on_surf = np.reshape( solution, solution_shape ) laplacian = filt.laplace(sol_on_surf , mode='reflect') result = fid/2.0 - beta* np.dot( laplacian.flatten(), sol_on_surf.flatten() ) /2 grad[:] += beta * laplacian.flatten() # if myrank==0: # print " fid ----<" , result, parallel return result def cg( data , solution , s2d, d2s, solution_shape ): maxdim = max( s2d.shape[0], s2d.shape[1] ) auxs = [ np.zeros( [maxdim] ,"f") for i in range(5) ] nsol, ndata = d2s.shape grad=auxs[0][:nsol] grad_old=auxs[1][:nsol] p=auxs[2][:nsol] err = 0.0 err=calculate_grad(grad,data ,solution , s2d, d2s, solution_shape ) # calculate_grad(grad, Volume, donnees, dim, n_row,n_col, nnz, Bp, Bj, Bx, beta,auxs+3 ) ; p[:] = grad[:] rrold=0.0 rr = 0.0 rrold += np.dot( grad, grad ) rr=rrold; for iter in range(500): grad_old[:] = grad[:] calculate_grad(grad,None ,p , s2d, d2s, solution_shape ) pap = np.dot( p, grad ) solution[:] -= p*(rr/pap) err=calculate_grad(grad , data ,solution , s2d, d2s, solution_shape ) rrold=rr; rr = np.dot( grad, (grad-grad_old) ) beta = rr/rrold if beta<0 : beta = 0 p[:] = grad[:]+ p[:]*beta if myrank==0: if iter%1 ==0: sys.stdout.write(" "*60+"\r"+indent+( "iter %d errore est %e mod_grad est %e" % ( iter, err,rr ) ) ) sys.stdout.flush() if myrank==0: print( " ") def Fista( data , solution , s2d, d2s, solution_shape , parallel = 0 , niter=500, beta=0.1 ): global indent maxdim = max( s2d.shape[0], s2d.shape[1] ) auxs = [ np.zeros( [maxdim] ,"f") for i in range(5) ] nsol, ndata = d2s.shape grad =auxs[0][:nsol] grad2=auxs[1][:nsol] x_old=auxs[2][:nsol] y =auxs[3][:nsol] err = 0.0 err=calculate_grad(grad,data ,solution , s2d, d2s, solution_shape , parallel = parallel , beta=beta) # calculate_grad(grad, Volume, donnees, dim, n_row,n_col, nnz, Bp, Bj, Bx, beta,auxs+3 ) ; if myrank==0: print( indent+"CALCULATING LIPSCHITZ FACTOR ") for i in range(10): calculate_grad(grad2,None ,grad , s2d, d2s, solution_shape , parallel = parallel , beta=beta) Lip = math.sqrt( np.linalg.norm(grad2/100000) )*100000 grad[:] = grad2/ Lip if myrank==0: sys.stdout.write( " "*60+"\r"+indent+"LIP %e"% Lip ) sys.stdout.flush() #if myrank==0: #print "" t=1.0 y[:] = solution x_old[:] = solution for iter in range(abs(niter)): err = calculate_grad(grad,data ,y , s2d, d2s, solution_shape , parallel = parallel, beta=beta ) solution[:] = y + grad/Lip solution[:] = np.maximum(solution, 0) tnew = ( 1+math.sqrt(1.0+4*t*t) )/2 y[:] = solution +(t-1)/tnew *( solution - x_old ) t = tnew if niter<0: t=1 x_old[:] = solution if myrank==0: if iter%1 ==0: # sys.stdout.write(" "*60+"\r"+indent+("FISTA iter %d errore est %e mod_grad est %e" % ( iter, err, grad.std()) )) sys.stdout.write(indent+("FISTA iter %d errore est %e mod_grad est %e\n" % ( iter, err, grad.std()) )) sys.stdout.flush() if myrank==0: print( " " ) def trajectory_error( XYXY , O_spots, opticalPSF, nref, reponse_pixel , retrieve_spots, suggerimento=None, ROI = None): if myrank==0: print( XYXY) Nspots = O_spots.shape[0] if(len(XYXY)==4): X1,Y1,X2,Y2 = XYXY else: X1,X2 = XYXY Y1 = suggerimento[0] + X1* suggerimento[1] Y2 = suggerimento[0] + X2* suggerimento[1] trajectory = Trajectory() trajectory.set_extrema(X1,Y1,X2,Y2, Nspots ) err = 0.0 dd = 0.0 ss = 0.0 ds = 0.0 total_I = [] total_J = [] total_F = [] total_data = [] for n, data in enumerate( O_spots ): # print n center_pic = [ trajectory.Y.intercept + n* trajectory.Y.slope , trajectory.X.intercept + n* trajectory.X.slope ] # lut1, lut2 = get_LUT(data , opticalPSF , center_pic, nref, reponse_pixel, doproduct = 0) LUT = get_LUT(data , opticalPSF , center_pic, nref, reponse_pixel, doproduct = 1, ROI=ROI) if LUT is not None: I,J,F = np.array(LUT).T assert(J.max()< opticalPSF.size ) total_I.append( n*data.size + I ) total_J.append( J ) total_F.append(F) total_data.append( data.flatten() ) # tmp = np.zeros( [ data.shape[0] , opticalPSF.shape[1] ],"f" ) # tmp2= np.zeros( [ data.shape[0] , data.shape[1] ],"f" ) # for i,j,f, dum1, dum2 in lut1: # tmp [i,:] += opticalPSF[j,: ] *f # for i,j,f , dum1, dum2 in lut2: # tmp2[:,i] += tmp[:,j] *f # ss += (tmp2*tmp2).sum() # dd += (data*data).sum() # ds += (tmp2*data).sum() #print " CONCATENO " I = np.concatenate(total_I) J = np.concatenate(total_J) F = np.concatenate(total_F) data = np.concatenate(total_data ) s2d_coo = scipy.sparse.coo_matrix( (F,(I,J)) , shape = [ O_spots.size , opticalPSF.size ]) s2d = s2d_coo.tocsr() sim = s2d.dot( opticalPSF.flatten()) if retrieve_spots: return sim dd = (data*data).sum() ss=(sim*sim).sum() ds=(data*sim).sum() error = dd - ds*ds/ss ### f2 * ss + dd -2*ds*f ==> 2f*ss -2 ds *f = 0 ### f = ds/ss error = ds*ds/ss +dd -2ds*ds/ss = dd - ds*ds/ss if myrank==0: sys.stdout.write( " "*60+"\r"+indent+"trajectory error %e" % error ) sys.stdout.flush() return error # class Tobject: # def __init__(self, variables , O_spots, opticalPSF, nref , reponse_pixel ) : # self.variables = variables # self.O_spots = O_spots # self.opticalPSF = opticalPSF # self.nref = nref # self.reponse_pixel = reponse_pixel # def error(self): # X1 = self.variables[0].value # X2 = self.variables[1].value # Y1 = self.variables[2].value # Y2 = self.variables[3].value # res = trajectory_error(np.array([X1,Y1,X2,Y2]), self.O_spots, self.opticalPSF, self.nref , self.reponse_pixel,0 ) # if myrank==0: # print " error = " , res # return res def refine_trajectory(O_spots, opticalPSF, trajectory , nref , reponse_pixel , retrieve_spots = 0 , suggerimento = None , ROI = None): global indent Nspots = O_spots.shape[0] X1,Y1 = trajectory.get_coords(0) X2,Y2 = trajectory.get_coords(Nspots-1) if retrieve_spots: res = trajectory_error(np.array([X1,Y1,X2,Y2]), O_spots, opticalPSF, nref , reponse_pixel , 1 , None, ROI) return res # if not scipymin or 0: # variables = [ minimiser.Variable( X1,X1-2, X1+2), # minimiser.Variable( X2,X2-2, X2+2), # minimiser.Variable( Y1,Y1-2, Y1+2), # minimiser.Variable( Y2,Y2-2, Y2+2) # ] # tominimise=Tobject(variables , O_spots, opticalPSF, nref , reponse_pixel ) # miniob=minimiser.minimiser(tominimise,variables) # miniob.amoeba(0.0001) # X1 = variables[0].value # X2 = variables[1].value # Y1 = variables[2].value # Y2 = variables[3].value # trajectory.set_extrema( X1,Y1,X2,Y2 , Nspots) # return trajectory if suggerimento is not None: if myrank==0: print( indent +"IMPROVING TRAJECTORY ") res = minimize(trajectory_error,np.array([X1,X2]),( O_spots, opticalPSF, nref , reponse_pixel ,0 ,suggerimento , None, ROI), # method='Powell', method='Nelder-Mead', options={'disp': False, 'maxiter': 40, 'return_all': False, 'maxfev': None, 'xtol': 0.001, 'ftol': 0.001 } ) if myrank==0: print( " ... MINIMO IN ", res.x,) print( " INIZIALE ", [X1,X2]) # print " FINALE ", res.x[0], res.x[1] trajectory.set_extrema_suggestion( res.x[0], res.x[1], Nspots , suggerimento) else: dodebug = False if myrank==0: print( indent + "IMPROVING TRAJECTORY ") dodebug = True res = minimize(trajectory_error,np.array([X1,Y1,X2,Y2]),( O_spots, opticalPSF, nref , reponse_pixel ,0, None, ROI ), # method='Powell', method='Nelder-Mead', options={'disp': dodebug, 'maxiter': 40, 'return_all': False, 'maxfev': None, 'ftol': 0.001}) # options={'disp': dodebug, 'maxiter': 40, 'return_all': False, 'maxfev': None, 'xtol': None, 'ftol': 0.001}) # res = minimize(trajectory_error,np.array([X1,Y1,X2,Y2]),( O_spots, opticalPSF, nref )) if myrank==0: print( " ... MINIMO IN ", res.x,) print( " INIZIALE ", [X1,Y1,X2,Y2]) # print " FINALE ", res.x[0], res.x[1], res.x[2], res.x[3] trajectory.set_extrema( res.x[0], res.x[1], res.x[2], res.x[3], Nspots) return trajectory def fit_reponse(trajectory, O_spots , nref, reponse_pixel, beta=0.1, niter=500, ROI = None ) : solution = np.zeros( [O_spots[0].shape[0]*nref, O_spots[0].shape[1]*nref ] , "f" ) total_I = [] total_J = [] total_F = [] total_data = [] for n, data in enumerate( O_spots ): # if myrank==0: # print n center_pic = [ trajectory.Y.intercept + n* trajectory.Y.slope , trajectory.X.intercept + n* trajectory.X.slope ] LUT = get_LUT(data ,solution , center_pic, nref, reponse_pixel, ROI=ROI) if LUT is not None: I,J,F = np.array(LUT).T total_I.append( n*data.size + I ) total_J.append( J ) total_F.append(F) total_data.append( data.flatten() ) # if myrank==0: # print " CONCATENO " I = np.concatenate(total_I) J = np.concatenate(total_J) F = np.concatenate(total_F) data = np.concatenate(total_data ) # if myrank==0: # print " CREO MATRICE " # print data.shape # print I.shape # print J.shape s2d_coo = scipy.sparse.coo_matrix( (F,(I,J)) , shape = [ O_spots.size , solution.size ] ) d2s_coo = scipy.sparse.coo_matrix( (F,(J,I)) , shape = [ solution.size , O_spots.size ] ) # if myrank==0: # print " CAMBIO FORMATO " s2d = s2d_coo.tocsr() d2s = d2s_coo.tocsr() # if myrank==0: # print " FISTA " b=solution[:] solution_shape = solution.shape solution=np.reshape(solution,[-1]) # cg( data , solution , s2d, d2s, solution_shape ) Fista( data , solution , s2d, d2s, solution_shape, niter=niter, beta=beta ) solution.shape = solution_shape return solution def fit_reponse_pixel(trajectory_list, O_spots_list , nref, reponse_pixel, solution_list, niter=500, beta=100 , rois = None): if type(trajectory_list)!=type([]): trajectory_list = [trajectory_list] O_spots_list = [O_spots_list] solution_list = [solution_list] total_I = [] total_J = [] total_F = [] total_data = [] tot_size_data = 0 for trajectory , O_spots, solution, ROI in zip(trajectory_list,O_spots_list, solution_list, rois ) : if trajectory is None: continue assert( solution.shape == (O_spots[0].shape[0]*nref, O_spots[0].shape[1]*nref ) ) for n, data in enumerate( O_spots ): # if myrank==0: # print n center_pic = [ trajectory.Y.intercept + n* trajectory.Y.slope , trajectory.X.intercept + n* trajectory.X.slope ] LUT = get_LUT(data ,solution , center_pic, nref, reponse_pixel, soluzione = solution, doproduct = 2, ROI = ROI ) if LUT is not None: I,J,F = np.array(LUT).T total_I.append( tot_size_data+ n*data.size + I ) total_J.append( J ) total_F.append(F) total_data.append( data.flatten() ) tot_size_data +=O_spots .size if myrank==0: print( " CONCATENO ", ) I = np.concatenate(total_I) J = np.concatenate(total_J) F = np.concatenate(total_F) data = np.concatenate(total_data ) # if myrank==0: # print " CREO MATRICE " # print data.shape # print I.shape # print J.shape s2d_coo = scipy.sparse.coo_matrix( (F,(I,J)) , shape = [ tot_size_data , reponse_pixel.size ] ) d2s_coo = scipy.sparse.coo_matrix( (F,(J,I)) , shape = [ reponse_pixel.size , tot_size_data ] ) # if myrank==0: # print " CAMBIO FORMATO " s2d = s2d_coo.tocsr() d2s = d2s_coo.tocsr() datasim = s2d.dot( reponse_pixel.flatten() ) datasim = datasim - data if myrank==0: print( " FIDELITY ", (datasim*datasim).sum(),) print( " FISTA ",) reponse_pixel_shape = reponse_pixel.shape reponse_pixel=np.reshape(reponse_pixel,[-1]) Fista( data , reponse_pixel , s2d, d2s, reponse_pixel_shape , parallel = 1 , niter=niter, beta=beta ) reponse_pixel.shape = reponse_pixel_shape return reponse_pixel def get_spots_list( filename , groupname , filter_rois =1 ): res=[] nomi_scan = [] stats = [] origini = {} h5 = h5py.File(filename,"r") xscales = {} enescan = None print( " ===========================\n"*10 ) print( filename ) print( groupname ) tmp_list = list(filterRoiList(list(h5[groupname].keys()))) tmp_list = sorted(tmp_list, key = int ) for sn in tmp_list: print( groupname+"/"+sn+"/matrix") m = h5[groupname+"/"+sn+"/matrix"][:] print( m.shape) if groupname+"/"+sn+"/xscale" in h5: xscales[sn] = h5[groupname+"/"+sn+"/xscale"][:] else: xscales[sn] = np.arange( m.shape[0]).astype("f") if groupname+"/motorDict/energy" in h5: enescan = h5[groupname+"/motorDict/energy"][()] if m.shape!=(0,): stat = m.sum(axis=-1).sum(axis=-1) if (not filter_rois) or (stat.min()>stat.max()/2.0 and stat.min() >250.0): res.append(m) nomi_scan.append( sn ) stats.append(stat) origini[sn] = h5[groupname+"/"+sn+"/cornerpos"][:] h5.close() rois = [] ## METTERE D UFFICIO ANCHE LE ROI QUANDO SI SCRIVE UNO SCAN E CAMBIARE QUI IL ALLINDIETRO for i in range(1): pos = groupname.rfind("/") groupname=groupname[:pos] # groupname=groupname+"/rois_definition/rois_dict/" print( " NOMI SCAN ", nomi_scan) h5 = h5py.File(filename,"r") for sn in nomi_scan: print( " DATANAME ", groupname+"/ROI%02d/mask" %int(sn), filename) m = h5[groupname+"/ROI%02d/mask" %int(sn)][:] rois.append(m) h5.close() print( res) for m in res: print( m.sum()) print( nomi_scan) print( rois) return res, nomi_scan, stats, origini, rois, xscales, enescan class pippo: pass class Trajectory : def __init__(self): self.X=pippo() self.Y=pippo() def get_coords(self,n): X1 = self.X.intercept + self.X.slope*n Y1 = self.Y.intercept + self.Y.slope*n return X1, Y1 def set_extrema(self, X1, Y1, X2, Y2,N): self.N = N self.X.intercept = X1 self.Y.intercept = Y1 self.X.slope = (X2-X1)/(N-1) self.Y.slope = (Y2-Y1)/(N-1) def set_extrema_suggestion( self, X1, X2, Nspots , suggerimento): Nspots = self.N Y1 = suggerimento[0] + X1* suggerimento[1] Y2 = suggerimento[0] + X2* suggerimento[1] self. set_extrema( X1, Y1, X2, Y2,Nspots) def projectOnHint( self, suggerimento ): Nspots = self.N X1,Y1 = self.get_coords(0) X2,Y2 = self.get_coords(Nspots-1) Y1 = suggerimento[0] + X1* suggerimento[1] Y2 = suggerimento[0] + X2* suggerimento[1] self. set_extrema( X1, Y1, X2, Y2,Nspots) def __repr__(self): s="" s=s+" X.intercept = %e " % self.X.intercept s=s+" X.slope = %e " % self.X.slope s=s+" Y.intercept = %e " % self.Y.intercept s=s+" Y.slope = %e " % self.Y.slope s=s+" Nspots = %d " % self.N return s class NoSignal(Exception): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) def get_Trajectory_byregression( O_spots ): Xs=[] Ys=[] Ws=[] for slice in O_spots: mmax = slice.max() thres = mmax / 20 slice = np.array(slice) slice[ slice0: X = ( slice * np.arange(slice.shape[1] ) ).sum()/m0 Y = ( slice.T * np.arange(slice.shape[0] ) ).sum()/m0 Xs.append(X) Ys.append(Y) Ws.append(m0) if len(Xs)<=1: raise NoSignal(" All images were zero ") ret_val = Trajectory() ret_val.X.slope, ret_val.X.intercept = np.polyfit( np.arange(len(Xs)), Xs ,1, w = Ws) ret_val.Y.slope, ret_val.Y.intercept = np.polyfit( np.arange(len(Ys)), Ys ,1,w= Ws ) # ret_val.X.slope, ret_val.X.intercept, Xr_value, Xp_value, Xstd_err = scipy.stats.linregress(np.arange(len(Xs)),Xs , ) # ret_val.Y.slope, ret_val.Y.intercept, Yr_value, Yp_value, Ystd_err = scipy.stats.linregress(np.arange(len(Ys)),Ys , ) ret_val.N = len(O_spots) return ret_val def DOFIT(filename=None, groupname=None, nref=5, niter_optical=500, beta_optical=0.1 , beta_pixel=1000.0, niter_pixel = -20, niter_global = 50, pixel_dim=6, simmetrizza=1, do_refine_trajectory=1, target_file="responses.h5",target_groupname="FIT", trajectory_reference_scansequence_filename = None, trajectory_reference_scansequence_groupname = None , trajectory_threshold = None, trajectory_file=None , filter_rois=1 , fit_lines = False): ###### filename = "../nonregressions/demo_imaging.hdf5" ###### groupname = "ROI_B/foil_scanXX/scans/Scan273/" # print filename, groupname O_spots_list, nomi_rois, stats, origini_foil, rois, xscales, energy = get_spots_list( filename, groupname, filter_rois= filter_rois ) stats = np.array(stats) if do_refine_trajectory ==2: h5f = h5py.File(trajectory_reference_scansequence_filename,"r") h5 = h5f[trajectory_reference_scansequence_groupname] zscan_keys = sorted( list(filterRoiList(list(h5.keys()))) , key = lambda x: int(list(filter(str.isdigit, str(x) ))) ) ZDIM = len(zscan_keys) myrange = np.array_split( np.arange( ZDIM ), nprocs )[myrank] myZDIM = len(myrange) roi_keys = nomi_rois integrated = {} origini_ref = {} for rk in roi_keys: zkey = zscan_keys[ 0 ] m = h5[zkey][rk]["matrix"][:] mm = m.sum(axis=0) integrated[rk ] = np.zeros_like( mm ) .astype("d") origini_ref [rk ] = h5[zkey][rk]["cornerpos"][:] for iz in range(myZDIM): zkey = zscan_keys[myrange[iz] ] for rk in roi_keys: m = h5[ zkey ][ rk ]["matrix"][:] msum = m.sum(axis=0) integrated[rk] = integrated[rk]+msum if nprocs>1: for n in integrated.keys(): comm.Allreduce( [np.array(integrated[n]), MPI.DOUBLE], [integrated[n], MPI.DOUBLE], op=MPI.SUM) trajectory_hints={} for n in integrated.keys(): C = integrated[n] pesiC = C.sum(axis=0) medieC = (np.arange(C.shape[0])[:,None]*C).sum(axis=0)/pesiC coords = np.arange(len( medieC )) maxP=pesiC.max() whereBigger = np.where( pesiC > maxP*trajectory_threshold )[0] inizio = whereBigger.min() fine = whereBigger.max() pesifit = np.zeros( len(pesiC),"f") pesifit[inizio:fine+1] = ( pesiC[inizio:fine+1]> maxP*trajectory_threshold )*1 pfit=np.polynomial.polynomial.polyfit(coords, medieC, 1, w=pesifit) tmp_intercept = pfit[0] tmp_slope = pfit[1] DY = origini_foil[n][0] -origini_ref[n][0] DX = origini_foil[n][1] -origini_ref[n][1] hint_slope = tmp_slope hint_intercept = tmp_intercept - DY + hint_slope*DX trajectory_hints[n] = hint_intercept,hint_slope else: trajectory_hints={} global SYMM_RESPO SYMM_RESPO = simmetrizza reponse_pixel = np.ones([ pixel_dim , pixel_dim ],"f") #### nref = 5 trajectory_list = [] solution_list = [] trajectories_from_file = None if trajectory_file is not None: ### file = open(trajectory_file,"r") print( "RELOADING TRAJECTORIES ") trajectories_from_file = reload_trajectories(trajectory_file, nomi_rois) for iterm, (O_spots, name, ROI) in enumerate(zip(O_spots_list, nomi_rois, rois)): # print "MYRANK %d fa "%myrank, iterm if (iterm)%nprocs == myrank: if trajectories_from_file is not None: " REUSING TRAJECTORIES " trajectory = trajectories_from_file[iterm] trajectory.N = O_spots.shape[0] else: try: trajectory = get_Trajectory_byregression( O_spots ) except NoSignal as inst: msg = " Was not able to determine trajactory because not enough signal in ROI %s"%name print(msg) inst.value = msg raise print( " TRAJECTORY ", trajectory) if do_refine_trajectory ==2 : PRIMA = trajectory.Y.intercept, trajectory.Y.slope print( "============= projectOnHint============= ", ) trajectory.projectOnHint( trajectory_hints[ name ] ) print( "Intercept : ", PRIMA[0] , "===>", trajectory.Y.intercept , "Slope : ", PRIMA[1] , "===>", trajectory.Y.slope) if myrank ==0: print( "Process 0 FITTING OPTICAL RESPONSE for scan ", name) solution = fit_reponse( trajectory, O_spots , nref , reponse_pixel,niter=niter_optical, beta= beta_optical, ROI=ROI) ## niter 500 beta_optical 0.1 else: solution = None trajectory = None solution_list.append(solution) trajectory_list.append(trajectory) if myrank ==0: print( "NOW ALL PROCESS FITTING PIXEL RESPONSE") if pixel_dim>1: newreponse = fit_reponse_pixel(trajectory_list , O_spots_list , nref, reponse_pixel, solution_list ,beta = beta_pixel , niter = niter_pixel , rois=rois) global indent indent = "" for iter in range(niter_global): indent = " " if myrank ==0: print( "ITERATIVE REFINEMENT, cycle No", iter) print( nomi_rois) for iterm, (O_spots, name, ROI ) in enumerate(zip(O_spots_list, nomi_rois, rois)): if (iterm)%nprocs == myrank: # print " MYRANK %d fa "%myrank, iterm trajectory = trajectory_list[iterm] if myrank ==0: print( " Process 0 FITTING OPTICAL RESPONSE for roi ", name) solution = fit_reponse( trajectory, O_spots , nref , reponse_pixel,niter=niter_optical, beta=beta_optical, ROI=ROI) if do_refine_trajectory: suggerimento = None if do_refine_trajectory ==2 : suggerimento = trajectory_hints[name] if myrank ==0: print( " Process %d REFINING TRAJECTORY for roi "%myrank, name) trajectory = refine_trajectory(O_spots, solution , trajectory , nref , reponse_pixel , suggerimento = suggerimento, ROI=ROI ) #####solution = fit_reponse( trajectory, O_spots , nref , reponse_pixel) else: solution = None trajectory = None solution_list[iterm]= solution trajectory_list[iterm] = trajectory if nprocs>1: # print " process ", myrank, " aspetta " comm.Barrier() # print " TUTTI I PROCESSI SONO ARRIVATI QUI " #for i in range(nprocs): # if i==myrank: # print (" %d " %i )*10 # for t in trajectory_list: # if t is not None: # print t # comm.Barrier() if myrank ==0: print( "REFITTING PIXEL RESPONSE ",) # import pickle # file = open("tra%d"%myrank,"r") # # pickle.dump( trajectory_list, file ) # trajectory_list = pickle.load( file ) # file.close() if pixel_dim>1: newreponse = fit_reponse_pixel(trajectory_list, O_spots_list , nref, reponse_pixel, solution_list ,beta = beta_pixel, niter = niter_pixel , rois = rois) # reponse_pixel=newreponse if myrank ==0: print( "FINISHED. NOW WRITING ") trajectory_dic = {} for i,t in enumerate(trajectory_list): if t is not None: trajectory_dic[i] = t tosend = pickle.dumps(trajectory_dic) for iproc in range(nprocs): if iproc: if myrank==iproc: comm.send( tosend, dest=0, tag=11) elif myrank==0: data = comm.recv(source=iproc, tag=11) tdic = pickle.loads(data) trajectory_dic.update(tdic) if fit_lines : lines = [] for solution in solution_list : if solution is None: lines.append(None) continue weigths = np.array(solution.flat) indici = np.indices(solution.shape) ind_y = np.array(indici[0].flat) ind_x = np.array(indici[1].flat) print( " SHAPES ", ind_x.shape, ind_y.shape, weigths.shape) pfit = np.polynomial.polynomial.polyfit(ind_x.astype("d"),ind_y.astype("d"),1,w= weigths.astype("d") ) lines.append( [pfit[0], pfit[1] ] ) if nprocs>1: comm.Barrier() if iproc==myrank: if myrank==0: h5f = h5py.File(target_file,"a") h5 = h5f.require_group(target_groupname) if "response" in h5: del h5["response"] h5["response"] = reponse_pixel if energy is not None: h5.require_group("motorDict") if "energy" in h5["motorDict"]: del h5["motorDict"]["energy"] h5["motorDict/energy"] = energy else: os.system("touch %s" %target_file ) h5f = h5py.File(target_file,"a") h5 = h5f[target_groupname] for iterm, (O_spots, name, stat) in enumerate(zip(O_spots_list, nomi_rois, stats)): if (iterm)%nprocs == myrank: trajectory = trajectory_list[iterm] solution = solution_list[iterm] if name in h5: del h5[name] h5group = h5.require_group(name ) README="" h5group[ "data" ] = solution README +="data : The reponse which fits all the respones if translated as below\n" h5group["Xintercept"] = trajectory.X.intercept README +="Xintercept : The X-shift of the fitted response for response numero 0\n" h5group["Xslope" ] = trajectory.X.slope README +="Xslope : The step of X-shift of the fitted response going from response n + n+1\n" h5group["Yintercept"] = trajectory.Y.intercept README +="Yintercept : same as for Xintercept but for Y \n" h5group["Yslope" ] = trajectory.Y.slope README +="Yslope : same as for Xslope but for Y \n" h5group["stat" ] = stat README +="stat : this relates to original responses. it is the total energy contained in reference n for all the n's\n" h5group["nref" ] = nref README +="nref : how many pixel of the fitted response we have for one pixel of the original response. this rescale the response but not all the other coordinates which are referred to the detector,\n" h5group["xscale" ] = xscales[name] README +="xscale : the scan varying variable values for the range over which the fit has been done\n" if fit_lines: h5group["line" ] = lines[iterm] README +="nref : how many pixel of the fitted response we have for one pixel of the original response\n" h5group["python_plot_imageAndLine"]=("fig,ax=pylab.plt.subplots()\n" "ax.imshow(self.data)\n" "xs= pylab.arange(self.data.shape[1])\n" "ys= xs*self.line[1]+self.line[0]\n" "ax.plot(xs,ys)\n" "pylab.plt.show()\n") README +="python_plot_imageAndLine : double click on it to execute the code and have a plot\n" h5group["README" ] = README h5f.flush() h5f.close() h5f = None # if myrank == 0: # tl = [trajectory_dic[i] for i in range( len(trajectory_list) )] # file = open( "trajectories.pickle" ,"w") # pickle.dump( tl , file ) # file.close() def reload_trajectories(trajectory_file, nomi_rois) : ### , trajectory_file_group, nomi_rois): h5 = h5py.File( trajectory_file ,"r") ### h5group = h5[trajectory_file_group] trajectory_list = [] for iterm, name in enumerate( nomi_rois[:]): h5group = h5[name] trajectory = Trajectory() trajectory.X.intercept = h5group["Xintercept"][()] trajectory.X.slope = h5group["Xslope"][()] trajectory.Y.intercept = h5group["Yintercept"][()] trajectory.Y.slope = h5group["Yslope"][()] # trajectory.N = O_spots.shape[0] trajectory_list.append( trajectory ) return trajectory_list def DOROIS(filename = "../nonregressions/demo_imaging.hdf5" , groupname = "ROI_B/foil_scanXX/scans/Scan273/", roi_filename = None, roisgroupname = "ROI_B/", target_filename="newscan.h5", roisgroupname_target = "ROI_B_FIT8/", newscanstarget = "scanXX/scans/Scan273", responsefilename = "responses.h5", resynth_z_square = None, responsepath = "fit", nex = 3, filter_rois=1, recenterings= None, filterMask = None): if roi_filename is None: roi_filename = filename O_spots_list, nomi_rois, stats , origini, rois, xscales, energy= get_spots_list( filename, groupname , filter_rois= filter_rois ) trajectory_list = [] solution_list = [] h5f = h5py.File( responsefilename ,"r") print( responsefilename) h5 = h5f[responsepath] print( responsepath) for iterm, (O_spots, name) in enumerate(zip(O_spots_list[:], nomi_rois[:])): if (iterm)%nprocs == myrank: if myrank ==0 : print( "Process 0 reading responses and trajectories for roi ", name) h5group = h5[name] print( name, "data") solution = h5group["data"][()] trajectory = Trajectory() trajectory.X.intercept = h5group["Xintercept"][()] trajectory.X.slope = h5group["Xslope"][()] trajectory.Y.intercept = h5group["Yintercept"][()] trajectory.Y.slope = h5group["Yslope"][()] nref = h5group["nref"][()] if resynth_z_square is not None: solution = gaussian_filter( solution, [ nref*resynth_z_square , 0 ]) trajectory.N = O_spots.shape[0] if recenterings is not None: rec = recenterings[int(name)] recy,recx = rec irecy, irecx = int(round(recy)), int(round(recx)) recy,recx = recy-irecy, recx-irecx else: recy,recx = 0,0 irecy,irecx = 0,0 trajectory.X.intercept += -recx trajectory.Y.intercept += -recy if "line" in h5group: trajectory.line = h5group["line"][()] lh,lslope =trajectory.line lh = lh-recy + recx*lslope trajectory.line = np.array([lh,lslope]) trajectory.recy = recy trajectory.recx = recx trajectory.irecy = irecy trajectory.irecx = irecx else: solution = None trajectory = None solution_list.append(solution) trajectory_list.append(trajectory) reponse_pixel = h5["response" ][:] h5f.close() del solution del trajectory masklist = [] for iterm, (O_spots, name, ROI) in enumerate(zip(O_spots_list[:], nomi_rois[:], rois)): if (iterm)%nprocs == myrank: # print " MYRANK fa ", myrank, iterm, solution_list[iterm] if myrank ==0 : print( "Process 0 expanding roi ", name) assert( (np.array(solution_list[iterm].shape) - np.array(O_spots[0].shape)*nref).sum() == 0 ) trajectory = trajectory_list[iterm] # trajectory = refine_trajectory(O_spots, solution_list[iterm] , trajectory , nref , reponse_pixel ) # solution = fit_reponse( trajectory, O_spots , nref , reponse_pixel,niter=500, beta=0.001) ## solution_list[iterm] = solution # print( filename) # print( roisgroupname+ "/rois_definition/image") h5f = h5py.File( roi_filename,"r") # imagealldect = h5f [roisgroupname+ "/rois_definition/image"][:] print( roi_filename) print( roisgroupname+ "/image" ) imagealldect = h5f [roisgroupname+ "/image"][:] # h5 = h5f [roisgroupname+ ("/rois_definition/rois_dict/ROI%02d"%int(name)) ] h5 = h5f [roisgroupname+ ("/rois_dict/ROI%02d"%int(name)) ] mask = h5["mask" ][:] origin = h5["origin"][:] h5f.close() if nex==0: newmask = mask neworigin=origin h,w = mask.shape del mask nspots = trajectory.N sezione = [int(nspots*(nex)) ,int(nspots*(nex))+nspots ] if nex: newmask = np.zeros([ (1+4*nex)*h,(1+4*nex)*w ] ) # print " CI SONO nspots" , nspots for i in range( - int(nspots*(nex+0.5)), int(nspots*(1+nex+0.5))): X1,Y1 = trajectory.get_coords(i) DX = int( trajectory.X.slope ) X1shift = X1 + 2*nex*w Y1shift = Y1 + 2*nex*h newmask[ max(0,int(Y1shift)-h//2) : min(int(Y1shift)+h//2+1, newmask.shape[0]) , max(0,int(X1shift-1+DX)):min(int(X1shift+1+DX)+1 , newmask.shape[1]) ] =1 yinds, xinds = np.where(newmask) ymin,ymax = yinds.min(), yinds.max() xmin,xmax = xinds.min(), xinds.max() # print " VA DA " , ymin,ymax, xmin,xmax newmask = newmask[ ymin:ymax+1, xmin:xmax+1 ] SHIFTORIGIN = np.array( [ (ymin-2*nex*h), (xmin-2*nex*w) ] ) neworigin = origin + SHIFTORIGIN if neworigin[0] <0: newmask = newmask[-neworigin[0]:] neworigin[0]=0 if neworigin[1] <0: newmask = newmask[ : , :-neworigin[1] ] neworigin[1]=0 corry = neworigin[0] + newmask.shape[0] - imagealldect.shape[0] corrx = neworigin[1] + newmask.shape[1] - imagealldect.shape[1] if corry>0: newmask = newmask[:-corry] if corrx>0: newmask = newmask[:,:-corrx] new_Ospots = np.zeros( [ nspots*(1+2*nex) , newmask.shape[0], newmask.shape[1] ] ,"f" ) trajectory.X.intercept = trajectory.X.intercept - nex*nspots*trajectory.X.slope - SHIFTORIGIN[1] trajectory.Y.intercept = trajectory.Y.intercept - nex*nspots*trajectory.Y.slope - SHIFTORIGIN[0] if filterMask is not None: newmask[:] = newmask * filterMask[ neworigin[0]:neworigin[0]+newmask.shape[0], neworigin[1]:neworigin[1]+newmask.shape[1] ] if nex>0: ROI=None else: new_Ospots = O_spots expandedSpots = refine_trajectory(new_Ospots, solution_list[iterm] , trajectory , nref , reponse_pixel , retrieve_spots = 1, ROI=ROI ) expandedSpots.shape = new_Ospots.shape else: newmask = None neworigin = None expandedSpots = None sezione = None masklist .append( [newmask,neworigin, expandedSpots , sezione , trajectory ] ) if target_filename!=filename: Oh5f = h5py.File(filename,"r") for iterm, (O_spots, name) in enumerate(zip(O_spots_list[:], nomi_rois[:])): if nprocs>1: comm.Barrier() if (iterm)%nprocs == myrank: newmask,neworigin, newspots, sezione, trajectory = masklist[iterm] solution = solution_list[iterm] if(iterm==0): h5f = h5py.File(target_filename,"a") else : h5f = h5py.File(target_filename,"a") if target_filename==filename: Oh5f = h5f if iterm ==0 and (roisgroupname_target in h5f): del h5f[roisgroupname_target] h5f.require_group( roisgroupname_target ) print( " h5g " , target_filename , roisgroupname_target) h5g = h5f [roisgroupname_target] h5g.require_group( "rois_definition" ) h5g.require_group( "rois_definition/rois_dict/" ) h5g.require_group( "rois_definition/rois_dict/ROI%02d"%int(name) ) if iterm==0: h5g [ "rois_definition/image"] = imagealldect h5 = h5g [ "rois_definition/rois_dict/ROI%02d"%int(name) ] h5["mask"] = newmask h5["origin"] = neworigin - np.array( [trajectory.irecy, trajectory.irecx] ) h5["sezioneold"] = sezione print( "newscanstarget " , newscanstarget) h5g.require_group( newscanstarget ) h5 = h5g [newscanstarget ] h5.require_group( str(name) ) h5 = h5 [str(name) ] for i in range(len(newspots)): spot_sum = newspots[i].sum() if spot_sum: newspots[i] = newspots[i]/spot_sum h5["matrix"] = newspots h5["Xintercept"] =trajectory.X.intercept h5["Yintercept"] =trajectory.Y.intercept h5["Xslope"] =trajectory.X.slope h5["Yslope"] =trajectory.Y.slope h5["monitor"] = np.ones([newspots[0].shape[0]],"d") h5["monitor_divider"] = 1.0 if hasattr(trajectory,"line"): h5["line"] =trajectory.line h5[ "optical_response" ] = solution h5["nref"] = nref Oh5 = Oh5f[groupname] Oh5g = Oh5[name] for key in Oh5g.keys(): if key=="cornerpos": h5[key] = neworigin - np.array( [trajectory.irecy, trajectory.irecx] ) elif key!="matrix" and key not in h5 : print( " aggiungo " , key) h5.copy( Oh5g[key], key) if iterm == 0 : if "motorDict" in Oh5: print( newscanstarget) print( list(h5g [newscanstarget ].keys())) print( list(Oh5.keys())) h5g [newscanstarget ].copy( Oh5 ["motorDict"], h5g [newscanstarget] ) h5f.close() if nprocs>1: comm.Barrier() if target_filename!=filename: Oh5f.close() if __name__ == "__main__": # todo="fit" todo="rois" if todo=="fit": filename = "../nonregressions/demo_imaging.hdf5" groupname = "ROI_B/foil_scanXX/scans/Scan273/" nref=5 niter_optical=500 beta_optical=0.1 beta_pixel=1000.0 niter_pixel = -20 niter_global = 10 pixel_dim=6 simmetrizza=1 do_refine_trajectory=0 target_file="responses.h5" DOFIT(filename=filename, groupname=groupname, nref=nref, niter_optical=niter_optical, beta_optical=beta_optical , beta_pixel=beta_pixel , niter_pixel = niter_pixel, niter_global = niter_global, pixel_dim=pixel_dim , simmetrizza=simmetrizza , do_refine_trajectory=do_refine_trajectory, target_file=target_file ) if todo=="rois": filename = "../nonregressions/demo_imaging.hdf5" # dove c' e lo scan originale groupname = "ROI_B/foil_scanXX/scans/Scan273/" # Dove si trova esattament lo scan roisgroupname = "ROI_B/" # dove c' e' la roi originale target_filename = "newscan.h5" roisgroupname_target = "ROI_B_FIT8/" newscanstarget = "scanXX/scans/Scan273" responsefilename = "responses.h5" nex = 3 DOROIS(filename = filename , groupname = groupname, roisgroupname = roisgroupname , target_filename = target_filename, roisgroupname_target = roisgroupname_target , newscanstarget = newscanstarget, responsefilename = "responses.h5", nex = nex) xrstools-0.15.0+git20210910+c147919d/XRStools/resintesi.py000066400000000000000000000152411412732462000222710ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from scipy import interpolate, signal, integrate, constants, optimize, ndimage def FandGrad( Y, factors, base_functs ): simu = np.dot( factors, base_functs ) diff = -Y+simu error = 0.5*(diff*diff).sum() grad = np.dot( base_functs, diff ) return error, grad def resintetizza( Xexp, Yexp, XY ): X,Y = XY norm_Yexp = Yexp.max() norm_Y = Y .max() Yexp = Yexp / norm_Yexp Y = Y / norm_Y base_shifts_facts = [] base_functs = [] mpos_XY = X[np.argmax(Y)] for eX in Xexp: base_functs.append( np.interp( Xexp + mpos_XY -eX, X, Y) ) base_functs = np.array(base_functs) factors = np.ones_like( Xexp) for iter in range(100): factors = factors / np.linalg.norm(factors) error, grad = FandGrad( Yexp, factors, base_functs ) factors = grad Lip = abs(np.linalg.norm(factors)) print("Lipschitz = ", Lip) factors = np.zeros_like( Xexp) factors_old = factors t=1.0 told = t for iter in range(10000): error, grad = FandGrad( Yexp, factors, base_functs ) # factors = factors-1000000000.0/Lip factors = np.maximum(0,factors-grad/Lip) told = t t = (1+np.sqrt(1+4*t*t))/2 step = (factors-factors_old)*(told-1)/t factors_old = factors factors = factors + step print(" error ", error ) return factors * norm_Yexp / norm_Y # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ def get_resynth( STW_infos, synth_coeffs , Xexp): todo_list = { "RC_curve", "A_curve", "B_curve", "C_curve" } result = {} synth_coeffs = synth_coeffs/np.sum( synth_coeffs ) X, Y = STW_infos["RC_curve"] mpos_XY = X[np.argmax(Y)] e_baricenter = (synth_coeffs*Xexp).sum()/synth_coeffs.sum() for todo in todo_list: resintesi = 0 dum, Y = STW_infos[todo] for eX, f in zip(Xexp, synth_coeffs): ## resintesi = resintesi + f* np.interp( X + mpos_XY -eX, X, Y) resintesi = resintesi + f* np.interp( X + (e_baricenter -eX), X, Y) result[todo] = np.array([ X, resintesi ] ) return result def operator_norm(linop, u, maxiter=100): u = u / np.linalg.norm(u) for i in range(maxiter): v = linop(u) L=(u[:] * v[:]).sum() u = v / np.linalg.norm(v[:]) return L def CP(prox_fs, prox_g, K, KT, x0, maxiter=100, error=None): """ This routine has been copied from pyprox project by Samuel Vaiter https://github.com/svaiter/pyprox References ---------- A. Chambolle and T. Pock, A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging, JOURNAL OF MATHEMATICAL IMAGING AND VISION Volume 40, Number 1 (2011) """ theta = 1 L = operator_norm( lambda x: KT(K(x)), np.random.random(x0.shape) ) sigma = 1.0/np.sqrt(L) tau = .9 / (sigma * L) x = x0.copy() x1 = x0.copy() xold = x0.copy() y = K(x) fx = [] iterations = 1 while iterations < maxiter: xold = x.copy() y = prox_fs(y + sigma * K(x1), sigma) x = prox_g(x - tau * KT(y), tau) x1 = x + theta * (x - xold) if error is not None: fx.append(error(x)) print(" CP iter ", iterations, " " , fx[-1]) iterations += 1 return x, fx def gradient(x): g = np.zeros_like(x) g [ :-1 ] = x [ 1: ] - x [ :-1 ] return g def gradientT(p): res = np.zeros_like(p) res[1: ] = p [ :-1 ] res[ :-1] = res[ :-1] - p [ :-1 ] return res # Minimization of F(x) + G(gradient*x) class G_functor: def __init__(self, alpha): self.alpha = alpha def __call__(self,u): return self.alpha * np.sum(np.abs(u)) class F_functor: def __init__(self, Yexp, base_functs): self.Yexp = Yexp self.base_functs = base_functs def __call__(self, factors): simu = np.dot( factors, self.base_functs ) diff = -self.Yexp+simu error = 0.5*(diff*diff).sum() return error ## grad = np.dot( base_functs, diff ) class Error_functor: def __init__(self, F, G): self.F = F self.G = G def __call__(self, x): error = self.F(x) + self.G(gradient(x)) print(" Error CP", error ) return error class K_functor: def __init__(self, base_functs): self.base_functs = base_functs def __call__(self,x): g = gradient(x) gp = np.dot( x, self.base_functs ) res = np.concatenate([g,gp], axis=0) return res class KT_functor: def __init__(self, base_functs): self.base_functs = base_functs def __call__(self,P): N = self.base_functs.shape[0] px = P[:N] res = gradientT(px) py = P[N:] res = res + np.dot( self.base_functs , py) return res def prox_f( x,tau): return x class prox_gs_functor: def __init__(self, alpha,data, N): self.alpha = alpha self.data = data self.N = N def __call__( self, P, tau ): print(" P size ", P.shape , " e N ", self.N) res=np.zeros_like(P) px=P[:self.N] py = P[self.N:] res[:self.N] = self.alpha*px/(np.maximum(self.alpha,np.abs(px))) res[self.N:] = (py - tau*self.data)/(1+tau) return res def resintetizzaTV( Xexp, Yexp, XY , beta ): X,Y = XY norm_Yexp = Yexp.max() norm_Y = Y .max() Yexp = Yexp / norm_Yexp Y = Y / norm_Y base_shifts_facts = [] base_functs = [] mpos_XY = X[np.argmax(Y)] for eX in Xexp: base_functs.append( np.interp( Xexp + mpos_XY -eX, X, Y) ) base_functs = np.array(base_functs) factors = np.ones_like( Xexp) N = Yexp.shape[0] prox_gs = prox_gs_functor(beta, Yexp,N ) K = K_functor( base_functs ) KT = KT_functor( base_functs ) G = G_functor( beta ) F = F_functor( Yexp, base_functs ) Error = Error_functor( F,G ) sol, fs = CP(prox_gs, prox_f, K, KT, factors , maxiter=1000, error=Error) print( " USCITO da CP ") # , fs) return sol xrstools-0.15.0+git20210910+c147919d/XRStools/resources/000077500000000000000000000000001412732462000217215ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/resources/WIZARD/000077500000000000000000000000001412732462000227215ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/resources/WIZARD/creationtab.ui000066400000000000000000000031661412732462000255610ustar00rootroot00000000000000 Form 0 0 533 545 Form Creation Creation Creation Qt::Vertical 20 458 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/WIZARD/creationtab2.ui000066400000000000000000000065201412732462000256400ustar00rootroot00000000000000 Form 0 0 533 545 Form 0 1 Qt::Horizontal 2 0 true 0 0 224 463 1 0 true 0 0 223 463 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/WIZARD/filepath_widget.ui000066400000000000000000000032051412732462000264170ustar00rootroot00000000000000 Form 0 0 919 450 Form TextLabel valid entry true false ready entry true false xrstools-0.15.0+git20210910+c147919d/XRStools/resources/WIZARD/hdf5dialog.ui000066400000000000000000000034001412732462000252630ustar00rootroot00000000000000 Dialog 0 0 502 308 Dialog Qt::Horizontal QDialogButtonBox::Cancel|QDialogButtonBox::Ok buttonBox buttonBox accepted() Dialog accept() 227 277 157 274 buttonBox rejected() Dialog reject() 295 283 286 274 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/WIZARD/hdf5filepath_widget.ui000066400000000000000000000032061412732462000271670ustar00rootroot00000000000000 Form 0 0 919 450 Form TextLabel valid entry true ready entry true xrstools-0.15.0+git20210910+c147919d/XRStools/resources/WIZARD/mainwindow.ui000066400000000000000000000217011412732462000254350ustar00rootroot00000000000000 MainWindow 0 0 919 907 XRS_WIZARD 16 16 true true 0 Creation Create New Calculation Qt::Horizontal 40 20 0 1 Qt::Horizontal 2 0 true 0 0 819 701 0 0 919 19 FIle toolBar TopToolBarArea false Load... Select Scan and files... subimage spot detection registration global spot detection showDatas showMasks push mask remotely write mask on file load mask from file remote load write mask oin file write mask on file write masksDict on file load masksDict from file Exit Save Parameters load image from hdf5 file Load Parameters load maskDict from hdf5 Console+1 true Store inputs/stdo/e xrstools-0.15.0+git20210910+c147919d/XRStools/resources/WIZARD/mainwindow2.ui000066400000000000000000000151561412732462000255260ustar00rootroot00000000000000 MainWindow 0 0 845 615 XRS_WIZARD true 0 Creation Create New Calculation Qt::Horizontal 40 20 Qt::Vertical 20 40 0 0 845 19 FIle toolBar TopToolBarArea false Load... Select Scan and files... subimage spot detection registration global spot detection showDatas showMasks push mask remotely write mask on file load mask from file remote load write mask oin file write mask on file write masksDict on file load masksDict from file Exit Save Parameters load image from hdf5 file Load Start Parameters load maskDict from hdf5 Load Parameters Load Parameters GUIDED xrstools-0.15.0+git20210910+c147919d/XRStools/resources/WIZARD/mainwindow_t.ui000066400000000000000000000154431412732462000257660ustar00rootroot00000000000000 MainWindow 0 0 845 615 XRS_WIZARD 16 16 true true 0 Creation Create New Calculation Qt::Horizontal 40 20 Qt::Vertical 20 40 0 0 845 19 FIle toolBar TopToolBarArea false Load... Select Scan and files... subimage spot detection registration global spot detection showDatas showMasks push mask remotely write mask on file load mask from file remote load write mask oin file write mask on file write masksDict on file load masksDict from file Exit Save Parameters load image from hdf5 file Load Parameters load maskDict from hdf5 Console+1 true Store inputs/stdo/e xrstools-0.15.0+git20210910+c147919d/XRStools/resources/WIZARD/multichoices_widget.ui000066400000000000000000000024511412732462000273150ustar00rootroot00000000000000 Form 0 0 919 450 Form TextLabel valid entry true false ready entry true false xrstools-0.15.0+git20210910+c147919d/XRStools/resources/WIZARD/text_widget.ui000066400000000000000000000023071412732462000256110ustar00rootroot00000000000000 Form 0 0 919 450 Form 0 TextLabel valid entry true ready entry true xrstools-0.15.0+git20210910+c147919d/XRStools/resources/__init__.py000066400000000000000000000123201412732462000240300ustar00rootroot00000000000000# coding: utf-8 # /*########################################################################## # # Copyright (C) 2016-2018 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # ###########################################################################*/ """Access project's data and documentation files. All access to data and documentation files MUST be made through the functions of this modules to ensure access across different distribution schemes: - Installing from source or from wheel - Installing package as a zip (through the use of pkg_resources) - Linux packaging willing to install data files (and doc files) in alternative folders. In this case, this file must be patched. - Frozen fat binary application using pyFAI (frozen with cx_Freeze or py2app). This needs special care for the resource files in the setup: - With cx_Freeze, add pyFAI/resources to include_files:: import pyFAI.resources pyFAI_include_files = (os.path.dirname(pyFAI.resources.__file__), os.path.join('pyFAI', 'resources')) setup(... options={'build_exe': {'include_files': [pyFAI_include_files]}} ) - With py2app, add pyFAI in the packages list of the py2app options:: setup(... options={'py2app': {'packages': ['pyFAI']}} ) """ __authors__ = ["V.A. Sole", "Thomas Vincent"] __license__ = "MIT" __date__ = "20/02/2018" import os import sys import logging logger = logging.getLogger(__name__) # pkg_resources is useful when this package is stored in a zip # When pkg_resources is not available, the resources dir defaults to the # directory containing this module. try: import pkg_resources except ImportError: logger.debug("Backtrace", exc_info=True) pkg_resources = None # For packaging purpose, patch this variable to use an alternative directory # E.g., replace with _RESOURCES_DIR = '/usr/share/pyFAI/data' _RESOURCES_DIR = None # For packaging purpose, patch this variable to use an alternative directory # E.g., replace with _RESOURCES_DIR = '/usr/share/pyFAI/doc' # Not in use, uncomment when functionnality is needed # _RESOURCES_DOC_DIR = None # cx_Freeze forzen support # See http://cx-freeze.readthedocs.io/en/latest/faq.html#using-data-files if getattr(sys, 'frozen', False): # Running in a frozen application: # We expect resources to be located either in a pyFAI/resources/ dir # relative to the executable or within this package. _dir = os.path.join(os.path.dirname(sys.executable), 'pyFAI', 'resources') if os.path.isdir(_dir): _RESOURCES_DIR = _dir def resource_filename(resource): """Return filename corresponding to resource. resource can be the name of either a file or a directory. The existence of the resource is not checked. :param str resource: Resource path relative to resource directory using '/' path separator. :return: Absolute resource path in the file system """ # Not in use, uncomment when functionnality is needed # If _RESOURCES_DOC_DIR is set, use it to get resources in doc/ subflodler # from an alternative directory. # if _RESOURCES_DOC_DIR is not None and (resource is 'doc' or # resource.startswith('doc/')): # # Remove doc folder from resource relative path # return os.path.join(_RESOURCES_DOC_DIR, *resource.split('/')[1:]) if _RESOURCES_DIR is not None: # if set, use this directory return os.path.join(_RESOURCES_DIR, *resource.split('/')) elif pkg_resources is None: # Fallback if pkg_resources is not available return os.path.join(os.path.abspath(os.path.dirname(__file__)), *resource.split('/')) else: # Preferred way to get resources as it supports zipfile package return pkg_resources.resource_filename(__name__, resource) _integrated = False def silx_integration(): """Provide pyFAI resources accessible throug silx using a prefix.""" global _integrated if _integrated: return import silx.resources silx.resources.register_resource_directory("XRStools", __name__, _RESOURCES_DIR) _integrated = True xrstools-0.15.0+git20210910+c147919d/XRStools/resources/data/000077500000000000000000000000001412732462000226325ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/resources/data/AtomicData.yaml000066400000000000000000001240121412732462000255240ustar00rootroot00000000000000H : Z : 1 AtomicRadius_A_ : 0.79 CovalentRadius_A_ : 0.32 AtomicMass : 1.00794 BoilingPoint_K_ : 20.268 MeltingPoint_K_ : 14.025 Density_g__ccm_ : 0.0899 AtomicVolume : 14.4 CoherentScatteringLength_1E-12cm_ : -0.374 IncoherentX-section_barn_ : 79.9 Absorption@1.8A_barn_ : 0.3326 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 He : Z : 2 AtomicRadius_A_ : 0.49 CovalentRadius_A_ : 0.93 AtomicMass : 4.002602 BoilingPoint_K_ : 4.215 MeltingPoint_K_ : 0.95 Density_g__ccm_ : 0.1787 AtomicVolume : 0.0 CoherentScatteringLength_1E-12cm_ : 0.326 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 0.00747 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Li : Z : 3 AtomicRadius_A_ : 2.05 CovalentRadius_A_ : 1.23 AtomicMass : 6.941 BoilingPoint_K_ : 1615 MeltingPoint_K_ : 453.7 Density_g__ccm_ : 0.53 AtomicVolume : 13.10 CoherentScatteringLength_1E-12cm_ : -0.190 IncoherentX-section_barn_ : 0.91 Absorption@1.8A_barn_ : 70.5 DebyeTemperature_K_ : 344 ThermalConductivity_W__cmK_ : 0.85 Be : Z : 4 AtomicRadius_A_ : 1.40 CovalentRadius_A_ : 0.90 AtomicMass : 9.012182 BoilingPoint_K_ : 2745 MeltingPoint_K_ : 1560.0 Density_g__ccm_ : 1.85 AtomicVolume : 5.0 CoherentScatteringLength_1E-12cm_ : 0.779 IncoherentX-section_barn_ : 0.005 Absorption@1.8A_barn_ : 0.0076 DebyeTemperature_K_ : 1440 ThermalConductivity_W__cmK_ : 2.00 B : Z : 5 AtomicRadius_A_ : 1.17 CovalentRadius_A_ : 0.82 AtomicMass : 10.811 BoilingPoint_K_ : 4275 MeltingPoint_K_ : 2300.0 Density_g__ccm_ : 2.34 AtomicVolume : 4.6 CoherentScatteringLength_1E-12cm_ : 0.530 IncoherentX-section_barn_ : 1.7 Absorption@1.8A_barn_ : 767.0 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.27 C : Z : 6 AtomicRadius_A_ : 0.91 CovalentRadius_A_ : 0.77 AtomicMass : 12.011 BoilingPoint_K_ : 4470.0 MeltingPoint_K_ : 4100.0 Density_g__ccm_ : 2.62 AtomicVolume : 4.58 CoherentScatteringLength_1E-12cm_ : 0.6648 IncoherentX-section_barn_ : 0.001 Absorption@1.8A_barn_ : 0.0035 DebyeTemperature_K_ : 2230 ThermalConductivity_W__cmK_ : 1.29 N : Z : 7 AtomicRadius_A_ : 0.75 CovalentRadius_A_ : 0.75 AtomicMass : 14.00674 BoilingPoint_K_ : 77.35 MeltingPoint_K_ : 63.14 Density_g__ccm_ : 1.251 AtomicVolume : 17.3 CoherentScatteringLength_1E-12cm_ : 0.936 IncoherentX-section_barn_ : 0.49 Absorption@1.8A_barn_ : 1.90 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 O : Z : 8 AtomicRadius_A_ : 0.65 CovalentRadius_A_ : 0.73 AtomicMass : 15.9994 BoilingPoint_K_ : 90.18 MeltingPoint_K_ : 50.35 Density_g__ccm_ : 1.429 AtomicVolume : 14.0 CoherentScatteringLength_1E-12cm_ : 0.5805 IncoherentX-section_barn_ : 0.000 Absorption@1.8A_barn_ : 0.00019 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 F : Z : 9 AtomicRadius_A_ : 0.57 CovalentRadius_A_ : 0.72 AtomicMass : 18.9984032 BoilingPoint_K_ : 84.95 MeltingPoint_K_ : 53.48 Density_g__ccm_ : 1.696 AtomicVolume : 17.1 CoherentScatteringLength_1E-12cm_ : 0.5654 IncoherentX-section_barn_ : 0.0008 Absorption@1.8A_barn_ : 0.0096 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Ne : Z : 10 AtomicRadius_A_ : 0.51 CovalentRadius_A_ : 0.71 AtomicMass : 20.1797 BoilingPoint_K_ : 27.096 MeltingPoint_K_ : 24.553 Density_g__ccm_ : 0.901 AtomicVolume : 16.7 CoherentScatteringLength_1E-12cm_ : 0.4547 IncoherentX-section_barn_ : 0.008 Absorption@1.8A_barn_ : 0.039 DebyeTemperature_K_ : 74 ThermalConductivity_W__cmK_ : -0.01 Na : Z : 11 AtomicRadius_A_ : 2.23 CovalentRadius_A_ : 1.54 AtomicMass : 22.989768 BoilingPoint_K_ : 1156 MeltingPoint_K_ : 371.0 Density_g__ccm_ : 0.97 AtomicVolume : 23.7 CoherentScatteringLength_1E-12cm_ : 0.363 IncoherentX-section_barn_ : 1.62 Absorption@1.8A_barn_ : 0.530 DebyeTemperature_K_ : 158 ThermalConductivity_W__cmK_ : 1.41 Mg : Z : 12 AtomicRadius_A_ : 1.72 CovalentRadius_A_ : 1.36 AtomicMass : 24.3050 BoilingPoint_K_ : 1363 MeltingPoint_K_ : 922 Density_g__ccm_ : 1.74 AtomicVolume : 13.97 CoherentScatteringLength_1E-12cm_ : 0.5375 IncoherentX-section_barn_ : 0.077 Absorption@1.8A_barn_ : 0.063 DebyeTemperature_K_ : 400 ThermalConductivity_W__cmK_ : 1.56 Al : Z : 13 AtomicRadius_A_ : 1.82 CovalentRadius_A_ : 1.18 AtomicMass : 26.981539 BoilingPoint_K_ : 2793 MeltingPoint_K_ : 933.25 Density_g__ccm_ : 2.70 AtomicVolume : 10.0 CoherentScatteringLength_1E-12cm_ : 0.3449 IncoherentX-section_barn_ : 0.0085 Absorption@1.8A_barn_ : 0.231 DebyeTemperature_K_ : 428 ThermalConductivity_W__cmK_ : 2.37 Si : Z : 14 AtomicRadius_A_ : 1.46 CovalentRadius_A_ : 1.11 AtomicMass : 28.0855 BoilingPoint_K_ : 3540.0 MeltingPoint_K_ : 1685 Density_g__ccm_ : 2.33 AtomicVolume : 12.1 CoherentScatteringLength_1E-12cm_ : 0.4149 IncoherentX-section_barn_ : 0.015 Absorption@1.8A_barn_ : 0.171 DebyeTemperature_K_ : 645 ThermalConductivity_W__cmK_ : 1.48 P : Z : 15 AtomicRadius_A_ : 1.23 CovalentRadius_A_ : 1.06 AtomicMass : 30.97362 BoilingPoint_K_ : 550.0 MeltingPoint_K_ : 317.30 Density_g__ccm_ : 1.82 AtomicVolume : 17.0 CoherentScatteringLength_1E-12cm_ : 0.513 IncoherentX-section_barn_ : 0.006 Absorption@1.8A_barn_ : 0.172 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 S : Z : 16 AtomicRadius_A_ : 1.09 CovalentRadius_A_ : 1.02 AtomicMass : 32.066 BoilingPoint_K_ : 717.75 MeltingPoint_K_ : 388.36 Density_g__ccm_ : 2.07 AtomicVolume : 15.5 CoherentScatteringLength_1E-12cm_ : 0.2847 IncoherentX-section_barn_ : 0.007 Absorption@1.8A_barn_ : 0.53 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Cl : Z : 17 AtomicRadius_A_ : 0.97 CovalentRadius_A_ : 0.99 AtomicMass : 35.4527 BoilingPoint_K_ : 239.1 MeltingPoint_K_ : 172.16 Density_g__ccm_ : 3.17 AtomicVolume : 22.7 CoherentScatteringLength_1E-12cm_ : 0.95792 IncoherentX-section_barn_ : 5.2 Absorption@1.8A_barn_ : 33.5 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Ar : Z : 18 AtomicRadius_A_ : 0.88 CovalentRadius_A_ : 0.98 AtomicMass : 39.948 BoilingPoint_K_ : 87.30 MeltingPoint_K_ : 83.81 Density_g__ccm_ : 1.784 AtomicVolume : 28.5 CoherentScatteringLength_1E-12cm_ : 0.1909 IncoherentX-section_barn_ : 0.22 Absorption@1.8A_barn_ : 0.675 DebyeTemperature_K_ : 92 ThermalConductivity_W__cmK_ : -0.01 K : Z : 19 AtomicRadius_A_ : 2.77 CovalentRadius_A_ : 2.03 AtomicMass : 39.0983 BoilingPoint_K_ : 1032 MeltingPoint_K_ : 336.35 Density_g__ccm_ : 0.86 AtomicVolume : 45.46 CoherentScatteringLength_1E-12cm_ : 0.371 IncoherentX-section_barn_ : 0.25 Absorption@1.8A_barn_ : 2.1 DebyeTemperature_K_ : 91 ThermalConductivity_W__cmK_ : 1.02 Ca : Z : 20 AtomicRadius_A_ : 2.23 CovalentRadius_A_ : 1.91 AtomicMass : 40.078 BoilingPoint_K_ : 1757 MeltingPoint_K_ : 1112 Density_g__ccm_ : 1.55 AtomicVolume : 29.9 CoherentScatteringLength_1E-12cm_ : 0.490 IncoherentX-section_barn_ : 0.03 Absorption@1.8A_barn_ : 0.43 DebyeTemperature_K_ : 230 ThermalConductivity_W__cmK_ : -0.01 Sc : Z : 21 AtomicRadius_A_ : 2.09 CovalentRadius_A_ : 1.62 AtomicMass : 44.955910 BoilingPoint_K_ : 3104 MeltingPoint_K_ : 1812 Density_g__ccm_ : 3.0 AtomicVolume : 15.0 CoherentScatteringLength_1E-12cm_ : 1.229 IncoherentX-section_barn_ : 4.5 Absorption@1.8A_barn_ : 27.2 DebyeTemperature_K_ : 360 ThermalConductivity_W__cmK_ : 0.16 Ti : Z : 22 AtomicRadius_A_ : 2.00 CovalentRadius_A_ : 1.45 AtomicMass : 47.88 BoilingPoint_K_ : 3562 MeltingPoint_K_ : 1943 Density_g__ccm_ : 4.50 AtomicVolume : 10.64 CoherentScatteringLength_1E-12cm_ : -0.330 IncoherentX-section_barn_ : 2.67 Absorption@1.8A_barn_ : 6.09 DebyeTemperature_K_ : 420 ThermalConductivity_W__cmK_ : 0.22 V : Z : 23 AtomicRadius_A_ : 1.92 CovalentRadius_A_ : 1.34 AtomicMass : 50.9415 BoilingPoint_K_ : 3682 MeltingPoint_K_ : 2175 Density_g__ccm_ : 5.8 AtomicVolume : 8.78 CoherentScatteringLength_1E-12cm_ : -0.0382 IncoherentX-section_barn_ : 5.187 Absorption@1.8A_barn_ : 5.08 DebyeTemperature_K_ : 380 ThermalConductivity_W__cmK_ : 0.31 Cr : Z : 24 AtomicRadius_A_ : 1.85 CovalentRadius_A_ : 1.18 AtomicMass : 51.9961 BoilingPoint_K_ : 2945 MeltingPoint_K_ : 2130.0 Density_g__ccm_ : 7.19 AtomicVolume : 7.23 CoherentScatteringLength_1E-12cm_ : 0.3635 IncoherentX-section_barn_ : 1.83 Absorption@1.8A_barn_ : 3.07 DebyeTemperature_K_ : 630 ThermalConductivity_W__cmK_ : 0.94 Mn : Z : 25 AtomicRadius_A_ : 1.79 CovalentRadius_A_ : 1.17 AtomicMass : 54.93085 BoilingPoint_K_ : 2335 MeltingPoint_K_ : 1517 Density_g__ccm_ : 7.43 AtomicVolume : 1.39 CoherentScatteringLength_1E-12cm_ : -0.373 IncoherentX-section_barn_ : 0.40 Absorption@1.8A_barn_ : 13.3 DebyeTemperature_K_ : 410 ThermalConductivity_W__cmK_ : 0.08 Fe : Z : 26 AtomicRadius_A_ : 1.72 CovalentRadius_A_ : 1.17 AtomicMass : 55.847 BoilingPoint_K_ : 3135 MeltingPoint_K_ : 1809 Density_g__ccm_ : 7.86 AtomicVolume : 7.1 CoherentScatteringLength_1E-12cm_ : 0.954 IncoherentX-section_barn_ : 0.39 Absorption@1.8A_barn_ : 2.56 DebyeTemperature_K_ : 470 ThermalConductivity_W__cmK_ : 0.80 Co : Z : 27 AtomicRadius_A_ : 1.67 CovalentRadius_A_ : 1.16 AtomicMass : 58.93320 BoilingPoint_K_ : 3201 MeltingPoint_K_ : 1768 Density_g__ccm_ : 8.90 AtomicVolume : 6.7 CoherentScatteringLength_1E-12cm_ : 0.250 IncoherentX-section_barn_ : 4.8 Absorption@1.8A_barn_ : 37.18 DebyeTemperature_K_ : 445 ThermalConductivity_W__cmK_ : 1.00 Ni : Z : 28 AtomicRadius_A_ : 1.62 CovalentRadius_A_ : 1.15 AtomicMass : 58.69 BoilingPoint_K_ : 3187 MeltingPoint_K_ : 1726 Density_g__ccm_ : 8.90 AtomicVolume : 6.59 CoherentScatteringLength_1E-12cm_ : 1.03 IncoherentX-section_barn_ : 5.2 Absorption@1.8A_barn_ : 4.49 DebyeTemperature_K_ : 450 ThermalConductivity_W__cmK_ : 0.91 Cu : Z : 29 AtomicRadius_A_ : 1.57 CovalentRadius_A_ : 1.17 AtomicMass : 63.546 BoilingPoint_K_ : 2836 MeltingPoint_K_ : 1357.6 Density_g__ccm_ : 8.96 AtomicVolume : 7.1 CoherentScatteringLength_1E-12cm_ : 0.7718 IncoherentX-section_barn_ : 0.52 Absorption@1.8A_barn_ : 3.78 DebyeTemperature_K_ : 343 ThermalConductivity_W__cmK_ : 4.01 Zn : Z : 30 AtomicRadius_A_ : 1.53 CovalentRadius_A_ : 1.25 AtomicMass : 65.39 BoilingPoint_K_ : 1180.0 MeltingPoint_K_ : 692.73 Density_g__ccm_ : 7.14 AtomicVolume : 9.2 CoherentScatteringLength_1E-12cm_ : 0.5680 IncoherentX-section_barn_ : 0.077 Absorption@1.8A_barn_ : 1.11 DebyeTemperature_K_ : 327 ThermalConductivity_W__cmK_ : 1.16 Ga : Z : 31 AtomicRadius_A_ : 1.81 CovalentRadius_A_ : 1.26 AtomicMass : 69.723 BoilingPoint_K_ : 2478 MeltingPoint_K_ : 302.90 Density_g__ccm_ : 5.91 AtomicVolume : 11.8 CoherentScatteringLength_1E-12cm_ : 0.7288 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 2.9 DebyeTemperature_K_ : 320 ThermalConductivity_W__cmK_ : 0.41 Ge : Z : 32 AtomicRadius_A_ : 1.52 CovalentRadius_A_ : 1.22 AtomicMass : 72.61 BoilingPoint_K_ : 3107 MeltingPoint_K_ : 1210.4 Density_g__ccm_ : 5.32 AtomicVolume : 13.6 CoherentScatteringLength_1E-12cm_ : 0.81929 IncoherentX-section_barn_ : 0.17 Absorption@1.8A_barn_ : 2.3 DebyeTemperature_K_ : 374 ThermalConductivity_W__cmK_ : 0.6 As : Z : 33 AtomicRadius_A_ : 1.33 CovalentRadius_A_ : 1.20 AtomicMass : 74.92159 BoilingPoint_K_ : 876 MeltingPoint_K_ : 1081 Density_g__ccm_ : 5.72 AtomicVolume : 13.1 CoherentScatteringLength_1E-12cm_ : 0.658 IncoherentX-section_barn_ : 0.060 Absorption@1.8A_barn_ : 4.5 DebyeTemperature_K_ : 282 ThermalConductivity_W__cmK_ : 0.50 Se : Z : 34 AtomicRadius_A_ : 1.22 CovalentRadius_A_ : 1.16 AtomicMass : 78.96 BoilingPoint_K_ : 958 MeltingPoint_K_ : 494 Density_g__ccm_ : 4.80 AtomicVolume : 16.45 CoherentScatteringLength_1E-12cm_ : 0.797 IncoherentX-section_barn_ : 0.33 Absorption@1.8A_barn_ : 11.7 DebyeTemperature_K_ : 90 ThermalConductivity_W__cmK_ : 0.02 Br : Z : 35 AtomicRadius_A_ : 1.12 CovalentRadius_A_ : 1.14 AtomicMass : 79.904 BoilingPoint_K_ : 332.25 MeltingPoint_K_ : 265.90 Density_g__ccm_ : 3.12 AtomicVolume : 23.5 CoherentScatteringLength_1E-12cm_ : 0.679 IncoherentX-section_barn_ : 0.10 Absorption@1.8A_barn_ : 6.9 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Kr : Z : 36 AtomicRadius_A_ : 1.03 CovalentRadius_A_ : 1.12 AtomicMass : 83.80 BoilingPoint_K_ : 119.80 MeltingPoint_K_ : 115.78 Density_g__ccm_ : 3.74 AtomicVolume : 38.9 CoherentScatteringLength_1E-12cm_ : 0.780 IncoherentX-section_barn_ : 0.03 Absorption@1.8A_barn_ : 25. DebyeTemperature_K_ : 72 ThermalConductivity_W__cmK_ : -0.01 Rb : Z : 37 AtomicRadius_A_ : 2.98 CovalentRadius_A_ : 2.16 AtomicMass : 85.4678 BoilingPoint_K_ : 961 MeltingPoint_K_ : 312.64 Density_g__ccm_ : 1.53 AtomicVolume : 55.9 CoherentScatteringLength_1E-12cm_ : 0.708 IncoherentX-section_barn_ : 0.3 Absorption@1.8A_barn_ : 0.38 DebyeTemperature_K_ : 56 ThermalConductivity_W__cmK_ : 0.58 Sr : Z : 38 AtomicRadius_A_ : 2.45 CovalentRadius_A_ : 1.91 AtomicMass : 87.62 BoilingPoint_K_ : 1650.0 MeltingPoint_K_ : 1041 Density_g__ccm_ : 2.6 AtomicVolume : 33.7 CoherentScatteringLength_1E-12cm_ : 0.702 IncoherentX-section_barn_ : 0.04 Absorption@1.8A_barn_ : 1.28 DebyeTemperature_K_ : 147 ThermalConductivity_W__cmK_ : -0.01 Y : Z : 39 AtomicRadius_A_ : 2.27 CovalentRadius_A_ : 1.62 AtomicMass : 88.90585 BoilingPoint_K_ : 3611 MeltingPoint_K_ : 1799 Density_g__ccm_ : 4.5 AtomicVolume : 19.8 CoherentScatteringLength_1E-12cm_ : 0.775 IncoherentX-section_barn_ : 0.15 Absorption@1.8A_barn_ : 1.28 DebyeTemperature_K_ : 280 ThermalConductivity_W__cmK_ : 0.17 Zr : Z : 40 AtomicRadius_A_ : 2.16 CovalentRadius_A_ : 1.45 AtomicMass : 91.224 BoilingPoint_K_ : 4682 MeltingPoint_K_ : 2125 Density_g__ccm_ : 6.49 AtomicVolume : 14.1 CoherentScatteringLength_1E-12cm_ : 0.716 IncoherentX-section_barn_ : 0.16 Absorption@1.8A_barn_ : 0.185 DebyeTemperature_K_ : 291 ThermalConductivity_W__cmK_ : 0.23 Nb : Z : 41 AtomicRadius_A_ : 2.09 CovalentRadius_A_ : 1.34 AtomicMass : 92.90638 BoilingPoint_K_ : 5017 MeltingPoint_K_ : 2740.0 Density_g__ccm_ : 8.55 AtomicVolume : 10.87 CoherentScatteringLength_1E-12cm_ : 0.7054 IncoherentX-section_barn_ : 0.0024 Absorption@1.8A_barn_ : 1.15 DebyeTemperature_K_ : 275 ThermalConductivity_W__cmK_ : 0.54 Mo : Z : 42 AtomicRadius_A_ : 2.01 CovalentRadius_A_ : 1.30 AtomicMass : 95.94 BoilingPoint_K_ : 4912 MeltingPoint_K_ : 2890.0 Density_g__ccm_ : 10.2 AtomicVolume : 9.4 CoherentScatteringLength_1E-12cm_ : 0.695 IncoherentX-section_barn_ : 0.28 Absorption@1.8A_barn_ : 2.55 DebyeTemperature_K_ : 450 ThermalConductivity_W__cmK_ : 1.38 Tc : Z : 43 AtomicRadius_A_ : 1.95 CovalentRadius_A_ : 1.27 AtomicMass : 98.91 BoilingPoint_K_ : 4538 MeltingPoint_K_ : 2473 Density_g__ccm_ : 11.5 AtomicVolume : 8.5 CoherentScatteringLength_1E-12cm_ : 0.68 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 20.0 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.51 Ru : Z : 44 AtomicRadius_A_ : 1.89 CovalentRadius_A_ : 1.25 AtomicMass : 101.07 BoilingPoint_K_ : 4423 MeltingPoint_K_ : 2523 Density_g__ccm_ : 12.2 AtomicVolume : 8.3 CoherentScatteringLength_1E-12cm_ : 0.721 IncoherentX-section_barn_ : 0.07 Absorption@1.8A_barn_ : 2.56 DebyeTemperature_K_ : 600 ThermalConductivity_W__cmK_ : 1.17 Rh : Z : 45 AtomicRadius_A_ : 1.83 CovalentRadius_A_ : 1.25 AtomicMass : 102.90550 BoilingPoint_K_ : 3970.0 MeltingPoint_K_ : 2236 Density_g__ccm_ : 12.4 AtomicVolume : 8.3 CoherentScatteringLength_1E-12cm_ : 0.588 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 145.0 DebyeTemperature_K_ : 480 ThermalConductivity_W__cmK_ : 1.50 Pd : Z : 46 AtomicRadius_A_ : 1.79 CovalentRadius_A_ : 1.28 AtomicMass : 106.42 BoilingPoint_K_ : 3237 MeltingPoint_K_ : 1825 Density_g__ccm_ : 12.0 AtomicVolume : 8.9 CoherentScatteringLength_1E-12cm_ : 0.591 IncoherentX-section_barn_ : 0.093 Absorption@1.8A_barn_ : 6.9 DebyeTemperature_K_ : 274 ThermalConductivity_W__cmK_ : 0.72 Ag : Z : 47 AtomicRadius_A_ : 1.75 CovalentRadius_A_ : 1.34 AtomicMass : 107.8682 BoilingPoint_K_ : 2436 MeltingPoint_K_ : 1234 Density_g__ccm_ : 10.5 AtomicVolume : 10.3 CoherentScatteringLength_1E-12cm_ : 0.5922 IncoherentX-section_barn_ : 0.58 Absorption@1.8A_barn_ : 63.3 DebyeTemperature_K_ : 225 ThermalConductivity_W__cmK_ : 4.29 Cd : Z : 48 AtomicRadius_A_ : 1.71 CovalentRadius_A_ : 1.48 AtomicMass : 112.411 BoilingPoint_K_ : 1040.0 MeltingPoint_K_ : 594.18 Density_g__ccm_ : 8.65 AtomicVolume : 13.1 CoherentScatteringLength_1E-12cm_ : 0.51 IncoherentX-section_barn_ : 2.4 Absorption@1.8A_barn_ : 2520.0 DebyeTemperature_K_ : 209 ThermalConductivity_W__cmK_ : 0.97 In : Z : 49 AtomicRadius_A_ : 2.00 CovalentRadius_A_ : 1.44 AtomicMass : 114.82 BoilingPoint_K_ : 2346 MeltingPoint_K_ : 429.76 Density_g__ccm_ : 7.31 AtomicVolume : 15.7 CoherentScatteringLength_1E-12cm_ : 0.4065 IncoherentX-section_barn_ : 0.54 Absorption@1.8A_barn_ : 193.8 DebyeTemperature_K_ : 108 ThermalConductivity_W__cmK_ : 0.82 Sn : Z : 50 AtomicRadius_A_ : 1.72 CovalentRadius_A_ : 1.41 AtomicMass : 118.710 BoilingPoint_K_ : 2876 MeltingPoint_K_ : 505.06 Density_g__ccm_ : 7.30 AtomicVolume : 16.3 CoherentScatteringLength_1E-12cm_ : 0.6228 IncoherentX-section_barn_ : 0.022 Absorption@1.8A_barn_ : 0.626 DebyeTemperature_K_ : 200 ThermalConductivity_W__cmK_ : 0.67 Sb : Z : 51 AtomicRadius_A_ : 1.53 CovalentRadius_A_ : 1.40 AtomicMass : 121.75 BoilingPoint_K_ : 1860.0 MeltingPoint_K_ : 904 Density_g__ccm_ : 6.68 AtomicVolume : 18.23 CoherentScatteringLength_1E-12cm_ : 0.5641 IncoherentX-section_barn_ : 0.3 Absorption@1.8A_barn_ : 5.1 DebyeTemperature_K_ : 211 ThermalConductivity_W__cmK_ : 0.24 Te : Z : 52 AtomicRadius_A_ : 1.42 CovalentRadius_A_ : 1.36 AtomicMass : 127.60 BoilingPoint_K_ : 1261 MeltingPoint_K_ : 722.65 Density_g__ccm_ : 6.24 AtomicVolume : 20.5 CoherentScatteringLength_1E-12cm_ : 0.543 IncoherentX-section_barn_ : 0.02 Absorption@1.8A_barn_ : 4.7 DebyeTemperature_K_ : 153 ThermalConductivity_W__cmK_ : 0.02 I : Z : 53 AtomicRadius_A_ : 1.32 CovalentRadius_A_ : 1.33 AtomicMass : 126.90447 BoilingPoint_K_ : 458.4 MeltingPoint_K_ : 386.7 Density_g__ccm_ : 4.92 AtomicVolume : 25.74 CoherentScatteringLength_1E-12cm_ : 0.528 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 6.2 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Xe : Z : 54 AtomicRadius_A_ : 1.24 CovalentRadius_A_ : 1.31 AtomicMass : 131.29 BoilingPoint_K_ : 165.03 MeltingPoint_K_ : 161.36 Density_g__ccm_ : 5.89 AtomicVolume : 37.3 CoherentScatteringLength_1E-12cm_ : 0.485 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 23.9 DebyeTemperature_K_ : 64 ThermalConductivity_W__cmK_ : -0.01 Cs : Z : 55 AtomicRadius_A_ : 3.34 CovalentRadius_A_ : 2.35 AtomicMass : 132.90543 BoilingPoint_K_ : 944 MeltingPoint_K_ : 301.55 Density_g__ccm_ : 1.87 AtomicVolume : 71.07 CoherentScatteringLength_1E-12cm_ : 0.542 IncoherentX-section_barn_ : 0.21 Absorption@1.8A_barn_ : 29.0 DebyeTemperature_K_ : 38 ThermalConductivity_W__cmK_ : 0.36 Ba : Z : 56 AtomicRadius_A_ : 2.78 CovalentRadius_A_ : 1.98 AtomicMass : 137.327 BoilingPoint_K_ : 2171 MeltingPoint_K_ : 1002 Density_g__ccm_ : 3.5 AtomicVolume : 39.24 CoherentScatteringLength_1E-12cm_ : 0.525 IncoherentX-section_barn_ : 0.01 Absorption@1.8A_barn_ : 1.2 DebyeTemperature_K_ : 110 ThermalConductivity_W__cmK_ : -0.01 La : Z : 57 AtomicRadius_A_ : 2.74 CovalentRadius_A_ : 1.69 AtomicMass : 138.9055 BoilingPoint_K_ : 3730.0 MeltingPoint_K_ : 1193 Density_g__ccm_ : 6.7 AtomicVolume : 20.73 CoherentScatteringLength_1E-12cm_ : 0.824 IncoherentX-section_barn_ : 1.13 Absorption@1.8A_barn_ : 8.97 DebyeTemperature_K_ : 142 ThermalConductivity_W__cmK_ : 0.14 Ce : Z : 58 AtomicRadius_A_ : 2.70 CovalentRadius_A_ : 1.65 AtomicMass : 140.115 BoilingPoint_K_ : 3699 MeltingPoint_K_ : 1071 Density_g__ccm_ : 6.78 AtomicVolume : 20.67 CoherentScatteringLength_1E-12cm_ : 0.484 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 0.63 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.11 Pr : Z : 59 AtomicRadius_A_ : 2.67 CovalentRadius_A_ : 1.65 AtomicMass : 140.90765 BoilingPoint_K_ : 3785 MeltingPoint_K_ : 1204 Density_g__ccm_ : 6.77 AtomicVolume : 20.8 CoherentScatteringLength_1E-12cm_ : 0.445 IncoherentX-section_barn_ : 0.016 Absorption@1.8A_barn_ : 11.5 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.13 Nd : Z : 60 AtomicRadius_A_ : 2.64 CovalentRadius_A_ : 1.64 AtomicMass : 144.24 BoilingPoint_K_ : 3341 MeltingPoint_K_ : 1289 Density_g__ccm_ : 7.00 AtomicVolume : 20.6 CoherentScatteringLength_1E-12cm_ : 0.769 IncoherentX-section_barn_ : 11. Absorption@1.8A_barn_ : 50.5 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.16 Pm : Z : 61 AtomicRadius_A_ : 2.62 CovalentRadius_A_ : 1.63 AtomicMass : 145 BoilingPoint_K_ : 3785 MeltingPoint_K_ : 1204 Density_g__ccm_ : 6.475 AtomicVolume : 22.39 CoherentScatteringLength_1E-12cm_ : 1.26 IncoherentX-section_barn_ : 1.3 Absorption@1.8A_barn_ : 168.4 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Sm : Z : 62 AtomicRadius_A_ : 2.59 CovalentRadius_A_ : 1.62 AtomicMass : 150.36 BoilingPoint_K_ : 2064 MeltingPoint_K_ : 1345 Density_g__ccm_ : 7.54 AtomicVolume : 19.95 CoherentScatteringLength_1E-12cm_ : 0.42 IncoherentX-section_barn_ : 50. Absorption@1.8A_barn_ : 5670. DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.13 Eu : Z : 63 AtomicRadius_A_ : 2.56 CovalentRadius_A_ : 1.85 AtomicMass : 151.965 BoilingPoint_K_ : 1870.0 MeltingPoint_K_ : 1090.0 Density_g__ccm_ : 5.26 AtomicVolume : 28.9 CoherentScatteringLength_1E-12cm_ : 0.668 IncoherentX-section_barn_ : 2.2 Absorption@1.8A_barn_ : 4600. DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Gd : Z : 64 AtomicRadius_A_ : 2.54 CovalentRadius_A_ : 1.61 AtomicMass : 157.25 BoilingPoint_K_ : 3539 MeltingPoint_K_ : 1585 Density_g__ccm_ : 7.89 AtomicVolume : 19.9 CoherentScatteringLength_1E-12cm_ : 0.95 IncoherentX-section_barn_ : 158.0 Absorption@1.8A_barn_ : 48890. DebyeTemperature_K_ : 200 ThermalConductivity_W__cmK_ : 0.11 Tb : Z : 65 AtomicRadius_A_ : 2.51 CovalentRadius_A_ : 1.59 AtomicMass : 158.92534 BoilingPoint_K_ : 3496 MeltingPoint_K_ : 1630.0 Density_g__ccm_ : 8.27 AtomicVolume : 19.2 CoherentScatteringLength_1E-12cm_ : 0.738 IncoherentX-section_barn_ : 0.004 Absorption@1.8A_barn_ : 23.4 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.11 Dy : Z : 66 AtomicRadius_A_ : 2.49 CovalentRadius_A_ : 1.59 AtomicMass : 162.50 BoilingPoint_K_ : 2835 MeltingPoint_K_ : 1682 Density_g__ccm_ : 8.54 AtomicVolume : 19.0 CoherentScatteringLength_1E-12cm_ : 1.69 IncoherentX-section_barn_ : 54.5 Absorption@1.8A_barn_ : 940. DebyeTemperature_K_ : 210 ThermalConductivity_W__cmK_ : 0.11 Ho : Z : 67 AtomicRadius_A_ : 2.47 CovalentRadius_A_ : 1.58 AtomicMass : 164.93032 BoilingPoint_K_ : 2968 MeltingPoint_K_ : 1743 Density_g__ccm_ : 8.80 AtomicVolume : 18.7 CoherentScatteringLength_1E-12cm_ : 0.808 IncoherentX-section_barn_ : 0.36 Absorption@1.8A_barn_ : 64.7 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.16 Er : Z : 68 AtomicRadius_A_ : 2.45 CovalentRadius_A_ : 1.57 AtomicMass : 167.26 BoilingPoint_K_ : 3136 MeltingPoint_K_ : 1795 Density_g__ccm_ : 9.05 AtomicVolume : 18.4 CoherentScatteringLength_1E-12cm_ : 0.803 IncoherentX-section_barn_ : 1.2 Absorption@1.8A_barn_ : 159.2 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.14 Tm : Z : 69 AtomicRadius_A_ : 2.42 CovalentRadius_A_ : 1.56 AtomicMass : 168.93421 BoilingPoint_K_ : 2220.0 MeltingPoint_K_ : 1818 Density_g__ccm_ : 9.33 AtomicVolume : 18.1 CoherentScatteringLength_1E-12cm_ : 0.705 IncoherentX-section_barn_ : 0.41 Absorption@1.8A_barn_ : 105. DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.17 Yb : Z : 70 AtomicRadius_A_ : 2.40 CovalentRadius_A_ : 1.74 AtomicMass : 173.04 BoilingPoint_K_ : 1467 MeltingPoint_K_ : 1097 Density_g__ccm_ : 6.98 AtomicVolume : 24.79 CoherentScatteringLength_1E-12cm_ : 1.24 IncoherentX-section_barn_ : 3.0 Absorption@1.8A_barn_ : 35.1 DebyeTemperature_K_ : 120 ThermalConductivity_W__cmK_ : 0.35 Lu : Z : 71 AtomicRadius_A_ : 2.25 CovalentRadius_A_ : 1.56 AtomicMass : 174.967 BoilingPoint_K_ : 3668 MeltingPoint_K_ : 1936 Density_g__ccm_ : 9.84 AtomicVolume : 17.78 CoherentScatteringLength_1E-12cm_ : 0.73 IncoherentX-section_barn_ : 0.1 Absorption@1.8A_barn_ : 76.4 DebyeTemperature_K_ : 210 ThermalConductivity_W__cmK_ : 0.16 Hf : Z : 72 AtomicRadius_A_ : 2.16 CovalentRadius_A_ : 1.44 AtomicMass : 178.49 BoilingPoint_K_ : 4876 MeltingPoint_K_ : 2500.0 Density_g__ccm_ : 13.1 AtomicVolume : 13.6 CoherentScatteringLength_1E-12cm_ : 0.777 IncoherentX-section_barn_ : 2.6 Absorption@1.8A_barn_ : 104.1 DebyeTemperature_K_ : 252 ThermalConductivity_W__cmK_ : 0.23 Ta : Z : 73 AtomicRadius_A_ : 2.09 CovalentRadius_A_ : 1.34 AtomicMass : 180.9479 BoilingPoint_K_ : 5731 MeltingPoint_K_ : 3287 Density_g__ccm_ : 16.6 AtomicVolume : 10.90 CoherentScatteringLength_1E-12cm_ : 0.691 IncoherentX-section_barn_ : 0.02 Absorption@1.8A_barn_ : 20.6 DebyeTemperature_K_ : 240 ThermalConductivity_W__cmK_ : 0.58 W : Z : 74 AtomicRadius_A_ : 2.02 CovalentRadius_A_ : 1.30 AtomicMass : 183.85 BoilingPoint_K_ : 5828 MeltingPoint_K_ : 3680.0 Density_g__ccm_ : 19.3 AtomicVolume : 9.53 CoherentScatteringLength_1E-12cm_ : 0.477 IncoherentX-section_barn_ : 2.00 Absorption@1.8A_barn_ : 18.4 DebyeTemperature_K_ : 400 ThermalConductivity_W__cmK_ : 1.74 Re : Z : 75 AtomicRadius_A_ : 1.97 CovalentRadius_A_ : 1.28 AtomicMass : 186.207 BoilingPoint_K_ : 5869 MeltingPoint_K_ : 3453 Density_g__ccm_ : 21.0 AtomicVolume : 8.85 CoherentScatteringLength_1E-12cm_ : 0.92 IncoherentX-section_barn_ : 0.9 Absorption@1.8A_barn_ : 90.7 DebyeTemperature_K_ : 430 ThermalConductivity_W__cmK_ : 0.48 Os : Z : 76 AtomicRadius_A_ : 1.92 CovalentRadius_A_ : 1.26 AtomicMass : 190.2 BoilingPoint_K_ : 5285 MeltingPoint_K_ : 3300.0 Density_g__ccm_ : 22.4 AtomicVolume : 8.49 CoherentScatteringLength_1E-12cm_ : 1.10 IncoherentX-section_barn_ : 0.4 Absorption@1.8A_barn_ : 16.0 DebyeTemperature_K_ : 500 ThermalConductivity_W__cmK_ : 0.88 Ir : Z : 77 AtomicRadius_A_ : 1.87 CovalentRadius_A_ : 1.27 AtomicMass : 192.22 BoilingPoint_K_ : 4701 MeltingPoint_K_ : 2716 Density_g__ccm_ : 22.5 AtomicVolume : 8.54 CoherentScatteringLength_1E-12cm_ : 1.06 IncoherentX-section_barn_ : 0.2 Absorption@1.8A_barn_ : 425.3 DebyeTemperature_K_ : 420 ThermalConductivity_W__cmK_ : 1.47 Pt : Z : 78 AtomicRadius_A_ : 1.83 CovalentRadius_A_ : 1.30 AtomicMass : 195.08 BoilingPoint_K_ : 4100.0 MeltingPoint_K_ : 2045 Density_g__ccm_ : 21.4 AtomicVolume : 9.10 CoherentScatteringLength_1E-12cm_ : 0.963 IncoherentX-section_barn_ : 0.13 Absorption@1.8A_barn_ : 10.3 DebyeTemperature_K_ : 240 ThermalConductivity_W__cmK_ : 0.72 Au : Z : 79 AtomicRadius_A_ : 1.79 CovalentRadius_A_ : 1.34 AtomicMass : 196.96654 BoilingPoint_K_ : 3130.0 MeltingPoint_K_ : 1337.58 Density_g__ccm_ : 19.3 AtomicVolume : 10.2 CoherentScatteringLength_1E-12cm_ : 0.763 IncoherentX-section_barn_ : 0.36 Absorption@1.8A_barn_ : 98.65 DebyeTemperature_K_ : 165 ThermalConductivity_W__cmK_ : 3.17 Hg : Z : 80 AtomicRadius_A_ : 1.76 CovalentRadius_A_ : 1.49 AtomicMass : 200.59 BoilingPoint_K_ : 630.0 MeltingPoint_K_ : 234.28 Density_g__ccm_ : 13.53 AtomicVolume : 14.82 CoherentScatteringLength_1E-12cm_ : 1.266 IncoherentX-section_barn_ : 6.7 Absorption@1.8A_barn_ : 372.3 DebyeTemperature_K_ : 71.9 ThermalConductivity_W__cmK_ : -0.01 Tl : Z : 81 AtomicRadius_A_ : 2.08 CovalentRadius_A_ : 1.48 AtomicMass : 204.3833 BoilingPoint_K_ : 1746 MeltingPoint_K_ : 577 Density_g__ccm_ : 11.85 AtomicVolume : 17.2 CoherentScatteringLength_1E-12cm_ : 0.8785 IncoherentX-section_barn_ : 0.14 Absorption@1.8A_barn_ : 3.43 DebyeTemperature_K_ : 78.5 ThermalConductivity_W__cmK_ : 0.46 Pb : Z : 82 AtomicRadius_A_ : 1.81 CovalentRadius_A_ : 1.47 AtomicMass : 207.2 BoilingPoint_K_ : 2023 MeltingPoint_K_ : 600.6 Density_g__ccm_ : 11.4 AtomicVolume : 18.17 CoherentScatteringLength_1E-12cm_ : 0.94003 IncoherentX-section_barn_ : 0.003 Absorption@1.8A_barn_ : 0.171 DebyeTemperature_K_ : 105 ThermalConductivity_W__cmK_ : 0.35 Bi : Z : 83 AtomicRadius_A_ : 1.63 CovalentRadius_A_ : 1.46 AtomicMass : 208.98037 BoilingPoint_K_ : 1837 MeltingPoint_K_ : 544.52 Density_g__ccm_ : 9.8 AtomicVolume : 21.3 CoherentScatteringLength_1E-12cm_ : 0.85256 IncoherentX-section_barn_ : 0.0072 Absorption@1.8A_barn_ : 0.0338 DebyeTemperature_K_ : 119 ThermalConductivity_W__cmK_ : 0.08 Po : Z : 84 AtomicRadius_A_ : 1.53 CovalentRadius_A_ : 1.46 AtomicMass : 209 BoilingPoint_K_ : 1235 MeltingPoint_K_ : 527 Density_g__ccm_ : 9.4 AtomicVolume : 22.23 CoherentScatteringLength_1E-12cm_ : -0.01 IncoherentX-section_barn_ : -0.01 Absorption@1.8A_barn_ : -0.01 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 At : Z : 85 AtomicRadius_A_ : 1.43 CovalentRadius_A_ : 1.45 AtomicMass : 210.0 BoilingPoint_K_ : 610.0 MeltingPoint_K_ : 575 Density_g__ccm_ : -0.01 AtomicVolume : -0.01 CoherentScatteringLength_1E-12cm_ : -0.01 IncoherentX-section_barn_ : -0.01 Absorption@1.8A_barn_ : -0.01 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Rn : Z : 86 AtomicRadius_A_ : 1.34 CovalentRadius_A_ : 1.43 AtomicMass : 222 BoilingPoint_K_ : 211 MeltingPoint_K_ : 202 Density_g__ccm_ : 9.91 AtomicVolume : 50.5 CoherentScatteringLength_1E-12cm_ : -0.01 IncoherentX-section_barn_ : -0.01 Absorption@1.8A_barn_ : -0.01 DebyeTemperature_K_ : 64 ThermalConductivity_W__cmK_ : -0.01 Fr : Z : 87 AtomicRadius_A_ : 3.50 CovalentRadius_A_ : 2.50 AtomicMass : 223 BoilingPoint_K_ : 950.0 MeltingPoint_K_ : 300.0 Density_g__ccm_ : -0.01 AtomicVolume : -0.01 CoherentScatteringLength_1E-12cm_ : 0.8495 IncoherentX-section_barn_ : 0.0072 Absorption@1.8A_barn_ : 0.036 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Ra : Z : 88 AtomicRadius_A_ : 3.00 CovalentRadius_A_ : 2.40 AtomicMass : 226.025 BoilingPoint_K_ : 1809 MeltingPoint_K_ : 973 Density_g__ccm_ : 5 AtomicVolume : 45.20 CoherentScatteringLength_1E-12cm_ : 1.0 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 12.8 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Ac : Z : 89 AtomicRadius_A_ : 3.20 CovalentRadius_A_ : 2.20 AtomicMass : 227.028 BoilingPoint_K_ : 3473 MeltingPoint_K_ : 1323 Density_g__ccm_ : 10.07 AtomicVolume : 22.54 CoherentScatteringLength_1E-12cm_ : -0.01 IncoherentX-section_barn_ : -0.01 Absorption@1.8A_barn_ : -0.01 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Th : Z : 90 AtomicRadius_A_ : 3.16 CovalentRadius_A_ : 1.65 AtomicMass : 232.0381 BoilingPoint_K_ : 5061 MeltingPoint_K_ : 2028 Density_g__ccm_ : 11.7 AtomicVolume : 19.9 CoherentScatteringLength_1E-12cm_ : 0.984 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 7.37 DebyeTemperature_K_ : 163 ThermalConductivity_W__cmK_ : 0.54 Pa : Z : 91 AtomicRadius_A_ : 3.14 CovalentRadius_A_ : -0.01 AtomicMass : 231.03588 BoilingPoint_K_ : -0.01 MeltingPoint_K_ : -0.01 Density_g__ccm_ : 15.4 AtomicVolume : 15.0 CoherentScatteringLength_1E-12cm_ : 0.91 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 200.6 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 U : Z : 92 AtomicRadius_A_ : 3.11 CovalentRadius_A_ : 1.42 AtomicMass : 238.0289 BoilingPoint_K_ : 4407 MeltingPoint_K_ : 1405 Density_g__ccm_ : 18.90 AtomicVolume : 12.59 CoherentScatteringLength_1E-12cm_ : 0.8417 IncoherentX-section_barn_ : 0.004 Absorption@1.8A_barn_ : 7.57 DebyeTemperature_K_ : 207 ThermalConductivity_W__cmK_ : 0.28 Np : Z : 93 AtomicRadius_A_ : 3.08 CovalentRadius_A_ : -0.01 AtomicMass : 237.048 BoilingPoint_K_ : -0.01 MeltingPoint_K_ : 910.0 Density_g__ccm_ : 20.4 AtomicVolume : 11.62 CoherentScatteringLength_1E-12cm_ : 1.055 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 175.9 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.06 Pu : Z : 94 AtomicRadius_A_ : 3.05 CovalentRadius_A_ : -0.01 AtomicMass : 244 BoilingPoint_K_ : 3503 MeltingPoint_K_ : 913 Density_g__ccm_ : 19.8 AtomicVolume : 12.32 CoherentScatteringLength_1E-12cm_ : 1.41 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 558. DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : 0.07 Am : Z : 95 AtomicRadius_A_ : 3.02 CovalentRadius_A_ : -0.01 AtomicMass : 243 BoilingPoint_K_ : 2880.0 MeltingPoint_K_ : 1268 Density_g__ccm_ : 13.6 AtomicVolume : 17.86 CoherentScatteringLength_1E-12cm_ : 0.83 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 75.3 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Cm : Z : 96 AtomicRadius_A_ : 2.99 CovalentRadius_A_ : -0.01 AtomicMass : 247 BoilingPoint_K_ : -0.01 MeltingPoint_K_ : 1340.0 Density_g__ccm_ : 13.511 AtomicVolume : 18.28 CoherentScatteringLength_1E-12cm_ : 0.7 IncoherentX-section_barn_ : 0.0 Absorption@1.8A_barn_ : 0.0 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Bk : Z : 97 AtomicRadius_A_ : 2.97 CovalentRadius_A_ : -0.01 AtomicMass : 247 BoilingPoint_K_ : -0.01 MeltingPoint_K_ : -0.01 Density_g__ccm_ : -0.01 AtomicVolume : -0.01 CoherentScatteringLength_1E-12cm_ : -0.01 IncoherentX-section_barn_ : -0.01 Absorption@1.8A_barn_ : -0.01 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Cf : Z : 98 AtomicRadius_A_ : 2.95 CovalentRadius_A_ : -0.01 AtomicMass : 251 BoilingPoint_K_ : -0.01 MeltingPoint_K_ : 900.0 Density_g__ccm_ : -0.01 AtomicVolume : -0.01 CoherentScatteringLength_1E-12cm_ : -0.01 IncoherentX-section_barn_ : -0.01 Absorption@1.8A_barn_ : -0.01 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Es : Z : 99 AtomicRadius_A_ : 2.92 CovalentRadius_A_ : -0.01 AtomicMass : 254 BoilingPoint_K_ : -0.01 MeltingPoint_K_ : -0.01 Density_g__ccm_ : -0.01 AtomicVolume : -0.01 CoherentScatteringLength_1E-12cm_ : -0.01 IncoherentX-section_barn_ : -0.01 Absorption@1.8A_barn_ : -0.01 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Fm : Z : 100 AtomicRadius_A_ : 2.90 CovalentRadius_A_ : -0.01 AtomicMass : 257 BoilingPoint_K_ : -0.01 MeltingPoint_K_ : -0.01 Density_g__ccm_ : -0.01 AtomicVolume : -0.01 CoherentScatteringLength_1E-12cm_ : -0.01 IncoherentX-section_barn_ : -0.01 Absorption@1.8A_barn_ : -0.01 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Md : Z : 101 AtomicRadius_A_ : 2.87 CovalentRadius_A_ : -0.01 AtomicMass : 258 BoilingPoint_K_ : -0.01 MeltingPoint_K_ : -0.01 Density_g__ccm_ : -0.01 AtomicVolume : -0.01 CoherentScatteringLength_1E-12cm_ : -0.01 IncoherentX-section_barn_ : -0.01 Absorption@1.8A_barn_ : -0.01 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 No : Z : 102 AtomicRadius_A_ : 2.85 CovalentRadius_A_ : -0.01 AtomicMass : 259 BoilingPoint_K_ : -0.01 MeltingPoint_K_ : -0.01 Density_g__ccm_ : -0.01 AtomicVolume : -0.01 CoherentScatteringLength_1E-12cm_ : -0.01 IncoherentX-section_barn_ : -0.01 Absorption@1.8A_barn_ : -0.01 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 Lr : Z : 103 AtomicRadius_A_ : 2.82 CovalentRadius_A_ : -0.01 AtomicMass : 260 BoilingPoint_K_ : -0.01 MeltingPoint_K_ : -0.01 Density_g__ccm_ : -0.01 AtomicVolume : -0.01 CoherentScatteringLength_1E-12cm_ : -0.01 IncoherentX-section_barn_ : -0.01 Absorption@1.8A_barn_ : -0.01 DebyeTemperature_K_ : -0.01 ThermalConductivity_W__cmK_ : -0.01 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/data/ComptonProfiles.dat000066400000000000000000022056251412732462000264630ustar00rootroot00000000000000#F ComptonProfiles.dat #C This file has been created using ComptonProfiles.pro #D Wed Jan 29 16:17:10 2003 #C This file belongs to the DABAX library. More information on #C DABAX can be found at: #C http://www.esrf.fr/computing/scientific/dabax #UT Compton Profiles of the elements and their sub-shells #UD Compton Profiles of the elements and their sub-shells #UD This file contains the Compton Profiles of #UD the elements (Z=1-102) #UD #UD REFERENCE: #UD F. Biggs, L. B. Mendelsohn and J B Mann #UD Hartree-Fock Compton profiles for the elements #UD Atomic Data and Nuclear Data Tables 16, 201-309 (1975) #UD #UD Nota: The number of electrons that occupy the levels #UD are under the UOCCUP keyword. #UD The binding energies [in eV] are in the UBIND keyword. #UD #UD These data has been obtained from the LSCAT extension #UD to the EGS4 program from Namito et. al. #UD #UD Columns: #UD col1: pz in atomic units #UD col2: Total compton profile (sum over the atomic electrons #UD col3,...coln: Compton profile for the individual sub-shells #UD #S 1 H #N 3 #UOCCUP 1 #UBIND 14.00 #L pz total Shell_1 0.000 0.8490 0.8490 0.05000 0.8420 0.8420 0.1000 0.8240 0.8240 0.1500 0.7940 0.7940 0.2000 0.7550 0.7550 0.3000 0.6550 0.6550 0.4000 0.5440 0.5440 0.5000 0.4350 0.4350 0.6000 0.3370 0.3370 0.7000 0.2570 0.2570 0.8000 0.1920 0.1920 1.000 0.1060 0.1060 1.200 0.05840 0.05840 1.400 0.03270 0.03270 1.600 0.01880 0.01880 1.800 0.01110 0.01110 2.000 0.006790 0.006790 2.400 0.002750 0.002750 3.000 0.0008490 0.0008490 4.000 0.0001730 0.0001730 5.000 4.830E-05 4.830E-05 6.000 1.680E-05 1.680E-05 7.000 6.790E-06 6.790E-06 8.000 3.090E-06 3.090E-06 10.00 8.200E-07 8.200E-07 15.00 7.400E-08 7.400E-08 20.00 1.300E-08 1.300E-08 30.00 1.200E-09 1.200E-09 40.00 2.300E-10 2.300E-10 60.00 4.300E-11 4.300E-11 100.0 2.600E-11 2.600E-11 #S 2 He #N 3 #UOCCUP 2 #UBIND 24.60 #L pz total Shell_1 0.000 1.070 0.5350 0.05000 1.070 0.5340 0.1000 1.060 0.5280 0.1500 1.040 0.5200 0.2000 1.020 0.5090 0.3000 0.9560 0.4780 0.4000 0.8780 0.4390 0.5000 0.7910 0.3960 0.6000 0.7000 0.3500 0.7000 0.6110 0.3060 0.8000 0.5270 0.2640 1.000 0.3820 0.1910 1.200 0.2710 0.1360 1.400 0.1910 0.09550 1.600 0.1340 0.06720 1.800 0.09520 0.04760 2.000 0.06800 0.03400 2.400 0.03580 0.01790 3.000 0.01480 0.007400 4.000 0.004130 0.002060 5.000 0.001400 0.0006980 6.000 0.0005470 0.0002740 7.000 0.0002400 0.0001200 8.000 0.0001160 5.780E-05 10.00 3.300E-05 1.600E-05 15.00 3.200E-06 1.600E-06 20.00 5.800E-07 2.900E-07 30.00 5.200E-08 2.600E-08 40.00 9.300E-09 4.700E-09 60.00 8.600E-10 4.300E-10 100.0 7.800E-11 3.900E-11 #S 3 Li #N 4 #UOCCUP 2 1 #UBIND 54.80 1.0000 #L pz total Shell_1 Shell_2 0.000 2.590 0.3290 1.940 0.05000 2.530 0.3280 1.870 0.1000 2.340 0.3270 1.690 0.1500 2.080 0.3250 1.430 0.2000 1.780 0.3220 1.140 0.3000 1.240 0.3150 0.6120 0.4000 0.8840 0.3050 0.2740 0.5000 0.6930 0.2930 0.1080 0.6000 0.5980 0.2790 0.04110 0.7000 0.5460 0.2630 0.01940 0.8000 0.5080 0.2470 0.01410 1.000 0.4390 0.2130 0.01340 1.200 0.3720 0.1800 0.01200 1.400 0.3080 0.1490 0.009580 1.600 0.2510 0.1220 0.007240 1.800 0.2030 0.09900 0.005360 2.000 0.1640 0.07980 0.003950 2.400 0.1050 0.05130 0.002180 3.000 0.05410 0.02660 0.0009620 4.000 0.01920 0.009430 0.0002950 5.000 0.007530 0.003710 0.0001080 6.000 0.003270 0.001610 4.480E-05 7.000 0.001550 0.0007640 2.060E-05 8.000 0.0007880 0.0003890 1.030E-05 10.00 0.0002400 0.0001200 3.100E-06 15.00 2.500E-05 1.200E-05 3.100E-07 20.00 4.800E-06 2.400E-06 5.900E-08 30.00 4.400E-07 2.200E-07 5.400E-09 40.00 7.900E-08 3.900E-08 9.600E-10 60.00 7.100E-09 3.500E-09 8.600E-11 100.0 3.600E-10 1.800E-10 4.400E-12 #S 4 Be #N 4 #UOCCUP 2 2 #UBIND 111.7 3.000 #L pz total Shell_1 Shell_2 0.000 3.160 0.2370 1.340 0.05000 3.110 0.2370 1.320 0.1000 2.980 0.2360 1.250 0.1500 2.770 0.2360 1.150 0.2000 2.520 0.2350 1.020 0.3000 1.950 0.2320 0.7430 0.4000 1.430 0.2280 0.4860 0.5000 1.030 0.2230 0.2930 0.6000 0.7660 0.2180 0.1650 0.7000 0.6000 0.2110 0.08920 0.8000 0.5030 0.2040 0.04750 1.000 0.4090 0.1880 0.01640 1.200 0.3630 0.1710 0.01080 1.400 0.3270 0.1530 0.01040 1.600 0.2920 0.1360 0.01020 1.800 0.2570 0.1190 0.009390 2.000 0.2240 0.1040 0.008230 2.400 0.1660 0.07710 0.005800 3.000 0.1020 0.04810 0.003150 4.000 0.04520 0.02150 0.001150 5.000 0.02060 0.009860 0.0004610 6.000 0.009940 0.004770 0.0002040 7.000 0.005070 0.002440 9.870E-05 8.000 0.002730 0.001310 5.110E-05 10.00 0.0009100 0.0004400 1.600E-05 15.00 1.000E-04 5.000E-05 1.800E-06 20.00 2.100E-05 9.900E-06 3.400E-07 30.00 1.900E-06 9.400E-07 3.200E-08 40.00 3.600E-07 1.700E-07 5.800E-09 60.00 3.200E-08 1.500E-08 5.200E-10 100.0 1.500E-09 7.400E-10 2.500E-11 #S 5 B #N 5 #UOCCUP 2 2 1 #UBIND 191.0 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.990 0.1860 1.000 0.6150 0.05000 2.970 0.1860 0.9920 0.6150 0.1000 2.910 0.1860 0.9630 0.6140 0.1500 2.820 0.1850 0.9170 0.6120 0.2000 2.690 0.1850 0.8570 0.6060 0.3000 2.370 0.1840 0.7110 0.5810 0.4000 2.000 0.1820 0.5520 0.5330 0.5000 1.640 0.1790 0.4050 0.4670 0.6000 1.310 0.1760 0.2840 0.3930 0.7000 1.050 0.1730 0.1910 0.3210 0.8000 0.8430 0.1690 0.1240 0.2560 1.000 0.5780 0.1610 0.05000 0.1570 1.200 0.4390 0.1510 0.02090 0.09430 1.400 0.3620 0.1410 0.01150 0.05680 1.600 0.3140 0.1300 0.009240 0.03460 1.800 0.2780 0.1190 0.008970 0.02150 2.000 0.2490 0.1090 0.008930 0.01350 2.400 0.1990 0.08840 0.008080 0.005710 3.000 0.1390 0.06280 0.005730 0.001780 4.000 0.07330 0.03380 0.002680 0.0003450 5.000 0.03830 0.01790 0.001220 8.880E-05 6.000 0.02040 0.009610 0.0005860 2.820E-05 7.000 0.01120 0.005320 0.0002980 1.040E-05 8.000 0.006410 0.003040 0.0001610 4.290E-06 10.00 0.002300 0.001100 5.400E-05 9.300E-07 15.00 0.0002900 0.0001400 6.300E-06 5.000E-08 20.00 6.100E-05 2.900E-05 1.300E-06 5.700E-09 30.00 6.000E-06 2.900E-06 1.200E-07 2.500E-10 40.00 1.100E-06 5.300E-07 2.200E-08 2.500E-11 60.00 1.000E-07 4.800E-08 2.000E-09 1.000E-12 100.0 4.800E-09 2.300E-09 9.400E-11 1.700E-14 #S 6 C #N 5 #UOCCUP 2 2 2 #UBIND 284.7 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.890 0.1530 0.8040 0.4880 0.05000 2.880 0.1530 0.7980 0.4880 0.1000 2.850 0.1530 0.7830 0.4880 0.1500 2.800 0.1530 0.7590 0.4870 0.2000 2.730 0.1530 0.7260 0.4850 0.3000 2.540 0.1520 0.6410 0.4750 0.4000 2.290 0.1510 0.5410 0.4530 0.5000 2.020 0.1490 0.4390 0.4200 0.6000 1.740 0.1480 0.3430 0.3790 0.7000 1.480 0.1460 0.2600 0.3330 0.8000 1.240 0.1440 0.1920 0.2870 1.000 0.8810 0.1390 0.09820 0.2040 1.200 0.6410 0.1330 0.04770 0.1400 1.400 0.4900 0.1260 0.02340 0.09500 1.600 0.3940 0.1200 0.01300 0.06440 1.800 0.3310 0.1120 0.009090 0.04380 2.000 0.2860 0.1050 0.007950 0.03010 2.400 0.2260 0.09050 0.007750 0.01460 3.000 0.1650 0.07010 0.006950 0.005300 4.000 0.09760 0.04330 0.004250 0.001200 5.000 0.05690 0.02580 0.002270 0.0003340 6.000 0.03320 0.01530 0.001200 0.0001100 7.000 0.01960 0.009130 0.0006490 4.160E-05 8.000 0.01190 0.005550 0.0003650 1.750E-05 10.00 0.004600 0.002200 0.0001300 3.900E-06 15.00 0.0006600 0.0003100 1.600E-05 2.200E-07 20.00 0.0001400 6.800E-05 3.400E-06 2.600E-08 30.00 1.500E-05 7.000E-06 3.300E-07 1.200E-09 40.00 2.800E-06 1.300E-06 6.200E-08 1.200E-10 60.00 2.500E-07 1.200E-07 5.600E-09 4.800E-12 100.0 1.200E-08 5.800E-09 2.700E-10 8.000E-14 #S 7 N #N 5 #UOCCUP 2 2 3 #UBIND 409.9 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.830 0.1300 0.6720 0.4070 0.05000 2.820 0.1300 0.6690 0.4070 0.1000 2.800 0.1300 0.6600 0.4070 0.1500 2.770 0.1300 0.6460 0.4070 0.2000 2.730 0.1300 0.6250 0.4060 0.3000 2.610 0.1290 0.5720 0.4010 0.4000 2.440 0.1290 0.5070 0.3900 0.5000 2.240 0.1280 0.4350 0.3720 0.6000 2.020 0.1270 0.3640 0.3480 0.7000 1.800 0.1260 0.2960 0.3190 0.8000 1.580 0.1240 0.2360 0.2870 1.000 1.200 0.1210 0.1420 0.2240 1.200 0.9020 0.1170 0.08070 0.1680 1.400 0.6880 0.1130 0.04430 0.1240 1.600 0.5380 0.1090 0.02440 0.09080 1.800 0.4350 0.1040 0.01420 0.06620 2.000 0.3610 0.09870 0.009530 0.04840 2.400 0.2690 0.08810 0.006940 0.02610 3.000 0.1910 0.07240 0.006760 0.01090 4.000 0.1180 0.04950 0.005290 0.002870 5.000 0.07420 0.03250 0.003330 0.0008820 6.000 0.04650 0.02090 0.001950 0.0003090 7.000 0.02930 0.01330 0.001140 0.0001210 8.000 0.01870 0.008580 0.0006740 5.230E-05 10.00 0.007900 0.003700 0.0002600 1.200E-05 15.00 0.001300 0.0005900 3.500E-05 7.300E-07 20.00 0.0002900 0.0001400 7.500E-06 8.900E-08 30.00 3.100E-05 1.500E-05 7.600E-07 4.100E-09 40.00 6.000E-06 2.800E-06 1.400E-07 4.200E-10 60.00 5.500E-07 2.600E-07 1.300E-08 1.700E-11 100.0 2.700E-08 1.300E-08 6.200E-10 3.000E-13 #S 8 O #N 5 #UOCCUP 2 2 4 #UBIND 543.1 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.780 0.1130 0.5790 0.3500 0.05000 2.780 0.1130 0.5770 0.3500 0.1000 2.770 0.1130 0.5710 0.3500 0.1500 2.750 0.1130 0.5610 0.3490 0.2000 2.720 0.1130 0.5480 0.3490 0.3000 2.640 0.1130 0.5130 0.3460 0.4000 2.520 0.1120 0.4680 0.3400 0.5000 2.380 0.1120 0.4170 0.3300 0.6000 2.210 0.1110 0.3630 0.3150 0.7000 2.030 0.1100 0.3110 0.2970 0.8000 1.840 0.1090 0.2610 0.2750 1.000 1.480 0.1070 0.1750 0.2280 1.200 1.160 0.1050 0.1110 0.1830 1.400 0.9140 0.1020 0.06840 0.1430 1.600 0.7230 0.09860 0.04090 0.1110 1.800 0.5800 0.09520 0.02450 0.08520 2.000 0.4750 0.09150 0.01510 0.06530 2.400 0.3370 0.08380 0.007650 0.03850 3.000 0.2270 0.07180 0.006050 0.01790 4.000 0.1390 0.05290 0.005610 0.005420 5.000 0.09030 0.03730 0.004140 0.001840 6.000 0.05950 0.02570 0.002710 0.0006900 7.000 0.03940 0.01740 0.001710 0.0002840 8.000 0.02630 0.01180 0.001070 0.0001260 10.00 0.01200 0.005500 0.0004400 3.000E-05 15.00 0.002100 0.001000 6.500E-05 1.900E-06 20.00 0.0005200 0.0002400 1.500E-05 2.400E-07 30.00 5.800E-05 2.800E-05 1.500E-06 1.100E-08 40.00 1.100E-05 5.400E-06 2.900E-07 1.200E-09 60.00 1.100E-06 5.100E-07 2.700E-08 5.000E-11 100.0 5.200E-08 2.500E-08 1.300E-09 8.500E-13 #S 9 F #N 5 #UOCCUP 2 2 5 #UBIND 696.7 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.750 0.1000 0.5080 0.3070 0.05000 2.750 0.1000 0.5070 0.3070 0.1000 2.740 0.1000 0.5030 0.3070 0.1500 2.730 0.1000 0.4960 0.3070 0.2000 2.710 0.1000 0.4870 0.3060 0.3000 2.650 0.09990 0.4620 0.3050 0.4000 2.570 0.09960 0.4300 0.3010 0.5000 2.460 0.09920 0.3930 0.2950 0.6000 2.330 0.09870 0.3530 0.2860 0.7000 2.190 0.09820 0.3120 0.2740 0.8000 2.030 0.09750 0.2710 0.2590 1.000 1.710 0.09600 0.1970 0.2240 1.200 1.400 0.09420 0.1360 0.1880 1.400 1.140 0.09220 0.09110 0.1540 1.600 0.9220 0.08990 0.05920 0.1250 1.800 0.7500 0.08740 0.03780 0.09980 2.000 0.6150 0.08470 0.02410 0.07950 2.400 0.4310 0.07890 0.01070 0.05030 3.000 0.2790 0.06970 0.005790 0.02550 4.000 0.1630 0.05420 0.005390 0.008720 5.000 0.1060 0.04050 0.004580 0.003240 6.000 0.07220 0.02950 0.003350 0.001300 7.000 0.04960 0.02110 0.002280 0.0005630 8.000 0.03430 0.01500 0.001520 0.0002600 10.00 0.01700 0.007500 0.0006700 6.600E-05 15.00 0.003300 0.001500 0.0001100 4.400E-06 20.00 0.0008500 0.0004000 2.600E-05 5.700E-07 30.00 1.000E-04 4.800E-05 2.800E-06 2.800E-08 40.00 2.000E-05 9.600E-06 5.400E-07 3.000E-09 60.00 1.900E-06 9.200E-07 5.000E-08 1.300E-10 100.0 9.400E-08 4.500E-08 2.400E-09 2.100E-12 #S 10 Ne #N 5 #UOCCUP 2 2 6 #UBIND 870.4 48.50 21.60 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.730 0.09000 0.4530 0.2740 0.05000 2.720 0.09000 0.4520 0.2740 0.1000 2.720 0.08990 0.4490 0.2740 0.1500 2.710 0.08990 0.4440 0.2730 0.2000 2.700 0.08980 0.4380 0.2730 0.3000 2.650 0.08970 0.4200 0.2720 0.4000 2.590 0.08950 0.3970 0.2700 0.5000 2.510 0.08920 0.3690 0.2660 0.6000 2.410 0.08880 0.3380 0.2600 0.7000 2.300 0.08840 0.3050 0.2520 0.8000 2.170 0.08800 0.2720 0.2410 1.000 1.890 0.08690 0.2090 0.2160 1.200 1.610 0.08550 0.1550 0.1880 1.400 1.350 0.08400 0.1100 0.1590 1.600 1.120 0.08230 0.07670 0.1330 1.800 0.9270 0.08050 0.05220 0.1100 2.000 0.7710 0.07840 0.03510 0.09060 2.400 0.5440 0.07400 0.01590 0.06070 3.000 0.3460 0.06680 0.006560 0.03320 4.000 0.1940 0.05430 0.004930 0.01260 5.000 0.1240 0.04240 0.004660 0.005050 6.000 0.08510 0.03230 0.003770 0.002160 7.000 0.05970 0.02410 0.002780 0.0009840 8.000 0.04240 0.01780 0.001960 0.0004730 10.00 0.02200 0.009600 0.0009400 0.0001300 15.00 0.004800 0.002200 0.0001700 9.000E-06 20.00 0.001300 0.0006000 4.200E-05 1.200E-06 30.00 0.0001600 7.600E-05 4.700E-06 6.000E-08 40.00 3.300E-05 1.600E-05 9.300E-07 6.700E-09 60.00 3.300E-06 1.500E-06 8.700E-08 2.800E-10 100.0 1.600E-07 7.600E-08 4.200E-09 4.800E-12 #S 11 Na #N 6 #UOCCUP 2 2 6 1 #UBIND 1072. 63.40 30.40 1.0000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 0.000 4.360 0.08150 0.3900 0.2250 2.070 0.05000 4.280 0.08150 0.3900 0.2250 1.990 0.1000 4.060 0.08150 0.3880 0.2250 1.770 0.1500 3.740 0.08150 0.3850 0.2250 1.460 0.2000 3.400 0.08140 0.3810 0.2240 1.130 0.3000 2.800 0.08130 0.3690 0.2240 0.5560 0.4000 2.430 0.08110 0.3540 0.2230 0.2230 0.5000 2.240 0.08090 0.3350 0.2220 0.07960 0.6000 2.140 0.08070 0.3140 0.2200 0.03190 0.7000 2.060 0.08040 0.2910 0.2160 0.02050 0.8000 1.980 0.08000 0.2670 0.2120 0.01910 1.000 1.810 0.07920 0.2180 0.1990 0.01800 1.200 1.610 0.07820 0.1720 0.1820 0.01420 1.400 1.410 0.07710 0.1320 0.1640 0.010000 1.600 1.220 0.07580 0.09800 0.1440 0.006720 1.800 1.050 0.07430 0.07130 0.1250 0.004400 2.000 0.8950 0.07280 0.05100 0.1080 0.002860 2.400 0.6550 0.06940 0.02520 0.07740 0.001190 3.000 0.4230 0.06370 0.009430 0.04600 0.0003390 4.000 0.2320 0.05340 0.004840 0.01920 0.0001150 5.000 0.1460 0.04330 0.004720 0.008320 0.0001090 6.000 0.09960 0.03420 0.004200 0.003770 9.890E-05 7.000 0.07060 0.02650 0.003350 0.001800 7.940E-05 8.000 0.05100 0.02030 0.002510 0.0008960 5.960E-05 10.00 0.02700 0.01200 0.001300 0.0002500 3.100E-05 15.00 0.006600 0.003000 0.0002600 1.900E-05 6.100E-06 20.00 0.001900 0.0008700 6.700E-05 2.600E-06 1.500E-06 30.00 0.0002500 0.0001200 7.900E-06 1.400E-07 1.800E-07 40.00 5.200E-05 2.400E-05 1.600E-06 1.500E-08 3.600E-08 60.00 5.200E-06 2.400E-06 1.500E-07 6.500E-10 3.400E-09 100.0 2.600E-07 1.200E-07 7.400E-09 1.100E-11 1.700E-10 #S 12 Mg #N 6 #UOCCUP 2 2 6 2 #UBIND 1303. 88.60 49.30 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 0.000 5.160 0.07450 0.3420 0.1920 1.590 0.05000 5.080 0.07450 0.3420 0.1920 1.550 0.1000 4.860 0.07450 0.3400 0.1920 1.440 0.1500 4.540 0.07450 0.3380 0.1920 1.280 0.2000 4.150 0.07440 0.3360 0.1920 1.090 0.3000 3.350 0.07430 0.3280 0.1920 0.6960 0.4000 2.710 0.07420 0.3170 0.1910 0.3870 0.5000 2.290 0.07400 0.3050 0.1910 0.1930 0.6000 2.050 0.07380 0.2900 0.1900 0.09110 0.7000 1.910 0.07360 0.2730 0.1880 0.04490 0.8000 1.830 0.07330 0.2550 0.1860 0.02720 1.000 1.700 0.07270 0.2180 0.1790 0.02120 1.200 1.560 0.07200 0.1810 0.1690 0.02070 1.400 1.420 0.07110 0.1460 0.1580 0.01810 1.600 1.260 0.07010 0.1150 0.1440 0.01440 1.800 1.120 0.06900 0.08830 0.1300 0.01070 2.000 0.9800 0.06780 0.06670 0.1160 0.007630 2.400 0.7470 0.06510 0.03640 0.08940 0.003640 3.000 0.4990 0.06050 0.01430 0.05780 0.001120 4.000 0.2750 0.05210 0.005170 0.02670 0.0002370 5.000 0.1710 0.04350 0.004590 0.01240 0.0001740 6.000 0.1160 0.03540 0.004410 0.005930 0.0001700 7.000 0.08220 0.02830 0.003790 0.002950 0.0001480 8.000 0.06010 0.02230 0.003020 0.001520 0.0001180 10.00 0.03300 0.01400 0.001700 0.0004500 6.700E-05 15.00 0.008600 0.003800 0.0003900 3.700E-05 1.500E-05 20.00 0.002600 0.001200 1.000E-04 5.200E-06 3.900E-06 30.00 0.0003600 0.0001700 1.300E-05 2.800E-07 4.700E-07 40.00 7.800E-05 3.600E-05 2.600E-06 3.200E-08 9.600E-08 60.00 8.000E-06 3.700E-06 2.500E-07 1.400E-09 9.300E-09 100.0 4.000E-07 1.900E-07 1.200E-08 2.400E-11 4.500E-10 #S 13 Al #N 7 #UOCCUP 2 2 6 2 1 #UBIND 1558. 118.0 72.80 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.150 0.06860 0.3050 0.1680 1.240 0.9190 0.05000 5.110 0.06860 0.3040 0.1680 1.220 0.9180 0.1000 5.000 0.06860 0.3040 0.1680 1.170 0.9160 0.1500 4.820 0.06850 0.3020 0.1680 1.080 0.9040 0.2000 4.570 0.06850 0.3000 0.1680 0.9750 0.8790 0.3000 3.970 0.06840 0.2950 0.1670 0.7290 0.7760 0.4000 3.320 0.06830 0.2870 0.1670 0.4940 0.6190 0.5000 2.760 0.06820 0.2780 0.1670 0.3080 0.4520 0.6000 2.340 0.06810 0.2670 0.1660 0.1800 0.3070 0.7000 2.040 0.06790 0.2550 0.1660 0.1010 0.1990 0.8000 1.840 0.06770 0.2410 0.1640 0.05750 0.1240 1.000 1.620 0.06720 0.2130 0.1610 0.02710 0.04520 1.200 1.490 0.06660 0.1830 0.1550 0.02330 0.01620 1.400 1.380 0.06590 0.1530 0.1480 0.02310 0.006660 1.600 1.260 0.06510 0.1260 0.1390 0.02120 0.004020 1.800 1.140 0.06420 0.1010 0.1290 0.01790 0.003500 2.000 1.030 0.06330 0.08010 0.1180 0.01430 0.003460 2.400 0.8160 0.06110 0.04790 0.09640 0.008060 0.003280 3.000 0.5680 0.05750 0.02070 0.06730 0.002930 0.002490 4.000 0.3220 0.05050 0.006250 0.03430 0.0005410 0.001270 5.000 0.1990 0.04320 0.004450 0.01710 0.0002520 0.0006180 6.000 0.1340 0.03610 0.004390 0.008630 0.0002410 0.0003070 7.000 0.09480 0.02960 0.004040 0.004470 0.0002270 0.0001570 8.000 0.06960 0.02390 0.003420 0.002390 0.0001940 8.330E-05 10.00 0.03900 0.01500 0.002100 0.0007500 0.0001200 2.600E-05 15.00 0.01100 0.004700 0.0005300 6.600E-05 3.000E-05 2.200E-06 20.00 0.003500 0.001600 0.0001500 9.700E-06 8.200E-06 3.200E-07 30.00 0.0005100 0.0002400 1.900E-05 5.300E-07 1.000E-06 1.700E-08 40.00 0.0001100 5.200E-05 4.000E-06 6.100E-08 2.100E-07 2.000E-09 60.00 1.200E-05 5.500E-06 3.900E-07 2.700E-09 2.100E-08 8.600E-11 100.0 6.000E-07 2.800E-07 1.900E-08 4.800E-11 1.000E-09 1.600E-12 #S 14 Si #N 7 #UOCCUP 2 2 6 2 2 #UBIND 1839. 149.0 99.30 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.130 0.06350 0.2750 0.1490 1.040 0.7440 0.05000 5.110 0.06350 0.2750 0.1490 1.030 0.7440 0.1000 5.040 0.06350 0.2740 0.1490 0.9920 0.7430 0.1500 4.920 0.06350 0.2730 0.1490 0.9400 0.7390 0.2000 4.760 0.06350 0.2720 0.1490 0.8720 0.7280 0.3000 4.330 0.06340 0.2680 0.1490 0.7070 0.6810 0.4000 3.800 0.06330 0.2620 0.1490 0.5310 0.5970 0.5000 3.260 0.06320 0.2550 0.1490 0.3740 0.4900 0.6000 2.770 0.06310 0.2470 0.1480 0.2480 0.3800 0.7000 2.370 0.06300 0.2370 0.1480 0.1580 0.2810 0.8000 2.060 0.06280 0.2270 0.1470 0.09790 0.2010 1.000 1.670 0.06240 0.2050 0.1450 0.04070 0.09460 1.200 1.470 0.06190 0.1810 0.1420 0.02560 0.04190 1.400 1.340 0.06140 0.1560 0.1370 0.02370 0.01830 1.600 1.240 0.06080 0.1320 0.1310 0.02350 0.008720 1.800 1.140 0.06010 0.1100 0.1240 0.02210 0.005240 2.000 1.040 0.05930 0.09060 0.1160 0.01940 0.004210 2.400 0.8610 0.05760 0.05840 0.09920 0.01290 0.004010 3.000 0.6280 0.05450 0.02790 0.07400 0.005660 0.003600 4.000 0.3690 0.04880 0.008220 0.04140 0.001160 0.002170 5.000 0.2300 0.04250 0.004500 0.02210 0.0003610 0.001150 6.000 0.1530 0.03630 0.004250 0.01180 0.0002870 0.0005990 7.000 0.1080 0.03040 0.004120 0.006360 0.0002830 0.0003190 8.000 0.07970 0.02520 0.003690 0.003520 0.0002580 0.0001740 10.00 0.04600 0.01700 0.002500 0.001200 0.0001800 5.600E-05 15.00 0.01300 0.005600 0.0007000 0.0001100 4.900E-05 5.200E-06 20.00 0.004500 0.002000 0.0002100 1.700E-05 1.400E-05 7.700E-07 30.00 0.0007000 0.0003200 2.800E-05 9.400E-07 1.900E-06 4.300E-08 40.00 0.0001600 7.300E-05 5.900E-06 1.100E-07 3.900E-07 5.000E-09 60.00 1.700E-05 7.800E-06 5.900E-07 4.900E-09 3.900E-08 2.200E-10 100.0 8.700E-07 4.000E-07 2.900E-08 8.900E-11 1.900E-09 4.100E-12 #S 15 P #N 7 #UOCCUP 2 2 6 2 3 #UBIND 2149. 189.0 135.0 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.110 0.05920 0.2510 0.1340 0.8970 0.6310 0.05000 5.100 0.05920 0.2500 0.1340 0.8900 0.6310 0.1000 5.050 0.05910 0.2500 0.1340 0.8680 0.6310 0.1500 4.970 0.05910 0.2490 0.1340 0.8330 0.6290 0.2000 4.860 0.05910 0.2480 0.1340 0.7860 0.6240 0.3000 4.550 0.05910 0.2450 0.1340 0.6690 0.5990 0.4000 4.130 0.05900 0.2410 0.1340 0.5360 0.5510 0.5000 3.660 0.05890 0.2350 0.1340 0.4070 0.4830 0.6000 3.180 0.05880 0.2290 0.1340 0.2950 0.4040 0.7000 2.750 0.05870 0.2220 0.1340 0.2050 0.3250 0.8000 2.380 0.05860 0.2140 0.1330 0.1380 0.2530 1.000 1.850 0.05830 0.1960 0.1320 0.06110 0.1420 1.200 1.530 0.05790 0.1760 0.1300 0.03200 0.07410 1.400 1.350 0.05740 0.1560 0.1270 0.02430 0.03710 1.600 1.220 0.05690 0.1360 0.1230 0.02330 0.01850 1.800 1.130 0.05640 0.1160 0.1180 0.02320 0.009800 2.000 1.040 0.05570 0.09820 0.1120 0.02200 0.006090 2.400 0.8850 0.05430 0.06740 0.09910 0.01700 0.004330 3.000 0.6730 0.05180 0.03530 0.07810 0.008930 0.004200 4.000 0.4150 0.04700 0.01110 0.04750 0.002210 0.003040 5.000 0.2530 0.04160 0.004920 0.02720 0.0005790 0.001780 6.000 0.1750 0.03620 0.004090 0.01520 0.0003300 0.0009820 7.000 0.1240 0.03090 0.004060 0.008560 0.0003170 0.0005440 8.000 0.09070 0.02610 0.003820 0.004890 0.0003060 0.0003060 10.00 0.05300 0.01800 0.002800 0.001700 0.0002300 1.000E-04 15.00 0.01600 0.006600 0.0008900 0.0001700 7.200E-05 1.000E-05 20.00 0.005600 0.002400 0.0002800 2.700E-05 2.200E-05 1.600E-06 30.00 0.0009200 0.0004200 3.900E-05 1.600E-06 3.000E-06 9.100E-08 40.00 0.0002200 9.800E-05 8.400E-06 1.900E-07 6.500E-07 1.100E-08 60.00 2.300E-05 1.100E-05 8.500E-07 8.500E-09 6.500E-08 4.800E-10 100.0 1.200E-06 5.700E-07 4.300E-08 1.500E-10 3.300E-09 8.700E-12 #S 16 S #N 7 #UOCCUP 2 2 6 2 4 #UBIND 2472. 229.0 164.0 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.100 0.05530 0.2300 0.1220 0.7940 0.5510 0.05000 5.080 0.05530 0.2300 0.1220 0.7880 0.5510 0.1000 5.050 0.05530 0.2300 0.1220 0.7730 0.5500 0.1500 5.000 0.05530 0.2290 0.1220 0.7480 0.5490 0.2000 4.920 0.05530 0.2280 0.1220 0.7150 0.5470 0.3000 4.680 0.05530 0.2260 0.1220 0.6280 0.5330 0.4000 4.360 0.05520 0.2230 0.1220 0.5260 0.5040 0.5000 3.960 0.05520 0.2180 0.1220 0.4210 0.4590 0.6000 3.530 0.05510 0.2130 0.1220 0.3240 0.4040 0.7000 3.110 0.05500 0.2080 0.1220 0.2400 0.3430 0.8000 2.720 0.05490 0.2010 0.1220 0.1730 0.2830 1.000 2.090 0.05460 0.1870 0.1210 0.08470 0.1790 1.200 1.680 0.05430 0.1710 0.1190 0.04290 0.1060 1.400 1.410 0.05390 0.1540 0.1170 0.02720 0.05960 1.600 1.240 0.05350 0.1370 0.1150 0.02310 0.03260 1.800 1.130 0.05300 0.1200 0.1110 0.02270 0.01790 2.000 1.040 0.05250 0.1030 0.1070 0.02250 0.01030 2.400 0.8940 0.05130 0.07460 0.09710 0.01960 0.005080 3.000 0.7050 0.04930 0.04230 0.08000 0.01220 0.004360 4.000 0.4560 0.04520 0.01460 0.05240 0.003710 0.003720 5.000 0.2960 0.04060 0.005820 0.03190 0.0009830 0.002430 6.000 0.1990 0.03580 0.004040 0.01880 0.0004070 0.001440 7.000 0.1400 0.03110 0.003920 0.01100 0.0003390 0.0008290 8.000 0.1030 0.02670 0.003830 0.006500 0.0003360 0.0004820 10.00 0.06000 0.01900 0.003100 0.002400 0.0002800 0.0001700 15.00 0.01900 0.007500 0.001100 0.0002600 9.900E-05 1.800E-05 20.00 0.006900 0.002900 0.0003600 4.200E-05 3.200E-05 2.900E-06 30.00 0.001200 0.0005300 5.300E-05 2.600E-06 4.600E-06 1.700E-07 40.00 0.0002900 0.0001300 1.200E-05 3.100E-07 1.000E-06 2.000E-08 60.00 3.200E-05 1.500E-05 1.200E-06 1.400E-08 1.000E-07 9.300E-10 100.0 1.700E-06 7.800E-07 6.100E-08 2.600E-10 5.200E-09 1.700E-11 #S 17 Cl #N 7 #UOCCUP 2 2 6 2 5 #UBIND 2823. 270.0 201.0 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.080 0.05200 0.2130 0.1120 0.7130 0.4900 0.05000 5.070 0.05200 0.2130 0.1120 0.7090 0.4900 0.1000 5.050 0.05200 0.2130 0.1120 0.6980 0.4890 0.1500 5.010 0.05200 0.2120 0.1120 0.6800 0.4890 0.2000 4.950 0.05200 0.2120 0.1120 0.6550 0.4870 0.3000 4.770 0.05190 0.2100 0.1120 0.5890 0.4790 0.4000 4.510 0.05190 0.2070 0.1120 0.5090 0.4600 0.5000 4.190 0.05180 0.2040 0.1120 0.4230 0.4310 0.6000 3.810 0.05180 0.2000 0.1120 0.3400 0.3920 0.7000 3.420 0.05170 0.1950 0.1120 0.2650 0.3460 0.8000 3.040 0.05160 0.1900 0.1120 0.2010 0.2980 1.000 2.370 0.05140 0.1780 0.1110 0.1080 0.2060 1.200 1.880 0.05110 0.1650 0.1100 0.05690 0.1340 1.400 1.540 0.05080 0.1500 0.1090 0.03310 0.08260 1.600 1.310 0.05050 0.1360 0.1070 0.02430 0.04930 1.800 1.160 0.05010 0.1210 0.1050 0.02210 0.02880 2.000 1.050 0.04970 0.1070 0.1010 0.02190 0.01690 2.400 0.8980 0.04870 0.08020 0.09400 0.02080 0.006950 3.000 0.7250 0.04690 0.04870 0.08030 0.01500 0.004400 4.000 0.4920 0.04350 0.01850 0.05600 0.005560 0.004120 5.000 0.3290 0.03950 0.007200 0.03610 0.001630 0.003020 6.000 0.2240 0.03530 0.004200 0.02240 0.0005600 0.001930 7.000 0.1580 0.03110 0.003770 0.01360 0.0003660 0.001170 8.000 0.1160 0.02710 0.003740 0.008310 0.0003520 0.0007020 10.00 0.06700 0.02000 0.003200 0.003200 0.0003200 0.0002600 15.00 0.02200 0.008400 0.001300 0.0003800 0.0001300 3.000E-05 20.00 0.008200 0.003400 0.0004500 6.400E-05 4.400E-05 4.900E-06 30.00 0.001500 0.0006600 7.000E-05 4.000E-06 6.600E-06 3.000E-07 40.00 0.0003700 0.0001700 1.600E-05 4.900E-07 1.500E-06 3.600E-08 60.00 4.200E-05 1.900E-05 1.700E-06 2.300E-08 1.500E-07 1.600E-09 100.0 2.300E-06 1.000E-06 8.500E-08 4.200E-10 7.800E-09 3.000E-11 #S 18 Ar #N 7 #UOCCUP 2 2 6 2 6 #UBIND 3206. 326.3 249.3 29.20 15.80 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.060 0.04900 0.1980 0.1040 0.6490 0.4420 0.05000 5.060 0.04900 0.1980 0.1040 0.6460 0.4420 0.1000 5.040 0.04900 0.1980 0.1040 0.6370 0.4410 0.1500 5.010 0.04900 0.1980 0.1040 0.6230 0.4410 0.2000 4.960 0.04900 0.1970 0.1040 0.6040 0.4400 0.3000 4.820 0.04900 0.1960 0.1040 0.5520 0.4350 0.4000 4.620 0.04890 0.1930 0.1040 0.4890 0.4230 0.5000 4.350 0.04890 0.1910 0.1040 0.4190 0.4020 0.6000 4.040 0.04880 0.1870 0.1040 0.3480 0.3740 0.7000 3.690 0.04880 0.1840 0.1040 0.2810 0.3400 0.8000 3.330 0.04870 0.1790 0.1030 0.2220 0.3020 1.000 2.660 0.04850 0.1700 0.1030 0.1300 0.2240 1.200 2.110 0.04830 0.1590 0.1020 0.07230 0.1560 1.400 1.700 0.04800 0.1460 0.1010 0.04150 0.1040 1.600 1.420 0.04780 0.1340 0.1000 0.02750 0.06650 1.800 1.220 0.04740 0.1210 0.09830 0.02240 0.04160 2.000 1.080 0.04710 0.1080 0.09610 0.02110 0.02570 2.400 0.9070 0.04620 0.08430 0.09040 0.02080 0.01020 3.000 0.7360 0.04470 0.05420 0.07940 0.01700 0.004650 4.000 0.5200 0.04180 0.02260 0.05860 0.007580 0.004200 5.000 0.3590 0.03830 0.009020 0.03980 0.002530 0.003450 6.000 0.2490 0.03470 0.004620 0.02580 0.0008310 0.002380 7.000 0.1770 0.03090 0.003680 0.01630 0.0004200 0.001520 8.000 0.1300 0.02720 0.003610 0.01030 0.0003620 0.0009490 10.00 0.07500 0.02100 0.003300 0.004100 0.0003500 0.0003700 15.00 0.02500 0.009200 0.001500 0.0005300 0.0001600 4.500E-05 20.00 0.009700 0.003900 0.0005600 9.300E-05 5.800E-05 7.700E-06 30.00 0.001900 0.0008100 9.100E-05 6.000E-06 9.200E-06 4.800E-07 40.00 0.0004700 0.0002100 2.100E-05 7.500E-07 2.100E-06 6.000E-08 60.00 5.500E-05 2.500E-05 2.200E-06 3.500E-08 2.200E-07 2.800E-09 100.0 3.000E-06 1.400E-06 1.200E-07 6.500E-10 1.100E-08 5.100E-11 #S 19 K #N 8 #UOCCUP 2 2 6 2 6 1 #UBIND 3608. 378.6 295.5 34.80 18.30 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 0.000 6.900 0.04640 0.1850 0.09650 0.5700 0.3760 2.460 0.05000 6.760 0.04640 0.1850 0.09650 0.5680 0.3760 2.320 0.1000 6.390 0.04640 0.1850 0.09650 0.5620 0.3760 1.960 0.1500 5.890 0.04640 0.1850 0.09650 0.5520 0.3760 1.490 0.2000 5.400 0.04640 0.1840 0.09650 0.5390 0.3750 1.020 0.3000 4.650 0.04630 0.1830 0.09650 0.5030 0.3730 0.3710 0.4000 4.260 0.04630 0.1810 0.09650 0.4570 0.3670 0.1110 0.5000 4.030 0.04630 0.1790 0.09650 0.4050 0.3570 0.04910 0.6000 3.820 0.04620 0.1760 0.09640 0.3490 0.3420 0.04270 0.7000 3.580 0.04620 0.1730 0.09640 0.2950 0.3220 0.04190 0.8000 3.320 0.04610 0.1700 0.09630 0.2430 0.2980 0.03730 1.000 2.780 0.04600 0.1620 0.09600 0.1560 0.2420 0.02330 1.200 2.280 0.04580 0.1520 0.09560 0.09460 0.1850 0.01230 1.400 1.870 0.04550 0.1420 0.09490 0.05630 0.1340 0.006040 1.600 1.550 0.04530 0.1310 0.09390 0.03550 0.09330 0.002950 1.800 1.320 0.04500 0.1200 0.09260 0.02570 0.06280 0.001570 2.000 1.150 0.04470 0.1090 0.09090 0.02210 0.04130 0.001030 2.400 0.9290 0.04400 0.08720 0.08660 0.02120 0.01740 0.0008230 3.000 0.7470 0.04270 0.05880 0.07780 0.01920 0.006250 0.0007780 4.000 0.5420 0.04020 0.02670 0.06000 0.01020 0.004590 0.0004350 5.000 0.3860 0.03720 0.01120 0.04280 0.003900 0.004170 0.0001670 6.000 0.2730 0.03390 0.005350 0.02890 0.001330 0.003120 5.590E-05 7.000 0.1960 0.03060 0.003720 0.01900 0.0005620 0.002120 2.230E-05 8.000 0.1440 0.02720 0.003480 0.01230 0.0004010 0.001370 1.510E-05 10.00 0.08400 0.02100 0.003300 0.005200 0.0003800 0.0005700 1.400E-05 15.00 0.02800 0.010000 0.001700 0.0007100 0.0002000 7.300E-05 7.600E-06 20.00 0.01100 0.004500 0.0006700 0.0001300 7.800E-05 1.300E-05 2.900E-06 30.00 0.002300 0.0009700 0.0001200 8.800E-06 1.300E-05 8.500E-07 4.900E-07 40.00 0.0005900 0.0002600 2.700E-05 1.100E-06 3.000E-06 1.100E-07 1.100E-07 60.00 7.000E-05 3.200E-05 2.900E-06 5.300E-08 3.200E-07 5.000E-09 1.200E-08 100.0 3.900E-06 1.800E-06 1.500E-07 9.900E-10 1.700E-08 9.000E-11 6.300E-10 #S 20 Ca #N 8 #UOCCUP 2 2 6 2 6 2 #UBIND 4039. 438.0 348.0 44.00 24.80 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 0.000 7.870 0.04400 0.1740 0.09020 0.5080 0.3300 1.950 0.05000 7.720 0.04400 0.1740 0.09020 0.5070 0.3300 1.870 0.1000 7.320 0.04400 0.1740 0.09020 0.5030 0.3300 1.680 0.1500 6.740 0.04400 0.1740 0.09020 0.4960 0.3300 1.400 0.2000 6.100 0.04400 0.1730 0.09020 0.4860 0.3300 1.090 0.3000 4.960 0.04400 0.1720 0.09020 0.4600 0.3290 0.5470 0.4000 4.230 0.04390 0.1710 0.09020 0.4260 0.3260 0.2280 0.5000 3.850 0.04390 0.1690 0.09020 0.3860 0.3200 0.09350 0.6000 3.620 0.04390 0.1670 0.09010 0.3420 0.3110 0.05320 0.7000 3.440 0.04380 0.1640 0.09010 0.2980 0.2990 0.04660 0.8000 3.250 0.04380 0.1610 0.09000 0.2540 0.2830 0.04630 1.000 2.830 0.04360 0.1540 0.08990 0.1760 0.2440 0.03920 1.200 2.400 0.04350 0.1460 0.08950 0.1150 0.1990 0.02640 1.400 2.000 0.04330 0.1380 0.08900 0.07240 0.1550 0.01540 1.600 1.680 0.04310 0.1280 0.08830 0.04600 0.1160 0.008310 1.800 1.420 0.04280 0.1180 0.08730 0.03140 0.08350 0.004390 2.000 1.220 0.04260 0.1090 0.08610 0.02450 0.05840 0.002460 2.400 0.9660 0.04200 0.08910 0.08270 0.02120 0.02710 0.001340 3.000 0.7590 0.04090 0.06260 0.07580 0.02040 0.009050 0.001260 4.000 0.5590 0.03870 0.03070 0.06070 0.01280 0.004830 0.0008480 5.000 0.4100 0.03600 0.01370 0.04510 0.005590 0.004650 0.0003760 6.000 0.2960 0.03320 0.006370 0.03170 0.002060 0.003790 0.0001370 7.000 0.2160 0.03020 0.003930 0.02160 0.0008060 0.002730 5.120E-05 8.000 0.1600 0.02710 0.003390 0.01440 0.0004660 0.001850 2.760E-05 10.00 0.09300 0.02100 0.003300 0.006400 0.0004100 0.0008100 2.300E-05 15.00 0.03200 0.01100 0.001900 0.0009400 0.0002500 0.0001100 1.400E-05 20.00 0.01300 0.005000 0.0007900 0.0001800 1.000E-04 2.100E-05 5.800E-06 30.00 0.002700 0.001200 0.0001400 1.300E-05 1.800E-05 1.400E-06 1.000E-06 40.00 0.0007200 0.0003200 3.400E-05 1.600E-06 4.200E-06 1.800E-07 2.400E-07 60.00 8.900E-05 4.000E-05 3.800E-06 7.800E-08 4.600E-07 8.400E-09 2.600E-08 100.0 5.000E-06 2.300E-06 2.000E-07 1.500E-09 2.400E-08 1.700E-10 1.400E-09 #S 21 Sc #N 9 #UOCCUP 2 2 6 2 6 1 2 #UBIND 4490. 498.0 400.3 51.10 28.30 0.8000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 7.670 0.04180 0.1640 0.08470 0.4710 0.3040 0.3100 1.840 0.05000 7.550 0.04180 0.1640 0.08470 0.4700 0.3040 0.3100 1.780 0.1000 7.200 0.04180 0.1640 0.08470 0.4670 0.3040 0.3100 1.610 0.1500 6.700 0.04180 0.1640 0.08470 0.4610 0.3040 0.3100 1.370 0.2000 6.140 0.04180 0.1630 0.08470 0.4530 0.3040 0.3100 1.090 0.3000 5.080 0.04180 0.1620 0.08470 0.4320 0.3030 0.3090 0.5890 0.4000 4.370 0.04180 0.1610 0.08470 0.4040 0.3010 0.3070 0.2660 0.5000 3.960 0.04180 0.1600 0.08470 0.3710 0.2970 0.3030 0.1120 0.6000 3.730 0.04170 0.1580 0.08460 0.3340 0.2900 0.2940 0.05690 0.7000 3.550 0.04170 0.1550 0.08460 0.2960 0.2810 0.2820 0.04350 0.8000 3.380 0.04160 0.1530 0.08460 0.2580 0.2700 0.2660 0.04220 1.000 3.000 0.04150 0.1470 0.08440 0.1870 0.2400 0.2290 0.03890 1.200 2.590 0.04140 0.1400 0.08420 0.1280 0.2030 0.1900 0.02890 1.400 2.200 0.04120 0.1330 0.08380 0.08440 0.1650 0.1540 0.01850 1.600 1.860 0.04110 0.1250 0.08330 0.05490 0.1290 0.1230 0.01080 1.800 1.580 0.04080 0.1160 0.08250 0.03690 0.09710 0.09740 0.006030 2.000 1.350 0.04060 0.1080 0.08160 0.02710 0.07120 0.07680 0.003380 2.400 1.040 0.04010 0.09010 0.07900 0.02080 0.03590 0.04760 0.001470 3.000 0.7910 0.03910 0.06570 0.07340 0.02020 0.01240 0.02350 0.001200 4.000 0.5760 0.03720 0.03440 0.06070 0.01450 0.004900 0.007660 0.0009430 5.000 0.4290 0.03490 0.01630 0.04670 0.007190 0.004690 0.002700 0.0004790 6.000 0.3160 0.03240 0.007650 0.03410 0.002920 0.004130 0.001030 0.0001940 7.000 0.2340 0.02970 0.004330 0.02390 0.001140 0.003160 0.0004240 7.360E-05 8.000 0.1740 0.02690 0.003380 0.01650 0.0005620 0.002250 0.0001870 3.400E-05 10.00 0.1000 0.02200 0.003200 0.007600 0.0004100 0.001000 4.300E-05 2.300E-05 15.00 0.03500 0.01100 0.002100 0.001200 0.0002800 0.0001600 2.300E-06 1.600E-05 20.00 0.01500 0.005500 0.0009200 0.0002400 0.0001200 3.000E-05 2.300E-07 7.000E-06 30.00 0.003200 0.001300 0.0001800 1.700E-05 2.300E-05 2.100E-06 6.600E-09 1.300E-06 40.00 0.0008800 0.0003800 4.300E-05 2.300E-06 5.500E-06 2.700E-07 4.600E-10 3.100E-07 60.00 0.0001100 5.000E-05 4.900E-06 1.100E-07 6.200E-07 1.300E-08 9.800E-12 3.500E-08 100.0 6.400E-06 2.900E-06 2.600E-07 2.100E-09 3.300E-08 2.400E-10 1.500E-13 1.800E-09 #S 22 Ti #N 9 #UOCCUP 2 2 6 2 6 2 2 #UBIND 4966. 561.4 456.9 58.40 32.60 0.8000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 7.510 0.03990 0.1550 0.07980 0.4410 0.2820 0.2750 1.760 0.05000 7.400 0.03990 0.1550 0.07980 0.4400 0.2820 0.2750 1.710 0.1000 7.100 0.03990 0.1550 0.07980 0.4370 0.2820 0.2750 1.560 0.1500 6.660 0.03990 0.1550 0.07980 0.4320 0.2820 0.2750 1.340 0.2000 6.140 0.03990 0.1540 0.07980 0.4260 0.2820 0.2750 1.090 0.3000 5.160 0.03990 0.1540 0.07980 0.4080 0.2820 0.2750 0.6190 0.4000 4.460 0.03980 0.1530 0.07980 0.3850 0.2800 0.2740 0.2970 0.5000 4.040 0.03980 0.1510 0.07980 0.3570 0.2770 0.2710 0.1300 0.6000 3.800 0.03980 0.1500 0.07980 0.3260 0.2730 0.2660 0.06230 0.7000 3.640 0.03980 0.1480 0.07980 0.2930 0.2660 0.2590 0.04190 0.8000 3.480 0.03970 0.1460 0.07970 0.2590 0.2570 0.2490 0.03840 1.000 3.150 0.03960 0.1410 0.07960 0.1940 0.2330 0.2230 0.03700 1.200 2.770 0.03950 0.1350 0.07940 0.1390 0.2030 0.1920 0.02970 1.400 2.390 0.03940 0.1280 0.07920 0.09500 0.1700 0.1620 0.02050 1.600 2.040 0.03920 0.1210 0.07870 0.06370 0.1380 0.1340 0.01280 1.800 1.740 0.03900 0.1140 0.07820 0.04310 0.1080 0.1100 0.007570 2.000 1.500 0.03880 0.1060 0.07740 0.03060 0.08220 0.08940 0.004370 2.400 1.140 0.03840 0.09050 0.07540 0.02090 0.04470 0.05860 0.001700 3.000 0.8390 0.03750 0.06800 0.07090 0.01950 0.01650 0.03090 0.001120 4.000 0.5960 0.03590 0.03780 0.06030 0.01570 0.005220 0.01100 0.0009740 5.000 0.4470 0.03380 0.01890 0.04790 0.008720 0.004600 0.004140 0.0005610 6.000 0.3350 0.03160 0.009140 0.03600 0.003890 0.004300 0.001660 0.0002520 7.000 0.2510 0.02910 0.004920 0.02610 0.001590 0.003500 0.0007050 0.0001010 8.000 0.1890 0.02670 0.003470 0.01850 0.0007170 0.002610 0.0003180 4.340E-05 10.00 0.1100 0.02200 0.003100 0.009000 0.0004100 0.001300 7.600E-05 2.200E-05 15.00 0.03900 0.01200 0.002200 0.001500 0.0003100 0.0002100 4.200E-06 1.700E-05 20.00 0.01700 0.006000 0.001000 0.0003100 0.0001500 4.100E-05 4.300E-07 8.000E-06 30.00 0.003700 0.001500 0.0002100 2.400E-05 2.900E-05 3.000E-06 1.300E-08 1.600E-06 40.00 0.001100 0.0004500 5.300E-05 3.200E-06 7.100E-06 3.900E-07 9.000E-10 3.900E-07 60.00 0.0001400 6.100E-05 6.200E-06 1.600E-07 8.000E-07 1.900E-08 1.900E-11 4.400E-08 100.0 8.000E-06 3.600E-06 3.300E-07 3.000E-09 4.300E-08 3.500E-10 6.500E-14 2.400E-09 #S 23 V #N 9 #UOCCUP 2 2 6 2 6 3 2 #UBIND 5466. 627.2 516.0 66.30 37.20 0.7000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 7.380 0.03810 0.1470 0.07550 0.4150 0.2640 0.2500 1.690 0.05000 7.280 0.03810 0.1470 0.07550 0.4140 0.2640 0.2500 1.650 0.1000 7.010 0.03810 0.1470 0.07550 0.4120 0.2640 0.2500 1.510 0.1500 6.610 0.03810 0.1470 0.07550 0.4080 0.2640 0.2500 1.320 0.2000 6.140 0.03810 0.1460 0.07550 0.4030 0.2640 0.2500 1.090 0.3000 5.210 0.03810 0.1460 0.07550 0.3880 0.2640 0.2500 0.6430 0.4000 4.530 0.03810 0.1450 0.07550 0.3680 0.2630 0.2490 0.3240 0.5000 4.110 0.03810 0.1440 0.07550 0.3440 0.2610 0.2480 0.1470 0.6000 3.860 0.03800 0.1420 0.07550 0.3170 0.2570 0.2450 0.06910 0.7000 3.700 0.03800 0.1410 0.07550 0.2880 0.2520 0.2400 0.04200 0.8000 3.560 0.03800 0.1390 0.07540 0.2580 0.2450 0.2330 0.03560 1.000 3.260 0.03790 0.1350 0.07540 0.1990 0.2260 0.2140 0.03460 1.200 2.920 0.03780 0.1300 0.07520 0.1470 0.2010 0.1900 0.02960 1.400 2.560 0.03760 0.1240 0.07500 0.1040 0.1730 0.1650 0.02170 1.600 2.220 0.03750 0.1180 0.07470 0.07210 0.1440 0.1400 0.01440 1.800 1.910 0.03740 0.1110 0.07420 0.04950 0.1160 0.1180 0.008970 2.000 1.650 0.03720 0.1040 0.07370 0.03490 0.09150 0.09840 0.005380 2.400 1.260 0.03680 0.09040 0.07200 0.02160 0.05320 0.06740 0.002030 3.000 0.9020 0.03610 0.06970 0.06840 0.01870 0.02120 0.03770 0.001050 4.000 0.6210 0.03460 0.04080 0.05950 0.01640 0.005870 0.01450 0.0009610 5.000 0.4650 0.03280 0.02150 0.04850 0.01010 0.004460 0.005790 0.0006220 6.000 0.3530 0.03080 0.01080 0.03760 0.004940 0.004340 0.002430 0.0003080 7.000 0.2670 0.02860 0.005700 0.02800 0.002150 0.003750 0.001070 0.0001330 8.000 0.2030 0.02630 0.003690 0.02040 0.0009460 0.002920 0.0004960 5.650E-05 10.00 0.1200 0.02200 0.003000 0.010000 0.0004200 0.001500 0.0001200 2.200E-05 15.00 0.04300 0.01200 0.002300 0.001900 0.0003300 0.0002700 7.100E-06 1.800E-05 20.00 0.01800 0.006500 0.001200 0.0004000 0.0001700 5.500E-05 7.500E-07 8.900E-06 30.00 0.004300 0.001800 0.0002500 3.200E-05 3.500E-05 4.100E-06 2.300E-08 1.900E-06 40.00 0.001200 0.0005300 6.500E-05 4.300E-06 8.900E-06 5.500E-07 1.600E-09 4.700E-07 60.00 0.0001700 7.300E-05 7.700E-06 2.200E-07 1.000E-06 2.700E-08 3.500E-11 5.400E-08 100.0 9.900E-06 4.500E-06 4.200E-07 4.300E-09 5.600E-08 5.400E-10 1.200E-13 2.900E-09 #S 24 Cr #N 9 #UOCCUP 2 2 6 2 6 5 1 #UBIND 5991. 697.8 579.1 75.20 43.10 2.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 6.250 0.03650 0.1400 0.07160 0.3980 0.2530 0.2610 1.850 0.05000 6.190 0.03650 0.1400 0.07160 0.3980 0.2530 0.2610 1.790 0.1000 6.020 0.03650 0.1400 0.07160 0.3950 0.2530 0.2610 1.620 0.1500 5.770 0.03650 0.1400 0.07160 0.3920 0.2520 0.2610 1.380 0.2000 5.490 0.03650 0.1390 0.07160 0.3870 0.2520 0.2610 1.110 0.3000 4.960 0.03650 0.1390 0.07160 0.3740 0.2520 0.2610 0.6110 0.4000 4.580 0.03640 0.1380 0.07160 0.3560 0.2510 0.2600 0.2820 0.5000 4.340 0.03640 0.1370 0.07160 0.3340 0.2490 0.2570 0.1170 0.6000 4.180 0.03640 0.1360 0.07160 0.3100 0.2460 0.2510 0.05120 0.7000 4.040 0.03640 0.1340 0.07160 0.2840 0.2420 0.2440 0.03130 0.8000 3.890 0.03630 0.1330 0.07160 0.2560 0.2360 0.2340 0.02750 1.000 3.560 0.03630 0.1290 0.07150 0.2020 0.2200 0.2090 0.02670 1.200 3.180 0.03620 0.1250 0.07140 0.1520 0.1980 0.1830 0.02220 1.400 2.800 0.03610 0.1200 0.07120 0.1110 0.1730 0.1560 0.01610 1.600 2.440 0.03600 0.1140 0.07100 0.07820 0.1470 0.1330 0.01080 1.800 2.110 0.03580 0.1090 0.07060 0.05460 0.1210 0.1110 0.006840 2.000 1.830 0.03570 0.1030 0.07020 0.03850 0.09740 0.09330 0.004210 2.400 1.390 0.03530 0.08990 0.06890 0.02250 0.05940 0.06500 0.001630 3.000 0.9840 0.03470 0.07090 0.06590 0.01770 0.02550 0.03760 0.0007400 4.000 0.6550 0.03340 0.04340 0.05840 0.01640 0.006730 0.01530 0.0006730 5.000 0.4850 0.03180 0.02410 0.04880 0.01110 0.004270 0.006460 0.0004820 6.000 0.3690 0.03000 0.01260 0.03880 0.005910 0.004200 0.002830 0.0002610 7.000 0.2820 0.02800 0.006630 0.02970 0.002740 0.003810 0.001290 0.0001210 8.000 0.2170 0.02600 0.004050 0.02210 0.001230 0.003120 0.0006140 5.330E-05 10.00 0.1300 0.02200 0.002900 0.01200 0.0004300 0.001800 0.0001600 1.700E-05 15.00 0.04700 0.01300 0.002400 0.002300 0.0003500 0.0003300 9.600E-06 1.300E-05 20.00 0.02000 0.007000 0.001300 0.0005100 0.0001900 7.100E-05 1.000E-06 7.100E-06 30.00 0.004900 0.002000 0.0003000 4.200E-05 4.200E-05 5.500E-06 3.200E-08 1.600E-06 40.00 0.001500 0.0006200 7.800E-05 5.800E-06 1.100E-05 7.400E-07 2.400E-09 4.100E-07 60.00 0.0002000 8.800E-05 9.400E-06 3.000E-07 1.300E-06 3.700E-08 5.300E-11 4.800E-08 100.0 1.200E-05 5.400E-06 5.300E-07 5.900E-09 7.000E-08 7.400E-10 3.100E-13 2.600E-09 #S 25 Mn #N 9 #UOCCUP 2 2 6 2 6 5 2 #UBIND 6538. 769.0 643.1 82.40 47.30 1.100 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 7.160 0.03500 0.1330 0.06810 0.3730 0.2350 0.2150 1.590 0.05000 7.080 0.03500 0.1330 0.06810 0.3720 0.2350 0.2150 1.550 0.1000 6.850 0.03500 0.1330 0.06810 0.3700 0.2350 0.2150 1.440 0.1500 6.510 0.03500 0.1330 0.06810 0.3670 0.2350 0.2150 1.270 0.2000 6.110 0.03500 0.1330 0.06810 0.3640 0.2350 0.2150 1.080 0.3000 5.280 0.03500 0.1320 0.06810 0.3520 0.2350 0.2150 0.6770 0.4000 4.630 0.03500 0.1320 0.06810 0.3380 0.2340 0.2140 0.3680 0.5000 4.210 0.03490 0.1310 0.06810 0.3200 0.2330 0.2140 0.1800 0.6000 3.950 0.03490 0.1300 0.06810 0.2990 0.2310 0.2120 0.08560 0.7000 3.790 0.03490 0.1290 0.06810 0.2760 0.2280 0.2100 0.04580 0.8000 3.670 0.03490 0.1270 0.06810 0.2530 0.2230 0.2060 0.03260 1.000 3.430 0.03480 0.1240 0.06810 0.2050 0.2110 0.1950 0.02960 1.200 3.150 0.03470 0.1200 0.06800 0.1590 0.1940 0.1800 0.02770 1.400 2.840 0.03460 0.1160 0.06780 0.1190 0.1730 0.1620 0.02250 1.600 2.520 0.03450 0.1110 0.06760 0.08700 0.1500 0.1440 0.01650 1.800 2.220 0.03440 0.1060 0.06740 0.06230 0.1270 0.1260 0.01120 2.000 1.950 0.03430 0.1000 0.06700 0.04450 0.1050 0.1090 0.007310 2.400 1.510 0.03400 0.08910 0.06600 0.02500 0.06800 0.08010 0.002920 3.000 1.060 0.03340 0.07170 0.06350 0.01750 0.03150 0.04930 0.001050 4.000 0.6920 0.03220 0.04570 0.05720 0.01660 0.008370 0.02160 0.0008630 5.000 0.5070 0.03080 0.02650 0.04880 0.01230 0.004380 0.009590 0.0006830 6.000 0.3870 0.02920 0.01440 0.03970 0.007070 0.004170 0.004380 0.0004050 7.000 0.2980 0.02740 0.007710 0.03110 0.003520 0.003950 0.002070 0.0002030 8.000 0.2310 0.02550 0.004530 0.02360 0.001640 0.003380 0.001010 9.300E-05 10.00 0.1400 0.02200 0.002900 0.01300 0.0004900 0.002000 0.0002700 2.500E-05 15.00 0.05100 0.01300 0.002500 0.002700 0.0003600 0.0004200 1.700E-05 1.800E-05 20.00 0.02200 0.007400 0.001400 0.0006300 0.0002100 9.300E-05 1.900E-06 1.000E-05 30.00 0.005600 0.002200 0.0003400 5.400E-05 5.000E-05 7.500E-06 6.200E-08 2.400E-06 40.00 0.001700 0.0007100 9.300E-05 7.600E-06 1.300E-05 1.000E-06 4.600E-09 6.500E-07 60.00 0.0002400 1.000E-04 1.100E-05 4.000E-07 1.600E-06 5.200E-08 1.000E-10 7.700E-08 100.0 1.500E-05 6.600E-06 6.500E-07 8.000E-09 8.900E-08 1.000E-09 8.200E-13 4.300E-09 #S 26 Fe #N 9 #UOCCUP 2 2 6 2 6 6 2 #UBIND 7111. 848.0 711.9 91.60 53.00 0.8000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 7.060 0.03360 0.1270 0.06500 0.3550 0.2230 0.2010 1.550 0.05000 6.990 0.03360 0.1270 0.06500 0.3540 0.2230 0.2010 1.510 0.1000 6.780 0.03360 0.1270 0.06500 0.3530 0.2230 0.2010 1.410 0.1500 6.470 0.03360 0.1270 0.06500 0.3500 0.2230 0.2010 1.250 0.2000 6.090 0.03360 0.1270 0.06500 0.3470 0.2230 0.2010 1.070 0.3000 5.310 0.03360 0.1270 0.06500 0.3370 0.2230 0.2010 0.6900 0.4000 4.670 0.03360 0.1260 0.06500 0.3240 0.2220 0.2010 0.3870 0.5000 4.250 0.03360 0.1250 0.06500 0.3090 0.2210 0.2000 0.1960 0.6000 3.990 0.03350 0.1240 0.06500 0.2900 0.2200 0.1990 0.09460 0.7000 3.830 0.03350 0.1230 0.06500 0.2700 0.2170 0.1970 0.04920 0.8000 3.710 0.03350 0.1220 0.06500 0.2490 0.2140 0.1950 0.03240 1.000 3.500 0.03340 0.1190 0.06490 0.2050 0.2030 0.1860 0.02730 1.200 3.240 0.03340 0.1160 0.06490 0.1630 0.1890 0.1740 0.02640 1.400 2.960 0.03330 0.1120 0.06480 0.1250 0.1710 0.1590 0.02240 1.600 2.660 0.03320 0.1080 0.06460 0.09350 0.1510 0.1430 0.01710 1.800 2.370 0.03310 0.1030 0.06440 0.06840 0.1310 0.1270 0.01210 2.000 2.090 0.03300 0.09830 0.06410 0.04960 0.1100 0.1120 0.008150 2.400 1.640 0.03270 0.08800 0.06320 0.02750 0.07430 0.08450 0.003430 3.000 1.160 0.03220 0.07210 0.06120 0.01730 0.03670 0.05410 0.001130 4.000 0.7380 0.03120 0.04760 0.05590 0.01630 0.01020 0.02500 0.0008000 5.000 0.5330 0.02990 0.02870 0.04850 0.01300 0.004540 0.01170 0.0006860 6.000 0.4050 0.02840 0.01620 0.04030 0.008060 0.004030 0.005530 0.0004420 7.000 0.3140 0.02680 0.008880 0.03220 0.004290 0.003940 0.002700 0.0002370 8.000 0.2440 0.02510 0.005130 0.02500 0.002090 0.003510 0.001360 0.0001150 10.00 0.1500 0.02200 0.002900 0.01400 0.0005700 0.002300 0.0003800 2.900E-05 15.00 0.05500 0.01400 0.002500 0.003200 0.0003700 0.0005100 2.500E-05 1.700E-05 20.00 0.02500 0.007800 0.001500 0.0007700 0.0002300 0.0001200 2.900E-06 1.100E-05 30.00 0.006300 0.002400 0.0003900 6.900E-05 5.900E-05 9.900E-06 9.600E-08 2.700E-06 40.00 0.001900 0.0008100 0.0001100 9.900E-06 1.600E-05 1.400E-06 7.200E-09 7.500E-07 60.00 0.0002800 0.0001200 1.400E-05 5.300E-07 2.000E-06 7.100E-08 1.600E-10 9.100E-08 100.0 1.800E-05 7.900E-06 7.900E-07 1.100E-08 1.100E-07 1.400E-09 1.300E-12 5.100E-09 #S 27 Co #N 9 #UOCCUP 2 2 6 2 6 7 2 #UBIND 7711. 926.6 786.0 101.4 59.40 0.4000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 6.980 0.03230 0.1220 0.06210 0.3390 0.2120 0.1900 1.510 0.05000 6.910 0.03230 0.1220 0.06210 0.3390 0.2120 0.1900 1.480 0.1000 6.720 0.03230 0.1220 0.06210 0.3370 0.2120 0.1900 1.380 0.1500 6.420 0.03230 0.1220 0.06210 0.3350 0.2120 0.1900 1.240 0.2000 6.070 0.03230 0.1220 0.06210 0.3320 0.2120 0.1900 1.060 0.3000 5.330 0.03230 0.1210 0.06210 0.3240 0.2120 0.1890 0.7000 0.4000 4.710 0.03230 0.1210 0.06210 0.3120 0.2120 0.1890 0.4040 0.5000 4.280 0.03230 0.1200 0.06210 0.2980 0.2110 0.1890 0.2110 0.6000 4.020 0.03230 0.1190 0.06210 0.2820 0.2090 0.1880 0.1040 0.7000 3.860 0.03230 0.1180 0.06210 0.2640 0.2070 0.1870 0.05330 0.8000 3.750 0.03220 0.1170 0.06210 0.2450 0.2050 0.1850 0.03300 1.000 3.550 0.03220 0.1150 0.06210 0.2050 0.1960 0.1780 0.02540 1.200 3.320 0.03210 0.1120 0.06200 0.1660 0.1840 0.1680 0.02490 1.400 3.060 0.03210 0.1080 0.06190 0.1300 0.1690 0.1550 0.02200 1.600 2.780 0.03200 0.1050 0.06180 0.09920 0.1510 0.1410 0.01740 1.800 2.500 0.03190 0.1010 0.06160 0.07410 0.1330 0.1270 0.01280 2.000 2.230 0.03180 0.09610 0.06140 0.05460 0.1140 0.1130 0.008900 2.400 1.760 0.03150 0.08680 0.06070 0.03030 0.07970 0.08790 0.003960 3.000 1.260 0.03110 0.07220 0.05900 0.01750 0.04170 0.05820 0.001250 4.000 0.7910 0.03010 0.04920 0.05450 0.01580 0.01230 0.02830 0.0007400 5.000 0.5620 0.02900 0.03080 0.04810 0.01340 0.004890 0.01380 0.0006750 6.000 0.4250 0.02770 0.01800 0.04060 0.008950 0.003910 0.006780 0.0004690 7.000 0.3290 0.02620 0.01010 0.03310 0.005070 0.003870 0.003410 0.0002690 8.000 0.2580 0.02470 0.005850 0.02620 0.002600 0.003580 0.001760 0.0001380 10.00 0.1600 0.02100 0.002900 0.01600 0.0006900 0.002500 0.0005100 3.500E-05 15.00 0.06000 0.01400 0.002500 0.003700 0.0003700 0.0006100 3.600E-05 1.600E-05 20.00 0.02700 0.008200 0.001600 0.0009400 0.0002500 0.0001500 4.200E-06 1.100E-05 30.00 0.007000 0.002700 0.0004500 8.700E-05 6.800E-05 1.300E-05 1.400E-07 3.100E-06 40.00 0.002200 0.0009100 0.0001300 1.300E-05 1.900E-05 1.800E-06 1.100E-08 8.500E-07 60.00 0.0003300 0.0001400 1.700E-05 6.900E-07 2.400E-06 9.400E-08 2.500E-10 1.100E-07 100.0 2.100E-05 9.400E-06 9.600E-07 1.400E-08 1.300E-07 1.900E-09 1.700E-12 6.000E-09 #S 28 Ni #N 9 #UOCCUP 2 2 6 2 6 8 2 #UBIND 8332. 1010. 859.9 110.9 66.60 0.6000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 6.900 0.03110 0.1170 0.05950 0.3250 0.2020 0.1790 1.470 0.05000 6.840 0.03110 0.1170 0.05950 0.3240 0.2020 0.1790 1.440 0.1000 6.660 0.03110 0.1170 0.05950 0.3230 0.2020 0.1790 1.350 0.1500 6.380 0.03110 0.1170 0.05950 0.3210 0.2020 0.1790 1.220 0.2000 6.050 0.03110 0.1170 0.05950 0.3180 0.2020 0.1790 1.050 0.3000 5.340 0.03110 0.1160 0.05950 0.3110 0.2020 0.1790 0.7090 0.4000 4.740 0.03110 0.1160 0.05950 0.3010 0.2020 0.1790 0.4200 0.5000 4.320 0.03110 0.1150 0.05950 0.2880 0.2010 0.1790 0.2250 0.6000 4.050 0.03110 0.1150 0.05950 0.2740 0.2000 0.1780 0.1130 0.7000 3.890 0.03110 0.1140 0.05950 0.2580 0.1990 0.1770 0.05800 0.8000 3.780 0.03110 0.1130 0.05950 0.2410 0.1960 0.1760 0.03420 1.000 3.590 0.03100 0.1110 0.05950 0.2040 0.1890 0.1700 0.02380 1.200 3.390 0.03100 0.1080 0.05940 0.1680 0.1790 0.1620 0.02340 1.400 3.150 0.03090 0.1050 0.05930 0.1340 0.1660 0.1510 0.02130 1.600 2.880 0.03080 0.1020 0.05920 0.1040 0.1510 0.1390 0.01750 1.800 2.610 0.03070 0.09790 0.05910 0.07930 0.1340 0.1270 0.01330 2.000 2.350 0.03060 0.09390 0.05890 0.05950 0.1170 0.1140 0.009540 2.400 1.890 0.03040 0.08550 0.05830 0.03340 0.08440 0.09040 0.004480 3.000 1.360 0.03000 0.07210 0.05690 0.01800 0.04660 0.06170 0.001420 4.000 0.8490 0.02920 0.05050 0.05310 0.01520 0.01470 0.03140 0.0006880 5.000 0.5950 0.02820 0.03260 0.04750 0.01370 0.005440 0.01590 0.0006520 6.000 0.4460 0.02700 0.01970 0.04080 0.009720 0.003840 0.008090 0.0004870 7.000 0.3460 0.02560 0.01140 0.03380 0.005840 0.003760 0.004190 0.0002980 8.000 0.2710 0.02420 0.006650 0.02730 0.003160 0.003600 0.002220 0.0001620 10.00 0.1700 0.02100 0.003100 0.01700 0.0008500 0.002600 0.0006700 4.200E-05 15.00 0.06400 0.01400 0.002500 0.004300 0.0003600 0.0007100 4.900E-05 1.600E-05 20.00 0.02900 0.008500 0.001700 0.001100 0.0002700 0.0001800 5.900E-06 1.200E-05 30.00 0.007800 0.002900 0.0005000 0.0001100 7.700E-05 1.600E-05 2.100E-07 3.400E-06 40.00 0.002500 0.001000 0.0001500 1.600E-05 2.200E-05 2.300E-06 1.600E-08 9.600E-07 60.00 0.0003800 0.0001600 2.000E-05 8.900E-07 2.800E-06 1.200E-07 3.600E-10 1.200E-07 100.0 2.500E-05 1.100E-05 1.200E-06 1.800E-08 1.600E-07 2.500E-09 2.400E-12 7.100E-09 #S 29 Cu #N 9 #UOCCUP 2 2 6 2 6 10 1 #UBIND 8980. 1099. 939.4 122.5 75.80 2.800 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 5.930 0.03010 0.1120 0.05710 0.3150 0.1960 0.1850 1.640 0.05000 5.880 0.03010 0.1120 0.05710 0.3150 0.1960 0.1850 1.600 0.1000 5.760 0.03000 0.1120 0.05710 0.3140 0.1960 0.1850 1.480 0.1500 5.580 0.03000 0.1120 0.05710 0.3120 0.1960 0.1850 1.310 0.2000 5.370 0.03000 0.1120 0.05710 0.3090 0.1960 0.1850 1.100 0.3000 4.940 0.03000 0.1120 0.05710 0.3020 0.1960 0.1850 0.6860 0.4000 4.600 0.03000 0.1110 0.05710 0.2930 0.1950 0.1850 0.3700 0.5000 4.380 0.03000 0.1110 0.05710 0.2820 0.1950 0.1840 0.1790 0.6000 4.240 0.03000 0.1100 0.05710 0.2680 0.1940 0.1830 0.08190 0.7000 4.140 0.03000 0.1100 0.05710 0.2530 0.1930 0.1810 0.03920 0.8000 4.050 0.03000 0.1090 0.05710 0.2370 0.1900 0.1790 0.02340 1.000 3.860 0.02990 0.1070 0.05710 0.2030 0.1840 0.1710 0.01800 1.200 3.620 0.02990 0.1040 0.05700 0.1690 0.1750 0.1610 0.01770 1.400 3.360 0.02980 0.1020 0.05700 0.1370 0.1630 0.1480 0.01560 1.600 3.070 0.02980 0.09860 0.05690 0.1080 0.1490 0.1350 0.01250 1.800 2.790 0.02970 0.09530 0.05680 0.08310 0.1340 0.1220 0.009360 2.000 2.520 0.02960 0.09170 0.05660 0.06310 0.1180 0.1100 0.006750 2.400 2.030 0.02940 0.08410 0.05610 0.03600 0.08720 0.08690 0.003250 3.000 1.470 0.02900 0.07180 0.05500 0.01850 0.05020 0.05990 0.001060 4.000 0.9150 0.02830 0.05160 0.05170 0.01450 0.01690 0.03140 0.0004610 5.000 0.6320 0.02730 0.03430 0.04670 0.01360 0.006070 0.01640 0.0004430 6.000 0.4700 0.02630 0.02140 0.04070 0.01020 0.003780 0.008620 0.0003510 7.000 0.3620 0.02510 0.01280 0.03430 0.006500 0.003590 0.004590 0.0002290 8.000 0.2850 0.02380 0.007530 0.02820 0.003690 0.003520 0.002490 0.0001310 10.00 0.1800 0.02100 0.003300 0.01800 0.001000 0.002700 0.0007800 3.600E-05 15.00 0.06800 0.01400 0.002400 0.004800 0.0003500 0.0008100 6.100E-05 1.100E-05 20.00 0.03100 0.008900 0.001800 0.001300 0.0002800 0.0002100 7.500E-06 8.400E-06 30.00 0.008500 0.003200 0.0005600 0.0001300 8.600E-05 2.000E-05 2.800E-07 2.600E-06 40.00 0.002800 0.001100 0.0001700 2.000E-05 2.500E-05 3.000E-06 2.200E-08 7.700E-07 60.00 0.0004400 0.0001900 2.300E-05 1.100E-06 3.300E-06 1.600E-07 5.100E-10 1.000E-07 100.0 2.900E-05 1.300E-05 1.400E-06 2.400E-08 1.900E-07 3.300E-09 3.500E-12 5.900E-09 #S 30 Zn #N 9 #UOCCUP 2 2 6 2 6 10 2 #UBIND 9661. 1196. 1030. 139.9 89.60 10.20 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 6.760 0.02900 0.1080 0.05490 0.2990 0.1860 0.1620 1.410 0.05000 6.700 0.02900 0.1080 0.05490 0.2990 0.1860 0.1620 1.380 0.1000 6.550 0.02900 0.1080 0.05490 0.2980 0.1860 0.1620 1.300 0.1500 6.300 0.02900 0.1080 0.05490 0.2970 0.1860 0.1620 1.180 0.2000 6.010 0.02900 0.1080 0.05490 0.2940 0.1860 0.1620 1.040 0.3000 5.360 0.02900 0.1080 0.05490 0.2890 0.1850 0.1620 0.7210 0.4000 4.790 0.02900 0.1070 0.05490 0.2810 0.1850 0.1620 0.4460 0.5000 4.380 0.02900 0.1070 0.05490 0.2710 0.1850 0.1620 0.2510 0.6000 4.110 0.02900 0.1060 0.05490 0.2590 0.1840 0.1620 0.1320 0.7000 3.940 0.02900 0.1060 0.05490 0.2460 0.1830 0.1610 0.06870 0.8000 3.830 0.02900 0.1050 0.05490 0.2320 0.1810 0.1600 0.03830 1.000 3.660 0.02890 0.1030 0.05490 0.2010 0.1760 0.1570 0.02160 1.200 3.490 0.02890 0.1010 0.05480 0.1700 0.1690 0.1510 0.02060 1.400 3.290 0.02880 0.09850 0.05480 0.1400 0.1590 0.1430 0.01980 1.600 3.060 0.02880 0.09580 0.05470 0.1130 0.1470 0.1340 0.01720 1.800 2.820 0.02870 0.09280 0.05460 0.08850 0.1340 0.1240 0.01380 2.000 2.580 0.02860 0.08960 0.05450 0.06850 0.1200 0.1140 0.01050 2.400 2.120 0.02840 0.08260 0.05410 0.04000 0.09150 0.09330 0.005450 3.000 1.560 0.02810 0.07130 0.05310 0.02000 0.05530 0.06710 0.001830 4.000 0.9810 0.02740 0.05240 0.05030 0.01420 0.01990 0.03700 0.0006240 5.000 0.6720 0.02660 0.03580 0.04600 0.01360 0.007140 0.02010 0.0005880 6.000 0.4960 0.02560 0.02300 0.04060 0.01090 0.003940 0.01090 0.0004970 7.000 0.3810 0.02450 0.01410 0.03470 0.007270 0.003530 0.005940 0.0003430 8.000 0.3000 0.02330 0.008470 0.02890 0.004340 0.003500 0.003300 0.0002070 10.00 0.1900 0.02100 0.003500 0.01900 0.001300 0.002900 0.001100 6.100E-05 15.00 0.07300 0.01400 0.002400 0.005400 0.0003500 0.0009300 8.900E-05 1.400E-05 20.00 0.03300 0.009200 0.001900 0.001500 0.0002900 0.0002600 1.100E-05 1.200E-05 30.00 0.009400 0.003400 0.0006200 0.0001600 9.700E-05 2.500E-05 4.300E-07 3.900E-06 40.00 0.003100 0.001300 0.0001900 2.500E-05 3.000E-05 3.800E-06 3.400E-08 1.200E-06 60.00 0.0005000 0.0002200 2.700E-05 1.400E-06 4.000E-06 2.100E-07 8.100E-10 1.600E-07 100.0 3.400E-05 1.500E-05 1.600E-06 3.000E-08 2.300E-07 4.300E-09 6.200E-12 9.400E-09 #S 31 Ga #N 10 #UOCCUP 2 2 6 2 6 10 2 1 #UBIND 1.037E+04 1298. 1126. 158.0 104.0 18.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 0.000 6.930 0.02810 0.1040 0.05280 0.2850 0.1760 0.1460 1.180 0.9150 0.05000 6.900 0.02810 0.1040 0.05280 0.2840 0.1760 0.1460 1.160 0.9140 0.1000 6.800 0.02810 0.1040 0.05280 0.2840 0.1760 0.1460 1.110 0.9120 0.1500 6.630 0.02810 0.1040 0.05280 0.2820 0.1760 0.1460 1.040 0.9010 0.2000 6.420 0.02810 0.1040 0.05280 0.2810 0.1760 0.1460 0.9440 0.8760 0.3000 5.860 0.02810 0.1040 0.05280 0.2750 0.1760 0.1460 0.7230 0.7730 0.4000 5.260 0.02810 0.1030 0.05280 0.2690 0.1750 0.1460 0.5060 0.6180 0.5000 4.710 0.02800 0.1030 0.05280 0.2600 0.1750 0.1460 0.3270 0.4520 0.6000 4.290 0.02800 0.1030 0.05280 0.2500 0.1750 0.1450 0.1980 0.3090 0.7000 3.980 0.02800 0.1020 0.05280 0.2380 0.1740 0.1450 0.1160 0.2010 0.8000 3.770 0.02800 0.1010 0.05280 0.2260 0.1730 0.1450 0.06730 0.1260 1.000 3.520 0.02800 0.09970 0.05280 0.1990 0.1690 0.1430 0.03020 0.04640 1.200 3.350 0.02790 0.09780 0.05280 0.1700 0.1630 0.1400 0.02440 0.01680 1.400 3.180 0.02790 0.09560 0.05270 0.1430 0.1550 0.1350 0.02410 0.007120 1.600 3.000 0.02780 0.09310 0.05270 0.1170 0.1450 0.1290 0.02270 0.004470 1.800 2.800 0.02780 0.09040 0.05260 0.09340 0.1330 0.1220 0.01960 0.003980 2.000 2.590 0.02770 0.08750 0.05250 0.07360 0.1210 0.1140 0.01580 0.003950 2.400 2.180 0.02750 0.08110 0.05210 0.04430 0.09520 0.09710 0.008990 0.003670 3.000 1.650 0.02720 0.07070 0.05130 0.02190 0.06020 0.07290 0.003220 0.002550 4.000 1.050 0.02660 0.05300 0.04890 0.01390 0.02330 0.04240 0.0008910 0.0009810 5.000 0.7150 0.02590 0.03710 0.04510 0.01360 0.008500 0.02380 0.0007840 0.0003260 6.000 0.5240 0.02500 0.02450 0.04030 0.01140 0.004240 0.01330 0.0007010 0.0001310 7.000 0.4010 0.02390 0.01540 0.03490 0.008020 0.003510 0.007470 0.0005120 9.210E-05 8.000 0.3150 0.02280 0.009450 0.02950 0.005010 0.003480 0.004240 0.0003250 8.920E-05 10.00 0.2000 0.02000 0.003900 0.02000 0.001600 0.003000 0.001400 1.000E-04 8.000E-05 15.00 0.07800 0.01400 0.002300 0.006000 0.0003500 0.001100 0.0001200 1.900E-05 2.900E-05 20.00 0.03600 0.009400 0.001900 0.001800 0.0003000 0.0003100 1.600E-05 1.700E-05 8.300E-06 30.00 0.010000 0.003600 0.0006800 0.0001900 0.0001100 3.200E-05 6.400E-07 6.000E-06 8.500E-07 40.00 0.003500 0.001400 0.0002200 3.100E-05 3.400E-05 4.800E-06 5.200E-08 1.900E-06 1.300E-07 60.00 0.0005700 0.0002400 3.100E-05 1.800E-06 4.700E-06 2.700E-07 1.300E-09 2.600E-07 7.100E-09 100.0 4.000E-05 1.800E-05 1.900E-06 3.900E-08 2.800E-07 5.600E-09 1.000E-11 1.500E-08 1.500E-10 #S 32 Ge #N 10 #UOCCUP 2 2 6 2 6 10 2 2 #UBIND 1.110E+04 1413. 1228. 181.0 124.0 29.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 0.000 7.030 0.02720 0.1010 0.05090 0.2710 0.1670 0.1330 1.030 0.7690 0.05000 7.010 0.02720 0.1010 0.05090 0.2710 0.1670 0.1330 1.020 0.7690 0.1000 6.940 0.02720 0.1010 0.05090 0.2700 0.1670 0.1330 0.9850 0.7680 0.1500 6.820 0.02720 0.1000 0.05090 0.2690 0.1670 0.1330 0.9340 0.7630 0.2000 6.660 0.02720 0.1000 0.05090 0.2670 0.1670 0.1330 0.8660 0.7510 0.3000 6.210 0.02720 0.1000 0.05090 0.2630 0.1670 0.1330 0.7020 0.6980 0.4000 5.660 0.02720 0.09990 0.05090 0.2570 0.1660 0.1330 0.5270 0.6050 0.5000 5.100 0.02720 0.09950 0.05090 0.2500 0.1660 0.1330 0.3710 0.4890 0.6000 4.600 0.02710 0.09910 0.05090 0.2410 0.1660 0.1330 0.2460 0.3710 0.7000 4.190 0.02710 0.09850 0.05090 0.2310 0.1650 0.1330 0.1560 0.2690 0.8000 3.870 0.02710 0.09790 0.05090 0.2200 0.1640 0.1320 0.09710 0.1870 1.000 3.480 0.02710 0.09650 0.05090 0.1950 0.1610 0.1310 0.04150 0.08370 1.200 3.250 0.02710 0.09480 0.05090 0.1700 0.1560 0.1300 0.02770 0.03510 1.400 3.080 0.02700 0.09280 0.05080 0.1440 0.1500 0.1270 0.02620 0.01500 1.600 2.930 0.02700 0.09050 0.05080 0.1200 0.1420 0.1230 0.02580 0.007750 1.800 2.760 0.02690 0.08810 0.05070 0.09780 0.1320 0.1180 0.02380 0.005610 2.000 2.580 0.02680 0.08540 0.05060 0.07840 0.1210 0.1120 0.02040 0.005200 2.400 2.220 0.02670 0.07960 0.05030 0.04860 0.09800 0.09840 0.01280 0.005090 3.000 1.720 0.02640 0.07000 0.04960 0.02410 0.06480 0.07710 0.005010 0.003950 4.000 1.110 0.02590 0.05350 0.04750 0.01380 0.02700 0.04720 0.001210 0.001700 5.000 0.7590 0.02520 0.03830 0.04420 0.01350 0.01010 0.02760 0.0009380 0.0005950 6.000 0.5530 0.02430 0.02590 0.03990 0.01180 0.004700 0.01590 0.0008800 0.0002260 7.000 0.4220 0.02340 0.01670 0.03500 0.008730 0.003530 0.009130 0.0006800 0.0001370 8.000 0.3310 0.02240 0.01050 0.02990 0.005700 0.003440 0.005290 0.0004530 0.0001260 10.00 0.2100 0.02000 0.004300 0.02100 0.001900 0.003100 0.001800 0.0001500 0.0001200 15.00 0.08300 0.01500 0.002300 0.006600 0.0003500 0.001200 0.0001700 2.300E-05 4.800E-05 20.00 0.03800 0.009700 0.001900 0.002000 0.0003100 0.0003600 2.300E-05 2.100E-05 1.400E-05 30.00 0.01100 0.003900 0.0007400 0.0002300 0.0001200 3.900E-05 9.300E-07 8.100E-06 1.500E-06 40.00 0.003900 0.001500 0.0002500 3.800E-05 3.900E-05 6.100E-06 7.700E-08 2.600E-06 2.300E-07 60.00 0.0006500 0.0002800 3.600E-05 2.200E-06 5.600E-06 3.400E-07 1.900E-09 3.700E-07 1.300E-08 100.0 4.700E-05 2.000E-05 2.200E-06 4.800E-08 3.400E-07 7.200E-09 1.500E-11 2.200E-08 2.800E-10 #S 33 As #N 10 #UOCCUP 2 2 6 2 6 10 2 3 #UBIND 1.187E+04 1527. 1335. 204.0 143.0 42.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 0.000 7.100 0.02630 0.09710 0.04910 0.2580 0.1580 0.1230 0.9220 0.6740 0.05000 7.080 0.02630 0.09710 0.04910 0.2580 0.1580 0.1230 0.9140 0.6740 0.1000 7.030 0.02630 0.09710 0.04910 0.2580 0.1580 0.1230 0.8900 0.6730 0.1500 6.940 0.02630 0.09700 0.04910 0.2570 0.1580 0.1230 0.8520 0.6700 0.2000 6.820 0.02630 0.09700 0.04910 0.2550 0.1580 0.1230 0.8010 0.6640 0.3000 6.460 0.02630 0.09680 0.04910 0.2520 0.1580 0.1230 0.6730 0.6320 0.4000 5.990 0.02630 0.09650 0.04910 0.2460 0.1580 0.1230 0.5310 0.5730 0.5000 5.460 0.02630 0.09620 0.04910 0.2400 0.1580 0.1230 0.3960 0.4910 0.6000 4.930 0.02630 0.09580 0.04910 0.2320 0.1580 0.1230 0.2800 0.3990 0.7000 4.460 0.02630 0.09530 0.04910 0.2230 0.1570 0.1220 0.1900 0.3100 0.8000 4.070 0.02630 0.09480 0.04910 0.2130 0.1560 0.1220 0.1250 0.2320 1.000 3.530 0.02630 0.09350 0.04910 0.1920 0.1540 0.1220 0.05520 0.1200 1.200 3.210 0.02620 0.09190 0.04910 0.1690 0.1500 0.1210 0.03210 0.05690 1.400 3.010 0.02620 0.09010 0.04910 0.1450 0.1450 0.1190 0.02750 0.02630 1.600 2.850 0.02610 0.08810 0.04900 0.1220 0.1380 0.1160 0.02730 0.01290 1.800 2.710 0.02610 0.08580 0.04900 0.1010 0.1300 0.1130 0.02640 0.007890 2.000 2.550 0.02600 0.08340 0.04890 0.08260 0.1210 0.1090 0.02390 0.006360 2.400 2.240 0.02590 0.07810 0.04870 0.05280 0.09990 0.09790 0.01650 0.006070 3.000 1.770 0.02570 0.06930 0.04810 0.02660 0.06890 0.07970 0.007190 0.005240 4.000 1.170 0.02510 0.05380 0.04630 0.01390 0.03070 0.05140 0.001650 0.002550 5.000 0.8040 0.02450 0.03930 0.04330 0.01330 0.01200 0.03120 0.001070 0.0009540 6.000 0.5830 0.02370 0.02720 0.03950 0.01210 0.005320 0.01850 0.001030 0.0003540 7.000 0.4440 0.02290 0.01790 0.03500 0.009370 0.003620 0.01090 0.0008450 0.0001880 8.000 0.3480 0.02200 0.01150 0.03020 0.006390 0.003410 0.006460 0.0005900 0.0001600 10.00 0.2300 0.02000 0.004700 0.02100 0.002300 0.003200 0.002300 0.0002200 0.0001500 15.00 0.08800 0.01500 0.002200 0.007200 0.0003500 0.001400 0.0002300 2.700E-05 6.800E-05 20.00 0.04100 0.009900 0.002000 0.002300 0.0003200 0.0004300 3.200E-05 2.500E-05 2.100E-05 30.00 0.01200 0.004100 0.0008000 0.0002700 0.0001300 4.800E-05 1.300E-06 1.000E-05 2.400E-06 40.00 0.004200 0.001600 0.0002700 4.600E-05 4.500E-05 7.600E-06 1.100E-07 3.500E-06 3.700E-07 60.00 0.0007300 0.0003100 4.100E-05 2.700E-06 6.500E-06 4.300E-07 2.800E-09 5.000E-07 2.100E-08 100.0 5.300E-05 2.300E-05 2.600E-06 6.000E-08 4.000E-07 9.300E-09 2.100E-11 3.100E-08 4.600E-10 #S 34 Se #N 10 #UOCCUP 2 2 6 2 6 10 2 4 #UBIND 1.266E+04 1654. 1449. 231.0 164.0 56.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 0.000 7.160 0.02550 0.09390 0.04750 0.2470 0.1510 0.1140 0.8390 0.6040 0.05000 7.140 0.02550 0.09390 0.04750 0.2470 0.1510 0.1140 0.8330 0.6040 0.1000 7.100 0.02550 0.09390 0.04750 0.2460 0.1510 0.1140 0.8140 0.6030 0.1500 7.040 0.02550 0.09390 0.04750 0.2450 0.1510 0.1140 0.7850 0.6020 0.2000 6.940 0.02550 0.09380 0.04750 0.2440 0.1510 0.1140 0.7450 0.5980 0.3000 6.650 0.02550 0.09360 0.04750 0.2410 0.1510 0.1140 0.6430 0.5780 0.4000 6.240 0.02550 0.09340 0.04750 0.2360 0.1510 0.1140 0.5260 0.5380 0.5000 5.760 0.02550 0.09310 0.04750 0.2300 0.1500 0.1140 0.4090 0.4790 0.6000 5.250 0.02550 0.09270 0.04750 0.2230 0.1500 0.1140 0.3040 0.4080 0.7000 4.760 0.02550 0.09230 0.04750 0.2160 0.1500 0.1140 0.2170 0.3330 0.8000 4.320 0.02550 0.09180 0.04750 0.2070 0.1490 0.1140 0.1500 0.2630 1.000 3.660 0.02550 0.09060 0.04750 0.1880 0.1470 0.1140 0.07020 0.1510 1.200 3.240 0.02540 0.08920 0.04750 0.1670 0.1440 0.1130 0.03810 0.07970 1.400 2.980 0.02540 0.08760 0.04740 0.1450 0.1400 0.1120 0.02910 0.04000 1.600 2.800 0.02540 0.08570 0.04740 0.1240 0.1340 0.1100 0.02800 0.02020 1.800 2.650 0.02530 0.08370 0.04730 0.1040 0.1270 0.1080 0.02770 0.01130 2.000 2.510 0.02530 0.08150 0.04730 0.08640 0.1190 0.1040 0.02620 0.007910 2.400 2.230 0.02510 0.07660 0.04710 0.05680 0.1010 0.09630 0.01980 0.006730 3.000 1.810 0.02490 0.06840 0.04660 0.02930 0.07230 0.08090 0.009640 0.006280 4.000 1.230 0.02440 0.05400 0.04500 0.01420 0.03440 0.05490 0.002240 0.003490 5.000 0.8480 0.02390 0.04010 0.04240 0.01300 0.01420 0.03470 0.001190 0.001410 6.000 0.6150 0.02320 0.02830 0.03900 0.01230 0.006130 0.02120 0.001160 0.0005280 7.000 0.4660 0.02240 0.01910 0.03490 0.009940 0.003790 0.01280 0.001000 0.0002520 8.000 0.3650 0.02150 0.01250 0.03050 0.007060 0.003380 0.007730 0.0007340 0.0001920 10.00 0.2400 0.02000 0.005200 0.02200 0.002800 0.003300 0.002900 0.0002900 0.0001800 15.00 0.09400 0.01500 0.002200 0.007900 0.0003600 0.001500 0.0003000 3.200E-05 9.000E-05 20.00 0.04400 0.010000 0.002000 0.002600 0.0003300 0.0005000 4.200E-05 2.800E-05 3.000E-05 30.00 0.01300 0.004300 0.0008600 0.0003200 0.0001500 5.800E-05 1.800E-06 1.300E-05 3.500E-06 40.00 0.004600 0.001800 0.0003000 5.500E-05 5.100E-05 9.400E-06 1.600E-07 4.400E-06 5.600E-07 60.00 0.0008200 0.0003400 4.700E-05 3.300E-06 7.600E-06 5.500E-07 4.000E-09 6.500E-07 3.200E-08 100.0 6.100E-05 2.700E-05 3.000E-06 7.500E-08 4.800E-07 1.200E-08 2.900E-11 4.100E-08 7.000E-10 #S 35 Br #N 10 #UOCCUP 2 2 6 2 6 10 2 5 #UBIND 1.347E+04 1782. 1565. 257.0 184.0 69.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 0.000 7.200 0.02480 0.09090 0.04590 0.2360 0.1440 0.1070 0.7720 0.5490 0.05000 7.190 0.02480 0.09090 0.04590 0.2360 0.1440 0.1070 0.7670 0.5490 0.1000 7.160 0.02480 0.09090 0.04590 0.2360 0.1440 0.1070 0.7520 0.5490 0.1500 7.110 0.02480 0.09090 0.04590 0.2350 0.1440 0.1070 0.7290 0.5480 0.2000 7.030 0.02480 0.09080 0.04590 0.2340 0.1440 0.1070 0.6970 0.5450 0.3000 6.790 0.02480 0.09070 0.04590 0.2310 0.1440 0.1070 0.6140 0.5320 0.4000 6.440 0.02480 0.09040 0.04590 0.2270 0.1440 0.1070 0.5160 0.5040 0.5000 6.010 0.02480 0.09020 0.04590 0.2220 0.1430 0.1070 0.4150 0.4610 0.6000 5.530 0.02480 0.08980 0.04590 0.2160 0.1430 0.1070 0.3200 0.4060 0.7000 5.050 0.02480 0.08940 0.04590 0.2090 0.1430 0.1070 0.2380 0.3450 0.8000 4.590 0.02480 0.08900 0.04590 0.2010 0.1420 0.1070 0.1720 0.2830 1.000 3.840 0.02470 0.08790 0.04590 0.1840 0.1410 0.1070 0.08550 0.1770 1.200 3.320 0.02470 0.08660 0.04590 0.1650 0.1380 0.1060 0.04540 0.1020 1.400 2.990 0.02470 0.08510 0.04590 0.1450 0.1350 0.1050 0.03140 0.05510 1.600 2.770 0.02460 0.08340 0.04590 0.1250 0.1300 0.1040 0.02840 0.02920 1.800 2.610 0.02460 0.08160 0.04580 0.1070 0.1240 0.1020 0.02820 0.01610 2.000 2.470 0.02450 0.07960 0.04580 0.08950 0.1170 0.1000 0.02760 0.01020 2.400 2.220 0.02440 0.07510 0.04560 0.06050 0.1010 0.09380 0.02260 0.007280 3.000 1.840 0.02420 0.06750 0.04510 0.03210 0.07510 0.08120 0.01220 0.007030 4.000 1.280 0.02380 0.05400 0.04380 0.01470 0.03800 0.05770 0.003030 0.004440 5.000 0.8920 0.02330 0.04090 0.04150 0.01280 0.01650 0.03780 0.001340 0.001950 6.000 0.6470 0.02260 0.02940 0.03840 0.01240 0.007120 0.02380 0.001260 0.0007560 7.000 0.4890 0.02190 0.02030 0.03470 0.01040 0.004050 0.01470 0.001140 0.0003350 8.000 0.3820 0.02110 0.01350 0.03060 0.007690 0.003390 0.009090 0.0008780 0.0002270 10.00 0.2500 0.01900 0.005800 0.02300 0.003200 0.003300 0.003500 0.0003800 0.0002100 15.00 0.09900 0.01500 0.002100 0.008500 0.0003900 0.001700 0.0003800 3.900E-05 0.0001100 20.00 0.04700 0.010000 0.002000 0.002900 0.0003300 0.0005700 5.600E-05 3.100E-05 3.900E-05 30.00 0.01400 0.004500 0.0009200 0.0003700 0.0001600 7.000E-05 2.500E-06 1.500E-05 4.800E-06 40.00 0.005100 0.001900 0.0003400 6.500E-05 5.800E-05 1.200E-05 2.200E-07 5.500E-06 7.800E-07 60.00 0.0009100 0.0003800 5.300E-05 4.000E-06 8.800E-06 6.800E-07 5.600E-09 8.300E-07 4.600E-08 100.0 7.000E-05 3.000E-05 3.500E-06 9.200E-08 5.600E-07 1.500E-08 3.800E-11 6.300E-08 1.000E-09 #S 36 Kr #N 14 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 #UBIND 1.433E+04 1925. 1731. 1678. 293.0 222.0 214.0 95.00 94.00 27.00 14.00 14.00 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 0.000 7.190 0.02340 0.08620 0.04330 0.04430 0.2220 0.1340 0.1370 0.1010 0.1010 0.7050 0.4960 0.5080 0.05000 7.180 0.02340 0.08620 0.04330 0.04430 0.2220 0.1340 0.1370 0.1010 0.1010 0.7010 0.4960 0.5080 0.1000 7.150 0.02340 0.08620 0.04330 0.04430 0.2220 0.1340 0.1370 0.1010 0.1010 0.6890 0.4960 0.5080 0.1500 7.110 0.02340 0.08610 0.04330 0.04430 0.2210 0.1340 0.1370 0.1010 0.1010 0.6710 0.4950 0.5070 0.2000 7.050 0.02340 0.08610 0.04330 0.04430 0.2200 0.1340 0.1370 0.1010 0.1010 0.6460 0.4940 0.5050 0.3000 6.860 0.02340 0.08590 0.04330 0.04430 0.2180 0.1340 0.1370 0.1010 0.1010 0.5810 0.4850 0.4960 0.4000 6.570 0.02340 0.08580 0.04330 0.04430 0.2140 0.1340 0.1370 0.1010 0.1010 0.5010 0.4670 0.4750 0.5000 6.200 0.02340 0.08550 0.04330 0.04430 0.2100 0.1340 0.1370 0.1010 0.1010 0.4160 0.4360 0.4420 0.6000 5.770 0.02340 0.08520 0.04330 0.04430 0.2050 0.1340 0.1370 0.1010 0.1010 0.3330 0.3960 0.3980 0.7000 5.300 0.02340 0.08490 0.04330 0.04430 0.1990 0.1340 0.1360 0.1010 0.1010 0.2590 0.3480 0.3480 0.8000 4.850 0.02340 0.08450 0.04330 0.04430 0.1920 0.1340 0.1360 0.1010 0.1010 0.1950 0.2980 0.2950 1.000 4.040 0.02340 0.08360 0.04330 0.04430 0.1780 0.1320 0.1350 0.1000 0.1010 0.1040 0.2020 0.1960 1.200 3.440 0.02330 0.08250 0.04330 0.04430 0.1610 0.1310 0.1330 0.1000 0.1010 0.05610 0.1260 0.1210 1.400 3.030 0.02330 0.08120 0.04330 0.04420 0.1440 0.1280 0.1300 0.09960 0.1000 0.03530 0.07450 0.06980 1.600 2.760 0.02330 0.07980 0.04330 0.04420 0.1260 0.1240 0.1260 0.09880 0.09940 0.02880 0.04230 0.03900 1.800 2.580 0.02320 0.07820 0.04320 0.04420 0.1090 0.1200 0.1210 0.09750 0.09810 0.02770 0.02390 0.02190 2.000 2.440 0.02320 0.07640 0.04320 0.04410 0.09340 0.1140 0.1150 0.09580 0.09630 0.02760 0.01430 0.01320 2.400 2.200 0.02310 0.07250 0.04310 0.04400 0.06550 0.1010 0.1010 0.09090 0.09130 0.02470 0.007970 0.007820 3.000 1.860 0.02290 0.06590 0.04270 0.04360 0.03630 0.07810 0.07740 0.08050 0.08070 0.01540 0.007310 0.007340 4.000 1.330 0.02260 0.05380 0.04160 0.04240 0.01560 0.04290 0.04150 0.05970 0.05950 0.004380 0.005450 0.005270 5.000 0.9330 0.02210 0.04180 0.03980 0.04050 0.01230 0.02010 0.01900 0.04060 0.04030 0.001560 0.002730 0.002540 6.000 0.6770 0.02150 0.03100 0.03730 0.03770 0.01210 0.008920 0.008370 0.02640 0.02610 0.001310 0.001140 0.001040 7.000 0.5120 0.02090 0.02200 0.03410 0.03440 0.01080 0.004620 0.004450 0.01680 0.01650 0.001250 0.0004830 0.0004420 8.000 0.4000 0.02020 0.01520 0.03050 0.03060 0.008500 0.003390 0.003420 0.01060 0.01040 0.001040 0.0002740 0.0002640 10.00 0.2600 0.01900 0.006800 0.02300 0.02300 0.004000 0.003200 0.003300 0.004200 0.004100 0.0005100 0.0002200 0.0002300 15.00 0.1100 0.01400 0.002000 0.009500 0.009100 0.0004500 0.001900 0.001800 0.0005000 0.0004800 5.000E-05 0.0001400 0.0001400 20.00 0.05100 0.010000 0.001900 0.003500 0.003200 0.0003200 0.0007200 0.0006700 7.700E-05 7.300E-05 3.300E-05 5.500E-05 5.100E-05 30.00 0.01600 0.004900 0.001000 0.0005200 0.0004500 0.0001900 1.000E-04 8.800E-05 3.700E-06 3.400E-06 1.900E-05 7.600E-06 6.600E-06 40.00 0.006000 0.002200 0.0004100 1.000E-04 8.200E-05 7.300E-05 1.900E-05 1.500E-05 3.500E-07 3.100E-07 7.600E-06 1.400E-06 1.100E-06 60.00 0.001200 0.0004900 7.500E-05 7.800E-06 5.600E-06 1.300E-05 1.400E-06 9.900E-07 1.100E-08 8.600E-09 1.300E-06 1.000E-07 7.300E-08 100.0 0.0001100 4.900E-05 6.100E-06 2.800E-07 1.500E-07 1.000E-06 4.800E-08 2.600E-08 1.300E-10 9.300E-11 1.000E-07 3.500E-09 1.900E-09 #S 37 Rb #N 15 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 1 #UBIND 1.520E+04 2065. 1865. 1805. 321.0 248.0 239.0 1120. 111.0 29.00 14.00 14.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 0.000 9.080 0.02270 0.08350 0.04190 0.04290 0.2130 0.1290 0.1310 0.09500 0.09570 0.6310 0.4330 0.4430 2.560 0.05000 8.930 0.02270 0.08350 0.04190 0.04290 0.2130 0.1290 0.1310 0.09500 0.09570 0.6290 0.4330 0.4430 2.400 0.1000 8.500 0.02270 0.08350 0.04190 0.04290 0.2130 0.1290 0.1310 0.09500 0.09570 0.6200 0.4330 0.4430 2.000 0.1500 7.950 0.02270 0.08340 0.04190 0.04290 0.2120 0.1290 0.1310 0.09500 0.09570 0.6070 0.4330 0.4420 1.480 0.2000 7.410 0.02270 0.08340 0.04190 0.04290 0.2110 0.1290 0.1310 0.09500 0.09570 0.5890 0.4320 0.4410 0.9770 0.3000 6.630 0.02270 0.08330 0.04190 0.04290 0.2090 0.1290 0.1310 0.09500 0.09570 0.5390 0.4280 0.4370 0.3240 0.4000 6.200 0.02270 0.08310 0.04190 0.04290 0.2060 0.1290 0.1310 0.09500 0.09570 0.4780 0.4180 0.4250 0.09720 0.5000 5.910 0.02270 0.08290 0.04190 0.04290 0.2020 0.1280 0.1310 0.09500 0.09570 0.4100 0.4000 0.4060 0.05560 0.6000 5.600 0.02270 0.08260 0.04190 0.04290 0.1980 0.1280 0.1310 0.09500 0.09570 0.3410 0.3750 0.3790 0.05370 0.7000 5.250 0.02270 0.08230 0.04190 0.04290 0.1920 0.1280 0.1300 0.09500 0.09570 0.2760 0.3430 0.3450 0.05000 0.8000 4.880 0.02270 0.08200 0.04190 0.04290 0.1870 0.1280 0.1300 0.09500 0.09560 0.2170 0.3070 0.3060 0.04140 1.000 4.160 0.02270 0.08110 0.04190 0.04290 0.1730 0.1270 0.1290 0.09490 0.09560 0.1260 0.2280 0.2250 0.02240 1.200 3.560 0.02270 0.08010 0.04190 0.04290 0.1580 0.1260 0.1280 0.09470 0.09540 0.07110 0.1570 0.1520 0.01040 1.400 3.110 0.02260 0.07900 0.04190 0.04290 0.1430 0.1230 0.1250 0.09440 0.09500 0.04320 0.1010 0.09580 0.004610 1.600 2.800 0.02260 0.07760 0.04190 0.04290 0.1270 0.1200 0.1220 0.09380 0.09440 0.03200 0.06130 0.05750 0.002300 1.800 2.580 0.02260 0.07620 0.04190 0.04280 0.1110 0.1160 0.1180 0.09290 0.09340 0.02900 0.03640 0.03370 0.001540 2.000 2.420 0.02250 0.07450 0.04180 0.04280 0.09550 0.1120 0.1130 0.09160 0.09210 0.02870 0.02180 0.02010 0.001370 2.400 2.180 0.02250 0.07100 0.04170 0.04270 0.06860 0.1000 0.1010 0.08790 0.08830 0.02720 0.01030 0.009930 0.001340 3.000 1.860 0.02230 0.06490 0.04140 0.04230 0.03920 0.07960 0.07900 0.07950 0.07970 0.01880 0.008260 0.008380 0.0009880 4.000 1.370 0.02200 0.05360 0.04050 0.04130 0.01670 0.04600 0.04470 0.06110 0.06100 0.006020 0.006860 0.006730 0.0003230 5.000 0.9750 0.02150 0.04220 0.03890 0.03960 0.01210 0.02270 0.02150 0.04310 0.04290 0.001960 0.003780 0.003570 9.670E-05 6.000 0.7100 0.02100 0.03180 0.03660 0.03710 0.01190 0.01040 0.009710 0.02880 0.02860 0.001440 0.001680 0.001540 6.560E-05 7.000 0.5360 0.02040 0.02310 0.03370 0.03410 0.01100 0.005200 0.004960 0.01880 0.01850 0.001410 0.0007140 0.0006500 6.430E-05 8.000 0.4190 0.01980 0.01610 0.03050 0.03060 0.008990 0.003520 0.003520 0.01210 0.01190 0.001230 0.0003690 0.0003510 5.630E-05 10.00 0.2700 0.01800 0.007400 0.02400 0.02300 0.004500 0.003200 0.003300 0.005000 0.004900 0.0006500 0.0002700 0.0002700 3.000E-05 15.00 0.1100 0.01400 0.002000 0.010000 0.009700 0.0005100 0.002000 0.002000 0.0006200 0.0006000 6.600E-05 0.0001800 0.0001800 3.000E-06 20.00 0.05400 0.01100 0.001900 0.003900 0.003600 0.0003200 0.0008100 0.0007600 9.800E-05 9.300E-05 3.600E-05 7.500E-05 6.900E-05 1.600E-06 30.00 0.01700 0.005100 0.001100 0.0006000 0.0005200 0.0002000 0.0001200 1.000E-04 4.900E-06 4.500E-06 2.300E-05 1.100E-05 9.500E-06 1.000E-06 40.00 0.006500 0.002300 0.0004500 0.0001200 9.700E-05 8.100E-05 2.300E-05 1.900E-05 4.700E-07 4.200E-07 9.400E-06 2.100E-06 1.700E-06 4.300E-07 60.00 0.001300 0.0005400 8.400E-05 9.400E-06 6.700E-06 1.500E-05 1.700E-06 1.200E-06 1.400E-08 1.200E-08 1.700E-06 1.500E-07 1.100E-07 7.600E-08 100.0 0.0001300 5.600E-05 7.100E-06 3.400E-07 1.800E-07 1.200E-06 6.000E-08 3.200E-08 1.800E-10 1.300E-10 1.400E-07 5.400E-09 2.800E-09 6.200E-09 #S 38 Sr #N 15 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 2 #UBIND 1.610E+04 2216. 2007. 1940. 358.0 280.0 269.0 135.0 133.0 38.00 22.00 22.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 0.000 10.10 0.02210 0.08090 0.04060 0.04160 0.2040 0.1230 0.1250 0.09010 0.09070 0.5730 0.3890 0.3970 2.060 0.05000 9.970 0.02210 0.08090 0.04060 0.04160 0.2040 0.1230 0.1250 0.09010 0.09070 0.5710 0.3890 0.3970 1.970 0.1000 9.480 0.02210 0.08090 0.04060 0.04160 0.2040 0.1230 0.1250 0.09010 0.09070 0.5640 0.3890 0.3970 1.740 0.1500 8.810 0.02210 0.08090 0.04060 0.04160 0.2040 0.1230 0.1250 0.09010 0.09070 0.5540 0.3890 0.3970 1.410 0.2000 8.070 0.02210 0.08080 0.04060 0.04160 0.2030 0.1230 0.1250 0.09010 0.09070 0.5400 0.3880 0.3960 1.060 0.3000 6.830 0.02210 0.08070 0.04060 0.04160 0.2010 0.1230 0.1250 0.09010 0.09070 0.5020 0.3860 0.3930 0.4860 0.4000 6.090 0.02210 0.08060 0.04060 0.04160 0.1980 0.1230 0.1250 0.09010 0.09070 0.4540 0.3790 0.3870 0.1870 0.5000 5.690 0.02210 0.08040 0.04060 0.04160 0.1950 0.1230 0.1250 0.09010 0.09070 0.3990 0.3680 0.3740 0.08210 0.6000 5.420 0.02210 0.08010 0.04060 0.04160 0.1910 0.1230 0.1250 0.09010 0.09070 0.3410 0.3520 0.3560 0.06040 0.7000 5.150 0.02210 0.07990 0.04060 0.04160 0.1860 0.1230 0.1250 0.09010 0.09070 0.2850 0.3300 0.3330 0.05910 0.8000 4.860 0.02210 0.07950 0.04060 0.04160 0.1810 0.1230 0.1250 0.09010 0.09070 0.2320 0.3030 0.3040 0.05680 1.000 4.240 0.02210 0.07880 0.04060 0.04160 0.1690 0.1220 0.1240 0.09000 0.09060 0.1450 0.2410 0.2390 0.04100 1.200 3.660 0.02200 0.07790 0.04060 0.04160 0.1550 0.1210 0.1230 0.08990 0.09050 0.08620 0.1780 0.1740 0.02330 1.400 3.200 0.02200 0.07680 0.04060 0.04160 0.1410 0.1190 0.1210 0.08960 0.09020 0.05250 0.1230 0.1180 0.01170 1.600 2.850 0.02200 0.07560 0.04060 0.04160 0.1260 0.1160 0.1180 0.08920 0.08980 0.03650 0.08030 0.07610 0.005650 1.800 2.590 0.02190 0.07420 0.04060 0.04150 0.1110 0.1130 0.1150 0.08860 0.08910 0.03070 0.05050 0.04720 0.003120 2.000 2.410 0.02190 0.07280 0.04050 0.04150 0.09720 0.1090 0.1100 0.08760 0.08820 0.02950 0.03120 0.02900 0.002290 2.400 2.160 0.02180 0.06950 0.04040 0.04140 0.07140 0.09910 0.09960 0.08480 0.08520 0.02890 0.01360 0.01290 0.002110 3.000 1.870 0.02170 0.06380 0.04020 0.04110 0.04210 0.08060 0.08020 0.07800 0.07830 0.02200 0.009020 0.009150 0.001750 4.000 1.400 0.02140 0.05330 0.03940 0.04020 0.01790 0.04890 0.04760 0.06210 0.06200 0.008010 0.008080 0.008010 0.0006660 5.000 1.010 0.02100 0.04260 0.03790 0.03870 0.01210 0.02530 0.02410 0.04520 0.04500 0.002520 0.004900 0.004670 0.0001950 6.000 0.7430 0.02050 0.03260 0.03590 0.03650 0.01170 0.01200 0.01120 0.03120 0.03090 0.001590 0.002330 0.002150 0.0001090 7.000 0.5610 0.02000 0.02400 0.03330 0.03370 0.01110 0.005920 0.005590 0.02080 0.02050 0.001550 0.001010 0.0009220 0.0001050 8.000 0.4380 0.01940 0.01710 0.03030 0.03050 0.009410 0.003730 0.003680 0.01360 0.01340 0.001410 0.0004940 0.0004610 9.640E-05 10.00 0.2900 0.01800 0.008100 0.02400 0.02400 0.005100 0.003100 0.003200 0.005800 0.005700 0.0008100 0.0003000 0.0003100 5.600E-05 15.00 0.1200 0.01400 0.002100 0.01100 0.010000 0.0005900 0.002200 0.002100 0.0007600 0.0007300 8.800E-05 0.0002300 0.0002200 6.000E-06 20.00 0.05700 0.01100 0.001900 0.004200 0.003900 0.0003100 0.0009100 0.0008500 0.0001200 0.0001200 4.000E-05 9.700E-05 9.000E-05 2.700E-06 30.00 0.01800 0.005300 0.001100 0.0006800 0.0005900 0.0002100 0.0001400 0.0001200 6.400E-06 5.900E-06 2.700E-05 1.500E-05 1.300E-05 1.800E-06 40.00 0.007000 0.002500 0.0004900 0.0001400 0.0001100 9.000E-05 2.700E-05 2.200E-05 6.300E-07 5.500E-07 1.200E-05 2.900E-06 2.300E-06 7.800E-07 60.00 0.001500 0.0005900 9.400E-05 1.100E-05 8.000E-06 1.700E-05 2.100E-06 1.500E-06 1.900E-08 1.600E-08 2.100E-06 2.200E-07 1.600E-07 1.400E-07 100.0 0.0001500 6.300E-05 8.100E-06 4.200E-07 2.200E-07 1.400E-06 7.500E-08 4.000E-08 2.500E-10 1.700E-10 1.800E-07 7.800E-09 4.100E-09 1.200E-08 #S 39 Y #N 16 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 1 2 #UBIND 1.704E+04 2373. 2155. 2080. 395.0 313.0 301.0 160.0 158.0 46.00 26.00 26.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 0.000 9.920 0.02150 0.07850 0.03940 0.04040 0.1960 0.1180 0.1200 0.08560 0.08630 0.5330 0.3610 0.3660 0.4540 1.900 0.05000 9.780 0.02150 0.07850 0.03940 0.04040 0.1960 0.1180 0.1200 0.08560 0.08630 0.5310 0.3610 0.3660 0.4540 1.830 0.1000 9.390 0.02150 0.07850 0.03940 0.04040 0.1960 0.1180 0.1200 0.08560 0.08630 0.5260 0.3610 0.3660 0.4540 1.640 0.1500 8.830 0.02150 0.07850 0.03940 0.04040 0.1960 0.1180 0.1200 0.08560 0.08630 0.5180 0.3610 0.3660 0.4540 1.370 0.2000 8.200 0.02150 0.07840 0.03940 0.04040 0.1950 0.1180 0.1200 0.08560 0.08630 0.5060 0.3610 0.3660 0.4540 1.070 0.3000 7.070 0.02150 0.07830 0.03940 0.04040 0.1930 0.1180 0.1200 0.08560 0.08630 0.4750 0.3590 0.3640 0.4500 0.5440 0.4000 6.320 0.02150 0.07820 0.03940 0.04040 0.1910 0.1180 0.1200 0.08560 0.08630 0.4340 0.3540 0.3590 0.4390 0.2310 0.5000 5.880 0.02150 0.07800 0.03940 0.04040 0.1880 0.1180 0.1200 0.08560 0.08630 0.3880 0.3460 0.3510 0.4170 0.09980 0.6000 5.580 0.02150 0.07780 0.03940 0.04040 0.1840 0.1180 0.1200 0.08560 0.08630 0.3380 0.3330 0.3370 0.3840 0.06200 0.7000 5.320 0.02150 0.07750 0.03940 0.04040 0.1800 0.1180 0.1200 0.08560 0.08620 0.2880 0.3160 0.3190 0.3440 0.05660 0.8000 5.030 0.02150 0.07730 0.03940 0.04040 0.1750 0.1180 0.1200 0.08560 0.08620 0.2400 0.2950 0.2970 0.3010 0.05620 1.000 4.420 0.02140 0.07660 0.03940 0.04040 0.1650 0.1170 0.1190 0.08560 0.08620 0.1570 0.2440 0.2430 0.2180 0.04600 1.200 3.830 0.02140 0.07570 0.03940 0.04040 0.1520 0.1160 0.1180 0.08550 0.08610 0.09790 0.1880 0.1860 0.1510 0.02950 1.400 3.330 0.02140 0.07470 0.03940 0.04040 0.1390 0.1150 0.1170 0.08530 0.08590 0.06080 0.1370 0.1340 0.1010 0.01630 1.600 2.940 0.02140 0.07360 0.03930 0.04040 0.1260 0.1130 0.1140 0.08500 0.08560 0.04100 0.09430 0.09100 0.06660 0.008360 1.800 2.640 0.02130 0.07240 0.03930 0.04030 0.1120 0.1100 0.1110 0.08450 0.08510 0.03240 0.06230 0.05940 0.04320 0.004440 2.000 2.430 0.02130 0.07100 0.03930 0.04030 0.09840 0.1060 0.1080 0.08390 0.08440 0.02980 0.04010 0.03780 0.02770 0.002840 2.400 2.150 0.02120 0.06800 0.03920 0.04020 0.07380 0.09760 0.09840 0.08170 0.08220 0.02930 0.01730 0.01640 0.01120 0.002260 3.000 1.860 0.02110 0.06280 0.03900 0.04000 0.04480 0.08110 0.08090 0.07620 0.07660 0.02420 0.009510 0.009660 0.003260 0.002050 4.000 1.420 0.02080 0.05300 0.03830 0.03920 0.01920 0.05140 0.05020 0.06250 0.06260 0.010000 0.008770 0.008860 0.001600 0.0009100 5.000 1.050 0.02040 0.04280 0.03700 0.03780 0.01220 0.02790 0.02660 0.04700 0.04680 0.003190 0.005850 0.005680 0.001540 0.0002760 6.000 0.7750 0.02000 0.03320 0.03520 0.03580 0.01150 0.01370 0.01280 0.03330 0.03300 0.001720 0.002980 0.002800 0.001280 0.0001290 7.000 0.5870 0.01950 0.02490 0.03290 0.03330 0.01120 0.006780 0.006340 0.02270 0.02240 0.001640 0.001350 0.001240 0.0009450 0.0001190 8.000 0.4570 0.01900 0.01800 0.03010 0.03040 0.009740 0.004030 0.003920 0.01520 0.01500 0.001540 0.0006360 0.0005910 0.0006580 0.0001130 10.00 0.3000 0.01800 0.008800 0.02400 0.02400 0.005600 0.003100 0.003200 0.006700 0.006500 0.0009600 0.0003300 0.0003400 0.0003000 7.100E-05 15.00 0.1200 0.01400 0.002100 0.01100 0.01100 0.0006900 0.002300 0.002200 0.0009200 0.0008900 0.0001100 0.0002600 0.0002600 4.200E-05 8.300E-06 20.00 0.06000 0.01100 0.001800 0.004600 0.004300 0.0003100 0.001000 0.0009500 0.0001600 0.0001500 4.200E-05 0.0001200 0.0001100 7.100E-06 3.100E-06 30.00 0.01900 0.005400 0.001200 0.0007700 0.0006700 0.0002200 0.0001600 0.0001400 8.300E-06 7.600E-06 3.100E-05 1.900E-05 1.700E-05 3.800E-07 2.200E-06 40.00 0.007600 0.002600 0.0005300 0.0001600 0.0001300 9.900E-05 3.300E-05 2.700E-05 8.200E-07 7.300E-07 1.400E-05 3.700E-06 3.100E-06 3.700E-08 9.800E-07 60.00 0.001600 0.0006500 0.0001100 1.300E-05 9.500E-06 1.900E-05 2.600E-06 1.800E-06 2.600E-08 2.100E-08 2.600E-06 2.900E-07 2.100E-07 1.200E-09 1.900E-07 100.0 0.0001600 7.000E-05 9.200E-06 5.100E-07 2.700E-07 1.600E-06 9.300E-08 4.900E-08 3.300E-10 2.200E-10 2.200E-07 1.100E-08 5.600E-09 1.500E-11 1.600E-08 #S 40 Zr #N 16 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 2 2 #UBIND 1.800E+04 2534. 2309. 2225. 433.0 347.0 333.0 185.0 182.0 54.00 31.00 31.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 0.000 9.760 0.02090 0.07620 0.03820 0.03920 0.1890 0.1140 0.1160 0.08160 0.08230 0.5010 0.3390 0.3420 0.3930 1.800 0.05000 9.640 0.02090 0.07620 0.03820 0.03920 0.1890 0.1140 0.1160 0.08160 0.08230 0.5000 0.3390 0.3420 0.3930 1.740 0.1000 9.300 0.02090 0.07620 0.03820 0.03920 0.1890 0.1140 0.1160 0.08160 0.08230 0.4960 0.3390 0.3420 0.3930 1.580 0.1500 8.810 0.02090 0.07620 0.03820 0.03920 0.1880 0.1140 0.1160 0.08160 0.08230 0.4880 0.3390 0.3420 0.3930 1.340 0.2000 8.240 0.02090 0.07610 0.03820 0.03920 0.1880 0.1140 0.1160 0.08160 0.08230 0.4790 0.3380 0.3420 0.3930 1.070 0.3000 7.190 0.02090 0.07610 0.03820 0.03920 0.1860 0.1140 0.1160 0.08160 0.08230 0.4520 0.3370 0.3410 0.3920 0.5790 0.4000 6.460 0.02090 0.07590 0.03820 0.03920 0.1840 0.1140 0.1160 0.08160 0.08230 0.4170 0.3330 0.3370 0.3870 0.2640 0.5000 6.010 0.02090 0.07580 0.03820 0.03920 0.1810 0.1130 0.1160 0.08160 0.08230 0.3770 0.3270 0.3310 0.3750 0.1150 0.6000 5.720 0.02090 0.07560 0.03820 0.03920 0.1780 0.1130 0.1160 0.08160 0.08230 0.3330 0.3170 0.3210 0.3570 0.06450 0.7000 5.470 0.02090 0.07530 0.03820 0.03920 0.1740 0.1130 0.1160 0.08160 0.08230 0.2880 0.3040 0.3060 0.3320 0.05370 0.8000 5.200 0.02090 0.07510 0.03820 0.03920 0.1700 0.1130 0.1150 0.08160 0.08230 0.2450 0.2870 0.2890 0.3030 0.05310 1.000 4.620 0.02090 0.07440 0.03820 0.03920 0.1600 0.1130 0.1150 0.08160 0.08220 0.1670 0.2440 0.2440 0.2380 0.04710 1.200 4.020 0.02080 0.07370 0.03820 0.03920 0.1490 0.1120 0.1140 0.08150 0.08220 0.1080 0.1950 0.1940 0.1780 0.03310 1.400 3.490 0.02080 0.07280 0.03820 0.03920 0.1370 0.1110 0.1130 0.08140 0.08200 0.06860 0.1470 0.1450 0.1280 0.01980 1.600 3.060 0.02080 0.07170 0.03820 0.03920 0.1250 0.1090 0.1110 0.08120 0.08180 0.04580 0.1060 0.1030 0.08940 0.01080 1.800 2.730 0.02080 0.07060 0.03820 0.03920 0.1120 0.1060 0.1080 0.08080 0.08150 0.03450 0.07310 0.07050 0.06130 0.005810 2.000 2.480 0.02070 0.06930 0.03810 0.03920 0.09930 0.1040 0.1050 0.08030 0.08090 0.03020 0.04880 0.04660 0.04140 0.003450 2.400 2.150 0.02070 0.06660 0.03810 0.03910 0.07580 0.09600 0.09690 0.07860 0.07920 0.02910 0.02150 0.02040 0.01830 0.002290 3.000 1.850 0.02050 0.06170 0.03790 0.03890 0.04740 0.08130 0.08120 0.07430 0.07470 0.02580 0.01020 0.01030 0.005480 0.002170 4.000 1.440 0.02030 0.05260 0.03720 0.03820 0.02070 0.05360 0.05240 0.06260 0.06270 0.01210 0.009170 0.009380 0.001970 0.001120 5.000 1.080 0.01990 0.04300 0.03610 0.03700 0.01240 0.03040 0.02900 0.04840 0.04820 0.004010 0.006690 0.006590 0.001880 0.0003630 6.000 0.8070 0.01950 0.03380 0.03450 0.03520 0.01130 0.01550 0.01450 0.03520 0.03490 0.001900 0.003670 0.003490 0.001660 0.0001490 7.000 0.6130 0.01910 0.02570 0.03240 0.03290 0.01110 0.007750 0.007210 0.02460 0.02430 0.001690 0.001730 0.001600 0.001280 0.0001240 8.000 0.4780 0.01860 0.01890 0.02990 0.03020 0.009980 0.004420 0.004230 0.01680 0.01650 0.001640 0.0008140 0.0007530 0.0009200 0.0001220 10.00 0.3100 0.01700 0.009500 0.02400 0.02400 0.006100 0.003000 0.003100 0.007600 0.007400 0.001100 0.0003600 0.0003700 0.0004400 8.300E-05 15.00 0.1300 0.01400 0.002200 0.01200 0.01100 0.0008100 0.002400 0.002400 0.001100 0.001100 0.0001400 0.0002900 0.0002900 6.500E-05 1.100E-05 20.00 0.06300 0.01100 0.001800 0.005000 0.004600 0.0003100 0.001100 0.001100 0.0001900 0.0001800 4.500E-05 0.0001400 0.0001300 1.100E-05 3.300E-06 30.00 0.02000 0.005600 0.001200 0.0008700 0.0007600 0.0002300 0.0001900 0.0001700 1.100E-05 9.700E-06 3.400E-05 2.300E-05 2.100E-05 6.200E-07 2.500E-06 40.00 0.008100 0.002800 0.0005600 0.0001900 0.0001500 0.0001100 3.900E-05 3.100E-05 1.100E-06 9.400E-07 1.600E-05 4.700E-06 3.900E-06 6.100E-08 1.200E-06 60.00 0.001800 0.0007000 0.0001200 1.600E-05 1.100E-05 2.200E-05 3.100E-06 2.200E-06 3.400E-08 2.800E-08 3.100E-06 3.800E-07 2.700E-07 2.000E-09 2.300E-07 100.0 0.0001800 7.800E-05 1.000E-05 6.100E-07 3.200E-07 1.900E-06 1.200E-07 6.000E-08 4.400E-10 2.900E-10 2.700E-07 1.400E-08 7.300E-09 2.600E-11 2.000E-08 #S 41 Nb #N 16 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 1 #UBIND 1.898E+04 2695. 2462. 2368. 466.0 376.0 360.0 205.0 202.0 55.00 31.00 31.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 0.000 8.560 0.02030 0.07410 0.03710 0.03810 0.1820 0.1090 0.1120 0.07790 0.07870 0.4800 0.3240 0.3260 0.3850 1.880 0.05000 8.490 0.02030 0.07400 0.03710 0.03810 0.1820 0.1090 0.1120 0.07790 0.07870 0.4790 0.3240 0.3260 0.3850 1.810 0.1000 8.300 0.02030 0.07400 0.03710 0.03810 0.1820 0.1090 0.1120 0.07790 0.07870 0.4750 0.3240 0.3260 0.3850 1.630 0.1500 8.030 0.02030 0.07400 0.03710 0.03810 0.1820 0.1090 0.1120 0.07790 0.07870 0.4690 0.3240 0.3260 0.3850 1.370 0.2000 7.720 0.02030 0.07400 0.03710 0.03810 0.1810 0.1090 0.1120 0.07790 0.07870 0.4600 0.3240 0.3250 0.3840 1.080 0.3000 7.140 0.02030 0.07390 0.03710 0.03810 0.1800 0.1090 0.1120 0.07790 0.07870 0.4360 0.3230 0.3240 0.3830 0.5670 0.4000 6.710 0.02030 0.07380 0.03710 0.03810 0.1780 0.1090 0.1120 0.07790 0.07870 0.4050 0.3200 0.3220 0.3770 0.2480 0.5000 6.410 0.02030 0.07360 0.03710 0.03810 0.1750 0.1090 0.1120 0.07790 0.07870 0.3680 0.3150 0.3160 0.3660 0.1030 0.6000 6.150 0.02030 0.07350 0.03710 0.03810 0.1720 0.1090 0.1110 0.07790 0.07870 0.3280 0.3060 0.3080 0.3480 0.05550 0.7000 5.890 0.02030 0.07320 0.03710 0.03810 0.1690 0.1090 0.1110 0.07790 0.07870 0.2870 0.2940 0.2960 0.3230 0.04570 0.8000 5.590 0.02030 0.07300 0.03710 0.03810 0.1650 0.1090 0.1110 0.07790 0.07870 0.2470 0.2790 0.2810 0.2950 0.04520 1.000 4.950 0.02030 0.07240 0.03710 0.03810 0.1560 0.1090 0.1110 0.07790 0.07870 0.1730 0.2410 0.2430 0.2350 0.04020 1.200 4.300 0.02030 0.07170 0.03710 0.03810 0.1460 0.1080 0.1100 0.07790 0.07860 0.1150 0.1980 0.1980 0.1790 0.02860 1.400 3.720 0.02030 0.07090 0.03710 0.03810 0.1350 0.1070 0.1090 0.07780 0.07850 0.07460 0.1530 0.1530 0.1320 0.01760 1.600 3.240 0.02030 0.06990 0.03710 0.03810 0.1240 0.1050 0.1070 0.07760 0.07840 0.04970 0.1140 0.1120 0.09500 0.009930 1.800 2.860 0.02020 0.06890 0.03700 0.03810 0.1120 0.1030 0.1050 0.07740 0.07810 0.03640 0.08080 0.07910 0.06740 0.005470 2.000 2.570 0.02020 0.06770 0.03700 0.03810 0.09990 0.1010 0.1020 0.07700 0.07770 0.03050 0.05570 0.05400 0.04710 0.003210 2.400 2.180 0.02010 0.06510 0.03700 0.03800 0.07760 0.09420 0.09520 0.07570 0.07630 0.02840 0.02550 0.02440 0.02230 0.001890 3.000 1.850 0.02000 0.06070 0.03680 0.03780 0.04970 0.08110 0.08120 0.07220 0.07270 0.02630 0.01080 0.01090 0.007110 0.001790 4.000 1.450 0.01980 0.05210 0.03630 0.03720 0.02220 0.05540 0.05440 0.06230 0.06240 0.01380 0.009150 0.009520 0.002050 0.001050 5.000 1.110 0.01950 0.04310 0.03530 0.03610 0.01270 0.03270 0.03130 0.04950 0.04930 0.004900 0.007240 0.007260 0.001820 0.0003730 6.000 0.8380 0.01910 0.03430 0.03380 0.03450 0.01110 0.01740 0.01620 0.03690 0.03660 0.002100 0.004260 0.004120 0.001690 0.0001410 7.000 0.6390 0.01870 0.02640 0.03190 0.03250 0.01100 0.008840 0.008180 0.02640 0.02600 0.001710 0.002120 0.001990 0.001370 0.0001050 8.000 0.4990 0.01820 0.01970 0.02960 0.03000 0.01010 0.004900 0.004640 0.01840 0.01810 0.001690 0.001010 0.0009360 0.001020 0.0001040 10.00 0.3200 0.01700 0.010000 0.02400 0.02400 0.006600 0.003000 0.003100 0.008600 0.008400 0.001200 0.0003800 0.0003900 0.0005000 7.600E-05 15.00 0.1400 0.01400 0.002300 0.01200 0.01200 0.0009500 0.002500 0.002500 0.001300 0.001300 0.0001800 0.0003000 0.0003100 7.900E-05 1.100E-05 20.00 0.06700 0.01100 0.001700 0.005300 0.005000 0.0003100 0.001200 0.001200 0.0002400 0.0002200 4.700E-05 0.0001600 0.0001500 1.400E-05 2.900E-06 30.00 0.02100 0.005800 0.001300 0.0009800 0.0008500 0.0002400 0.0002200 0.0001900 1.300E-05 1.200E-05 3.600E-05 2.800E-05 2.500E-05 8.000E-07 2.200E-06 40.00 0.008700 0.002900 0.0006000 0.0002100 0.0001700 0.0001200 4.600E-05 3.700E-05 1.400E-06 1.200E-06 1.800E-05 5.800E-06 4.800E-06 8.100E-08 1.100E-06 60.00 0.001900 0.0007600 0.0001300 1.900E-05 1.300E-05 2.400E-05 3.800E-06 2.700E-06 4.500E-08 3.600E-08 3.600E-06 4.700E-07 3.400E-07 2.600E-09 2.200E-07 100.0 0.0002100 8.700E-05 1.200E-05 7.400E-07 3.800E-07 2.100E-06 1.400E-07 7.300E-08 5.700E-10 3.800E-10 3.200E-07 1.800E-08 9.400E-09 3.500E-11 1.900E-08 #S 42 Mo #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 1 1 #UBIND 2.000E+04 2866. 2625. 2520. 505.0 410.0 393.0 230.0 226.0 62.00 35.00 35.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 8.480 0.01980 0.07200 0.03600 0.03710 0.1760 0.1050 0.1080 0.07470 0.07540 0.4560 0.3060 0.3090 0.3470 0.3570 1.820 0.05000 8.420 0.01980 0.07200 0.03600 0.03710 0.1760 0.1050 0.1080 0.07470 0.07540 0.4550 0.3060 0.3090 0.3470 0.3570 1.760 0.1000 8.240 0.01980 0.07200 0.03600 0.03710 0.1760 0.1050 0.1080 0.07470 0.07540 0.4520 0.3060 0.3090 0.3470 0.3570 1.600 0.1500 7.990 0.01980 0.07200 0.03600 0.03710 0.1750 0.1050 0.1080 0.07470 0.07540 0.4460 0.3060 0.3090 0.3470 0.3570 1.360 0.2000 7.700 0.01980 0.07190 0.03600 0.03710 0.1750 0.1050 0.1080 0.07470 0.07540 0.4390 0.3060 0.3090 0.3470 0.3570 1.090 0.3000 7.150 0.01980 0.07190 0.03600 0.03710 0.1740 0.1050 0.1080 0.07470 0.07540 0.4180 0.3050 0.3080 0.3460 0.3560 0.5900 0.4000 6.740 0.01980 0.07170 0.03600 0.03710 0.1720 0.1050 0.1080 0.07470 0.07540 0.3900 0.3030 0.3060 0.3430 0.3520 0.2690 0.5000 6.460 0.01980 0.07160 0.03600 0.03710 0.1700 0.1050 0.1080 0.07470 0.07540 0.3580 0.2980 0.3020 0.3360 0.3440 0.1140 0.6000 6.220 0.01980 0.07140 0.03600 0.03710 0.1670 0.1050 0.1080 0.07470 0.07540 0.3220 0.2920 0.2950 0.3250 0.3300 0.05760 0.7000 5.980 0.01980 0.07120 0.03600 0.03710 0.1640 0.1050 0.1070 0.07470 0.07540 0.2850 0.2830 0.2850 0.3080 0.3110 0.04350 0.8000 5.720 0.01980 0.07100 0.03600 0.03710 0.1600 0.1050 0.1070 0.07470 0.07540 0.2480 0.2700 0.2730 0.2870 0.2880 0.04200 1.000 5.120 0.01980 0.07050 0.03600 0.03710 0.1520 0.1050 0.1070 0.07470 0.07540 0.1790 0.2390 0.2400 0.2400 0.2370 0.03900 1.200 4.500 0.01980 0.06980 0.03600 0.03710 0.1430 0.1040 0.1060 0.07460 0.07530 0.1230 0.2000 0.2000 0.1910 0.1870 0.02950 1.400 3.910 0.01970 0.06910 0.03600 0.03710 0.1330 0.1030 0.1050 0.07460 0.07530 0.08170 0.1600 0.1590 0.1470 0.1430 0.01920 1.600 3.410 0.01970 0.06820 0.03600 0.03710 0.1220 0.1020 0.1040 0.07440 0.07520 0.05480 0.1230 0.1210 0.1110 0.1060 0.01140 1.800 2.990 0.01970 0.06720 0.03600 0.03710 0.1110 0.1000 0.1020 0.07430 0.07490 0.03930 0.09020 0.08780 0.08150 0.07800 0.006510 2.000 2.670 0.01970 0.06620 0.03600 0.03700 0.1000 0.09810 0.09970 0.07400 0.07460 0.03160 0.06430 0.06180 0.05920 0.05630 0.003790 2.400 2.220 0.01960 0.06380 0.03590 0.03700 0.07900 0.09240 0.09350 0.07300 0.07360 0.02790 0.03080 0.02920 0.03000 0.02830 0.001930 3.000 1.850 0.01950 0.05960 0.03580 0.03680 0.05190 0.08070 0.08100 0.07010 0.07060 0.02680 0.01220 0.01200 0.01030 0.009630 0.001740 4.000 1.460 0.01930 0.05160 0.03530 0.03630 0.02380 0.05690 0.05600 0.06170 0.06190 0.01560 0.009260 0.009590 0.002560 0.002450 0.001150 5.000 1.140 0.01900 0.04310 0.03440 0.03530 0.01310 0.03490 0.03360 0.05020 0.05010 0.005970 0.007870 0.007890 0.002000 0.001970 0.0004510 6.000 0.8670 0.01860 0.03470 0.03310 0.03390 0.01090 0.01920 0.01800 0.03840 0.03810 0.002410 0.004980 0.004800 0.001930 0.001890 0.0001640 7.000 0.6650 0.01820 0.02710 0.03140 0.03200 0.01080 0.010000 0.009260 0.02800 0.02770 0.001760 0.002610 0.002440 0.001630 0.001590 0.0001070 8.000 0.5200 0.01780 0.02050 0.02930 0.02970 0.01020 0.005480 0.005130 0.01990 0.01960 0.001730 0.001270 0.001170 0.001250 0.001210 0.0001040 10.00 0.3400 0.01700 0.01100 0.02400 0.02400 0.007000 0.003000 0.003100 0.009600 0.009400 0.001300 0.0004300 0.0004300 0.0006500 0.0006200 8.200E-05 15.00 0.1400 0.01400 0.002400 0.01300 0.01200 0.001100 0.002500 0.002500 0.001500 0.001500 0.0002200 0.0003300 0.0003400 0.0001100 1.000E-04 1.300E-05 20.00 0.07000 0.01100 0.001700 0.005700 0.005300 0.0003200 0.001300 0.001300 0.0002900 0.0002700 5.000E-05 0.0001800 0.0001700 2.000E-05 1.900E-05 3.000E-06 30.00 0.02300 0.005900 0.001300 0.001100 0.0009500 0.0002500 0.0002500 0.0002200 1.700E-05 1.500E-05 3.900E-05 3.400E-05 3.000E-05 1.200E-06 1.000E-06 2.300E-06 40.00 0.009300 0.003000 0.0006400 0.0002500 0.0002000 0.0001300 5.300E-05 4.300E-05 1.800E-06 1.500E-06 2.000E-05 7.100E-06 5.900E-06 1.200E-07 1.000E-07 1.200E-06 60.00 0.002100 0.0008200 0.0001400 2.200E-05 1.500E-05 2.700E-05 4.500E-06 3.200E-06 5.800E-08 4.600E-08 4.200E-06 6.000E-07 4.300E-07 4.000E-09 3.100E-09 2.500E-07 100.0 0.0002300 9.700E-05 1.300E-05 8.800E-07 4.500E-07 2.500E-06 1.700E-07 8.900E-08 7.400E-10 4.900E-10 3.800E-07 2.300E-08 1.200E-08 5.200E-11 3.400E-11 2.300E-08 #S 43 Tc #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 1 2 #UBIND 2.104E+04 3042. 2793. 2677. 544.0 445.0 425.0 257.0 253.0 68.00 39.00 39.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 9.410 0.01930 0.07000 0.03500 0.03610 0.1700 0.1020 0.1040 0.07170 0.07240 0.4300 0.2870 0.2910 0.2990 0.3050 1.620 0.05000 9.330 0.01930 0.07000 0.03500 0.03610 0.1700 0.1020 0.1040 0.07170 0.07240 0.4290 0.2870 0.2910 0.2990 0.3050 1.580 0.1000 9.080 0.01930 0.07000 0.03500 0.03610 0.1700 0.1020 0.1040 0.07170 0.07240 0.4260 0.2870 0.2910 0.2990 0.3050 1.460 0.1500 8.700 0.01930 0.07000 0.03500 0.03610 0.1690 0.1020 0.1040 0.07170 0.07240 0.4220 0.2870 0.2910 0.2990 0.3050 1.280 0.2000 8.260 0.01930 0.07000 0.03500 0.03610 0.1690 0.1020 0.1040 0.07170 0.07240 0.4150 0.2870 0.2910 0.2990 0.3050 1.060 0.3000 7.380 0.01930 0.06990 0.03500 0.03610 0.1680 0.1020 0.1040 0.07170 0.07240 0.3970 0.2860 0.2900 0.2980 0.3040 0.6430 0.4000 6.700 0.01930 0.06980 0.03500 0.03610 0.1660 0.1020 0.1040 0.07170 0.07240 0.3740 0.2850 0.2890 0.2970 0.3030 0.3340 0.5000 6.250 0.01930 0.06970 0.03500 0.03610 0.1640 0.1020 0.1040 0.07170 0.07240 0.3460 0.2820 0.2850 0.2950 0.3000 0.1580 0.6000 5.970 0.01930 0.06950 0.03500 0.03610 0.1620 0.1020 0.1040 0.07170 0.07240 0.3150 0.2770 0.2800 0.2900 0.2950 0.07840 0.7000 5.760 0.01930 0.06930 0.03500 0.03610 0.1590 0.1020 0.1040 0.07170 0.07240 0.2820 0.2690 0.2730 0.2820 0.2860 0.05050 0.8000 5.550 0.01930 0.06910 0.03500 0.03610 0.1560 0.1010 0.1040 0.07170 0.07240 0.2490 0.2600 0.2630 0.2710 0.2730 0.04410 1.000 5.090 0.01930 0.06860 0.03500 0.03610 0.1490 0.1010 0.1030 0.07170 0.07240 0.1850 0.2340 0.2360 0.2410 0.2410 0.04290 1.200 4.570 0.01930 0.06800 0.03500 0.03610 0.1400 0.1010 0.1030 0.07170 0.07240 0.1310 0.2020 0.2020 0.2040 0.2030 0.03630 1.400 4.030 0.01920 0.06730 0.03500 0.03610 0.1310 0.09990 0.1020 0.07160 0.07230 0.08960 0.1660 0.1650 0.1670 0.1640 0.02600 1.600 3.540 0.01920 0.06650 0.03500 0.03610 0.1210 0.09880 0.1010 0.07150 0.07220 0.06110 0.1310 0.1290 0.1320 0.1290 0.01670 1.800 3.120 0.01920 0.06560 0.03500 0.03610 0.1110 0.09730 0.09920 0.07140 0.07210 0.04330 0.09990 0.09710 0.1020 0.09920 0.010000 2.000 2.770 0.01920 0.06470 0.03500 0.03610 0.1000 0.09540 0.09710 0.07110 0.07180 0.03370 0.07350 0.07070 0.07730 0.07470 0.005900 2.400 2.280 0.01910 0.06240 0.03490 0.03600 0.08020 0.09040 0.09170 0.07040 0.07100 0.02790 0.03710 0.03510 0.04220 0.04040 0.002580 3.000 1.860 0.01900 0.05860 0.03480 0.03590 0.05400 0.08000 0.08050 0.06810 0.06860 0.02710 0.01430 0.01370 0.01570 0.01490 0.002050 4.000 1.470 0.01880 0.05110 0.03440 0.03540 0.02550 0.05820 0.05740 0.06090 0.06120 0.01750 0.009390 0.009720 0.003610 0.003460 0.001530 5.000 1.160 0.01850 0.04300 0.03360 0.03450 0.01370 0.03700 0.03560 0.05070 0.05060 0.007260 0.008470 0.008520 0.002350 0.002340 0.0006590 6.000 0.8930 0.01820 0.03500 0.03250 0.03330 0.01080 0.02110 0.01980 0.03960 0.03930 0.002840 0.005740 0.005560 0.002300 0.002290 0.0002390 7.000 0.6900 0.01790 0.02760 0.03090 0.03150 0.01060 0.01130 0.01040 0.02950 0.02920 0.001850 0.003190 0.002980 0.002040 0.002010 0.0001350 8.000 0.5410 0.01740 0.02120 0.02900 0.02940 0.01020 0.006160 0.005710 0.02140 0.02100 0.001780 0.001600 0.001460 0.001630 0.001590 0.0001260 10.00 0.3500 0.01600 0.01200 0.02400 0.02500 0.007400 0.003100 0.003100 0.01100 0.010000 0.001500 0.0005000 0.0004800 0.0008800 0.0008600 0.0001100 15.00 0.1500 0.01400 0.002500 0.01300 0.01300 0.001300 0.002600 0.002600 0.001800 0.001700 0.0002700 0.0003500 0.0003600 0.0001600 0.0001500 2.000E-05 20.00 0.07400 0.01100 0.001700 0.006100 0.005700 0.0003300 0.001400 0.001400 0.0003400 0.0003200 5.500E-05 0.0002000 0.0002000 3.000E-05 2.800E-05 3.900E-06 30.00 0.02400 0.006000 0.001300 0.001200 0.001100 0.0002600 0.0002800 0.0002500 2.100E-05 1.900E-05 4.200E-05 4.100E-05 3.600E-05 1.800E-06 1.600E-06 3.000E-06 40.00 0.009900 0.003200 0.0006800 0.0002800 0.0002200 0.0001400 6.200E-05 5.000E-05 2.200E-06 1.900E-06 2.200E-05 8.800E-06 7.200E-06 1.900E-07 1.600E-07 1.600E-06 60.00 0.002300 0.0008800 0.0001600 2.600E-05 1.800E-05 3.000E-05 5.400E-06 3.800E-06 7.400E-08 5.900E-08 4.900E-06 7.600E-07 5.400E-07 6.300E-09 5.000E-09 3.400E-07 100.0 0.0002600 0.0001100 1.500E-05 1.000E-06 5.300E-07 2.800E-06 2.100E-07 1.100E-07 9.600E-10 6.300E-10 4.500E-07 2.900E-08 1.500E-08 8.300E-11 5.500E-11 3.200E-08 #S 44 Ru #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 3 1 #UBIND 2.212E+04 3224. 2967. 2838. 585.0 483.0 461.0 284.0 279.0 75.00 43.00 43.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 8.340 0.01880 0.06820 0.03410 0.03520 0.1640 0.09820 0.1010 0.06900 0.06970 0.4160 0.2760 0.2810 0.2960 0.3030 1.740 0.05000 8.290 0.01880 0.06820 0.03410 0.03520 0.1640 0.09820 0.1010 0.06900 0.06970 0.4150 0.2760 0.2810 0.2960 0.3030 1.690 0.1000 8.140 0.01880 0.06820 0.03410 0.03520 0.1640 0.09820 0.1010 0.06900 0.06970 0.4120 0.2760 0.2810 0.2960 0.3030 1.540 0.1500 7.920 0.01880 0.06810 0.03410 0.03520 0.1640 0.09820 0.1010 0.06900 0.06970 0.4080 0.2760 0.2810 0.2960 0.3030 1.330 0.2000 7.660 0.01880 0.06810 0.03410 0.03520 0.1630 0.09820 0.1010 0.06900 0.06970 0.4020 0.2760 0.2810 0.2960 0.3030 1.090 0.3000 7.160 0.01880 0.06810 0.03410 0.03520 0.1620 0.09820 0.1010 0.06900 0.06970 0.3860 0.2750 0.2810 0.2960 0.3020 0.6240 0.4000 6.780 0.01880 0.06800 0.03410 0.03520 0.1610 0.09820 0.1000 0.06900 0.06970 0.3650 0.2740 0.2790 0.2940 0.3010 0.3040 0.5000 6.510 0.01880 0.06780 0.03410 0.03520 0.1590 0.09810 0.1000 0.06900 0.06970 0.3390 0.2710 0.2760 0.2920 0.2980 0.1350 0.6000 6.310 0.01880 0.06770 0.03410 0.03520 0.1570 0.09810 0.1000 0.06900 0.06970 0.3100 0.2670 0.2720 0.2860 0.2910 0.06390 0.7000 6.120 0.01880 0.06750 0.03410 0.03520 0.1540 0.09810 0.1000 0.06900 0.06970 0.2790 0.2610 0.2650 0.2770 0.2810 0.04110 0.8000 5.910 0.01880 0.06730 0.03410 0.03520 0.1520 0.09800 0.1000 0.06900 0.06970 0.2480 0.2520 0.2560 0.2660 0.2690 0.03650 1.000 5.410 0.01880 0.06690 0.03410 0.03520 0.1450 0.09780 0.1000 0.06900 0.06960 0.1880 0.2300 0.2320 0.2360 0.2360 0.03550 1.200 4.840 0.01880 0.06630 0.03410 0.03520 0.1370 0.09730 0.09960 0.06900 0.06960 0.1350 0.2010 0.2010 0.2000 0.1990 0.02960 1.400 4.280 0.01880 0.06570 0.03410 0.03520 0.1290 0.09670 0.09880 0.06890 0.06960 0.09440 0.1680 0.1670 0.1650 0.1620 0.02120 1.600 3.750 0.01870 0.06490 0.03400 0.03520 0.1190 0.09570 0.09780 0.06890 0.06950 0.06500 0.1360 0.1330 0.1320 0.1290 0.01380 1.800 3.300 0.01870 0.06410 0.03400 0.03520 0.1100 0.09450 0.09640 0.06870 0.06940 0.04600 0.1060 0.1020 0.1040 0.1010 0.008410 2.000 2.920 0.01870 0.06320 0.03400 0.03510 0.1000 0.09290 0.09460 0.06860 0.06920 0.03500 0.07960 0.07610 0.07980 0.07700 0.005040 2.400 2.360 0.01870 0.06110 0.03400 0.03510 0.08110 0.08850 0.08980 0.06790 0.06850 0.02740 0.04200 0.03930 0.04550 0.04340 0.002150 3.000 1.880 0.01860 0.05750 0.03390 0.03500 0.05580 0.07920 0.07980 0.06610 0.06660 0.02660 0.01620 0.01520 0.01810 0.01710 0.001570 4.000 1.470 0.01840 0.05060 0.03350 0.03460 0.02710 0.05920 0.05850 0.06000 0.06030 0.01870 0.009230 0.009490 0.004150 0.003930 0.001270 5.000 1.170 0.01810 0.04290 0.03280 0.03380 0.01430 0.03890 0.03750 0.05090 0.05090 0.008390 0.008680 0.008720 0.002270 0.002240 0.0006000 6.000 0.9180 0.01780 0.03530 0.03180 0.03260 0.01080 0.02290 0.02160 0.04060 0.04040 0.003290 0.006290 0.006070 0.002210 0.002190 0.0002250 7.000 0.7150 0.01750 0.02820 0.03040 0.03110 0.01040 0.01260 0.01160 0.03090 0.03060 0.001920 0.003690 0.003430 0.002030 0.002000 0.0001140 8.000 0.5630 0.01710 0.02190 0.02860 0.02910 0.01020 0.006910 0.006360 0.02270 0.02240 0.001770 0.001930 0.001740 0.001680 0.001640 9.950E-05 10.00 0.3700 0.01600 0.01200 0.02400 0.02500 0.007700 0.003200 0.003200 0.01200 0.01100 0.001500 0.0005700 0.0005400 0.0009600 0.0009200 8.900E-05 15.00 0.1600 0.01400 0.002700 0.01400 0.01300 0.001500 0.002600 0.002700 0.002100 0.002000 0.0003200 0.0003600 0.0003700 0.0001800 0.0001700 1.900E-05 20.00 0.07700 0.01100 0.001600 0.006500 0.006000 0.0003400 0.001500 0.001500 0.0004100 0.0003900 6.000E-05 0.0002200 0.0002100 3.600E-05 3.300E-05 3.400E-06 30.00 0.02500 0.006200 0.001300 0.001300 0.001200 0.0002600 0.0003200 0.0002800 2.600E-05 2.300E-05 4.400E-05 4.700E-05 4.200E-05 2.200E-06 2.000E-06 2.400E-06 40.00 0.010000 0.003300 0.0007200 0.0003200 0.0002500 0.0001500 7.200E-05 5.800E-05 2.800E-06 2.400E-06 2.400E-05 1.100E-05 8.500E-06 2.400E-07 2.000E-07 1.400E-06 60.00 0.002500 0.0009400 0.0001700 3.000E-05 2.100E-05 3.300E-05 6.400E-06 4.500E-06 9.400E-08 7.500E-08 5.500E-06 9.300E-07 6.500E-07 8.000E-09 6.300E-09 3.100E-07 100.0 0.0002800 0.0001200 1.700E-05 1.200E-06 6.200E-07 3.200E-06 2.500E-07 1.300E-07 1.200E-09 8.100E-10 5.200E-07 3.600E-08 1.900E-08 1.100E-10 6.900E-11 2.900E-08 #S 45 Rh #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 4 1 #UBIND 2.322E+04 3412. 3146. 3004. 627.0 521.0 496.0 3120. 307.0 81.00 48.00 48.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 8.280 0.01840 0.06640 0.03320 0.03430 0.1590 0.09490 0.09730 0.06650 0.06710 0.3990 0.2630 0.2700 0.2770 0.2830 1.700 0.05000 8.230 0.01840 0.06640 0.03320 0.03430 0.1590 0.09490 0.09730 0.06650 0.06710 0.3980 0.2630 0.2700 0.2770 0.2830 1.650 0.1000 8.090 0.01840 0.06640 0.03320 0.03430 0.1590 0.09490 0.09730 0.06650 0.06710 0.3960 0.2630 0.2700 0.2770 0.2830 1.520 0.1500 7.880 0.01840 0.06640 0.03320 0.03430 0.1590 0.09490 0.09730 0.06650 0.06710 0.3920 0.2630 0.2700 0.2770 0.2830 1.320 0.2000 7.640 0.01840 0.06630 0.03320 0.03430 0.1580 0.09490 0.09730 0.06650 0.06710 0.3870 0.2630 0.2690 0.2770 0.2830 1.090 0.3000 7.160 0.01840 0.06630 0.03320 0.03430 0.1570 0.09490 0.09730 0.06650 0.06710 0.3720 0.2630 0.2690 0.2770 0.2830 0.6380 0.4000 6.780 0.01840 0.06620 0.03320 0.03430 0.1560 0.09490 0.09730 0.06650 0.06710 0.3530 0.2620 0.2680 0.2760 0.2820 0.3190 0.5000 6.530 0.01840 0.06610 0.03320 0.03430 0.1540 0.09490 0.09730 0.06650 0.06710 0.3300 0.2590 0.2650 0.2740 0.2790 0.1450 0.6000 6.340 0.01830 0.06600 0.03320 0.03430 0.1520 0.09490 0.09720 0.06650 0.06710 0.3040 0.2560 0.2620 0.2700 0.2750 0.06770 0.7000 6.160 0.01830 0.06580 0.03320 0.03430 0.1500 0.09490 0.09720 0.06650 0.06710 0.2760 0.2510 0.2560 0.2640 0.2680 0.04090 0.8000 5.970 0.01830 0.06560 0.03320 0.03430 0.1470 0.09480 0.09710 0.06650 0.06710 0.2470 0.2440 0.2480 0.2550 0.2580 0.03450 1.000 5.520 0.01830 0.06520 0.03310 0.03430 0.1410 0.09460 0.09690 0.06650 0.06710 0.1910 0.2250 0.2280 0.2310 0.2320 0.03350 1.200 4.990 0.01830 0.06470 0.03310 0.03430 0.1340 0.09420 0.09650 0.06640 0.06710 0.1400 0.2000 0.2000 0.2010 0.2000 0.02900 1.400 4.450 0.01830 0.06410 0.03310 0.03430 0.1260 0.09370 0.09590 0.06640 0.06710 0.1000 0.1700 0.1690 0.1690 0.1670 0.02160 1.600 3.920 0.01830 0.06340 0.03310 0.03430 0.1180 0.09290 0.09500 0.06640 0.06700 0.07000 0.1400 0.1380 0.1390 0.1370 0.01460 1.800 3.460 0.01830 0.06260 0.03310 0.03430 0.1090 0.09180 0.09380 0.06630 0.06690 0.04970 0.1120 0.1080 0.1120 0.1090 0.009230 2.000 3.050 0.01830 0.06180 0.03310 0.03430 0.09980 0.09040 0.09220 0.06610 0.06680 0.03720 0.08620 0.08240 0.08820 0.08560 0.005660 2.400 2.450 0.01820 0.05990 0.03310 0.03420 0.08180 0.08650 0.08800 0.06560 0.06620 0.02740 0.04760 0.04440 0.05270 0.05060 0.002330 3.000 1.920 0.01810 0.05650 0.03300 0.03410 0.05740 0.07830 0.07900 0.06410 0.06460 0.02620 0.01870 0.01730 0.02250 0.02130 0.001490 4.000 1.480 0.01790 0.05000 0.03270 0.03370 0.02880 0.05990 0.05930 0.05900 0.05930 0.01990 0.009240 0.009430 0.005290 0.005000 0.001280 5.000 1.190 0.01770 0.04280 0.03210 0.03310 0.01510 0.04050 0.03920 0.05090 0.05090 0.009650 0.008870 0.008950 0.002440 0.002400 0.0006670 6.000 0.9390 0.01740 0.03550 0.03110 0.03200 0.01080 0.02470 0.02330 0.04140 0.04120 0.003880 0.006840 0.006620 0.002280 0.002270 0.0002610 7.000 0.7380 0.01710 0.02860 0.02990 0.03060 0.01020 0.01400 0.01290 0.03210 0.03180 0.002050 0.004250 0.003950 0.002170 0.002150 0.0001220 8.000 0.5840 0.01670 0.02250 0.02830 0.02880 0.01010 0.007750 0.007100 0.02400 0.02370 0.001780 0.002320 0.002090 0.001860 0.001830 9.700E-05 10.00 0.3800 0.01600 0.01300 0.02400 0.02500 0.008000 0.003300 0.003300 0.01300 0.01200 0.001600 0.0006700 0.0006100 0.001100 0.001100 9.000E-05 15.00 0.1600 0.01300 0.002900 0.01400 0.01400 0.001700 0.002700 0.002700 0.002400 0.002300 0.0003800 0.0003800 0.0003900 0.0002200 0.0002100 2.100E-05 20.00 0.08100 0.01100 0.001600 0.006800 0.006400 0.0003600 0.001600 0.001600 0.0004800 0.0004500 6.800E-05 0.0002400 0.0002400 4.600E-05 4.300E-05 3.700E-06 30.00 0.02700 0.006300 0.001400 0.001500 0.001300 0.0002700 0.0003600 0.0003100 3.100E-05 2.800E-05 4.500E-05 5.500E-05 4.800E-05 2.900E-06 2.600E-06 2.500E-06 40.00 0.01100 0.003400 0.0007500 0.0003600 0.0002900 0.0001500 8.300E-05 6.700E-05 3.400E-06 3.000E-06 2.600E-05 1.300E-05 1.000E-05 3.200E-07 2.700E-07 1.400E-06 60.00 0.002700 0.001000 0.0001800 3.500E-05 2.400E-05 3.700E-05 7.500E-06 5.200E-06 1.200E-07 9.500E-08 6.300E-06 1.100E-06 7.800E-07 1.100E-08 8.600E-09 3.400E-07 100.0 0.0003100 0.0001300 1.900E-05 1.500E-06 7.300E-07 3.600E-06 3.000E-07 1.500E-07 1.600E-09 1.000E-09 6.100E-07 4.500E-08 2.300E-08 1.400E-10 9.400E-11 3.300E-08 #S 46 Pd #N 16 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 #UBIND 2.435E+04 3605. 3331. 3174. 670.0 559.0 531.0 340.0 335.0 86.00 51.00 51.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 0.000 7.020 0.01790 0.06470 0.03230 0.03350 0.1540 0.09190 0.09430 0.06420 0.06470 0.3860 0.2540 0.2610 0.2780 0.2850 0.05000 7.020 0.01790 0.06470 0.03230 0.03350 0.1540 0.09190 0.09430 0.06420 0.06470 0.3850 0.2540 0.2610 0.2780 0.2850 0.1000 7.020 0.01790 0.06470 0.03230 0.03350 0.1540 0.09190 0.09430 0.06420 0.06470 0.3830 0.2540 0.2610 0.2780 0.2850 0.1500 7.010 0.01790 0.06470 0.03230 0.03350 0.1540 0.09190 0.09430 0.06420 0.06470 0.3800 0.2540 0.2610 0.2780 0.2850 0.2000 7.000 0.01790 0.06460 0.03230 0.03350 0.1530 0.09190 0.09430 0.06420 0.06470 0.3750 0.2540 0.2610 0.2780 0.2850 0.3000 6.960 0.01790 0.06460 0.03230 0.03350 0.1530 0.09190 0.09430 0.06420 0.06470 0.3620 0.2530 0.2610 0.2770 0.2840 0.4000 6.910 0.01790 0.06450 0.03230 0.03350 0.1510 0.09190 0.09430 0.06420 0.06470 0.3440 0.2520 0.2600 0.2760 0.2830 0.5000 6.820 0.01790 0.06440 0.03230 0.03350 0.1500 0.09190 0.09430 0.06420 0.06470 0.3230 0.2510 0.2580 0.2740 0.2800 0.6000 6.690 0.01790 0.06430 0.03230 0.03350 0.1480 0.09190 0.09420 0.06420 0.06470 0.2990 0.2480 0.2540 0.2690 0.2740 0.7000 6.530 0.01790 0.06420 0.03230 0.03350 0.1460 0.09180 0.09420 0.06420 0.06470 0.2720 0.2430 0.2490 0.2610 0.2650 0.8000 6.320 0.01790 0.06400 0.03230 0.03350 0.1440 0.09180 0.09410 0.06420 0.06470 0.2460 0.2370 0.2420 0.2510 0.2540 1.000 5.820 0.01790 0.06360 0.03230 0.03350 0.1380 0.09160 0.09390 0.06410 0.06470 0.1920 0.2200 0.2240 0.2250 0.2260 1.200 5.250 0.01790 0.06310 0.03230 0.03350 0.1310 0.09130 0.09360 0.06410 0.06470 0.1440 0.1980 0.1990 0.1960 0.1950 1.400 4.680 0.01790 0.06260 0.03230 0.03340 0.1240 0.09080 0.09310 0.06410 0.06470 0.1040 0.1710 0.1700 0.1650 0.1630 1.600 4.130 0.01790 0.06190 0.03230 0.03340 0.1160 0.09010 0.09230 0.06410 0.06460 0.07390 0.1430 0.1400 0.1370 0.1340 1.800 3.640 0.01780 0.06120 0.03230 0.03340 0.1080 0.08920 0.09120 0.06400 0.06460 0.05270 0.1160 0.1120 0.1110 0.1080 2.000 3.210 0.01780 0.06040 0.03230 0.03340 0.09930 0.08790 0.08980 0.06390 0.06440 0.03920 0.09110 0.08690 0.08880 0.08610 2.400 2.560 0.01780 0.05870 0.03220 0.03340 0.08230 0.08460 0.08610 0.06350 0.06400 0.02750 0.05240 0.04860 0.05480 0.05260 3.000 1.960 0.01770 0.05550 0.03210 0.03330 0.05890 0.07720 0.07800 0.06230 0.06270 0.02550 0.02120 0.01930 0.02470 0.02340 4.000 1.490 0.01750 0.04940 0.03180 0.03300 0.03040 0.06040 0.05990 0.05790 0.05820 0.02070 0.009240 0.009300 0.006110 0.005720 5.000 1.200 0.01730 0.04260 0.03130 0.03230 0.01590 0.04200 0.04080 0.05070 0.05080 0.01080 0.008860 0.008950 0.002480 0.002390 6.000 0.9580 0.01700 0.03570 0.03050 0.03140 0.01090 0.02640 0.02500 0.04200 0.04190 0.004510 0.007240 0.007010 0.002160 0.002120 7.000 0.7600 0.01670 0.02910 0.02930 0.03010 0.010000 0.01540 0.01420 0.03310 0.03290 0.002230 0.004750 0.004410 0.002110 0.002060 8.000 0.6050 0.01640 0.02310 0.02790 0.02850 0.009960 0.008660 0.007900 0.02520 0.02500 0.001790 0.002710 0.002430 0.001870 0.001810 10.00 0.4000 0.01600 0.01400 0.02400 0.02400 0.008300 0.003500 0.003400 0.01400 0.01300 0.001700 0.0007900 0.0007000 0.001200 0.001100 15.00 0.1700 0.01300 0.003100 0.01400 0.01400 0.002000 0.002700 0.002700 0.002700 0.002600 0.0004400 0.0003800 0.0003900 0.0002500 0.0002300 20.00 0.08400 0.01100 0.001600 0.007200 0.006700 0.0003900 0.001700 0.001600 0.0005600 0.0005300 7.700E-05 0.0002600 0.0002500 5.300E-05 4.900E-05 30.00 0.02800 0.006400 0.001400 0.001600 0.001400 0.0002700 0.0004000 0.0003500 3.800E-05 3.400E-05 4.700E-05 6.300E-05 5.500E-05 3.500E-06 3.100E-06 40.00 0.01200 0.003500 0.0007900 0.0004000 0.0003200 0.0001600 9.500E-05 7.600E-05 4.200E-06 3.700E-06 2.800E-05 1.500E-05 1.200E-05 3.900E-07 3.300E-07 60.00 0.002900 0.001100 0.0002000 4.000E-05 2.700E-05 4.100E-05 8.900E-06 6.100E-06 1.500E-07 1.200E-07 7.000E-06 1.400E-06 9.300E-07 1.400E-08 1.100E-08 100.0 0.0003400 0.0001400 2.100E-05 1.700E-06 8.500E-07 4.100E-06 3.600E-07 1.800E-07 2.000E-09 1.300E-09 6.900E-07 5.500E-08 2.700E-08 1.800E-10 1.200E-10 #S 47 Ag #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 1 #UBIND 2.551E+04 3806. 3524. 3352. 717.0 602.0 573.0 374.0 368.0 95.00 59.00 59.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 8.180 0.01750 0.06310 0.03150 0.03270 0.1490 0.08910 0.09150 0.06200 0.06250 0.3690 0.2420 0.2490 0.2470 0.2510 1.640 0.05000 8.130 0.01750 0.06310 0.03150 0.03270 0.1490 0.08910 0.09150 0.06200 0.06250 0.3680 0.2420 0.2490 0.2470 0.2510 1.600 0.1000 8.010 0.01750 0.06310 0.03150 0.03270 0.1490 0.08910 0.09150 0.06200 0.06250 0.3660 0.2420 0.2490 0.2470 0.2510 1.480 0.1500 7.820 0.01750 0.06310 0.03150 0.03270 0.1490 0.08910 0.09150 0.06200 0.06250 0.3630 0.2420 0.2490 0.2470 0.2510 1.300 0.2000 7.590 0.01750 0.06300 0.03150 0.03270 0.1490 0.08910 0.09150 0.06200 0.06250 0.3590 0.2420 0.2490 0.2470 0.2510 1.080 0.3000 7.140 0.01750 0.06300 0.03150 0.03270 0.1480 0.08900 0.09150 0.06200 0.06250 0.3470 0.2410 0.2490 0.2470 0.2510 0.6600 0.4000 6.790 0.01750 0.06290 0.03150 0.03270 0.1470 0.08900 0.09140 0.06200 0.06250 0.3320 0.2410 0.2480 0.2460 0.2510 0.3460 0.5000 6.540 0.01750 0.06280 0.03150 0.03270 0.1460 0.08900 0.09140 0.06200 0.06250 0.3130 0.2390 0.2460 0.2450 0.2500 0.1640 0.6000 6.370 0.01750 0.06270 0.03150 0.03270 0.1440 0.08900 0.09140 0.06200 0.06250 0.2910 0.2370 0.2430 0.2430 0.2470 0.07630 0.7000 6.220 0.01750 0.06260 0.03150 0.03270 0.1420 0.08900 0.09140 0.06200 0.06250 0.2680 0.2330 0.2390 0.2390 0.2430 0.04200 0.8000 6.070 0.01750 0.06240 0.03150 0.03270 0.1400 0.08890 0.09130 0.06200 0.06250 0.2430 0.2280 0.2340 0.2340 0.2370 0.03160 1.000 5.700 0.01750 0.06210 0.03150 0.03260 0.1350 0.08880 0.09120 0.06200 0.06250 0.1940 0.2140 0.2180 0.2180 0.2200 0.02960 1.200 5.240 0.01750 0.06160 0.03140 0.03260 0.1290 0.08850 0.09090 0.06200 0.06250 0.1480 0.1950 0.1970 0.1970 0.1970 0.02720 1.400 4.750 0.01740 0.06110 0.03140 0.03260 0.1220 0.08810 0.09040 0.06200 0.06250 0.1100 0.1720 0.1710 0.1720 0.1720 0.02180 1.600 4.240 0.01740 0.06050 0.03140 0.03260 0.1140 0.08750 0.08970 0.06190 0.06250 0.07930 0.1460 0.1440 0.1470 0.1460 0.01570 1.800 3.770 0.01740 0.05980 0.03140 0.03260 0.1070 0.08670 0.08880 0.06190 0.06240 0.05710 0.1210 0.1180 0.1230 0.1210 0.01060 2.000 3.350 0.01740 0.05910 0.03140 0.03260 0.09870 0.08560 0.08760 0.06180 0.06230 0.04230 0.09720 0.09320 0.1010 0.09930 0.006820 2.400 2.670 0.01740 0.05750 0.03140 0.03260 0.08270 0.08270 0.08430 0.06150 0.06200 0.02820 0.05830 0.05440 0.06570 0.06370 0.002810 3.000 2.020 0.01730 0.05450 0.03130 0.03250 0.06020 0.07610 0.07700 0.06040 0.06090 0.02510 0.02450 0.02230 0.03150 0.03010 0.001370 4.000 1.500 0.01710 0.04880 0.03110 0.03220 0.03190 0.06070 0.06040 0.05670 0.05710 0.02150 0.009540 0.009500 0.008280 0.007800 0.001240 5.000 1.210 0.01690 0.04240 0.03060 0.03170 0.01680 0.04340 0.04220 0.05040 0.05050 0.01210 0.008920 0.009070 0.003010 0.002900 0.0007710 6.000 0.9760 0.01670 0.03580 0.02980 0.03080 0.01110 0.02800 0.02660 0.04240 0.04230 0.005300 0.007680 0.007500 0.002370 0.002340 0.0003400 7.000 0.7810 0.01640 0.02940 0.02880 0.02960 0.009860 0.01680 0.01550 0.03400 0.03380 0.002500 0.005310 0.004970 0.002340 0.002310 0.0001460 8.000 0.6250 0.01610 0.02360 0.02750 0.02810 0.009800 0.009630 0.008760 0.02630 0.02610 0.001830 0.003170 0.002850 0.002140 0.002100 9.470E-05 10.00 0.4100 0.01500 0.01400 0.02400 0.02400 0.008500 0.003700 0.003600 0.01500 0.01400 0.001700 0.0009500 0.0008300 0.001400 0.001400 8.800E-05 15.00 0.1800 0.01300 0.003300 0.01500 0.01400 0.002200 0.002700 0.002800 0.003000 0.002900 0.0005200 0.0003900 0.0004100 0.0003200 0.0003000 2.700E-05 20.00 0.08800 0.01100 0.001600 0.007600 0.007100 0.0004300 0.001800 0.001700 0.0006600 0.0006200 8.900E-05 0.0002800 0.0002800 7.100E-05 6.500E-05 4.600E-06 30.00 0.02900 0.006500 0.001400 0.001800 0.001500 0.0002700 0.0004400 0.0003900 4.500E-05 4.100E-05 4.800E-05 7.200E-05 6.300E-05 4.900E-06 4.300E-06 2.400E-06 40.00 0.01200 0.003700 0.0008200 0.0004500 0.0003600 0.0001700 0.0001100 8.700E-05 5.200E-06 4.500E-06 3.100E-05 1.700E-05 1.400E-05 5.400E-07 4.700E-07 1.500E-06 60.00 0.003100 0.001100 0.0002200 4.600E-05 3.100E-05 4.500E-05 1.000E-05 7.100E-06 1.800E-07 1.500E-07 7.900E-06 1.600E-06 1.100E-06 1.900E-08 1.500E-08 4.000E-07 100.0 0.0003800 0.0001500 2.300E-05 2.000E-06 9.900E-07 4.600E-06 4.300E-07 2.100E-07 2.500E-09 1.600E-09 8.000E-07 6.800E-08 3.400E-08 2.600E-10 1.700E-10 4.000E-08 #S 48 Cd #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 #UBIND 2.671E+04 4018. 3727. 3538. 770.0 651.0 617.0 411.0 405.0 108.0 67.00 67.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 9.040 0.01710 0.06150 0.03070 0.03190 0.1450 0.08640 0.08880 0.06000 0.06050 0.3520 0.2300 0.2370 0.2240 0.2280 1.450 0.05000 8.980 0.01710 0.06150 0.03070 0.03190 0.1450 0.08640 0.08880 0.06000 0.06050 0.3510 0.2300 0.2370 0.2240 0.2280 1.420 0.1000 8.800 0.01710 0.06150 0.03070 0.03190 0.1450 0.08640 0.08880 0.06000 0.06050 0.3500 0.2300 0.2370 0.2240 0.2280 1.330 0.1500 8.530 0.01710 0.06150 0.03070 0.03190 0.1450 0.08640 0.08880 0.06000 0.06050 0.3470 0.2300 0.2370 0.2240 0.2280 1.200 0.2000 8.200 0.01710 0.06150 0.03070 0.03190 0.1440 0.08640 0.08880 0.06000 0.06050 0.3430 0.2300 0.2370 0.2240 0.2280 1.040 0.3000 7.490 0.01710 0.06140 0.03070 0.03190 0.1440 0.08640 0.08880 0.06000 0.06050 0.3330 0.2300 0.2370 0.2240 0.2280 0.6940 0.4000 6.880 0.01710 0.06140 0.03070 0.03190 0.1430 0.08640 0.08880 0.06000 0.06050 0.3200 0.2290 0.2360 0.2240 0.2280 0.4090 0.5000 6.450 0.01710 0.06130 0.03070 0.03190 0.1420 0.08630 0.08880 0.06000 0.06050 0.3030 0.2280 0.2350 0.2230 0.2270 0.2190 0.6000 6.180 0.01710 0.06120 0.03070 0.03190 0.1400 0.08630 0.08870 0.06000 0.06050 0.2840 0.2260 0.2330 0.2220 0.2260 0.1120 0.7000 5.990 0.01710 0.06110 0.03070 0.03190 0.1380 0.08630 0.08870 0.06000 0.06050 0.2630 0.2240 0.2300 0.2200 0.2240 0.06050 0.8000 5.840 0.01710 0.06090 0.03070 0.03190 0.1360 0.08630 0.08870 0.06000 0.06050 0.2400 0.2200 0.2250 0.2170 0.2200 0.03980 1.000 5.540 0.01710 0.06060 0.03070 0.03190 0.1310 0.08610 0.08850 0.06000 0.06050 0.1950 0.2080 0.2120 0.2070 0.2100 0.03250 1.200 5.180 0.01700 0.06020 0.03070 0.03190 0.1260 0.08590 0.08830 0.05990 0.06050 0.1520 0.1910 0.1940 0.1930 0.1940 0.03170 1.400 4.760 0.01700 0.05970 0.03070 0.03190 0.1200 0.08560 0.08790 0.05990 0.06050 0.1150 0.1710 0.1710 0.1740 0.1740 0.02750 1.600 4.310 0.01700 0.05910 0.03060 0.03190 0.1130 0.08500 0.08730 0.05990 0.06050 0.08460 0.1480 0.1470 0.1530 0.1530 0.02130 1.800 3.870 0.01700 0.05850 0.03060 0.03190 0.1060 0.08430 0.08640 0.05990 0.06040 0.06170 0.1250 0.1220 0.1320 0.1310 0.01520 2.000 3.460 0.01700 0.05780 0.03060 0.03190 0.09800 0.08340 0.08540 0.05980 0.06040 0.04570 0.1030 0.09890 0.1110 0.1100 0.01020 2.400 2.770 0.01700 0.05630 0.03060 0.03180 0.08290 0.08080 0.08250 0.05950 0.06010 0.02940 0.06410 0.06010 0.07560 0.07380 0.004320 3.000 2.080 0.01690 0.05360 0.03050 0.03170 0.06130 0.07490 0.07600 0.05870 0.05920 0.02480 0.02820 0.02560 0.03850 0.03710 0.001790 4.000 1.520 0.01670 0.04820 0.03030 0.03150 0.03340 0.06080 0.06070 0.05550 0.05590 0.02230 0.010000 0.009860 0.01090 0.01030 0.001560 5.000 1.220 0.01650 0.04210 0.02990 0.03100 0.01780 0.04450 0.04340 0.05000 0.05010 0.01350 0.008940 0.009150 0.003690 0.003540 0.001060 6.000 0.9910 0.01630 0.03580 0.02920 0.03020 0.01130 0.02960 0.02810 0.04260 0.04260 0.006190 0.008050 0.007940 0.002560 0.002540 0.0004980 7.000 0.8000 0.01610 0.02970 0.02830 0.02910 0.009730 0.01820 0.01680 0.03480 0.03460 0.002840 0.005870 0.005530 0.002520 0.002510 0.0002130 8.000 0.6440 0.01580 0.02410 0.02710 0.02770 0.009620 0.01070 0.009670 0.02740 0.02710 0.001900 0.003650 0.003310 0.002380 0.002350 0.0001240 10.00 0.4300 0.01500 0.01500 0.02400 0.02400 0.008600 0.004000 0.003800 0.01600 0.01500 0.001800 0.001100 0.0009900 0.001700 0.001600 0.0001100 15.00 0.1800 0.01300 0.003600 0.01500 0.01500 0.002500 0.002600 0.002800 0.003400 0.003300 0.0006000 0.0004000 0.0004200 0.0004000 0.0003800 3.900E-05 20.00 0.09200 0.01100 0.001600 0.007900 0.007400 0.0004800 0.001900 0.001800 0.0007600 0.0007100 0.0001100 0.0003100 0.0003000 9.100E-05 8.500E-05 6.700E-06 30.00 0.03100 0.006600 0.001400 0.001900 0.001700 0.0002700 0.0004900 0.0004300 5.400E-05 4.900E-05 4.900E-05 8.300E-05 7.200E-05 6.500E-06 5.800E-06 3.000E-06 40.00 0.01300 0.003800 0.0008500 0.0005000 0.0004000 0.0001800 0.0001200 9.800E-05 6.300E-06 5.500E-06 3.300E-05 2.000E-05 1.600E-05 7.400E-07 6.400E-07 2.000E-06 60.00 0.003300 0.001200 0.0002300 5.300E-05 3.500E-05 4.900E-05 1.200E-05 8.200E-06 2.300E-07 1.800E-07 8.900E-06 2.000E-06 1.400E-06 2.700E-08 2.100E-08 5.500E-07 100.0 0.0004100 0.0001700 2.600E-05 2.400E-06 1.100E-06 5.100E-06 5.100E-07 2.500E-07 3.100E-09 2.000E-09 9.300E-07 8.400E-08 4.100E-08 3.600E-10 2.300E-10 5.700E-08 #S 49 In #N 18 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 1 #UBIND 2.794E+04 4238. 3938. 3730. 826.0 702.0 664.0 451.0 444.0 122.0 77.00 77.00 0.000 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 0.000 9.250 0.01670 0.06000 0.02990 0.03110 0.1410 0.08380 0.08630 0.05810 0.05860 0.3360 0.2190 0.2260 0.2060 0.2080 1.240 0.9620 0.05000 9.210 0.01670 0.06000 0.02990 0.03110 0.1410 0.08380 0.08630 0.05810 0.05860 0.3360 0.2190 0.2260 0.2060 0.2080 1.220 0.9610 0.1000 9.090 0.01670 0.06000 0.02990 0.03110 0.1410 0.08380 0.08630 0.05810 0.05860 0.3350 0.2190 0.2260 0.2060 0.2080 1.160 0.9580 0.1500 8.890 0.01670 0.06000 0.02990 0.03110 0.1410 0.08380 0.08630 0.05810 0.05860 0.3320 0.2190 0.2260 0.2060 0.2080 1.070 0.9440 0.2000 8.630 0.01670 0.06000 0.02990 0.03110 0.1400 0.08380 0.08630 0.05810 0.05860 0.3290 0.2190 0.2260 0.2060 0.2080 0.9610 0.9140 0.3000 7.970 0.01670 0.06000 0.02990 0.03110 0.1400 0.08380 0.08630 0.05810 0.05860 0.3200 0.2190 0.2260 0.2060 0.2080 0.7060 0.7920 0.4000 7.290 0.01670 0.05990 0.02990 0.03110 0.1390 0.08380 0.08620 0.05810 0.05860 0.3080 0.2190 0.2260 0.2060 0.2080 0.4670 0.6140 0.5000 6.700 0.01670 0.05980 0.02990 0.03110 0.1380 0.08380 0.08620 0.05810 0.05860 0.2930 0.2180 0.2250 0.2060 0.2080 0.2830 0.4300 0.6000 6.250 0.01670 0.05970 0.02990 0.03110 0.1360 0.08380 0.08620 0.05810 0.05860 0.2760 0.2160 0.2230 0.2050 0.2070 0.1620 0.2790 0.7000 5.930 0.01670 0.05960 0.02990 0.03110 0.1350 0.08380 0.08620 0.05810 0.05860 0.2570 0.2140 0.2200 0.2040 0.2060 0.09190 0.1700 0.8000 5.710 0.01670 0.05950 0.02990 0.03110 0.1330 0.08370 0.08620 0.05810 0.05860 0.2370 0.2110 0.2160 0.2020 0.2040 0.05680 0.09990 1.000 5.380 0.01670 0.05920 0.02990 0.03110 0.1280 0.08360 0.08600 0.05810 0.05860 0.1950 0.2010 0.2060 0.1960 0.1980 0.03740 0.03260 1.200 5.070 0.01670 0.05880 0.02990 0.03110 0.1230 0.08340 0.08580 0.05810 0.05860 0.1550 0.1870 0.1900 0.1860 0.1870 0.03650 0.01260 1.400 4.720 0.01660 0.05830 0.02990 0.03110 0.1170 0.08310 0.08540 0.05800 0.05860 0.1190 0.1700 0.1710 0.1720 0.1730 0.03410 0.008240 1.600 4.340 0.01660 0.05780 0.02990 0.03110 0.1110 0.08270 0.08490 0.05800 0.05860 0.08950 0.1500 0.1490 0.1550 0.1560 0.02840 0.007790 1.800 3.940 0.01660 0.05720 0.02990 0.03110 0.1040 0.08200 0.08420 0.05800 0.05860 0.06630 0.1290 0.1260 0.1370 0.1370 0.02150 0.007700 2.000 3.550 0.01660 0.05660 0.02990 0.03110 0.09730 0.08120 0.08330 0.05790 0.05850 0.04940 0.1070 0.1040 0.1190 0.1180 0.01520 0.007190 2.400 2.870 0.01660 0.05520 0.02990 0.03110 0.08290 0.07890 0.08070 0.05770 0.05830 0.03090 0.06990 0.06580 0.08430 0.08310 0.006760 0.005260 3.000 2.150 0.01650 0.05260 0.02980 0.03100 0.06230 0.07370 0.07480 0.05700 0.05750 0.02460 0.03230 0.02930 0.04570 0.04450 0.002460 0.002480 4.000 1.540 0.01640 0.04760 0.02960 0.03080 0.03490 0.06080 0.06080 0.05430 0.05470 0.02280 0.01080 0.01040 0.01390 0.01330 0.001970 0.0005940 5.000 1.230 0.01620 0.04180 0.02920 0.03030 0.01870 0.04550 0.04440 0.04940 0.04960 0.01470 0.008940 0.009180 0.004550 0.004380 0.001450 0.0003590 6.000 1.000 0.01600 0.03580 0.02860 0.02960 0.01170 0.03100 0.02950 0.04280 0.04280 0.007150 0.008360 0.008310 0.002780 0.002770 0.0007340 0.0003470 7.000 0.8170 0.01570 0.03000 0.02780 0.02860 0.009650 0.01960 0.01810 0.03540 0.03530 0.003270 0.006400 0.006080 0.002670 0.002680 0.0003190 0.0002790 8.000 0.6630 0.01550 0.02450 0.02660 0.02740 0.009440 0.01170 0.01060 0.02830 0.02800 0.002010 0.004170 0.003800 0.002580 0.002570 0.0001710 0.0001870 10.00 0.4400 0.01500 0.01500 0.02400 0.02400 0.008700 0.004400 0.004100 0.01700 0.01600 0.001800 0.001400 0.001200 0.001900 0.001900 0.0001400 6.200E-05 15.00 0.1900 0.01300 0.003900 0.01500 0.01500 0.002800 0.002600 0.002700 0.003800 0.003600 0.0006900 0.0004000 0.0004300 0.0005000 0.0004700 5.600E-05 1.600E-05 20.00 0.09600 0.01100 0.001600 0.008200 0.007700 0.0005400 0.001900 0.001900 0.0008700 0.0008200 0.0001200 0.0003300 0.0003200 0.0001200 0.0001100 1.000E-05 1.300E-05 30.00 0.03200 0.006700 0.001400 0.002100 0.001800 0.0002700 0.0005400 0.0004700 6.400E-05 5.900E-05 5.000E-05 9.400E-05 8.300E-05 8.500E-06 7.700E-06 3.900E-06 3.900E-06 40.00 0.01400 0.003900 0.0008800 0.0005600 0.0004400 0.0001900 0.0001400 0.0001100 7.600E-06 6.600E-06 3.500E-05 2.400E-05 1.900E-05 9.900E-07 8.600E-07 2.700E-06 9.800E-07 60.00 0.003500 0.001300 0.0002500 6.000E-05 4.000E-05 5.300E-05 1.400E-05 9.500E-06 2.800E-07 2.200E-07 9.900E-06 2.400E-06 1.600E-06 3.600E-08 2.900E-08 7.700E-07 9.800E-08 100.0 0.0004500 0.0001800 2.800E-05 2.700E-06 1.300E-06 5.700E-06 6.000E-07 2.900E-07 3.800E-09 2.400E-09 1.100E-06 1.000E-07 5.000E-08 4.900E-10 3.200E-10 8.200E-08 4.200E-09 #S 50 Sn #N 18 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 #UBIND 2.920E+04 4465. 4156. 3929. 884.0 757.0 715.0 496.0 485.0 137.0 89.00 89.00 25.00 24.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 0.000 9.400 0.01630 0.05860 0.02920 0.03040 0.1370 0.08140 0.08390 0.05630 0.05690 0.3220 0.2090 0.2160 0.1920 0.1930 1.100 0.8360 0.05000 9.370 0.01630 0.05860 0.02920 0.03040 0.1370 0.08140 0.08390 0.05630 0.05690 0.3220 0.2090 0.2160 0.1920 0.1930 1.090 0.8360 0.1000 9.280 0.01630 0.05860 0.02920 0.03040 0.1370 0.08140 0.08390 0.05630 0.05690 0.3200 0.2090 0.2160 0.1920 0.1930 1.050 0.8340 0.1500 9.130 0.01630 0.05860 0.02920 0.03040 0.1370 0.08140 0.08390 0.05630 0.05690 0.3180 0.2090 0.2160 0.1920 0.1930 0.9810 0.8270 0.2000 8.920 0.01630 0.05860 0.02920 0.03040 0.1360 0.08140 0.08390 0.05630 0.05690 0.3160 0.2090 0.2160 0.1920 0.1930 0.8970 0.8100 0.3000 8.350 0.01630 0.05850 0.02920 0.03040 0.1360 0.08140 0.08390 0.05630 0.05690 0.3080 0.2090 0.2160 0.1920 0.1930 0.6970 0.7360 0.4000 7.680 0.01630 0.05850 0.02920 0.03040 0.1350 0.08140 0.08390 0.05630 0.05690 0.2970 0.2080 0.2160 0.1920 0.1930 0.4950 0.6140 0.5000 7.020 0.01630 0.05840 0.02920 0.03040 0.1340 0.08140 0.08380 0.05630 0.05690 0.2830 0.2080 0.2150 0.1910 0.1930 0.3260 0.4720 0.6000 6.460 0.01630 0.05830 0.02920 0.03040 0.1330 0.08140 0.08380 0.05630 0.05690 0.2680 0.2070 0.2130 0.1910 0.1920 0.2020 0.3370 0.7000 6.020 0.01630 0.05820 0.02920 0.03040 0.1310 0.08130 0.08380 0.05630 0.05690 0.2510 0.2050 0.2110 0.1900 0.1920 0.1220 0.2280 0.8000 5.700 0.01630 0.05810 0.02920 0.03040 0.1290 0.08130 0.08380 0.05630 0.05690 0.2330 0.2020 0.2080 0.1890 0.1910 0.07540 0.1480 1.000 5.280 0.01630 0.05780 0.02920 0.03040 0.1250 0.08120 0.08370 0.05630 0.05690 0.1950 0.1940 0.1990 0.1850 0.1870 0.04240 0.05670 1.200 4.970 0.01630 0.05740 0.02920 0.03040 0.1210 0.08110 0.08350 0.05630 0.05680 0.1570 0.1830 0.1860 0.1780 0.1790 0.03890 0.02220 1.400 4.660 0.01630 0.05700 0.02920 0.03040 0.1150 0.08080 0.08320 0.05630 0.05680 0.1230 0.1680 0.1690 0.1670 0.1690 0.03800 0.01170 1.600 4.320 0.01630 0.05650 0.02920 0.03040 0.1090 0.08040 0.08270 0.05630 0.05680 0.09410 0.1500 0.1500 0.1540 0.1550 0.03380 0.009630 1.800 3.970 0.01620 0.05600 0.02920 0.03040 0.1030 0.07980 0.08210 0.05620 0.05680 0.07070 0.1310 0.1290 0.1390 0.1400 0.02720 0.009500 2.000 3.620 0.01620 0.05540 0.02920 0.03040 0.09640 0.07910 0.08120 0.05620 0.05670 0.05310 0.1110 0.1080 0.1230 0.1230 0.02020 0.009290 2.400 2.960 0.01620 0.05410 0.02910 0.03040 0.08290 0.07710 0.07890 0.05600 0.05660 0.03270 0.07530 0.07120 0.09130 0.09080 0.009650 0.007480 3.000 2.230 0.01610 0.05170 0.02910 0.03030 0.06310 0.07240 0.07370 0.05540 0.05600 0.02450 0.03660 0.03330 0.05250 0.05150 0.003280 0.003900 4.000 1.560 0.01600 0.04690 0.02890 0.03010 0.03630 0.06070 0.06080 0.05310 0.05360 0.02310 0.01180 0.01120 0.01720 0.01660 0.002300 0.0009530 5.000 1.240 0.01580 0.04150 0.02860 0.02970 0.01980 0.04630 0.04540 0.04880 0.04910 0.01590 0.008950 0.009190 0.005630 0.005420 0.001830 0.0004810 6.000 1.020 0.01560 0.03580 0.02800 0.02910 0.01210 0.03230 0.03080 0.04280 0.04290 0.008180 0.008600 0.008620 0.003040 0.003020 0.0009910 0.0004650 7.000 0.8330 0.01540 0.03020 0.02720 0.02820 0.009620 0.02090 0.01940 0.03590 0.03580 0.003780 0.006910 0.006610 0.002790 0.002810 0.0004440 0.0003950 8.000 0.6800 0.01520 0.02490 0.02620 0.02700 0.009260 0.01280 0.01160 0.02910 0.02890 0.002160 0.004700 0.004300 0.002740 0.002750 0.0002230 0.0002780 10.00 0.4600 0.01500 0.01600 0.02400 0.02400 0.008700 0.004800 0.004400 0.01800 0.01700 0.001800 0.001600 0.001400 0.002100 0.002100 0.0001600 9.800E-05 15.00 0.2000 0.01300 0.004200 0.01500 0.01500 0.003100 0.002600 0.002700 0.004200 0.004000 0.0007800 0.0004100 0.0004400 0.0005900 0.0005700 7.400E-05 2.200E-05 20.00 0.1000 0.01100 0.001600 0.008600 0.008000 0.0006100 0.002000 0.002000 0.0009900 0.0009300 0.0001500 0.0003500 0.0003500 0.0001400 0.0001400 1.400E-05 1.900E-05 30.00 0.03400 0.006800 0.001400 0.002300 0.001900 0.0002700 0.0005900 0.0005200 7.600E-05 6.900E-05 5.100E-05 0.0001100 9.400E-05 1.100E-05 9.900E-06 4.600E-06 5.800E-06 40.00 0.01400 0.004000 0.0009100 0.0006200 0.0004800 0.0002000 0.0001600 0.0001200 9.100E-06 7.900E-06 3.800E-05 2.800E-05 2.200E-05 1.300E-06 1.100E-06 3.400E-06 1.500E-06 60.00 0.003800 0.001300 0.0002700 6.800E-05 4.500E-05 5.700E-05 1.600E-05 1.100E-05 3.400E-07 2.700E-07 1.100E-05 2.900E-06 1.900E-06 4.800E-08 3.800E-08 1.000E-06 1.500E-07 100.0 0.0004900 0.0002000 3.100E-05 3.200E-06 1.500E-06 6.400E-06 7.100E-07 3.400E-07 4.700E-09 3.000E-09 1.200E-06 1.200E-07 6.000E-08 6.600E-10 4.200E-10 1.100E-07 6.700E-09 #S 51 Sb #N 19 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 1 #UBIND 3.049E+04 4699. 4381. 4132. 944.0 812.0 766.0 537.0 528.0 152.0 99.00 99.00 33.00 32.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 0.000 9.510 0.01590 0.05720 0.02850 0.02980 0.1330 0.07910 0.08160 0.05460 0.05520 0.3090 0.2000 0.2070 0.1790 0.1810 1.000 0.7370 0.7640 0.05000 9.480 0.01590 0.05720 0.02850 0.02980 0.1330 0.07910 0.08160 0.05460 0.05520 0.3080 0.2000 0.2070 0.1790 0.1810 0.9900 0.7370 0.7640 0.1000 9.410 0.01590 0.05720 0.02850 0.02980 0.1330 0.07910 0.08160 0.05460 0.05520 0.3070 0.2000 0.2070 0.1790 0.1810 0.9580 0.7360 0.7630 0.1500 9.300 0.01590 0.05720 0.02850 0.02980 0.1330 0.07910 0.08160 0.05460 0.05520 0.3050 0.2000 0.2070 0.1790 0.1810 0.9070 0.7320 0.7580 0.2000 9.130 0.01590 0.05720 0.02850 0.02980 0.1330 0.07910 0.08160 0.05460 0.05520 0.3030 0.2000 0.2070 0.1790 0.1810 0.8410 0.7230 0.7470 0.3000 8.650 0.01590 0.05720 0.02850 0.02980 0.1320 0.07910 0.08160 0.05460 0.05520 0.2960 0.2000 0.2070 0.1790 0.1800 0.6790 0.6780 0.6960 0.4000 8.030 0.01590 0.05710 0.02850 0.02980 0.1310 0.07910 0.08160 0.05460 0.05520 0.2860 0.1990 0.2060 0.1790 0.1800 0.5080 0.5960 0.6040 0.5000 7.360 0.01590 0.05710 0.02850 0.02980 0.1300 0.07910 0.08160 0.05460 0.05520 0.2740 0.1990 0.2060 0.1790 0.1800 0.3550 0.4890 0.4870 0.6000 6.730 0.01590 0.05700 0.02850 0.02980 0.1290 0.07910 0.08160 0.05460 0.05520 0.2610 0.1980 0.2040 0.1790 0.1800 0.2340 0.3770 0.3670 0.7000 6.210 0.01590 0.05690 0.02850 0.02980 0.1280 0.07910 0.08150 0.05460 0.05520 0.2450 0.1960 0.2030 0.1780 0.1800 0.1490 0.2750 0.2620 0.8000 5.790 0.01590 0.05680 0.02850 0.02980 0.1260 0.07900 0.08150 0.05460 0.05520 0.2290 0.1940 0.2000 0.1780 0.1790 0.09470 0.1920 0.1780 1.000 5.230 0.01590 0.05650 0.02850 0.02980 0.1220 0.07900 0.08140 0.05460 0.05520 0.1940 0.1880 0.1930 0.1750 0.1760 0.04850 0.08490 0.07460 1.200 4.880 0.01590 0.05620 0.02850 0.02980 0.1180 0.07880 0.08120 0.05460 0.05520 0.1590 0.1780 0.1820 0.1700 0.1710 0.04040 0.03560 0.03030 1.400 4.590 0.01590 0.05580 0.02850 0.02980 0.1130 0.07860 0.08100 0.05460 0.05520 0.1270 0.1650 0.1670 0.1620 0.1630 0.04010 0.01710 0.01530 1.600 4.290 0.01590 0.05530 0.02850 0.02980 0.1070 0.07820 0.08060 0.05460 0.05520 0.09820 0.1500 0.1500 0.1520 0.1530 0.03750 0.01190 0.01160 1.800 3.980 0.01590 0.05480 0.02850 0.02980 0.1020 0.07770 0.08000 0.05460 0.05510 0.07490 0.1320 0.1310 0.1400 0.1400 0.03190 0.01100 0.01130 2.000 3.660 0.01590 0.05430 0.02850 0.02970 0.09550 0.07710 0.07930 0.05450 0.05510 0.05680 0.1150 0.1120 0.1260 0.1260 0.02500 0.01100 0.01110 2.400 3.040 0.01580 0.05300 0.02840 0.02970 0.08270 0.07530 0.07720 0.05440 0.05490 0.03480 0.08010 0.07620 0.09710 0.09660 0.01290 0.009610 0.009260 3.000 2.300 0.01580 0.05080 0.02840 0.02970 0.06380 0.07110 0.07250 0.05390 0.05440 0.02450 0.04090 0.03740 0.05900 0.05800 0.004330 0.005580 0.004970 4.000 1.590 0.01570 0.04630 0.02820 0.02950 0.03760 0.06050 0.06070 0.05200 0.05240 0.02320 0.01310 0.01220 0.02100 0.02020 0.002560 0.001450 0.001220 5.000 1.250 0.01550 0.04120 0.02790 0.02910 0.02080 0.04700 0.04610 0.04820 0.04840 0.01700 0.008970 0.009210 0.006960 0.006670 0.002180 0.0006170 0.0006000 6.000 1.030 0.01530 0.03580 0.02740 0.02850 0.01250 0.03350 0.03210 0.04270 0.04280 0.009250 0.008750 0.008850 0.003390 0.003340 0.001270 0.0005790 0.0005790 7.000 0.8480 0.01510 0.03040 0.02670 0.02770 0.009640 0.02220 0.02070 0.03630 0.03630 0.004370 0.007350 0.007100 0.002900 0.002920 0.0005920 0.0005170 0.0004920 8.000 0.6960 0.01490 0.02520 0.02580 0.02660 0.009090 0.01390 0.01260 0.02980 0.02960 0.002370 0.005220 0.004820 0.002870 0.002890 0.0002850 0.0003820 0.0003460 10.00 0.4700 0.01400 0.01600 0.02300 0.02400 0.008700 0.005300 0.004800 0.01900 0.01800 0.001800 0.001900 0.001700 0.002300 0.002300 0.0001900 0.0001500 0.0001200 15.00 0.2100 0.01300 0.004500 0.01600 0.01500 0.003300 0.002500 0.002700 0.004600 0.004400 0.0008700 0.0004200 0.0004400 0.0007000 0.0006800 9.400E-05 2.800E-05 2.800E-05 20.00 0.1000 0.01100 0.001600 0.008900 0.008300 0.0007000 0.002100 0.002000 0.001100 0.001100 0.0001700 0.0003700 0.0003700 0.0001800 0.0001700 1.900E-05 2.400E-05 2.400E-05 30.00 0.03500 0.006800 0.001300 0.002400 0.002100 0.0002700 0.0006400 0.0005700 8.900E-05 8.100E-05 5.200E-05 0.0001200 0.0001100 1.400E-05 1.300E-05 5.300E-06 8.100E-06 6.900E-06 40.00 0.01500 0.004100 0.0009300 0.0006800 0.0005300 0.0002000 0.0001700 0.0001400 1.100E-05 9.400E-06 4.000E-05 3.200E-05 2.600E-05 1.700E-06 1.400E-06 4.100E-06 2.200E-06 1.700E-06 60.00 0.004000 0.001400 0.0002900 7.700E-05 5.100E-05 6.200E-05 1.800E-05 1.200E-05 4.200E-07 3.300E-07 1.200E-05 3.400E-06 2.300E-06 6.300E-08 5.000E-08 1.200E-06 2.300E-07 1.500E-07 100.0 0.0005300 0.0002100 3.400E-05 3.700E-06 1.700E-06 7.100E-06 8.300E-07 4.000E-07 5.700E-09 3.600E-09 1.400E-06 1.500E-07 7.300E-08 8.700E-10 5.600E-10 1.400E-07 1.000E-08 4.700E-09 #S 52 Te #N 19 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 2 #UBIND 3.181E+04 4939. 4612. 4341. 1006. 870.0 819.0 583.0 573.0 169.0 110.0 110.0 42.00 40.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 0.000 9.600 0.01560 0.05590 0.02780 0.02910 0.1300 0.07690 0.07940 0.05300 0.05360 0.2960 0.1910 0.1980 0.1680 0.1700 0.9200 0.6650 0.6930 0.05000 9.580 0.01560 0.05590 0.02780 0.02910 0.1300 0.07690 0.07940 0.05300 0.05360 0.2960 0.1910 0.1980 0.1680 0.1700 0.9110 0.6650 0.6930 0.1000 9.520 0.01560 0.05590 0.02780 0.02910 0.1290 0.07690 0.07940 0.05300 0.05360 0.2950 0.1910 0.1980 0.1680 0.1700 0.8860 0.6640 0.6930 0.1500 9.430 0.01560 0.05590 0.02780 0.02910 0.1290 0.07690 0.07940 0.05300 0.05360 0.2930 0.1910 0.1980 0.1680 0.1700 0.8450 0.6620 0.6900 0.2000 9.290 0.01560 0.05590 0.02780 0.02910 0.1290 0.07690 0.07940 0.05300 0.05360 0.2910 0.1910 0.1980 0.1680 0.1700 0.7920 0.6560 0.6820 0.3000 8.880 0.01560 0.05590 0.02780 0.02910 0.1290 0.07690 0.07940 0.05300 0.05360 0.2850 0.1910 0.1980 0.1680 0.1700 0.6590 0.6260 0.6470 0.4000 8.320 0.01560 0.05580 0.02780 0.02910 0.1280 0.07690 0.07940 0.05300 0.05360 0.2760 0.1910 0.1980 0.1680 0.1700 0.5120 0.5690 0.5810 0.5000 7.670 0.01560 0.05580 0.02780 0.02910 0.1270 0.07690 0.07940 0.05300 0.05360 0.2660 0.1910 0.1970 0.1680 0.1700 0.3740 0.4890 0.4900 0.6000 7.030 0.01560 0.05570 0.02780 0.02910 0.1260 0.07690 0.07940 0.05300 0.05360 0.2530 0.1900 0.1960 0.1680 0.1700 0.2590 0.3970 0.3900 0.7000 6.440 0.01560 0.05560 0.02780 0.02910 0.1250 0.07690 0.07940 0.05300 0.05360 0.2390 0.1890 0.1950 0.1680 0.1700 0.1730 0.3070 0.2940 0.8000 5.950 0.01560 0.05550 0.02780 0.02910 0.1230 0.07690 0.07940 0.05300 0.05360 0.2240 0.1870 0.1930 0.1670 0.1690 0.1140 0.2270 0.2120 1.000 5.250 0.01560 0.05520 0.02780 0.02910 0.1200 0.07680 0.07930 0.05300 0.05360 0.1920 0.1810 0.1860 0.1660 0.1670 0.05600 0.1130 0.09980 1.200 4.820 0.01560 0.05490 0.02780 0.02910 0.1160 0.07670 0.07910 0.05300 0.05360 0.1600 0.1730 0.1770 0.1620 0.1630 0.04200 0.05130 0.04370 1.400 4.520 0.01560 0.05460 0.02780 0.02910 0.1110 0.07650 0.07890 0.05300 0.05360 0.1290 0.1620 0.1640 0.1560 0.1580 0.04090 0.02430 0.02100 1.600 4.250 0.01560 0.05410 0.02780 0.02910 0.1060 0.07610 0.07850 0.05300 0.05360 0.1020 0.1490 0.1490 0.1480 0.1490 0.03970 0.01470 0.01380 1.800 3.970 0.01550 0.05370 0.02780 0.02910 0.1000 0.07570 0.07800 0.05300 0.05360 0.07880 0.1330 0.1320 0.1380 0.1390 0.03550 0.01230 0.01240 2.000 3.680 0.01550 0.05310 0.02780 0.02910 0.09450 0.07520 0.07740 0.05300 0.05350 0.06050 0.1170 0.1150 0.1270 0.1270 0.02920 0.01210 0.01230 2.400 3.110 0.01550 0.05200 0.02780 0.02910 0.08240 0.07360 0.07550 0.05280 0.05340 0.03700 0.08430 0.08070 0.1010 0.1010 0.01640 0.01130 0.01110 3.000 2.370 0.01540 0.04990 0.02770 0.02900 0.06440 0.06980 0.07130 0.05240 0.05300 0.02470 0.04520 0.04150 0.06490 0.06390 0.005650 0.007320 0.006600 4.000 1.630 0.01530 0.04570 0.02760 0.02890 0.03880 0.06010 0.06040 0.05080 0.05120 0.02320 0.01460 0.01340 0.02490 0.02400 0.002770 0.002080 0.001730 5.000 1.270 0.01520 0.04080 0.02730 0.02850 0.02190 0.04750 0.04680 0.04740 0.04780 0.01800 0.009040 0.009240 0.008540 0.008160 0.002500 0.0007710 0.0007260 6.000 1.040 0.01500 0.03570 0.02690 0.02800 0.01310 0.03460 0.03320 0.04250 0.04270 0.01030 0.008830 0.009010 0.003820 0.003740 0.001560 0.0006780 0.0006800 7.000 0.8620 0.01480 0.03050 0.02620 0.02720 0.009710 0.02340 0.02190 0.03660 0.03660 0.005030 0.007720 0.007540 0.003010 0.003020 0.0007630 0.0006300 0.0006050 8.000 0.7120 0.01460 0.02550 0.02540 0.02630 0.008950 0.01500 0.01360 0.03040 0.03020 0.002630 0.005720 0.005330 0.002970 0.002990 0.0003610 0.0004890 0.0004460 10.00 0.4900 0.01400 0.01700 0.02300 0.02400 0.008700 0.005800 0.005200 0.01900 0.01900 0.001800 0.002300 0.002000 0.002500 0.002500 0.0002100 0.0002000 0.0001700 15.00 0.2100 0.01200 0.004800 0.01600 0.01500 0.003600 0.002500 0.002700 0.005000 0.004900 0.0009700 0.0004300 0.0004500 0.0008200 0.0007900 0.0001100 3.300E-05 3.400E-05 20.00 0.1100 0.010000 0.001700 0.009200 0.008600 0.0007900 0.002100 0.002100 0.001300 0.001200 0.0002100 0.0003800 0.0003900 0.0002100 0.0002000 2.400E-05 3.000E-05 2.900E-05 30.00 0.03700 0.006900 0.001300 0.002600 0.002200 0.0002600 0.0006900 0.0006100 1.000E-04 9.400E-05 5.200E-05 0.0001300 0.0001200 1.700E-05 1.600E-05 5.800E-06 1.100E-05 9.100E-06 40.00 0.01600 0.004200 0.0009600 0.0007400 0.0005800 0.0002100 0.0001900 0.0001600 1.300E-05 1.100E-05 4.200E-05 3.700E-05 3.000E-05 2.100E-06 1.800E-06 4.700E-06 2.900E-06 2.300E-06 60.00 0.004300 0.001500 0.0003100 8.700E-05 5.700E-05 6.700E-05 2.100E-05 1.400E-05 5.000E-07 4.000E-07 1.400E-05 4.000E-06 2.700E-06 8.200E-08 6.400E-08 1.500E-06 3.100E-07 2.100E-07 100.0 0.0005800 0.0002300 3.800E-05 4.200E-06 2.000E-06 7.800E-06 9.700E-07 4.600E-07 7.000E-09 4.400E-09 1.600E-06 1.800E-07 8.700E-08 1.100E-09 7.200E-10 1.800E-07 1.400E-08 6.600E-09 #S 53 I #N 19 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 3 #UBIND 3.317E+04 5188. 4852. 4557. 1072. 931.0 875.0 631.0 620.0 186.0 123.0 123.0 50.00 50.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 0.000 9.670 0.01530 0.05470 0.02720 0.02850 0.1260 0.07480 0.07740 0.05150 0.05210 0.2850 0.1840 0.1900 0.1590 0.1610 0.8530 0.6080 0.6370 0.05000 9.660 0.01530 0.05470 0.02720 0.02850 0.1260 0.07480 0.07740 0.05150 0.05210 0.2840 0.1840 0.1900 0.1590 0.1610 0.8460 0.6080 0.6370 0.1000 9.610 0.01530 0.05470 0.02720 0.02850 0.1260 0.07480 0.07740 0.05150 0.05210 0.2840 0.1840 0.1900 0.1590 0.1610 0.8260 0.6080 0.6370 0.1500 9.530 0.01530 0.05470 0.02720 0.02850 0.1260 0.07480 0.07740 0.05150 0.05210 0.2820 0.1840 0.1900 0.1590 0.1610 0.7920 0.6060 0.6350 0.2000 9.420 0.01530 0.05470 0.02720 0.02850 0.1260 0.07480 0.07740 0.05150 0.05210 0.2800 0.1840 0.1900 0.1590 0.1610 0.7480 0.6020 0.6300 0.3000 9.070 0.01530 0.05460 0.02720 0.02850 0.1250 0.07480 0.07740 0.05150 0.05210 0.2750 0.1830 0.1900 0.1590 0.1610 0.6370 0.5820 0.6050 0.4000 8.570 0.01530 0.05460 0.02720 0.02850 0.1250 0.07480 0.07740 0.05150 0.05210 0.2670 0.1830 0.1900 0.1590 0.1610 0.5100 0.5410 0.5560 0.5000 7.960 0.01530 0.05450 0.02720 0.02850 0.1240 0.07480 0.07740 0.05150 0.05210 0.2570 0.1830 0.1890 0.1590 0.1610 0.3870 0.4790 0.4850 0.6000 7.310 0.01530 0.05440 0.02720 0.02850 0.1230 0.07480 0.07740 0.05150 0.05210 0.2460 0.1820 0.1880 0.1590 0.1610 0.2790 0.4050 0.4010 0.7000 6.700 0.01530 0.05440 0.02720 0.02850 0.1220 0.07480 0.07730 0.05150 0.05210 0.2330 0.1810 0.1870 0.1590 0.1610 0.1940 0.3270 0.3170 0.8000 6.150 0.01530 0.05430 0.02720 0.02850 0.1200 0.07480 0.07730 0.05150 0.05210 0.2200 0.1800 0.1860 0.1590 0.1600 0.1310 0.2540 0.2390 1.000 5.330 0.01520 0.05400 0.02720 0.02850 0.1170 0.07470 0.07720 0.05150 0.05210 0.1900 0.1750 0.1800 0.1570 0.1590 0.06450 0.1380 0.1240 1.200 4.810 0.01520 0.05370 0.02720 0.02850 0.1130 0.07460 0.07710 0.05150 0.05210 0.1600 0.1680 0.1720 0.1550 0.1560 0.04410 0.06830 0.05840 1.400 4.470 0.01520 0.05340 0.02720 0.02850 0.1090 0.07440 0.07690 0.05150 0.05210 0.1310 0.1590 0.1610 0.1500 0.1520 0.04130 0.03330 0.02810 1.600 4.200 0.01520 0.05300 0.02720 0.02850 0.1040 0.07420 0.07660 0.05150 0.05210 0.1050 0.1470 0.1480 0.1440 0.1450 0.04090 0.01870 0.01670 1.800 3.950 0.01520 0.05260 0.02720 0.02850 0.09890 0.07380 0.07610 0.05150 0.05210 0.08240 0.1330 0.1330 0.1360 0.1370 0.03810 0.01390 0.01350 2.000 3.690 0.01520 0.05210 0.02710 0.02850 0.09340 0.07330 0.07560 0.05150 0.05210 0.06400 0.1180 0.1170 0.1260 0.1270 0.03270 0.01300 0.01310 2.400 3.160 0.01520 0.05100 0.02710 0.02850 0.08210 0.07190 0.07390 0.05140 0.05200 0.03940 0.08790 0.08460 0.1040 0.1040 0.01990 0.01260 0.01240 3.000 2.440 0.01510 0.04900 0.02710 0.02840 0.06490 0.06850 0.07010 0.05110 0.05160 0.02510 0.04930 0.04560 0.06990 0.06900 0.007250 0.009020 0.008230 4.000 1.670 0.01500 0.04500 0.02700 0.02830 0.04000 0.05970 0.06010 0.04960 0.05010 0.02310 0.01630 0.01490 0.02890 0.02790 0.002980 0.002860 0.002370 5.000 1.280 0.01490 0.04050 0.02670 0.02800 0.02290 0.04800 0.04730 0.04670 0.04700 0.01880 0.009190 0.009320 0.01030 0.009870 0.002770 0.0009580 0.0008720 6.000 1.050 0.01470 0.03560 0.02630 0.02750 0.01370 0.03560 0.03430 0.04230 0.04240 0.01140 0.008850 0.009100 0.004380 0.004250 0.001860 0.0007640 0.0007670 7.000 0.8740 0.01450 0.03060 0.02570 0.02680 0.009830 0.02460 0.02310 0.03680 0.03680 0.005760 0.008030 0.007930 0.003130 0.003140 0.0009580 0.0007340 0.0007110 8.000 0.7260 0.01430 0.02580 0.02500 0.02590 0.008830 0.01610 0.01460 0.03090 0.03080 0.002950 0.006190 0.005830 0.003040 0.003070 0.0004540 0.0005970 0.0005480 10.00 0.5000 0.01400 0.01700 0.02300 0.02300 0.008600 0.006300 0.005700 0.02000 0.02000 0.001800 0.002600 0.002300 0.002700 0.002700 0.0002200 0.0002600 0.0002200 15.00 0.2200 0.01200 0.005100 0.01600 0.01600 0.003900 0.002500 0.002600 0.005500 0.005300 0.001100 0.0004400 0.0004600 0.0009500 0.0009100 0.0001400 3.900E-05 3.900E-05 20.00 0.1100 0.010000 0.001700 0.009500 0.008900 0.0008900 0.002100 0.002200 0.001400 0.001300 0.0002400 0.0004000 0.0004100 0.0002500 0.0002400 3.100E-05 3.500E-05 3.500E-05 30.00 0.03900 0.006900 0.001300 0.002800 0.002400 0.0002600 0.0007500 0.0006600 0.0001200 0.0001100 5.300E-05 0.0001500 0.0001300 2.100E-05 1.900E-05 6.300E-06 1.300E-05 1.100E-05 40.00 0.01700 0.004300 0.0009800 0.0008100 0.0006400 0.0002100 0.0002100 0.0001700 1.500E-05 1.300E-05 4.400E-05 4.300E-05 3.400E-05 2.700E-06 2.300E-06 5.300E-06 3.800E-06 2.900E-06 60.00 0.004500 0.001500 0.0003300 9.800E-05 6.400E-05 7.200E-05 2.400E-05 1.600E-05 6.100E-07 4.800E-07 1.500E-05 4.700E-06 3.200E-06 1.000E-07 8.200E-08 1.800E-06 4.200E-07 2.700E-07 100.0 0.0006200 0.0002500 4.100E-05 4.900E-06 2.300E-06 8.700E-06 1.100E-06 5.400E-07 8.500E-09 5.300E-09 1.800E-06 2.200E-07 1.000E-07 1.500E-09 9.300E-10 2.100E-07 1.900E-08 8.900E-09 #S 54 Xe #N 19 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 4 #UBIND 3.457E+04 5453. 5107. 4787. 1149. 1002. 941.0 689.0 677.0 213.0 146.0 146.0 69.00 68.00 23.00 13.00 12.00 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 0.000 9.740 0.01490 0.05350 0.02650 0.02790 0.1230 0.07290 0.07540 0.05010 0.05070 0.2740 0.1760 0.1820 0.1510 0.1530 0.7970 0.5620 0.5920 0.05000 9.720 0.01490 0.05350 0.02650 0.02790 0.1230 0.07290 0.07540 0.05010 0.05070 0.2740 0.1760 0.1820 0.1510 0.1530 0.7910 0.5620 0.5920 0.1000 9.690 0.01490 0.05350 0.02650 0.02790 0.1230 0.07290 0.07540 0.05010 0.05070 0.2730 0.1760 0.1820 0.1510 0.1530 0.7740 0.5610 0.5910 0.1500 9.620 0.01490 0.05350 0.02650 0.02790 0.1230 0.07290 0.07540 0.05010 0.05070 0.2720 0.1760 0.1820 0.1510 0.1530 0.7470 0.5600 0.5900 0.2000 9.520 0.01490 0.05340 0.02650 0.02790 0.1230 0.07290 0.07540 0.05010 0.05070 0.2700 0.1760 0.1820 0.1510 0.1530 0.7100 0.5580 0.5860 0.3000 9.220 0.01490 0.05340 0.02650 0.02790 0.1220 0.07290 0.07540 0.05010 0.05070 0.2650 0.1760 0.1820 0.1510 0.1530 0.6150 0.5430 0.5680 0.4000 8.770 0.01490 0.05340 0.02650 0.02790 0.1210 0.07290 0.07540 0.05010 0.05070 0.2580 0.1760 0.1820 0.1510 0.1530 0.5050 0.5130 0.5310 0.5000 8.210 0.01490 0.05330 0.02650 0.02790 0.1210 0.07290 0.07540 0.05010 0.05070 0.2490 0.1760 0.1820 0.1510 0.1530 0.3940 0.4660 0.4750 0.6000 7.590 0.01490 0.05320 0.02650 0.02790 0.1200 0.07290 0.07540 0.05010 0.05070 0.2390 0.1750 0.1810 0.1510 0.1530 0.2940 0.4060 0.4050 0.7000 6.960 0.01490 0.05320 0.02650 0.02790 0.1190 0.07280 0.07540 0.05010 0.05070 0.2280 0.1750 0.1800 0.1510 0.1530 0.2110 0.3390 0.3310 0.8000 6.380 0.01490 0.05310 0.02650 0.02790 0.1170 0.07280 0.07540 0.05010 0.05070 0.2150 0.1730 0.1790 0.1510 0.1520 0.1480 0.2730 0.2600 1.000 5.450 0.01490 0.05280 0.02650 0.02790 0.1140 0.07280 0.07530 0.05010 0.05070 0.1880 0.1700 0.1740 0.1500 0.1510 0.07380 0.1600 0.1450 1.200 4.840 0.01490 0.05260 0.02650 0.02790 0.1110 0.07270 0.07520 0.05010 0.05070 0.1600 0.1640 0.1680 0.1480 0.1490 0.04700 0.08550 0.07370 1.400 4.440 0.01490 0.05230 0.02650 0.02790 0.1070 0.07250 0.07500 0.05010 0.05070 0.1330 0.1550 0.1580 0.1450 0.1460 0.04150 0.04360 0.03660 1.600 4.160 0.01490 0.05190 0.02650 0.02790 0.1020 0.07230 0.07470 0.05010 0.05070 0.1080 0.1450 0.1460 0.1400 0.1410 0.04120 0.02370 0.02040 1.800 3.910 0.01490 0.05150 0.02650 0.02790 0.09750 0.07190 0.07430 0.05010 0.05070 0.08570 0.1330 0.1330 0.1330 0.1340 0.03960 0.01600 0.01480 2.000 3.680 0.01490 0.05100 0.02650 0.02790 0.09240 0.07150 0.07380 0.05010 0.05070 0.06730 0.1190 0.1180 0.1250 0.1260 0.03540 0.01380 0.01360 2.400 3.190 0.01490 0.05000 0.02650 0.02790 0.08160 0.07020 0.07230 0.05000 0.05060 0.04180 0.09090 0.08800 0.1060 0.1060 0.02330 0.01350 0.01330 3.000 2.500 0.01480 0.04810 0.02650 0.02780 0.06530 0.06720 0.06890 0.04970 0.05030 0.02560 0.05320 0.04960 0.07420 0.07330 0.009110 0.01060 0.009720 4.000 1.710 0.01470 0.04440 0.02640 0.02770 0.04110 0.05920 0.05980 0.04850 0.04900 0.02280 0.01830 0.01660 0.03290 0.03190 0.003190 0.003760 0.003120 5.000 1.300 0.01460 0.04010 0.02610 0.02740 0.02400 0.04830 0.04780 0.04590 0.04630 0.01950 0.009440 0.009470 0.01240 0.01180 0.003000 0.001200 0.001040 6.000 1.060 0.01440 0.03540 0.02580 0.02700 0.01430 0.03650 0.03520 0.04190 0.04210 0.01250 0.008820 0.009150 0.005070 0.004880 0.002160 0.0008430 0.0008360 7.000 0.8860 0.01420 0.03060 0.02530 0.02630 0.010000 0.02580 0.02420 0.03690 0.03690 0.006550 0.008260 0.008250 0.003300 0.003280 0.001170 0.0008240 0.0008000 8.000 0.7400 0.01400 0.02600 0.02450 0.02550 0.008740 0.01720 0.01570 0.03140 0.03130 0.003330 0.006620 0.006310 0.003100 0.003120 0.0005650 0.0007010 0.0006450 10.00 0.5100 0.01400 0.01800 0.02300 0.02300 0.008500 0.006900 0.006200 0.02100 0.02100 0.001900 0.003000 0.002600 0.002900 0.002900 0.0002400 0.0003300 0.0002800 15.00 0.2300 0.01200 0.005400 0.01600 0.01600 0.004200 0.002400 0.002600 0.006000 0.005700 0.001200 0.0004600 0.0004700 0.001100 0.001000 0.0001600 4.500E-05 4.400E-05 20.00 0.1200 0.010000 0.001800 0.009700 0.009200 0.001000 0.002200 0.002200 0.001600 0.001500 0.0002800 0.0004100 0.0004300 0.0003000 0.0002800 3.900E-05 4.000E-05 4.000E-05 30.00 0.04000 0.007000 0.001300 0.003000 0.002500 0.0002600 0.0008000 0.0007100 0.0001400 0.0001300 5.300E-05 0.0001600 0.0001500 2.600E-05 2.400E-05 6.800E-06 1.600E-05 1.400E-05 40.00 0.01800 0.004400 0.0009900 0.0008900 0.0006900 0.0002200 0.0002400 0.0001900 1.800E-05 1.500E-05 4.600E-05 4.800E-05 3.900E-05 3.300E-06 2.900E-06 5.900E-06 4.800E-06 3.600E-06 60.00 0.004800 0.001600 0.0003400 0.0001100 7.200E-05 7.700E-05 2.700E-05 1.800E-05 7.300E-07 5.700E-07 1.600E-05 5.500E-06 3.700E-06 1.300E-07 1.000E-07 2.100E-06 5.400E-07 3.500E-07 100.0 0.0006700 0.0002700 4.500E-05 5.600E-06 2.600E-06 9.500E-06 1.300E-06 6.200E-07 1.000E-08 6.400E-09 2.000E-06 2.600E-07 1.200E-07 1.900E-09 1.200E-09 2.600E-07 2.500E-08 1.200E-08 #S 55 Cs #N 20 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 4 1 #UBIND 3.599E+07 5713. 5360. 5012. 1217. 1065. 998.0 740.0 724.0 231.0 162.0 162.0 77.00 75.00 23.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 0.000 11.70 0.01460 0.05230 0.02590 0.02730 0.1200 0.07100 0.07350 0.04880 0.04940 0.2640 0.1700 0.1760 0.1440 0.1450 0.7200 0.4980 0.5220 2.740 0.05000 11.50 0.01460 0.05230 0.02590 0.02730 0.1200 0.07100 0.07350 0.04880 0.04940 0.2640 0.1700 0.1760 0.1440 0.1450 0.7150 0.4980 0.5220 2.550 0.1000 11.00 0.01460 0.05230 0.02590 0.02730 0.1200 0.07100 0.07350 0.04880 0.04940 0.2630 0.1700 0.1760 0.1440 0.1450 0.7020 0.4980 0.5220 2.050 0.1500 10.40 0.01460 0.05230 0.02590 0.02730 0.1200 0.07100 0.07350 0.04880 0.04940 0.2620 0.1700 0.1760 0.1440 0.1450 0.6820 0.4970 0.5210 1.440 0.2000 9.740 0.01460 0.05230 0.02590 0.02730 0.1190 0.07100 0.07350 0.04880 0.04940 0.2600 0.1700 0.1760 0.1440 0.1450 0.6540 0.4960 0.5190 0.8910 0.3000 8.890 0.01460 0.05220 0.02590 0.02730 0.1190 0.07100 0.07350 0.04880 0.04940 0.2560 0.1700 0.1750 0.1440 0.1450 0.5800 0.4880 0.5090 0.2540 0.4000 8.400 0.01460 0.05220 0.02590 0.02730 0.1180 0.07100 0.07350 0.04880 0.04940 0.2490 0.1690 0.1750 0.1440 0.1450 0.4920 0.4700 0.4870 0.08670 0.5000 7.980 0.01460 0.05220 0.02590 0.02730 0.1180 0.07100 0.07350 0.04880 0.04940 0.2420 0.1690 0.1750 0.1440 0.1450 0.3990 0.4390 0.4510 0.07100 0.6000 7.500 0.01460 0.05210 0.02590 0.02730 0.1170 0.07100 0.07350 0.04880 0.04940 0.2320 0.1690 0.1750 0.1440 0.1450 0.3110 0.3980 0.4020 0.06810 0.7000 6.980 0.01460 0.05200 0.02590 0.02730 0.1160 0.07100 0.07350 0.04880 0.04940 0.2220 0.1680 0.1740 0.1440 0.1450 0.2340 0.3480 0.3460 0.05610 0.8000 6.460 0.01460 0.05190 0.02590 0.02730 0.1150 0.07090 0.07350 0.04880 0.04940 0.2110 0.1670 0.1730 0.1430 0.1450 0.1710 0.2950 0.2870 0.04070 1.000 5.560 0.01460 0.05170 0.02590 0.02730 0.1120 0.07090 0.07340 0.04880 0.04940 0.1860 0.1640 0.1690 0.1430 0.1440 0.08960 0.1920 0.1790 0.01720 1.200 4.890 0.01460 0.05150 0.02590 0.02730 0.1090 0.07080 0.07330 0.04880 0.04940 0.1600 0.1590 0.1630 0.1410 0.1430 0.05410 0.1130 0.1010 0.006660 1.400 4.440 0.01460 0.05120 0.02590 0.02730 0.1050 0.07070 0.07320 0.04880 0.04940 0.1340 0.1520 0.1550 0.1390 0.1400 0.04390 0.06150 0.05310 0.003220 1.600 4.130 0.01460 0.05080 0.02590 0.02730 0.1010 0.07050 0.07290 0.04880 0.04940 0.1100 0.1430 0.1450 0.1350 0.1360 0.04280 0.03370 0.02900 0.002480 1.800 3.890 0.01460 0.05040 0.02590 0.02730 0.09610 0.07020 0.07260 0.04880 0.04940 0.08870 0.1320 0.1320 0.1300 0.1310 0.04220 0.02090 0.01890 0.002430 2.000 3.660 0.01460 0.05000 0.02590 0.02730 0.09130 0.06980 0.07210 0.04880 0.04930 0.07050 0.1200 0.1190 0.1230 0.1240 0.03930 0.01620 0.01580 0.002370 2.400 3.220 0.01450 0.04900 0.02590 0.02730 0.08110 0.06870 0.07080 0.04870 0.04930 0.04430 0.09340 0.09080 0.1070 0.1070 0.02800 0.01510 0.01530 0.001810 3.000 2.560 0.01450 0.04730 0.02590 0.02730 0.06550 0.06600 0.06770 0.04850 0.04900 0.02640 0.05690 0.05330 0.07780 0.07700 0.01190 0.01290 0.01230 0.0007930 4.000 1.750 0.01440 0.04380 0.02580 0.02710 0.04210 0.05860 0.05930 0.04740 0.04790 0.02250 0.02040 0.01840 0.03680 0.03580 0.003660 0.005220 0.004430 0.0002060 5.000 1.320 0.01430 0.03970 0.02560 0.02690 0.02500 0.04850 0.04810 0.04520 0.04550 0.02010 0.009820 0.009710 0.01460 0.01390 0.003360 0.001630 0.001400 0.0001810 6.000 1.070 0.01410 0.03520 0.02530 0.02650 0.01490 0.03730 0.03610 0.04160 0.04180 0.01350 0.008770 0.009140 0.005910 0.005660 0.002590 0.0009990 0.0009950 0.0001430 7.000 0.8970 0.01400 0.03070 0.02480 0.02590 0.01020 0.02680 0.02520 0.03690 0.03700 0.007380 0.008420 0.008500 0.003510 0.003480 0.001490 0.0009760 0.0009710 8.290E-05 8.000 0.7530 0.01380 0.02620 0.02410 0.02510 0.008680 0.01820 0.01670 0.03170 0.03160 0.003780 0.007010 0.006740 0.003150 0.003180 0.0007340 0.0008670 0.0008190 4.080E-05 10.00 0.5300 0.01300 0.01800 0.02200 0.02300 0.008400 0.007600 0.006700 0.02200 0.02100 0.001900 0.003400 0.003000 0.003000 0.003000 0.0002800 0.0004500 0.0003800 1.500E-05 15.00 0.2400 0.01200 0.005800 0.01600 0.01600 0.004400 0.002400 0.002600 0.006400 0.006200 0.001300 0.0004800 0.0004900 0.001200 0.001200 0.0001900 5.600E-05 5.400E-05 1.000E-05 20.00 0.1200 0.010000 0.001900 0.010000 0.009400 0.001100 0.002200 0.002200 0.001800 0.001700 0.0003300 0.0004200 0.0004400 0.0003500 0.0003300 5.000E-05 4.800E-05 4.900E-05 2.700E-06 30.00 0.04200 0.007000 0.001300 0.003100 0.002700 0.0002500 0.0008600 0.0007700 0.0001600 0.0001400 5.400E-05 0.0001800 0.0001600 3.200E-05 2.900E-05 7.600E-06 2.100E-05 1.800E-05 4.100E-07 40.00 0.01800 0.004500 0.001000 0.0009700 0.0007500 0.0002200 0.0002600 0.0002100 2.100E-05 1.800E-05 4.800E-05 5.500E-05 4.400E-05 4.100E-06 3.500E-06 6.800E-06 6.300E-06 4.900E-06 3.600E-07 60.00 0.005000 0.001700 0.0003600 0.0001200 7.900E-05 8.200E-05 3.100E-05 2.100E-05 8.600E-07 6.800E-07 1.800E-05 6.400E-06 4.300E-06 1.700E-07 1.300E-07 2.500E-06 7.400E-07 4.800E-07 1.300E-07 100.0 0.0007300 0.0002900 4.900E-05 6.400E-06 2.900E-06 1.000E-05 1.500E-06 7.100E-07 1.200E-08 7.700E-09 2.300E-06 3.100E-07 1.500E-07 2.400E-09 1.500E-09 3.200E-07 3.500E-08 1.600E-08 1.700E-08 #S 56 Ba #N 20 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 4 2 #UBIND 3.744E+04 5987. 5624. 5247. 1293. 1137. 1063. 796.0 781.0 253.0 180.0 180.0 93.00 90.00 40.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 0.000 12.90 0.01430 0.05120 0.02540 0.02680 0.1170 0.06920 0.07180 0.04750 0.04810 0.2550 0.1630 0.1690 0.1370 0.1390 0.6570 0.4520 0.4730 2.230 0.05000 12.70 0.01430 0.05120 0.02540 0.02680 0.1170 0.06920 0.07180 0.04750 0.04810 0.2540 0.1630 0.1690 0.1370 0.1390 0.6530 0.4520 0.4730 2.120 0.1000 12.00 0.01430 0.05120 0.02540 0.02680 0.1170 0.06920 0.07180 0.04750 0.04810 0.2540 0.1630 0.1690 0.1370 0.1390 0.6430 0.4520 0.4730 1.820 0.1500 11.20 0.01430 0.05120 0.02540 0.02680 0.1170 0.06920 0.07180 0.04750 0.04810 0.2530 0.1630 0.1690 0.1370 0.1390 0.6270 0.4520 0.4720 1.420 0.2000 10.30 0.01430 0.05120 0.02540 0.02680 0.1160 0.06920 0.07180 0.04750 0.04810 0.2510 0.1630 0.1690 0.1370 0.1390 0.6050 0.4510 0.4710 1.010 0.3000 8.950 0.01430 0.05110 0.02540 0.02680 0.1160 0.06920 0.07180 0.04750 0.04810 0.2470 0.1630 0.1690 0.1370 0.1390 0.5470 0.4460 0.4640 0.4010 0.4000 8.200 0.01430 0.05110 0.02540 0.02680 0.1160 0.06920 0.07180 0.04750 0.04810 0.2410 0.1630 0.1690 0.1370 0.1390 0.4750 0.4330 0.4500 0.1440 0.5000 7.760 0.01430 0.05100 0.02540 0.02680 0.1150 0.06920 0.07180 0.04750 0.04810 0.2340 0.1630 0.1690 0.1370 0.1390 0.3970 0.4130 0.4250 0.08240 0.6000 7.370 0.01430 0.05100 0.02540 0.02680 0.1140 0.06920 0.07180 0.04750 0.04810 0.2260 0.1630 0.1680 0.1370 0.1390 0.3200 0.3830 0.3900 0.07740 0.7000 6.950 0.01430 0.05090 0.02540 0.02680 0.1130 0.06920 0.07170 0.04750 0.04810 0.2170 0.1620 0.1680 0.1370 0.1390 0.2500 0.3450 0.3470 0.07450 0.8000 6.500 0.01430 0.05080 0.02540 0.02680 0.1120 0.06920 0.07170 0.04750 0.04810 0.2060 0.1610 0.1670 0.1370 0.1390 0.1900 0.3020 0.2990 0.06400 1.000 5.640 0.01430 0.05060 0.02540 0.02680 0.1090 0.06910 0.07170 0.04750 0.04810 0.1830 0.1590 0.1640 0.1360 0.1380 0.1050 0.2130 0.2030 0.03560 1.200 4.950 0.01430 0.05040 0.02540 0.02680 0.1060 0.06900 0.07160 0.04750 0.04810 0.1590 0.1540 0.1590 0.1350 0.1370 0.06240 0.1360 0.1240 0.01590 1.400 4.460 0.01430 0.05010 0.02540 0.02680 0.1030 0.06890 0.07140 0.04750 0.04810 0.1350 0.1480 0.1510 0.1340 0.1350 0.04690 0.07970 0.07030 0.007020 1.600 4.120 0.01430 0.04980 0.02540 0.02680 0.09890 0.06870 0.07120 0.04750 0.04810 0.1120 0.1400 0.1420 0.1310 0.1320 0.04390 0.04530 0.03920 0.004200 1.800 3.860 0.01430 0.04940 0.02540 0.02680 0.09470 0.06850 0.07090 0.04750 0.04810 0.09130 0.1300 0.1310 0.1260 0.1280 0.04370 0.02720 0.02410 0.003690 2.000 3.640 0.01430 0.04900 0.02540 0.02680 0.09020 0.06810 0.07050 0.04750 0.04810 0.07340 0.1200 0.1190 0.1210 0.1220 0.04210 0.01930 0.01830 0.003670 2.400 3.230 0.01420 0.04810 0.02540 0.02680 0.08060 0.06710 0.06930 0.04740 0.04800 0.04680 0.09530 0.09310 0.1070 0.1070 0.03250 0.01630 0.01660 0.003120 3.000 2.610 0.01420 0.04650 0.02530 0.02670 0.06570 0.06470 0.06650 0.04720 0.04780 0.02730 0.06030 0.05670 0.08070 0.08000 0.01520 0.01490 0.01450 0.001540 4.000 1.790 0.01410 0.04320 0.02520 0.02660 0.04300 0.05800 0.05880 0.04630 0.04690 0.02220 0.02270 0.02040 0.04060 0.03960 0.004240 0.006850 0.005920 0.0003670 5.000 1.340 0.01400 0.03930 0.02510 0.02640 0.02600 0.04860 0.04830 0.04440 0.04480 0.02040 0.01030 0.01010 0.01690 0.01620 0.003660 0.002190 0.001850 0.0002880 6.000 1.080 0.01390 0.03500 0.02480 0.02600 0.01560 0.03800 0.03680 0.04110 0.04140 0.01450 0.008720 0.009100 0.006900 0.006590 0.003020 0.001160 0.001140 0.0002440 7.000 0.9070 0.01370 0.03070 0.02430 0.02550 0.01050 0.02780 0.02620 0.03690 0.03700 0.008250 0.008520 0.008680 0.003800 0.003730 0.001840 0.001100 0.001120 0.0001510 8.000 0.7640 0.01350 0.02640 0.02370 0.02480 0.008660 0.01920 0.01760 0.03200 0.03190 0.004290 0.007340 0.007140 0.003210 0.003230 0.0009390 0.001020 0.0009840 7.700E-05 10.00 0.5400 0.01300 0.01900 0.02200 0.02300 0.008300 0.008300 0.007300 0.02200 0.02200 0.001900 0.003800 0.003300 0.003100 0.003100 0.0003200 0.0005700 0.0004900 2.600E-05 15.00 0.2400 0.01200 0.006100 0.01600 0.01600 0.004700 0.002400 0.002500 0.006900 0.006700 0.001300 0.0005100 0.0005100 0.001300 0.001300 0.0002200 6.900E-05 6.500E-05 1.700E-05 20.00 0.1300 0.010000 0.002000 0.010000 0.009700 0.001300 0.002200 0.002300 0.001900 0.001800 0.0003800 0.0004300 0.0004600 0.0004000 0.0003800 6.300E-05 5.500E-05 5.700E-05 5.000E-06 30.00 0.04400 0.007000 0.001200 0.003300 0.002900 0.0002500 0.0009100 0.0008200 0.0001800 0.0001600 5.500E-05 0.0002000 0.0001800 3.800E-05 3.400E-05 8.500E-06 2.600E-05 2.300E-05 6.600E-07 40.00 0.01900 0.004500 0.001000 0.001000 0.0008100 0.0002300 0.0002900 0.0002300 2.400E-05 2.100E-05 4.900E-05 6.200E-05 5.000E-05 5.000E-06 4.300E-06 7.700E-06 8.100E-06 6.400E-06 6.000E-07 60.00 0.005300 0.001800 0.0003800 0.0001400 8.800E-05 8.700E-05 3.500E-05 2.300E-05 1.000E-06 8.000E-07 1.900E-05 7.400E-06 5.000E-06 2.100E-07 1.600E-07 3.000E-06 9.700E-07 6.300E-07 2.300E-07 100.0 0.0007800 0.0003100 5.300E-05 7.300E-06 3.300E-06 1.100E-05 1.700E-06 8.100E-07 1.500E-08 9.200E-09 2.500E-06 3.600E-07 1.700E-07 3.000E-09 1.900E-09 3.900E-07 4.800E-08 2.200E-08 3.100E-08 #S 57 La #N 21 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 4 1 2 #UBIND 3.892E+04 6267. 5891. 5483. 1362. 1205. 1124. 852.0 835.0 271.0 196.0 196.0 103.0 103.0 36.00 18.00 18.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 0.000 12.70 0.01400 0.05010 0.02480 0.02630 0.1140 0.06750 0.07010 0.04630 0.04690 0.2460 0.1580 0.1630 0.1310 0.1330 0.6160 0.4240 0.4400 0.5090 2.070 0.05000 12.50 0.01400 0.05010 0.02480 0.02630 0.1140 0.06750 0.07010 0.04630 0.04690 0.2460 0.1580 0.1630 0.1310 0.1330 0.6130 0.4240 0.4400 0.5090 1.980 0.1000 12.00 0.01400 0.05010 0.02480 0.02630 0.1140 0.06750 0.07010 0.04630 0.04690 0.2450 0.1580 0.1630 0.1310 0.1330 0.6040 0.4240 0.4400 0.5090 1.730 0.1500 11.30 0.01400 0.05010 0.02480 0.02630 0.1140 0.06750 0.07010 0.04630 0.04690 0.2440 0.1580 0.1630 0.1310 0.1330 0.5910 0.4240 0.4400 0.5090 1.390 0.2000 10.50 0.01400 0.05010 0.02480 0.02630 0.1140 0.06750 0.07010 0.04630 0.04690 0.2430 0.1580 0.1630 0.1310 0.1330 0.5720 0.4230 0.4390 0.5090 1.030 0.3000 9.200 0.01400 0.05000 0.02480 0.02630 0.1130 0.06750 0.07010 0.04630 0.04690 0.2390 0.1580 0.1630 0.1310 0.1330 0.5220 0.4190 0.4340 0.5040 0.4570 0.4000 8.420 0.01400 0.05000 0.02480 0.02630 0.1130 0.06750 0.07010 0.04630 0.04690 0.2340 0.1570 0.1630 0.1310 0.1330 0.4600 0.4100 0.4230 0.4880 0.1750 0.5000 7.950 0.01400 0.05000 0.02480 0.02630 0.1120 0.06750 0.07010 0.04630 0.04690 0.2270 0.1570 0.1630 0.1310 0.1330 0.3920 0.3930 0.4040 0.4570 0.08800 0.6000 7.570 0.01400 0.04990 0.02480 0.02630 0.1110 0.06750 0.07010 0.04630 0.04690 0.2200 0.1570 0.1630 0.1310 0.1330 0.3230 0.3690 0.3770 0.4120 0.07480 0.7000 7.160 0.01400 0.04980 0.02480 0.02630 0.1110 0.06740 0.07010 0.04630 0.04690 0.2110 0.1560 0.1620 0.1310 0.1330 0.2580 0.3380 0.3420 0.3580 0.07400 0.8000 6.710 0.01400 0.04980 0.02480 0.02630 0.1090 0.06740 0.07000 0.04630 0.04690 0.2020 0.1560 0.1610 0.1310 0.1330 0.2010 0.3020 0.3020 0.3010 0.06790 1.000 5.830 0.01400 0.04960 0.02480 0.02630 0.1070 0.06740 0.07000 0.04630 0.04690 0.1810 0.1540 0.1580 0.1310 0.1320 0.1160 0.2230 0.2170 0.1980 0.04340 1.200 5.080 0.01400 0.04930 0.02480 0.02630 0.1040 0.06730 0.06990 0.04630 0.04690 0.1580 0.1500 0.1540 0.1300 0.1310 0.06940 0.1500 0.1410 0.1210 0.02170 1.400 4.520 0.01400 0.04910 0.02480 0.02630 0.1010 0.06720 0.06980 0.04630 0.04690 0.1350 0.1440 0.1480 0.1280 0.1300 0.04960 0.09330 0.08460 0.06950 0.009930 1.600 4.130 0.01400 0.04880 0.02480 0.02630 0.09720 0.06710 0.06960 0.04630 0.04690 0.1140 0.1370 0.1400 0.1260 0.1270 0.04420 0.05530 0.04900 0.03860 0.005300 1.800 3.850 0.01400 0.04840 0.02480 0.02620 0.09330 0.06680 0.06930 0.04630 0.04690 0.09360 0.1290 0.1300 0.1230 0.1240 0.04370 0.03330 0.02960 0.02100 0.004070 2.000 3.620 0.01400 0.04810 0.02480 0.02620 0.08900 0.06650 0.06890 0.04630 0.04690 0.07610 0.1190 0.1190 0.1180 0.1190 0.04300 0.02230 0.02080 0.01180 0.003940 2.400 3.230 0.01400 0.04720 0.02480 0.02620 0.08000 0.06560 0.06790 0.04630 0.04680 0.04930 0.09690 0.09500 0.1060 0.1070 0.03550 0.01670 0.01720 0.005400 0.003620 3.000 2.650 0.01390 0.04570 0.02480 0.02620 0.06580 0.06340 0.06530 0.04610 0.04670 0.02830 0.06340 0.06000 0.08290 0.08240 0.01830 0.01590 0.01590 0.004550 0.002020 4.000 1.840 0.01380 0.04250 0.02470 0.02610 0.04380 0.05730 0.05830 0.04530 0.04580 0.02190 0.02510 0.02250 0.04430 0.04320 0.004890 0.008290 0.007370 0.003520 0.0004780 5.000 1.360 0.01370 0.03890 0.02450 0.02590 0.02700 0.04860 0.04850 0.04360 0.04400 0.02070 0.01100 0.01050 0.01950 0.01860 0.003850 0.002770 0.002350 0.001750 0.0003240 6.000 1.090 0.01360 0.03480 0.02430 0.02560 0.01630 0.03850 0.03750 0.04070 0.04100 0.01530 0.008680 0.009060 0.008060 0.007670 0.003360 0.001300 0.001270 0.0007120 0.0002910 7.000 0.9160 0.01340 0.03070 0.02390 0.02510 0.01080 0.02870 0.02720 0.03680 0.03690 0.009130 0.008560 0.008800 0.004170 0.004060 0.002170 0.001180 0.001220 0.0003130 0.0001920 8.000 0.7750 0.01330 0.02650 0.02330 0.02440 0.008680 0.02020 0.01860 0.03220 0.03220 0.004850 0.007620 0.007490 0.003280 0.003300 0.001150 0.001120 0.001110 0.0002050 0.0001020 10.00 0.5500 0.01300 0.01900 0.02200 0.02300 0.008100 0.008900 0.007800 0.02300 0.02300 0.002000 0.004200 0.003700 0.003200 0.003200 0.0003700 0.0006700 0.0006000 0.0001900 3.200E-05 15.00 0.2500 0.01200 0.006400 0.01600 0.01600 0.004900 0.002300 0.002500 0.007400 0.007100 0.001400 0.0005500 0.0005300 0.001500 0.001400 0.0002500 8.200E-05 7.600E-05 9.600E-05 2.100E-05 20.00 0.1300 0.010000 0.002100 0.010000 0.009900 0.001400 0.002200 0.002300 0.002100 0.002000 0.0004300 0.0004400 0.0004700 0.0004600 0.0004400 7.700E-05 6.000E-05 6.400E-05 3.000E-05 6.600E-06 30.00 0.04500 0.007100 0.001200 0.003500 0.003000 0.0002500 0.0009700 0.0008700 0.0002100 0.0001900 5.600E-05 0.0002100 0.0001900 4.500E-05 4.100E-05 9.300E-06 3.000E-05 2.700E-05 2.900E-06 7.800E-07 40.00 0.02000 0.004400 0.001000 0.001100 0.0008800 0.0002300 0.0003100 0.0002500 2.800E-05 2.400E-05 5.100E-05 6.900E-05 5.600E-05 6.100E-06 5.200E-06 8.400E-06 9.700E-06 7.800E-06 3.900E-07 7.000E-07 60.00 0.005600 0.001800 0.0004000 0.0001500 9.700E-05 9.200E-05 3.900E-05 2.600E-05 1.200E-06 9.400E-07 2.100E-05 8.500E-06 5.700E-06 2.500E-07 2.000E-07 3.400E-06 1.200E-06 7.900E-07 1.600E-08 2.900E-07 100.0 0.0008400 0.0003300 5.800E-05 8.200E-06 3.700E-06 1.300E-05 2.000E-06 9.200E-07 1.800E-08 1.100E-08 2.800E-06 4.300E-07 2.000E-07 3.700E-09 2.300E-09 4.600E-07 6.000E-08 2.800E-08 2.400E-10 3.900E-08 #S 58 Ce #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 1 2 2 4 1 2 #UBIND 4.044E+04 6549. 6165. 5724. 1435. 1273. 1186. 901.0 883.0 289.0 207.0 207.0 112.0 108.0 0.000 38.00 18.00 18.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 12.60 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2390 0.1530 0.1590 0.1270 0.1290 0.1420 0.6020 0.4140 0.4310 0.4980 2.050 0.05000 12.40 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2390 0.1530 0.1590 0.1270 0.1290 0.1420 0.5990 0.4140 0.4310 0.4980 1.960 0.1000 11.90 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2380 0.1530 0.1590 0.1270 0.1290 0.1420 0.5910 0.4140 0.4310 0.4980 1.720 0.1500 11.20 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2380 0.1530 0.1590 0.1270 0.1290 0.1420 0.5790 0.4130 0.4310 0.4980 1.390 0.2000 10.40 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2360 0.1530 0.1590 0.1270 0.1290 0.1420 0.5610 0.4130 0.4300 0.4980 1.040 0.3000 9.170 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2330 0.1530 0.1590 0.1270 0.1290 0.1420 0.5150 0.4090 0.4260 0.4930 0.4690 0.4000 8.400 0.01370 0.04900 0.02430 0.02570 0.1100 0.06580 0.06840 0.04520 0.04580 0.2280 0.1530 0.1590 0.1270 0.1290 0.1420 0.4560 0.4010 0.4160 0.4790 0.1810 0.5000 7.940 0.01370 0.04890 0.02430 0.02570 0.1100 0.06580 0.06840 0.04520 0.04580 0.2220 0.1530 0.1590 0.1270 0.1290 0.1420 0.3910 0.3860 0.3980 0.4500 0.08800 0.6000 7.570 0.01370 0.04890 0.02430 0.02570 0.1090 0.06580 0.06840 0.04520 0.04580 0.2150 0.1530 0.1580 0.1270 0.1290 0.1420 0.3250 0.3640 0.3730 0.4080 0.07210 0.7000 7.180 0.01370 0.04880 0.02430 0.02570 0.1080 0.06580 0.06840 0.04520 0.04580 0.2070 0.1520 0.1580 0.1270 0.1290 0.1420 0.2620 0.3360 0.3400 0.3580 0.07150 0.8000 6.760 0.01370 0.04870 0.02430 0.02570 0.1070 0.06580 0.06840 0.04520 0.04580 0.1980 0.1520 0.1570 0.1270 0.1290 0.1420 0.2060 0.3020 0.3020 0.3040 0.06660 1.000 5.910 0.01370 0.04860 0.02430 0.02570 0.1050 0.06580 0.06840 0.04520 0.04580 0.1790 0.1500 0.1550 0.1270 0.1280 0.1400 0.1210 0.2270 0.2210 0.2030 0.04410 1.200 5.170 0.01370 0.04830 0.02430 0.02570 0.1020 0.06570 0.06830 0.04520 0.04580 0.1570 0.1460 0.1510 0.1260 0.1280 0.1380 0.07220 0.1560 0.1460 0.1270 0.02280 1.400 4.610 0.01370 0.04810 0.02430 0.02570 0.09900 0.06560 0.06820 0.04520 0.04580 0.1360 0.1420 0.1450 0.1250 0.1260 0.1340 0.05010 0.09930 0.08970 0.07470 0.01060 1.600 4.210 0.01370 0.04780 0.02430 0.02570 0.09560 0.06550 0.06800 0.04520 0.04580 0.1150 0.1350 0.1380 0.1230 0.1240 0.1280 0.04330 0.06000 0.05260 0.04240 0.005470 1.800 3.920 0.01370 0.04750 0.02430 0.02570 0.09190 0.06530 0.06780 0.04520 0.04580 0.09540 0.1270 0.1290 0.1200 0.1210 0.1210 0.04240 0.03620 0.03160 0.02360 0.003950 2.000 3.690 0.01370 0.04710 0.02430 0.02570 0.08790 0.06500 0.06740 0.04520 0.04570 0.07820 0.1180 0.1190 0.1160 0.1170 0.1140 0.04210 0.02370 0.02150 0.01330 0.003730 2.400 3.300 0.01370 0.04630 0.02430 0.02570 0.07930 0.06420 0.06650 0.04510 0.04570 0.05130 0.09780 0.09620 0.1050 0.1060 0.09780 0.03600 0.01640 0.01660 0.005640 0.003520 3.000 2.730 0.01360 0.04490 0.02420 0.02570 0.06590 0.06220 0.06410 0.04500 0.04550 0.02930 0.06580 0.06240 0.08390 0.08350 0.07430 0.01960 0.01570 0.01570 0.004370 0.002100 4.000 1.910 0.01360 0.04190 0.02420 0.02560 0.04460 0.05660 0.05770 0.04430 0.04480 0.02150 0.02710 0.02440 0.04680 0.04580 0.04380 0.005220 0.008900 0.007900 0.003580 0.0005090 5.000 1.400 0.01350 0.03840 0.02400 0.02540 0.02790 0.04850 0.04850 0.04280 0.04320 0.02060 0.01160 0.01090 0.02150 0.02060 0.02470 0.003750 0.003130 0.002600 0.001900 0.0003080 6.000 1.110 0.01330 0.03460 0.02380 0.02510 0.01710 0.03900 0.03810 0.04020 0.04050 0.01590 0.008600 0.008920 0.009090 0.008650 0.01380 0.003410 0.001360 0.001280 0.0008070 0.0002870 7.000 0.9300 0.01320 0.03060 0.02340 0.02470 0.01120 0.02960 0.02810 0.03660 0.03670 0.009900 0.008460 0.008770 0.004510 0.004370 0.007700 0.002330 0.001150 0.001190 0.0003500 0.0002000 8.000 0.7870 0.01300 0.02660 0.02290 0.02400 0.008740 0.02120 0.01950 0.03230 0.03230 0.005400 0.007760 0.007690 0.003320 0.003320 0.004340 0.001290 0.001120 0.001120 0.0002120 0.0001110 10.00 0.5700 0.01300 0.01900 0.02200 0.02200 0.008000 0.009700 0.008500 0.02300 0.02300 0.002100 0.004500 0.004100 0.003200 0.003200 0.001400 0.0003900 0.0007200 0.0006400 0.0001900 3.300E-05 15.00 0.2600 0.01100 0.006800 0.01600 0.01600 0.005200 0.002400 0.002500 0.007900 0.007600 0.001500 0.0005900 0.0005600 0.001600 0.001600 0.0001200 0.0002600 9.000E-05 8.000E-05 1.000E-04 2.100E-05 20.00 0.1400 0.010000 0.002200 0.01100 0.010000 0.001600 0.002200 0.002300 0.002300 0.002200 0.0004900 0.0004300 0.0004700 0.0005200 0.0004900 1.400E-05 8.700E-05 6.000E-05 6.300E-05 3.300E-05 7.100E-06 30.00 0.04700 0.007100 0.001200 0.003700 0.003200 0.0002500 0.001000 0.0009300 0.0002400 0.0002100 5.700E-05 0.0002300 0.0002100 5.200E-05 4.700E-05 4.700E-07 9.400E-06 3.200E-05 2.900E-05 3.400E-06 7.700E-07 40.00 0.02100 0.004700 0.001000 0.001200 0.0009500 0.0002300 0.0003400 0.0002700 3.300E-05 2.800E-05 5.100E-05 7.600E-05 6.200E-05 7.200E-06 6.100E-06 3.100E-08 8.500E-06 1.100E-05 8.500E-06 4.600E-07 6.900E-07 60.00 0.005900 0.001900 0.0004200 0.0001700 0.0001100 9.700E-05 4.400E-05 2.900E-05 1.400E-06 1.100E-06 2.200E-05 9.700E-06 6.400E-06 3.100E-07 2.400E-07 5.100E-10 3.700E-06 1.400E-06 8.900E-07 2.000E-08 3.000E-07 100.0 0.0009000 0.0003500 6.200E-05 9.400E-06 4.200E-06 1.400E-05 2.300E-06 1.000E-06 2.100E-08 1.300E-08 3.100E-06 5.000E-07 2.300E-07 4.500E-09 2.800E-09 3.500E-12 5.100E-07 6.900E-08 3.100E-08 2.900E-10 4.200E-08 #S 59 Pr #N 21 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 3 2 2 4 2 #UBIND 4.199E+04 6835. 6441. 5965. 1511. 1338. 1243. 951.0 931.0 304.0 218.0 218.0 118.0 115.0 0.000 38.00 23.00 23.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 0.000 12.50 0.01350 0.04800 0.02380 0.02520 0.1090 0.06430 0.06690 0.04410 0.04470 0.2340 0.1500 0.1560 0.1250 0.1260 0.1490 0.6090 0.4170 0.4420 2.150 0.05000 12.30 0.01350 0.04800 0.02380 0.02520 0.1090 0.06430 0.06690 0.04410 0.04470 0.2340 0.1500 0.1560 0.1250 0.1260 0.1490 0.6070 0.4170 0.4420 2.050 0.1000 11.80 0.01350 0.04800 0.02380 0.02520 0.1090 0.06430 0.06690 0.04410 0.04470 0.2330 0.1500 0.1560 0.1250 0.1260 0.1490 0.5990 0.4160 0.4420 1.780 0.1500 11.00 0.01350 0.04800 0.02380 0.02520 0.1080 0.06430 0.06690 0.04410 0.04470 0.2330 0.1500 0.1560 0.1250 0.1260 0.1490 0.5860 0.4160 0.4410 1.420 0.2000 10.20 0.01350 0.04800 0.02380 0.02520 0.1080 0.06430 0.06690 0.04410 0.04470 0.2320 0.1500 0.1560 0.1250 0.1260 0.1490 0.5680 0.4160 0.4400 1.030 0.3000 8.900 0.01350 0.04800 0.02380 0.02520 0.1080 0.06430 0.06690 0.04410 0.04470 0.2280 0.1500 0.1560 0.1250 0.1260 0.1490 0.5200 0.4120 0.4360 0.4410 0.4000 8.150 0.01350 0.04790 0.02380 0.02520 0.1080 0.06430 0.06690 0.04410 0.04470 0.2240 0.1500 0.1560 0.1250 0.1260 0.1480 0.4610 0.4030 0.4250 0.1620 0.5000 7.730 0.01350 0.04790 0.02380 0.02520 0.1070 0.06430 0.06690 0.04410 0.04470 0.2180 0.1500 0.1550 0.1250 0.1260 0.1480 0.3950 0.3880 0.4050 0.07990 0.6000 7.400 0.01350 0.04790 0.02380 0.02520 0.1060 0.06430 0.06690 0.04410 0.04470 0.2110 0.1490 0.1550 0.1250 0.1260 0.1480 0.3270 0.3660 0.3780 0.06860 0.7000 7.060 0.01350 0.04780 0.02380 0.02520 0.1060 0.06420 0.06690 0.04410 0.04470 0.2040 0.1490 0.1550 0.1250 0.1260 0.1480 0.2640 0.3370 0.3430 0.06770 0.8000 6.680 0.01350 0.04770 0.02380 0.02520 0.1050 0.06420 0.06690 0.04410 0.04470 0.1950 0.1480 0.1540 0.1250 0.1260 0.1470 0.2070 0.3030 0.3030 0.06160 1.000 5.900 0.01350 0.04760 0.02380 0.02520 0.1030 0.06420 0.06680 0.04410 0.04470 0.1770 0.1470 0.1520 0.1240 0.1260 0.1450 0.1210 0.2280 0.2180 0.03870 1.200 5.230 0.01340 0.04740 0.02380 0.02520 0.1000 0.06420 0.06680 0.04410 0.04470 0.1570 0.1440 0.1480 0.1240 0.1250 0.1400 0.07090 0.1560 0.1430 0.01920 1.400 4.720 0.01340 0.04710 0.02380 0.02520 0.09720 0.06410 0.06670 0.04410 0.04470 0.1360 0.1390 0.1430 0.1230 0.1240 0.1350 0.04830 0.09900 0.08650 0.008730 1.600 4.350 0.01340 0.04690 0.02380 0.02520 0.09400 0.06390 0.06650 0.04410 0.04470 0.1160 0.1330 0.1360 0.1210 0.1220 0.1280 0.04110 0.05960 0.05020 0.004500 1.800 4.070 0.01340 0.04660 0.02380 0.02520 0.09050 0.06380 0.06630 0.04410 0.04470 0.09680 0.1260 0.1280 0.1180 0.1190 0.1200 0.04010 0.03570 0.02970 0.003280 2.000 3.840 0.01340 0.04620 0.02380 0.02520 0.08670 0.06350 0.06600 0.04410 0.04460 0.07990 0.1180 0.1190 0.1150 0.1150 0.1120 0.03990 0.02300 0.01990 0.003110 2.400 3.440 0.01340 0.04550 0.02370 0.02520 0.07860 0.06280 0.06510 0.04410 0.04460 0.05290 0.09820 0.09690 0.1040 0.1050 0.09460 0.03450 0.01530 0.01510 0.002930 3.000 2.850 0.01340 0.04410 0.02370 0.02520 0.06580 0.06100 0.06300 0.04390 0.04450 0.03000 0.06750 0.06420 0.08420 0.08400 0.07120 0.01940 0.01470 0.01430 0.001780 4.000 1.990 0.01330 0.04130 0.02370 0.02510 0.04530 0.05590 0.05710 0.04340 0.04390 0.02100 0.02890 0.02590 0.04840 0.04750 0.04210 0.005170 0.008740 0.007500 0.0004420 5.000 1.450 0.01320 0.03800 0.02350 0.02490 0.02880 0.04840 0.04850 0.04200 0.04240 0.02040 0.01220 0.01130 0.02300 0.02220 0.02410 0.003450 0.003210 0.002550 0.0002490 6.000 1.140 0.01310 0.03440 0.02330 0.02470 0.01780 0.03940 0.03860 0.03960 0.04000 0.01630 0.008480 0.008740 0.009970 0.009510 0.01370 0.003220 0.001330 0.001180 0.0002370 7.000 0.9480 0.01290 0.03050 0.02300 0.02420 0.01160 0.03030 0.02890 0.03640 0.03660 0.01060 0.008280 0.008620 0.004820 0.004650 0.007780 0.002310 0.001060 0.001050 0.0001730 8.000 0.8010 0.01280 0.02670 0.02250 0.02370 0.008840 0.02210 0.02040 0.03240 0.03240 0.005920 0.007770 0.007770 0.003340 0.003310 0.004450 0.001330 0.001040 0.001010 1.000E-04 10.00 0.5800 0.01200 0.01900 0.02100 0.02200 0.007800 0.010000 0.009100 0.02400 0.02400 0.002200 0.004800 0.004400 0.003100 0.003100 0.001500 0.0004000 0.0007200 0.0006200 3.000E-05 15.00 0.2700 0.01100 0.007100 0.01600 0.01600 0.005400 0.002400 0.002500 0.008400 0.008100 0.001500 0.0006400 0.0005900 0.001700 0.001600 0.0001300 0.0002500 9.300E-05 7.700E-05 1.800E-05 20.00 0.1400 0.009900 0.002400 0.01100 0.010000 0.001700 0.002200 0.002300 0.002600 0.002400 0.0005400 0.0004300 0.0004700 0.0005600 0.0005300 1.600E-05 9.100E-05 5.500E-05 5.700E-05 6.600E-06 30.00 0.04900 0.007100 0.001200 0.003900 0.003300 0.0002500 0.001100 0.0009800 0.0002700 0.0002400 5.900E-05 0.0002400 0.0002200 5.900E-05 5.300E-05 5.400E-07 9.200E-06 3.200E-05 2.700E-05 6.600E-07 40.00 0.02200 0.004700 0.001000 0.001300 0.001000 0.0002300 0.0003700 0.0003000 3.800E-05 3.200E-05 5.200E-05 8.300E-05 6.700E-05 8.300E-06 7.100E-06 3.700E-08 8.000E-06 1.100E-05 8.400E-06 5.700E-07 60.00 0.006200 0.002000 0.0004400 0.0001900 0.0001200 1.000E-04 4.900E-05 3.200E-05 1.700E-06 1.300E-06 2.300E-05 1.100E-05 7.200E-06 3.600E-07 2.800E-07 6.000E-10 3.600E-06 1.400E-06 9.000E-07 2.600E-07 100.0 0.0009600 0.0003700 6.700E-05 1.100E-05 4.700E-06 1.500E-05 2.600E-06 1.200E-06 2.500E-08 1.500E-08 3.400E-06 5.700E-07 2.600E-07 5.400E-09 3.300E-09 3.800E-12 5.300E-07 7.500E-08 3.200E-08 3.800E-08 #S 60 Nd #N 21 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 4 2 2 4 2 #UBIND 4.357E+04 7126. 6722. 6208. 1576. 1403. 1298. 1000. 978.0 321.0 230.0 230.0 124.0 121.0 0.000 38.00 22.00 22.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 0.000 12.40 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2280 0.1460 0.1520 0.1210 0.1230 0.1410 0.5970 0.4070 0.4340 2.130 0.05000 12.20 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2280 0.1460 0.1520 0.1210 0.1230 0.1410 0.5950 0.4070 0.4340 2.030 0.1000 11.70 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2280 0.1460 0.1520 0.1210 0.1230 0.1410 0.5870 0.4070 0.4340 1.770 0.1500 11.00 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2270 0.1460 0.1520 0.1210 0.1230 0.1410 0.5750 0.4070 0.4340 1.420 0.2000 10.20 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2260 0.1460 0.1520 0.1210 0.1230 0.1410 0.5580 0.4060 0.4330 1.040 0.3000 8.880 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2230 0.1460 0.1520 0.1210 0.1230 0.1410 0.5130 0.4030 0.4280 0.4520 0.4000 8.130 0.01320 0.04700 0.02330 0.02480 0.1050 0.06280 0.06540 0.04310 0.04360 0.2190 0.1460 0.1520 0.1210 0.1230 0.1410 0.4570 0.3950 0.4180 0.1670 0.5000 7.720 0.01320 0.04690 0.02330 0.02480 0.1050 0.06280 0.06540 0.04310 0.04360 0.2130 0.1460 0.1520 0.1210 0.1230 0.1410 0.3930 0.3820 0.4000 0.07990 0.6000 7.400 0.01320 0.04690 0.02330 0.02480 0.1040 0.06280 0.06540 0.04310 0.04360 0.2070 0.1460 0.1510 0.1210 0.1230 0.1410 0.3290 0.3610 0.3740 0.06640 0.7000 7.070 0.01320 0.04680 0.02330 0.02480 0.1030 0.06280 0.06540 0.04310 0.04360 0.2000 0.1450 0.1510 0.1210 0.1230 0.1400 0.2670 0.3340 0.3420 0.06570 0.8000 6.710 0.01320 0.04680 0.02330 0.02480 0.1020 0.06270 0.06540 0.04310 0.04360 0.1920 0.1450 0.1500 0.1210 0.1230 0.1400 0.2110 0.3030 0.3030 0.06060 1.000 5.960 0.01320 0.04660 0.02330 0.02480 0.1010 0.06270 0.06530 0.04310 0.04360 0.1750 0.1430 0.1480 0.1210 0.1220 0.1380 0.1250 0.2310 0.2220 0.03940 1.200 5.310 0.01320 0.04640 0.02330 0.02480 0.09820 0.06270 0.06530 0.04310 0.04360 0.1560 0.1400 0.1450 0.1200 0.1220 0.1350 0.07380 0.1610 0.1480 0.02010 1.400 4.800 0.01320 0.04620 0.02330 0.02480 0.09540 0.06260 0.06520 0.04310 0.04360 0.1360 0.1360 0.1410 0.1190 0.1210 0.1300 0.04910 0.1040 0.09100 0.009290 1.600 4.420 0.01320 0.04600 0.02330 0.02480 0.09240 0.06250 0.06510 0.04310 0.04360 0.1160 0.1310 0.1340 0.1180 0.1190 0.1240 0.04050 0.06410 0.05350 0.004670 1.800 4.130 0.01320 0.04570 0.02330 0.02480 0.08910 0.06230 0.06490 0.04310 0.04360 0.09820 0.1250 0.1270 0.1160 0.1170 0.1180 0.03900 0.03870 0.03170 0.003210 2.000 3.910 0.01320 0.04540 0.02330 0.02480 0.08560 0.06210 0.06460 0.04310 0.04360 0.08160 0.1170 0.1180 0.1120 0.1130 0.1100 0.03890 0.02450 0.02060 0.002950 2.400 3.510 0.01310 0.04460 0.02320 0.02480 0.07790 0.06140 0.06380 0.04310 0.04360 0.05480 0.09870 0.09770 0.1030 0.1040 0.09510 0.03460 0.01520 0.01460 0.002840 3.000 2.920 0.01310 0.04340 0.02320 0.02470 0.06570 0.05980 0.06190 0.04290 0.04350 0.03110 0.06930 0.06620 0.08470 0.08470 0.07330 0.02050 0.01430 0.01400 0.001820 4.000 2.060 0.01300 0.04070 0.02320 0.02470 0.04590 0.05520 0.05640 0.04240 0.04290 0.02070 0.03090 0.02770 0.05050 0.04970 0.04480 0.005580 0.009190 0.007880 0.0004740 5.000 1.500 0.01290 0.03760 0.02300 0.02450 0.02970 0.04820 0.04840 0.04120 0.04170 0.02020 0.01310 0.01190 0.02490 0.02410 0.02640 0.003370 0.003570 0.002790 0.0002390 6.000 1.170 0.01280 0.03410 0.02280 0.02420 0.01860 0.03980 0.03900 0.03910 0.03940 0.01680 0.008500 0.008670 0.01110 0.01060 0.01530 0.003220 0.001430 0.001210 0.0002290 7.000 0.9650 0.01270 0.03040 0.02250 0.02390 0.01210 0.03100 0.02960 0.03610 0.03630 0.01120 0.008140 0.008520 0.005290 0.005070 0.008890 0.002420 0.001040 0.001020 0.0001760 8.000 0.8140 0.01260 0.02680 0.02210 0.02330 0.008970 0.02290 0.02130 0.03240 0.03250 0.006510 0.007800 0.007860 0.003440 0.003390 0.005180 0.001460 0.001030 0.0009960 0.0001070 10.00 0.5900 0.01200 0.02000 0.02100 0.02200 0.007700 0.01100 0.009800 0.02400 0.02400 0.002300 0.005100 0.004700 0.003100 0.003100 0.001800 0.0004400 0.0007500 0.0006500 3.200E-05 15.00 0.2700 0.01100 0.007500 0.01600 0.01600 0.005500 0.002400 0.002500 0.008900 0.008600 0.001600 0.0007100 0.0006300 0.001800 0.001700 0.0001600 0.0002500 1.000E-04 8.300E-05 1.800E-05 20.00 0.1400 0.009800 0.002500 0.01100 0.01100 0.001900 0.002200 0.002300 0.002800 0.002600 0.0006000 0.0004200 0.0004700 0.0006200 0.0005800 2.100E-05 1.000E-04 5.500E-05 5.600E-05 7.100E-06 30.00 0.05100 0.007100 0.001200 0.004100 0.003500 0.0002600 0.001100 0.001000 0.0003000 0.0002700 6.100E-05 0.0002500 0.0002300 6.800E-05 6.100E-05 7.100E-07 9.500E-06 3.300E-05 2.900E-05 6.700E-07 40.00 0.02300 0.004800 0.001000 0.001400 0.001100 0.0002300 0.0004000 0.0003200 4.300E-05 3.700E-05 5.200E-05 9.000E-05 7.300E-05 9.700E-06 8.300E-06 4.900E-08 8.000E-06 1.200E-05 9.100E-06 5.600E-07 60.00 0.006500 0.002000 0.0004600 0.0002000 0.0001300 0.0001100 5.500E-05 3.600E-05 2.000E-06 1.500E-06 2.500E-05 1.200E-05 8.100E-06 4.300E-07 3.300E-07 8.200E-10 3.800E-06 1.600E-06 9.900E-07 2.700E-07 100.0 0.001000 0.0003900 7.200E-05 1.200E-05 5.200E-06 1.600E-05 3.000E-06 1.300E-06 3.000E-08 1.800E-08 3.700E-06 6.500E-07 3.000E-07 6.400E-09 3.900E-09 4.800E-12 5.700E-07 8.600E-08 3.700E-08 4.000E-08 #S 61 Pm #N 21 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 5 2 2 4 2 #UBIND 4.518E+04 7430. 7015. 6465. 1656. 1478. 1364. 1060. 1034. 337.0 242.0 242.0 133.0 129.0 0.000 38.00 22.00 22.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 0.000 12.30 0.01290 0.04610 0.02280 0.02430 0.1040 0.06130 0.06400 0.04210 0.04260 0.2230 0.1420 0.1480 0.1180 0.1190 0.1340 0.5860 0.3990 0.4270 2.110 0.05000 12.20 0.01290 0.04610 0.02280 0.02430 0.1040 0.06130 0.06400 0.04210 0.04260 0.2230 0.1420 0.1480 0.1180 0.1190 0.1340 0.5840 0.3990 0.4270 2.010 0.1000 11.60 0.01290 0.04610 0.02280 0.02430 0.1040 0.06130 0.06400 0.04210 0.04260 0.2220 0.1420 0.1480 0.1180 0.1190 0.1340 0.5760 0.3990 0.4270 1.760 0.1500 10.90 0.01290 0.04610 0.02280 0.02430 0.1040 0.06130 0.06400 0.04210 0.04260 0.2210 0.1420 0.1480 0.1180 0.1190 0.1340 0.5650 0.3990 0.4270 1.410 0.2000 10.10 0.01290 0.04610 0.02280 0.02430 0.1040 0.06130 0.06400 0.04210 0.04260 0.2200 0.1420 0.1480 0.1180 0.1190 0.1340 0.5490 0.3980 0.4260 1.050 0.3000 8.860 0.01290 0.04600 0.02280 0.02430 0.1030 0.06130 0.06400 0.04210 0.04260 0.2180 0.1420 0.1480 0.1180 0.1190 0.1340 0.5060 0.3950 0.4220 0.4620 0.4000 8.110 0.01290 0.04600 0.02280 0.02430 0.1030 0.06130 0.06400 0.04210 0.04260 0.2140 0.1420 0.1480 0.1180 0.1190 0.1340 0.4530 0.3880 0.4120 0.1730 0.5000 7.700 0.01290 0.04600 0.02280 0.02430 0.1020 0.06130 0.06400 0.04210 0.04260 0.2090 0.1420 0.1480 0.1180 0.1190 0.1340 0.3920 0.3750 0.3960 0.08020 0.6000 7.390 0.01290 0.04590 0.02280 0.02430 0.1020 0.06130 0.06400 0.04210 0.04260 0.2030 0.1420 0.1480 0.1180 0.1190 0.1340 0.3300 0.3570 0.3710 0.06440 0.7000 7.080 0.01290 0.04590 0.02280 0.02430 0.1010 0.06130 0.06400 0.04210 0.04260 0.1960 0.1420 0.1470 0.1180 0.1190 0.1340 0.2700 0.3320 0.3400 0.06370 0.8000 6.740 0.01290 0.04580 0.02280 0.02430 0.1000 0.06130 0.06400 0.04210 0.04260 0.1890 0.1410 0.1470 0.1180 0.1190 0.1340 0.2160 0.3020 0.3040 0.05960 1.000 6.020 0.01290 0.04570 0.02280 0.02430 0.09850 0.06130 0.06390 0.04210 0.04260 0.1720 0.1400 0.1450 0.1180 0.1190 0.1320 0.1300 0.2330 0.2250 0.03990 1.200 5.370 0.01290 0.04550 0.02280 0.02430 0.09630 0.06120 0.06390 0.04210 0.04260 0.1540 0.1370 0.1420 0.1170 0.1190 0.1300 0.07670 0.1660 0.1520 0.02100 1.400 4.860 0.01290 0.04530 0.02280 0.02430 0.09370 0.06120 0.06380 0.04210 0.04260 0.1360 0.1340 0.1380 0.1170 0.1180 0.1260 0.05020 0.1100 0.09530 0.009860 1.600 4.480 0.01290 0.04510 0.02280 0.02430 0.09090 0.06110 0.06370 0.04210 0.04260 0.1170 0.1290 0.1320 0.1150 0.1160 0.1210 0.04020 0.06840 0.05670 0.004880 1.800 4.190 0.01290 0.04480 0.02280 0.02430 0.08780 0.06090 0.06350 0.04210 0.04260 0.09950 0.1230 0.1250 0.1130 0.1140 0.1150 0.03790 0.04170 0.03370 0.003180 2.000 3.960 0.01290 0.04450 0.02280 0.02430 0.08440 0.06070 0.06320 0.04210 0.04260 0.08330 0.1160 0.1170 0.1100 0.1110 0.1090 0.03780 0.02620 0.02150 0.002820 2.400 3.570 0.01290 0.04380 0.02280 0.02430 0.07720 0.06010 0.06250 0.04210 0.04260 0.05670 0.09890 0.09820 0.1020 0.1030 0.09500 0.03460 0.01520 0.01430 0.002730 3.000 3.000 0.01280 0.04260 0.02270 0.02430 0.06550 0.05870 0.06080 0.04200 0.04250 0.03220 0.07100 0.06800 0.08510 0.08510 0.07470 0.02150 0.01400 0.01370 0.001860 4.000 2.140 0.01280 0.04010 0.02270 0.02420 0.04650 0.05440 0.05580 0.04150 0.04200 0.02040 0.03290 0.02960 0.05240 0.05160 0.04700 0.006050 0.009560 0.008200 0.0005080 5.000 1.550 0.01270 0.03710 0.02260 0.02410 0.03060 0.04800 0.04830 0.04050 0.04090 0.01990 0.01400 0.01260 0.02680 0.02600 0.02840 0.003300 0.003940 0.003050 0.0002310 6.000 1.200 0.01260 0.03380 0.02240 0.02380 0.01930 0.04000 0.03940 0.03860 0.03890 0.01710 0.008570 0.008630 0.01230 0.01170 0.01690 0.003180 0.001540 0.001260 0.0002210 7.000 0.9840 0.01250 0.03030 0.02210 0.02350 0.01250 0.03160 0.03030 0.03580 0.03610 0.01190 0.007990 0.008390 0.005820 0.005560 0.009980 0.002510 0.001030 0.0009900 0.0001780 8.000 0.8280 0.01230 0.02680 0.02170 0.02300 0.009140 0.02380 0.02210 0.03240 0.03250 0.007100 0.007780 0.007910 0.003580 0.003500 0.005920 0.001580 0.001010 0.0009750 0.0001130 10.00 0.6000 0.01200 0.02000 0.02100 0.02200 0.007600 0.01200 0.010000 0.02500 0.02400 0.002400 0.005400 0.005000 0.003000 0.003000 0.002100 0.0004800 0.0007800 0.0006800 3.400E-05 15.00 0.2800 0.01100 0.007800 0.01600 0.01600 0.005700 0.002500 0.002500 0.009300 0.009100 0.001600 0.0007900 0.0006800 0.001900 0.001800 0.0002000 0.0002500 0.0001200 8.900E-05 1.700E-05 20.00 0.1500 0.009700 0.002700 0.01100 0.01100 0.002100 0.002100 0.002300 0.003000 0.002900 0.0006600 0.0004200 0.0004700 0.0006800 0.0006400 2.600E-05 0.0001100 5.400E-05 5.500E-05 7.500E-06 30.00 0.05300 0.007100 0.001100 0.004300 0.003700 0.0002600 0.001200 0.001100 0.0003400 0.0003000 6.400E-05 0.0002600 0.0002500 7.800E-05 6.900E-05 9.200E-07 1.000E-05 3.500E-05 3.000E-05 6.900E-07 40.00 0.02300 0.004900 0.001000 0.001500 0.001200 0.0002300 0.0004300 0.0003500 4.900E-05 4.200E-05 5.200E-05 9.800E-05 8.000E-05 1.100E-05 9.600E-06 6.400E-08 7.900E-06 1.300E-05 9.700E-06 5.400E-07 60.00 0.006800 0.002100 0.0004800 0.0002200 0.0001400 0.0001100 6.100E-05 4.000E-05 2.300E-06 1.800E-06 2.600E-05 1.400E-05 9.000E-06 5.100E-07 3.900E-07 1.100E-09 4.000E-06 1.800E-06 1.100E-06 2.800E-07 100.0 0.001100 0.0004200 7.800E-05 1.300E-05 5.800E-06 1.800E-05 3.400E-06 1.500E-06 3.500E-08 2.100E-08 4.000E-06 7.500E-07 3.400E-07 7.700E-09 4.700E-09 6.100E-12 6.200E-07 9.800E-08 4.100E-08 4.200E-08 #S 62 Sm #N 21 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 2 2 4 2 #UBIND 4.684E+04 7737. 7312. 6717. 1728. 1546. 1425. 1111. 1085. 351.0 251.0 251.0 137.0 132.0 0.000 33.00 26.00 26.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 0.000 12.30 0.01270 0.04520 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2170 0.1390 0.1450 0.1150 0.1160 0.1290 0.5750 0.3910 0.4200 2.090 0.05000 12.10 0.01270 0.04520 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2170 0.1390 0.1450 0.1150 0.1160 0.1290 0.5730 0.3910 0.4200 2.000 0.1000 11.60 0.01270 0.04520 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2170 0.1390 0.1450 0.1150 0.1160 0.1290 0.5660 0.3910 0.4200 1.750 0.1500 10.90 0.01270 0.04520 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2160 0.1390 0.1450 0.1150 0.1160 0.1290 0.5550 0.3910 0.4200 1.410 0.2000 10.10 0.01270 0.04520 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2150 0.1390 0.1450 0.1150 0.1160 0.1290 0.5400 0.3900 0.4190 1.050 0.3000 8.840 0.01270 0.04510 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2130 0.1390 0.1450 0.1150 0.1160 0.1290 0.5000 0.3870 0.4160 0.4720 0.4000 8.090 0.01270 0.04510 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2090 0.1390 0.1450 0.1150 0.1160 0.1290 0.4490 0.3810 0.4070 0.1780 0.5000 7.680 0.01270 0.04510 0.02230 0.02390 0.1000 0.05990 0.06260 0.04120 0.04170 0.2040 0.1390 0.1450 0.1150 0.1160 0.1290 0.3910 0.3690 0.3910 0.08080 0.6000 7.390 0.01270 0.04500 0.02230 0.02390 0.09960 0.05990 0.06260 0.04120 0.04170 0.1990 0.1390 0.1440 0.1150 0.1160 0.1290 0.3310 0.3520 0.3680 0.06260 0.7000 7.090 0.01270 0.04500 0.02230 0.02390 0.09900 0.05990 0.06260 0.04120 0.04170 0.1930 0.1380 0.1440 0.1150 0.1160 0.1290 0.2730 0.3290 0.3380 0.06180 0.8000 6.760 0.01270 0.04490 0.02230 0.02390 0.09830 0.05990 0.06260 0.04120 0.04170 0.1860 0.1380 0.1440 0.1150 0.1160 0.1280 0.2190 0.3010 0.3040 0.05850 1.000 6.060 0.01270 0.04480 0.02230 0.02390 0.09650 0.05990 0.06260 0.04120 0.04170 0.1700 0.1370 0.1420 0.1150 0.1160 0.1270 0.1340 0.2360 0.2280 0.04030 1.200 5.430 0.01270 0.04460 0.02230 0.02390 0.09440 0.05990 0.06250 0.04120 0.04170 0.1530 0.1340 0.1390 0.1140 0.1160 0.1250 0.07970 0.1700 0.1560 0.02180 1.400 4.920 0.01270 0.04440 0.02230 0.02390 0.09200 0.05980 0.06250 0.04120 0.04170 0.1350 0.1310 0.1360 0.1140 0.1150 0.1220 0.05140 0.1140 0.09930 0.01040 1.600 4.540 0.01270 0.04420 0.02230 0.02390 0.08940 0.05970 0.06230 0.04120 0.04170 0.1180 0.1270 0.1300 0.1120 0.1140 0.1180 0.04000 0.07260 0.05990 0.005120 1.800 4.250 0.01270 0.04400 0.02230 0.02390 0.08640 0.05960 0.06220 0.04120 0.04170 0.1010 0.1210 0.1240 0.1110 0.1120 0.1130 0.03700 0.04480 0.03570 0.003180 2.000 4.020 0.01260 0.04370 0.02230 0.02390 0.08330 0.05940 0.06200 0.04120 0.04170 0.08480 0.1150 0.1160 0.1080 0.1090 0.1070 0.03680 0.02810 0.02250 0.002710 2.400 3.630 0.01260 0.04300 0.02230 0.02390 0.07640 0.05890 0.06130 0.04120 0.04170 0.05840 0.09900 0.09860 0.1010 0.1020 0.09460 0.03430 0.01530 0.01400 0.002630 3.000 3.070 0.01260 0.04190 0.02230 0.02380 0.06530 0.05750 0.05970 0.04110 0.04160 0.03330 0.07250 0.06960 0.08520 0.08530 0.07560 0.02240 0.01360 0.01330 0.001880 4.000 2.210 0.01250 0.03950 0.02220 0.02380 0.04700 0.05370 0.05510 0.04070 0.04120 0.02020 0.03480 0.03130 0.05410 0.05330 0.04890 0.006550 0.009870 0.008460 0.0005450 5.000 1.600 0.01250 0.03670 0.02210 0.02360 0.03130 0.04770 0.04810 0.03970 0.04020 0.01960 0.01500 0.01340 0.02870 0.02780 0.03020 0.003250 0.004310 0.003300 0.0002260 6.000 1.230 0.01240 0.03360 0.02200 0.02340 0.02000 0.04020 0.03970 0.03800 0.03840 0.01730 0.008710 0.008640 0.01350 0.01290 0.01840 0.003130 0.001670 0.001320 0.0002130 7.000 1.000 0.01220 0.03020 0.02170 0.02310 0.01300 0.03220 0.03090 0.03550 0.03580 0.01250 0.007850 0.008250 0.006420 0.006100 0.01110 0.002570 0.001030 0.0009650 0.0001790 8.000 0.8430 0.01210 0.02680 0.02140 0.02260 0.009350 0.02450 0.02290 0.03230 0.03240 0.007700 0.007730 0.007920 0.003770 0.003650 0.006660 0.001690 0.0009850 0.0009500 0.0001190 10.00 0.6100 0.01200 0.02000 0.02000 0.02100 0.007500 0.01300 0.01100 0.02500 0.02500 0.002600 0.005600 0.005200 0.003000 0.003000 0.002500 0.0005300 0.0008100 0.0007000 3.700E-05 15.00 0.2900 0.01100 0.008100 0.01600 0.01600 0.005800 0.002500 0.002500 0.009800 0.009500 0.001600 0.0008800 0.0007300 0.002000 0.001900 0.0002400 0.0002500 0.0001300 9.700E-05 1.700E-05 20.00 0.1500 0.009700 0.002800 0.01100 0.01100 0.002300 0.002100 0.002300 0.003300 0.003100 0.0007200 0.0004200 0.0004600 0.0007400 0.0007000 3.200E-05 0.0001200 5.300E-05 5.400E-05 8.000E-06 30.00 0.05500 0.007100 0.001100 0.004500 0.003800 0.0002700 0.001200 0.001100 0.0003800 0.0003400 6.900E-05 0.0002800 0.0002600 8.800E-05 7.800E-05 1.200E-06 1.100E-05 3.600E-05 3.100E-05 7.200E-07 40.00 0.02400 0.004900 0.001000 0.001600 0.001200 0.0002300 0.0004600 0.0003800 5.600E-05 4.800E-05 5.200E-05 0.0001100 8.700E-05 1.300E-05 1.100E-05 8.200E-08 7.800E-06 1.400E-05 1.000E-05 5.200E-07 60.00 0.007100 0.002100 0.0005000 0.0002500 0.0001600 0.0001200 6.700E-05 4.400E-05 2.600E-06 2.000E-06 2.700E-05 1.500E-05 1.000E-05 6.000E-07 4.500E-07 1.400E-09 4.200E-06 2.000E-06 1.200E-06 2.800E-07 100.0 0.001200 0.0004400 8.400E-05 1.500E-05 6.500E-06 1.900E-05 3.800E-06 1.700E-06 4.100E-08 2.500E-08 4.400E-06 8.600E-07 3.800E-07 9.200E-09 5.500E-09 7.800E-12 6.700E-07 1.100E-07 4.600E-08 4.500E-08 #S 63 Eu #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 1 2 2 4 2 #UBIND 4.852E+04 8052. 7618. 6977. 1805. 1619. 1486. 1166. 1136. 366.0 261.0 261.0 141.0 136.0 0.000 0.000 37.00 27.00 27.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 12.20 0.01240 0.04430 0.02190 0.02340 0.09920 0.05860 0.06130 0.04030 0.04080 0.2120 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.5640 0.3840 0.4130 2.070 0.05000 12.00 0.01240 0.04430 0.02190 0.02340 0.09920 0.05860 0.06130 0.04030 0.04080 0.2120 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.5620 0.3840 0.4130 1.980 0.1000 11.50 0.01240 0.04430 0.02190 0.02340 0.09920 0.05860 0.06130 0.04030 0.04080 0.2120 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.5560 0.3840 0.4130 1.740 0.1500 10.80 0.01240 0.04430 0.02190 0.02340 0.09910 0.05860 0.06130 0.04030 0.04080 0.2110 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.5460 0.3830 0.4130 1.410 0.2000 10.10 0.01240 0.04430 0.02190 0.02340 0.09900 0.05860 0.06130 0.04030 0.04080 0.2100 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.5310 0.3830 0.4120 1.060 0.3000 8.820 0.01240 0.04420 0.02190 0.02340 0.09880 0.05860 0.06130 0.04030 0.04080 0.2080 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.4930 0.3810 0.4090 0.4820 0.4000 8.070 0.01240 0.04420 0.02190 0.02340 0.09850 0.05860 0.06130 0.04030 0.04080 0.2050 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.4440 0.3750 0.4000 0.1840 0.5000 7.670 0.01240 0.04420 0.02190 0.02340 0.09800 0.05860 0.06130 0.04030 0.04080 0.2000 0.1350 0.1410 0.1120 0.1140 0.1240 0.1300 0.3890 0.3640 0.3860 0.08170 0.6000 7.380 0.01240 0.04410 0.02190 0.02340 0.09750 0.05860 0.06130 0.04030 0.04080 0.1950 0.1350 0.1410 0.1120 0.1140 0.1240 0.1300 0.3310 0.3480 0.3640 0.06100 0.7000 7.090 0.01240 0.04410 0.02190 0.02340 0.09690 0.05860 0.06130 0.04030 0.04080 0.1890 0.1350 0.1410 0.1120 0.1140 0.1240 0.1300 0.2750 0.3260 0.3360 0.05990 0.8000 6.780 0.01240 0.04410 0.02190 0.02340 0.09620 0.05860 0.06130 0.04030 0.04080 0.1830 0.1350 0.1400 0.1120 0.1140 0.1240 0.1300 0.2230 0.2990 0.3030 0.05730 1.000 6.110 0.01240 0.04390 0.02190 0.02340 0.09460 0.05860 0.06130 0.04030 0.04080 0.1680 0.1330 0.1390 0.1120 0.1130 0.1230 0.1280 0.1380 0.2370 0.2300 0.04060 1.200 5.490 0.01240 0.04380 0.02190 0.02340 0.09270 0.05850 0.06120 0.04030 0.04080 0.1520 0.1310 0.1370 0.1120 0.1130 0.1210 0.1250 0.08290 0.1740 0.1600 0.02260 1.400 4.990 0.01240 0.04360 0.02190 0.02340 0.09040 0.05850 0.06120 0.04030 0.04080 0.1350 0.1290 0.1330 0.1110 0.1120 0.1180 0.1220 0.05290 0.1190 0.1040 0.01100 1.600 4.600 0.01240 0.04340 0.02190 0.02340 0.08790 0.05840 0.06110 0.04030 0.04080 0.1180 0.1250 0.1280 0.1100 0.1110 0.1150 0.1170 0.04000 0.07670 0.06350 0.005410 1.800 4.310 0.01240 0.04310 0.02180 0.02340 0.08510 0.05830 0.06090 0.04030 0.04080 0.1010 0.1200 0.1230 0.1080 0.1100 0.1100 0.1120 0.03620 0.04780 0.03810 0.003220 2.000 4.080 0.01240 0.04290 0.02180 0.02340 0.08210 0.05810 0.06070 0.04030 0.04080 0.08610 0.1140 0.1150 0.1060 0.1070 0.1050 0.1060 0.03580 0.03000 0.02380 0.002620 2.400 3.690 0.01240 0.04230 0.02180 0.02340 0.07560 0.05760 0.06010 0.04020 0.04080 0.06010 0.09890 0.09880 0.09960 0.1000 0.09390 0.09300 0.03400 0.01550 0.01390 0.002520 3.000 3.140 0.01240 0.04120 0.02180 0.02340 0.06510 0.05640 0.05870 0.04020 0.04070 0.03460 0.07380 0.07110 0.08520 0.08540 0.07620 0.07440 0.02320 0.01310 0.01290 0.001890 4.000 2.280 0.01230 0.03900 0.02180 0.02330 0.04740 0.05290 0.05440 0.03980 0.04030 0.02010 0.03680 0.03310 0.05570 0.05490 0.05050 0.04840 0.007120 0.01010 0.008750 0.0005840 5.000 1.660 0.01220 0.03630 0.02170 0.02320 0.03210 0.04730 0.04790 0.03900 0.03940 0.01920 0.01610 0.01420 0.03050 0.02960 0.03190 0.03030 0.003240 0.004650 0.003580 0.0002230 6.000 1.270 0.01210 0.03330 0.02150 0.02300 0.02080 0.04030 0.03990 0.03740 0.03780 0.01740 0.008930 0.008700 0.01480 0.01410 0.01980 0.01860 0.003080 0.001820 0.001410 0.0002040 7.000 1.030 0.01200 0.03010 0.02130 0.02270 0.01350 0.03260 0.03150 0.03510 0.03540 0.01300 0.007720 0.008110 0.007080 0.006700 0.01210 0.01140 0.002630 0.001040 0.0009520 0.0001780 8.000 0.8590 0.01190 0.02680 0.02100 0.02230 0.009590 0.02520 0.02360 0.03220 0.03240 0.008290 0.007640 0.007900 0.004000 0.003850 0.007420 0.006930 0.001800 0.0009550 0.0009300 0.0001230 10.00 0.6200 0.01190 0.02000 0.02000 0.02100 0.007400 0.01300 0.01200 0.02500 0.02500 0.002900 0.005900 0.005500 0.002900 0.003000 0.002800 0.002600 0.0005900 0.0008200 0.0007200 4.000E-05 15.00 0.3000 0.01100 0.008500 0.01600 0.01600 0.006000 0.002600 0.002600 0.010000 0.010000 0.001600 0.0009800 0.0008000 0.002100 0.002000 0.0002900 0.0002600 0.0002500 0.0001500 0.0001100 1.700E-05 20.00 0.1600 0.009600 0.003000 0.01100 0.01100 0.002400 0.002100 0.002300 0.003500 0.003300 0.0007800 0.0004100 0.0004600 0.0008100 0.0007600 3.900E-05 3.500E-05 0.0001300 5.200E-05 5.300E-05 8.400E-06 30.00 0.05700 0.007100 0.001100 4.610 0.004000 0.0002800 0.001300 0.001200 0.0004200 0.0003800 7.400E-05 0.0002900 0.0002700 9.900E-05 8.800E-05 1.500E-06 1.300E-06 1.200E-05 3.700E-05 3.200E-05 7.600E-07 40.00 0.02500 0.005000 0.001000 0.001700 0.001300 0.0002200 0.0005000 0.0004000 6.400E-05 5.500E-05 5.100E-05 0.0001100 9.400E-05 1.500E-05 1.300E-05 1.100E-07 8.900E-08 7.800E-06 1.500E-05 1.100E-05 5.100E-07 60.00 0.007500 0.002200 0.0005200 0.0002700 0.0001700 0.0001200 7.400E-05 4.900E-05 3.100E-06 2.400E-06 2.900E-05 1.700E-05 1.100E-05 7.000E-07 5.300E-07 1.800E-09 1.400E-09 4.400E-06 2.200E-06 1.300E-06 2.900E-07 100.0 0.001200 0.0004600 9.000E-05 1.700E-05 7.200E-06 2.100E-05 4.300E-06 1.900E-06 4.900E-08 2.900E-08 4.800E-06 9.800E-07 4.300E-07 1.100E-08 6.500E-09 9.900E-12 6.600E-12 7.300E-07 1.300E-07 5.100E-08 4.800E-08 #S 64 Gd #N 23 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 1 2 2 4 1 2 #UBIND 5.024E+04 8376. 7931. 7243. 1888. 1695. 1551. 1225. 1193. 383.0 311.0 272.0 147.0 142.0 0.000 0.000 43.00 28.00 28.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 0.000 12.00 0.01220 0.04340 0.02140 0.02300 0.09710 0.05730 0.06010 0.03940 0.03990 0.2070 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.5380 0.3660 0.3910 0.4680 1.930 0.05000 11.90 0.01220 0.04340 0.02140 0.02300 0.09710 0.05730 0.06010 0.03940 0.03990 0.2070 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.5360 0.3660 0.3910 0.4680 1.850 0.1000 11.40 0.01220 0.04340 0.02140 0.02300 0.09710 0.05730 0.06010 0.03940 0.03990 0.2060 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.5310 0.3660 0.3910 0.4680 1.660 0.1500 10.90 0.01220 0.04340 0.02140 0.02300 0.09700 0.05730 0.06010 0.03940 0.03990 0.2060 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.5220 0.3660 0.3910 0.4670 1.370 0.2000 10.20 0.01220 0.04340 0.02140 0.02300 0.09690 0.05730 0.06010 0.03940 0.03990 0.2050 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.5090 0.3660 0.3900 0.4670 1.060 0.3000 9.050 0.01220 0.04340 0.02140 0.02300 0.09670 0.05730 0.06010 0.03940 0.03990 0.2030 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.4750 0.3640 0.3880 0.4630 0.5300 0.4000 8.290 0.01220 0.04340 0.02140 0.02300 0.09640 0.05730 0.06010 0.03940 0.03990 0.2000 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.4320 0.3590 0.3810 0.4520 0.2210 0.5000 7.850 0.01220 0.04330 0.02140 0.02300 0.09600 0.05730 0.06010 0.03940 0.03990 0.1960 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.3820 0.3500 0.3700 0.4280 0.09630 0.6000 7.530 0.01220 0.04330 0.02140 0.02300 0.09550 0.05730 0.06010 0.03940 0.03990 0.1910 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.3300 0.3360 0.3520 0.3930 0.06330 0.7000 7.240 0.01220 0.04330 0.02140 0.02300 0.09500 0.05730 0.06010 0.03940 0.03990 0.1850 0.1310 0.1370 0.1080 0.1100 0.1130 0.1160 0.2780 0.3180 0.3290 0.3510 0.05950 0.8000 6.910 0.01220 0.04320 0.02140 0.02300 0.09430 0.05730 0.06010 0.03940 0.03990 0.1790 0.1310 0.1370 0.1080 0.1100 0.1130 0.1160 0.2290 0.2950 0.3010 0.3050 0.05870 1.000 6.220 0.01220 0.04310 0.02140 0.02300 0.09280 0.05730 0.06000 0.03940 0.03990 0.1660 0.1300 0.1360 0.1080 0.1100 0.1130 0.1150 0.1460 0.2400 0.2360 0.2160 0.04610 1.200 5.570 0.01220 0.04290 0.02140 0.02300 0.09090 0.05730 0.06000 0.03940 0.03990 0.1500 0.1280 0.1340 0.1080 0.1100 0.1120 0.1140 0.09000 0.1810 0.1700 0.1440 0.02810 1.400 5.030 0.01220 0.04280 0.02140 0.02300 0.08880 0.05720 0.05990 0.03940 0.03990 0.1340 0.1260 0.1300 0.1080 0.1090 0.1100 0.1130 0.05750 0.1280 0.1150 0.09240 0.01480 1.600 4.610 0.01220 0.04260 0.02140 0.02300 0.08640 0.05710 0.05980 0.03940 0.03990 0.1180 0.1220 0.1260 0.1070 0.1080 0.1080 0.1100 0.04210 0.08550 0.07300 0.05740 0.007440 1.800 4.290 0.01220 0.04230 0.02140 0.02300 0.08380 0.05700 0.05970 0.03940 0.03990 0.1020 0.1180 0.1210 0.1050 0.1070 0.1050 0.1070 0.03670 0.05490 0.04500 0.03480 0.004160 2.000 4.050 0.01220 0.04210 0.02140 0.02300 0.08100 0.05690 0.05950 0.03940 0.03990 0.08750 0.1120 0.1140 0.1040 0.1050 0.1020 0.1030 0.03570 0.03490 0.02820 0.02080 0.003040 2.400 3.670 0.01220 0.04150 0.02140 0.02300 0.07480 0.05650 0.05900 0.03940 0.03990 0.06190 0.09870 0.09890 0.09800 0.09900 0.09340 0.09370 0.03470 0.01710 0.01520 0.007770 0.002800 3.000 3.160 0.01210 0.04050 0.02140 0.02300 0.06480 0.05540 0.05770 0.03930 0.03990 0.03600 0.07510 0.07260 0.08510 0.08540 0.07850 0.07800 0.02520 0.01330 0.01340 0.003370 0.002270 4.000 2.330 0.01210 0.03840 0.02140 0.02290 0.04780 0.05210 0.05370 0.03900 0.03950 0.02020 0.03880 0.03510 0.05740 0.05670 0.05440 0.05330 0.008280 0.01090 0.009770 0.003050 0.0007700 5.000 1.700 0.01200 0.03580 0.02130 0.02280 0.03280 0.04700 0.04770 0.03830 0.03870 0.01890 0.01740 0.01520 0.03260 0.03160 0.03560 0.03450 0.003450 0.005380 0.004260 0.002210 0.0002690 6.000 1.300 0.01190 0.03300 0.02110 0.02260 0.02150 0.04040 0.04010 0.03690 0.03730 0.01760 0.009280 0.008880 0.01640 0.01560 0.02260 0.02180 0.003160 0.002150 0.001660 0.001220 0.0002310 7.000 1.050 0.01180 0.02990 0.02090 0.02240 0.01410 0.03310 0.03200 0.03480 0.03510 0.01360 0.007650 0.008010 0.007910 0.007450 0.01410 0.01350 0.002810 0.001130 0.001020 0.0005860 0.0002100 8.000 0.8750 0.01170 0.02680 0.02060 0.02200 0.009860 0.02590 0.02430 0.03200 0.03220 0.008910 0.007580 0.007890 0.004330 0.004130 0.008800 0.008390 0.002000 0.0009850 0.0009800 0.0002860 0.0001520 10.00 0.6300 0.01100 0.02100 0.02000 0.02100 0.007300 0.01400 0.01200 0.02500 0.02500 0.003100 0.006100 0.005700 0.002900 0.002900 0.003400 0.003200 0.0006900 0.0008800 0.0008000 0.0001500 5.200E-05 15.00 0.3000 0.01100 0.008800 0.01600 0.01600 0.006100 0.002800 0.002600 0.01100 0.010000 0.001600 0.001100 0.0008900 0.002200 0.002100 0.0003700 0.0003400 0.0002600 0.0001700 0.0001300 0.0001100 1.900E-05 20.00 0.1600 0.009500 0.003200 0.01200 0.01100 0.002600 0.002000 0.002300 0.003800 0.003600 0.0008500 0.0004100 0.0004600 0.0008800 0.0008300 5.100E-05 4.700E-05 0.0001400 5.500E-05 5.600E-05 4.700E-05 1.000E-05 30.00 0.05800 0.007100 0.001100 0.004800 0.004200 0.0003000 0.001300 0.001200 0.0004700 0.0004200 8.100E-05 0.0003000 0.0002900 0.0001100 1.000E-04 2.000E-06 1.800E-06 1.300E-05 4.000E-05 3.600E-05 6.100E-06 9.700E-07 40.00 0.02600 0.005000 0.001000 0.001800 0.001400 0.0002200 0.0005300 0.0004300 7.200E-05 6.200E-05 5.100E-05 0.0001200 1.000E-04 1.700E-05 1.500E-05 1.400E-07 1.200E-07 8.000E-06 1.700E-05 1.300E-05 9.300E-07 5.800E-07 60.00 0.007800 0.002300 0.0005400 0.0002900 0.0001800 0.0001300 8.200E-05 5.300E-05 3.500E-06 2.700E-06 3.000E-05 1.900E-05 1.200E-05 8.300E-07 6.200E-07 2.500E-09 2.000E-09 4.800E-06 2.600E-06 1.600E-06 4.400E-08 3.500E-07 100.0 0.001300 0.0004900 9.600E-05 1.900E-05 8.000E-06 2.200E-05 4.900E-06 2.200E-06 5.700E-08 3.400E-08 5.200E-06 1.100E-06 4.900E-07 1.300E-08 7.700E-09 1.400E-11 9.100E-12 8.200E-07 1.500E-07 6.200E-08 6.900E-10 5.900E-08 #S 65 Tb #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 3 2 2 4 2 #UBIND 5.200E+04 8708. 8252. 7515. 1970. 1770. 1614. 1278. 1244. 400.0 322.0 284.0 152.0 148.0 0.000 0.000 42.00 28.00 28.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 12.00 0.01200 0.04260 0.02100 0.02260 0.09500 0.05610 0.05890 0.03860 0.03910 0.2030 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.5440 0.3700 0.4000 2.030 0.05000 11.80 0.01200 0.04260 0.02100 0.02260 0.09500 0.05610 0.05890 0.03860 0.03910 0.2030 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.5420 0.3700 0.4000 1.940 0.1000 11.40 0.01200 0.04260 0.02100 0.02260 0.09500 0.05610 0.05890 0.03860 0.03910 0.2030 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.5370 0.3700 0.4000 1.720 0.1500 10.70 0.01200 0.04260 0.02100 0.02260 0.09500 0.05610 0.05890 0.03860 0.03910 0.2020 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.5280 0.3700 0.4000 1.410 0.2000 10.00 0.01200 0.04260 0.02100 0.02260 0.09490 0.05610 0.05890 0.03860 0.03910 0.2010 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.5150 0.3700 0.3990 1.070 0.3000 8.790 0.01200 0.04260 0.02100 0.02260 0.09470 0.05610 0.05890 0.03860 0.03910 0.1990 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.4800 0.3680 0.3960 0.5020 0.4000 8.040 0.01200 0.04250 0.02100 0.02260 0.09440 0.05610 0.05890 0.03860 0.03910 0.1960 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.4360 0.3630 0.3890 0.1970 0.5000 7.640 0.01200 0.04250 0.02100 0.02260 0.09400 0.05610 0.05890 0.03860 0.03910 0.1920 0.1290 0.1350 0.1070 0.1080 0.1160 0.1210 0.3850 0.3540 0.3760 0.08430 0.6000 7.360 0.01200 0.04250 0.02100 0.02260 0.09360 0.05610 0.05890 0.03860 0.03910 0.1880 0.1290 0.1350 0.1070 0.1080 0.1160 0.1210 0.3320 0.3390 0.3570 0.05840 0.7000 7.100 0.01200 0.04240 0.02100 0.02260 0.09300 0.05610 0.05890 0.03860 0.03910 0.1830 0.1290 0.1350 0.1070 0.1080 0.1160 0.1210 0.2790 0.3200 0.3320 0.05640 0.8000 6.810 0.01200 0.04240 0.02100 0.02260 0.09240 0.05610 0.05890 0.03860 0.03910 0.1770 0.1280 0.1350 0.1070 0.1080 0.1160 0.1200 0.2290 0.2960 0.3020 0.05480 1.000 6.190 0.01200 0.04230 0.02100 0.02260 0.09100 0.05610 0.05890 0.03860 0.03910 0.1640 0.1280 0.1340 0.1070 0.1080 0.1150 0.1190 0.1460 0.2400 0.2340 0.04100 1.200 5.600 0.01200 0.04210 0.02100 0.02260 0.08930 0.05600 0.05880 0.03860 0.03910 0.1490 0.1260 0.1320 0.1060 0.1080 0.1140 0.1180 0.08930 0.1810 0.1680 0.02400 1.400 5.100 0.01200 0.04200 0.02100 0.02260 0.08730 0.05600 0.05880 0.03860 0.03910 0.1340 0.1240 0.1290 0.1060 0.1070 0.1120 0.1150 0.05640 0.1270 0.1120 0.01230 1.600 4.710 0.01200 0.04180 0.02100 0.02260 0.08500 0.05590 0.05870 0.03860 0.03910 0.1180 0.1200 0.1250 0.1050 0.1070 0.1090 0.1110 0.04070 0.08450 0.07040 0.006050 1.800 4.410 0.01190 0.04160 0.02100 0.02260 0.08250 0.05580 0.05860 0.03860 0.03910 0.1030 0.1160 0.1200 0.1040 0.1050 0.1060 0.1070 0.03510 0.05400 0.04300 0.003390 2.000 4.180 0.01190 0.04130 0.02100 0.02260 0.07990 0.05570 0.05840 0.03860 0.03910 0.08850 0.1110 0.1130 0.1020 0.1030 0.1010 0.1020 0.03400 0.03420 0.02670 0.002510 2.400 3.800 0.01190 0.04080 0.02100 0.02260 0.07400 0.05530 0.05790 0.03850 0.03910 0.06320 0.09840 0.09880 0.09680 0.09780 0.09200 0.09180 0.03310 0.01630 0.01400 0.002320 3.000 3.270 0.01190 0.03980 0.02100 0.02260 0.06440 0.05430 0.05670 0.03850 0.03910 0.03700 0.07590 0.07350 0.08470 0.08500 0.07660 0.07540 0.02450 0.01230 0.01220 0.001890 4.000 2.420 0.01180 0.03780 0.02090 0.02250 0.04810 0.05130 0.05300 0.03820 0.03880 0.02020 0.04040 0.03650 0.05830 0.05760 0.05290 0.05130 0.008350 0.01030 0.009150 0.0006640 5.000 1.770 0.01180 0.03540 0.02090 0.02240 0.03350 0.04660 0.04740 0.03760 0.03810 0.01850 0.01850 0.01600 0.03390 0.03290 0.03480 0.03340 0.003310 0.005310 0.004140 0.0002270 6.000 1.350 0.01170 0.03270 0.02070 0.02230 0.02220 0.04040 0.04020 0.03630 0.03670 0.01750 0.009590 0.009000 0.01750 0.01660 0.02230 0.02130 0.002940 0.002170 0.001620 0.0001870 7.000 1.080 0.01160 0.02970 0.02050 0.02200 0.01460 0.03340 0.03240 0.03440 0.03470 0.01390 0.007530 0.007850 0.008560 0.008050 0.01410 0.01340 0.002680 0.001090 0.0009470 0.0001740 8.000 0.8940 0.01150 0.02670 0.02030 0.02170 0.01020 0.02650 0.02490 0.03190 0.03210 0.009430 0.007410 0.007770 0.004600 0.004370 0.008920 0.008420 0.001990 0.0009020 0.0008830 0.0001310 10.00 0.6500 0.01100 0.02100 0.01900 0.02100 0.007200 0.01500 0.01300 0.02600 0.02600 0.003400 0.006200 0.005900 0.002900 0.002900 0.003600 0.003300 0.0007200 0.0008300 0.0007500 4.800E-05 15.00 0.3100 0.010000 0.009100 0.01600 0.01600 0.006100 0.002900 0.002700 0.01100 0.01100 0.001600 0.001200 0.0009700 0.002200 0.002100 0.0004000 0.0003700 0.0002500 0.0001800 0.0001300 1.600E-05 20.00 0.1700 0.009400 0.003400 0.01200 0.01100 0.002800 0.002000 0.002200 0.004100 0.003800 0.0009000 0.0004100 0.0004500 0.0009400 0.0008800 5.700E-05 5.200E-05 0.0001400 5.100E-05 5.100E-05 9.200E-06 30.00 0.06000 0.007100 0.001100 0.005000 0.004300 0.0003200 0.001400 0.001300 0.0005200 0.0004600 8.900E-05 0.0003100 0.0003000 0.0001200 0.0001100 2.200E-06 2.000E-06 1.400E-05 3.900E-05 3.400E-05 8.900E-07 40.00 0.02700 0.005100 0.001000 0.001900 0.001500 0.0002200 0.0005700 0.0004600 8.100E-05 7.000E-05 5.000E-05 0.0001300 0.0001100 2.000E-05 1.600E-05 1.700E-07 1.400E-07 7.500E-06 1.700E-05 1.300E-05 4.700E-07 60.00 0.008100 0.002300 0.0005500 0.0003200 0.0002000 0.0001300 9.000E-05 5.900E-05 4.100E-06 3.100E-06 3.100E-05 2.100E-05 1.400E-05 9.500E-07 7.200E-07 3.000E-09 2.300E-09 4.700E-06 2.600E-06 1.600E-06 3.000E-07 100.0 0.001400 0.0005100 1.000E-04 2.100E-05 8.900E-06 2.400E-05 5.500E-06 2.400E-06 6.600E-08 4.000E-08 5.600E-06 1.300E-06 5.500E-07 1.500E-08 9.000E-09 1.600E-11 1.000E-11 8.400E-07 1.600E-07 6.400E-08 5.300E-08 #S 66 Dy #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 4 2 2 4 2 #UBIND 5.379E+04 9047. 8581. 7790. 2050. 1845. 1679. 1335. 1298. 419.0 339.0 297.0 158.0 155.0 0.000 0.000 66.00 29.00 29.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 11.90 0.01170 0.04180 0.02060 0.02220 0.09310 0.05490 0.05770 0.03780 0.03840 0.1990 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.5350 0.3640 0.3940 2.010 0.05000 11.80 0.01170 0.04180 0.02060 0.02220 0.09310 0.05490 0.05770 0.03780 0.03840 0.1980 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.5330 0.3640 0.3940 1.930 0.1000 11.30 0.01170 0.04180 0.02060 0.02220 0.09300 0.05490 0.05770 0.03780 0.03840 0.1980 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.5280 0.3640 0.3940 1.710 0.1500 10.70 0.01170 0.04180 0.02060 0.02220 0.09300 0.05490 0.05770 0.03780 0.03840 0.1980 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.5190 0.3640 0.3940 1.400 0.2000 9.970 0.01170 0.04180 0.02060 0.02220 0.09290 0.05490 0.05770 0.03780 0.03840 0.1970 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.5070 0.3640 0.3930 1.070 0.3000 8.770 0.01170 0.04170 0.02060 0.02220 0.09270 0.05490 0.05770 0.03780 0.03840 0.1950 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.4740 0.3620 0.3900 0.5110 0.4000 8.030 0.01170 0.04170 0.02060 0.02220 0.09250 0.05490 0.05770 0.03780 0.03840 0.1920 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.4320 0.3570 0.3840 0.2030 0.5000 7.620 0.01170 0.04170 0.02060 0.02220 0.09210 0.05490 0.05770 0.03780 0.03840 0.1890 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.3830 0.3490 0.3720 0.08590 0.6000 7.350 0.01170 0.04170 0.02060 0.02220 0.09170 0.05490 0.05770 0.03780 0.03840 0.1840 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.3320 0.3350 0.3540 0.05740 0.7000 7.100 0.01170 0.04160 0.02060 0.02220 0.09120 0.05490 0.05770 0.03780 0.03840 0.1800 0.1260 0.1320 0.1040 0.1060 0.1130 0.1170 0.2810 0.3170 0.3300 0.05470 0.8000 6.830 0.01170 0.04160 0.02060 0.02220 0.09060 0.05490 0.05770 0.03780 0.03840 0.1740 0.1260 0.1320 0.1040 0.1060 0.1130 0.1170 0.2320 0.2950 0.3010 0.05360 1.000 6.230 0.01170 0.04150 0.02060 0.02220 0.08930 0.05490 0.05770 0.03780 0.03840 0.1620 0.1250 0.1310 0.1040 0.1060 0.1120 0.1160 0.1500 0.2410 0.2360 0.04100 1.200 5.650 0.01170 0.04130 0.02060 0.02220 0.08760 0.05480 0.05770 0.03780 0.03840 0.1480 0.1230 0.1290 0.1040 0.1060 0.1110 0.1140 0.09250 0.1830 0.1710 0.02460 1.400 5.160 0.01170 0.04120 0.02060 0.02220 0.08570 0.05480 0.05760 0.03780 0.03840 0.1330 0.1210 0.1260 0.1030 0.1050 0.1090 0.1120 0.05830 0.1310 0.1150 0.01280 1.600 4.770 0.01170 0.04100 0.02060 0.02220 0.08360 0.05470 0.05750 0.03780 0.03840 0.1180 0.1180 0.1230 0.1030 0.1040 0.1070 0.1090 0.04120 0.08820 0.07370 0.006400 1.800 4.460 0.01170 0.04080 0.02060 0.02220 0.08130 0.05460 0.05740 0.03780 0.03840 0.1040 0.1140 0.1180 0.1020 0.1030 0.1030 0.1050 0.03470 0.05710 0.04550 0.003510 2.000 4.230 0.01170 0.04060 0.02060 0.02220 0.07880 0.05450 0.05730 0.03780 0.03830 0.08940 0.1100 0.1120 0.1000 0.1020 0.09960 0.1010 0.03320 0.03640 0.02830 0.002480 2.400 3.850 0.01170 0.04010 0.02060 0.02220 0.07320 0.05420 0.05680 0.03770 0.03830 0.06470 0.09800 0.09860 0.09540 0.09650 0.09090 0.09090 0.03260 0.01690 0.01420 0.002220 3.000 3.330 0.01170 0.03920 0.02060 0.02220 0.06410 0.05330 0.05570 0.03770 0.03830 0.03830 0.07670 0.07450 0.08430 0.08470 0.07650 0.07560 0.02500 0.01190 0.01190 0.001880 4.000 2.490 0.01160 0.03730 0.02050 0.02220 0.04830 0.05060 0.05230 0.03750 0.03800 0.02030 0.04210 0.03810 0.05940 0.05860 0.05380 0.05240 0.008990 0.01030 0.009280 0.0007030 5.000 1.830 0.01160 0.03500 0.02050 0.02210 0.03410 0.04610 0.04700 0.03690 0.03740 0.01810 0.01970 0.01700 0.03550 0.03440 0.03600 0.03480 0.003400 0.005610 0.004410 0.0002320 6.000 1.390 0.01150 0.03240 0.02030 0.02190 0.02290 0.04030 0.04030 0.03570 0.03620 0.01740 0.010000 0.009230 0.01880 0.01790 0.02350 0.02250 0.002860 0.002360 0.001740 0.0001790 7.000 1.100 0.01140 0.02960 0.02020 0.02170 0.01510 0.03370 0.03280 0.03400 0.03430 0.01430 0.007490 0.007740 0.009370 0.008790 0.01510 0.01440 0.002690 0.001130 0.0009560 0.0001700 8.000 0.9130 0.01130 0.02670 0.01990 0.02130 0.01050 0.02700 0.02550 0.03170 0.03190 0.009960 0.007280 0.007670 0.004960 0.004690 0.009660 0.009160 0.002060 0.0008820 0.0008590 0.0001330 10.00 0.6600 0.01100 0.02100 0.01900 0.02000 0.007200 0.01500 0.01400 0.02600 0.02600 0.003700 0.006300 0.006100 0.002900 0.002900 0.003900 0.003700 0.0007900 0.0008200 0.0007500 5.100E-05 15.00 0.3200 0.010000 0.009400 0.01600 0.01600 0.006200 0.003100 0.002800 0.01200 0.01100 0.001600 0.001400 0.001100 0.002300 0.002200 0.0004600 0.0004300 0.0002400 0.0002000 0.0001400 1.500E-05 20.00 0.1700 0.009300 0.003600 0.01200 0.01100 0.003000 0.002000 0.002200 0.004300 0.004100 0.0009600 0.0004100 0.0004500 0.001000 0.0009400 6.700E-05 6.100E-05 0.0001500 5.100E-05 5.100E-05 9.500E-06 30.00 0.06200 0.007100 0.001100 0.005200 0.004500 0.0003400 0.001400 0.001300 0.0005700 0.0005100 9.900E-05 0.0003200 0.0003100 0.0001400 0.0001200 2.700E-06 2.400E-06 1.500E-05 3.900E-05 3.500E-05 9.600E-07 40.00 0.02800 0.005100 0.001000 0.002000 0.001600 0.0002200 0.0006000 0.0004900 9.200E-05 7.800E-05 5.000E-05 0.0001400 0.0001200 2.200E-05 1.900E-05 2.000E-07 1.700E-07 7.400E-06 1.800E-05 1.300E-05 4.600E-07 60.00 0.008500 0.002400 0.0005700 0.0003500 0.0002200 0.0001400 9.900E-05 6.400E-05 4.700E-06 3.600E-06 3.200E-05 2.300E-05 1.500E-05 1.100E-06 8.300E-07 3.800E-09 3.000E-09 4.900E-06 2.900E-06 1.700E-06 3.000E-07 100.0 0.001500 0.0005400 0.0001100 2.400E-05 9.800E-06 2.600E-05 6.200E-06 2.700E-06 7.700E-08 4.600E-08 6.000E-06 1.400E-06 6.200E-07 1.800E-08 1.100E-08 2.000E-11 1.300E-11 9.000E-07 1.800E-07 7.100E-08 5.600E-08 #S 67 Ho #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 5 2 2 4 2 #UBIND 5.562E+04 9395. 8918. 8071. 2123. 1918. 1736. 1386. 1346. 431.0 349.0 309.0 164.0 161.0 0.000 0.000 51.00 20.00 20.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 11.80 0.01150 0.04100 0.02020 0.02190 0.09120 0.05370 0.05660 0.03700 0.03760 0.1940 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.5260 0.3580 0.3880 1.990 0.05000 11.70 0.01150 0.04100 0.02020 0.02190 0.09120 0.05370 0.05660 0.03700 0.03760 0.1940 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.5240 0.3580 0.3880 1.910 0.1000 11.20 0.01150 0.04100 0.02020 0.02190 0.09110 0.05370 0.05660 0.03700 0.03760 0.1940 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.5190 0.3580 0.3880 1.700 0.1500 10.60 0.01150 0.04100 0.02020 0.02190 0.09110 0.05370 0.05660 0.03700 0.03760 0.1940 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.5110 0.3580 0.3880 1.400 0.2000 9.940 0.01150 0.04100 0.02020 0.02190 0.09100 0.05370 0.05660 0.03700 0.03760 0.1930 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.4990 0.3580 0.3880 1.070 0.3000 8.750 0.01150 0.04100 0.02020 0.02190 0.09080 0.05370 0.05660 0.03700 0.03760 0.1910 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.4680 0.3560 0.3850 0.5200 0.4000 8.010 0.01150 0.04090 0.02020 0.02190 0.09060 0.05370 0.05660 0.03700 0.03760 0.1880 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.4280 0.3520 0.3790 0.2090 0.5000 7.610 0.01150 0.04090 0.02020 0.02190 0.09030 0.05370 0.05660 0.03700 0.03760 0.1850 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.3810 0.3440 0.3670 0.08770 0.6000 7.340 0.01150 0.04090 0.02020 0.02190 0.08990 0.05370 0.05660 0.03700 0.03760 0.1810 0.1230 0.1300 0.1020 0.1040 0.1100 0.1130 0.3320 0.3310 0.3500 0.05660 0.7000 7.100 0.01150 0.04080 0.02020 0.02190 0.08940 0.05370 0.05660 0.03700 0.03760 0.1760 0.1230 0.1300 0.1020 0.1040 0.1100 0.1130 0.2820 0.3140 0.3280 0.05320 0.8000 6.840 0.01150 0.04080 0.02020 0.02190 0.08890 0.05370 0.05660 0.03700 0.03760 0.1710 0.1230 0.1290 0.1020 0.1040 0.1100 0.1130 0.2350 0.2930 0.3000 0.05240 1.000 6.260 0.01150 0.04070 0.02020 0.02190 0.08760 0.05370 0.05660 0.03700 0.03760 0.1600 0.1220 0.1280 0.1020 0.1040 0.1090 0.1120 0.1540 0.2420 0.2370 0.04100 1.200 5.690 0.01150 0.04060 0.02020 0.02190 0.08600 0.05370 0.05660 0.03700 0.03760 0.1470 0.1210 0.1270 0.1020 0.1030 0.1080 0.1110 0.09570 0.1860 0.1740 0.02520 1.400 5.210 0.01150 0.04040 0.02020 0.02190 0.08430 0.05370 0.05650 0.03700 0.03760 0.1330 0.1190 0.1240 0.1010 0.1030 0.1060 0.1090 0.06030 0.1340 0.1190 0.01340 1.600 4.820 0.01150 0.04030 0.02020 0.02190 0.08230 0.05360 0.05650 0.03700 0.03760 0.1180 0.1160 0.1210 0.1010 0.1020 0.1040 0.1060 0.04200 0.09180 0.07690 0.006760 1.800 4.510 0.01150 0.04010 0.02020 0.02190 0.08000 0.05350 0.05640 0.03700 0.03760 0.1040 0.1130 0.1170 0.09960 0.1010 0.1010 0.1030 0.03450 0.06020 0.04800 0.003650 2.000 4.270 0.01150 0.03990 0.02020 0.02190 0.07770 0.05340 0.05620 0.03700 0.03760 0.09030 0.1080 0.1110 0.09820 0.09970 0.09790 0.09890 0.03240 0.03870 0.02990 0.002480 2.400 3.890 0.01150 0.03940 0.02020 0.02180 0.07240 0.05310 0.05580 0.03700 0.03760 0.06600 0.09740 0.09830 0.09400 0.09520 0.08980 0.09000 0.03190 0.01760 0.01450 0.002120 3.000 3.390 0.01150 0.03850 0.02020 0.02180 0.06370 0.05230 0.05480 0.03690 0.03750 0.03950 0.07730 0.07540 0.08380 0.08430 0.07620 0.07560 0.02540 0.01150 0.01150 0.001860 4.000 2.560 0.01140 0.03670 0.02010 0.02180 0.04850 0.04980 0.05170 0.03670 0.03730 0.02050 0.04380 0.03970 0.06030 0.05960 0.05450 0.05330 0.009650 0.01030 0.009370 0.0007410 5.000 1.890 0.01140 0.03450 0.02010 0.02170 0.03470 0.04570 0.04670 0.03620 0.03670 0.01780 0.02100 0.01800 0.03700 0.03590 0.03710 0.03600 0.003520 0.005890 0.004660 0.0002410 6.000 1.430 0.01130 0.03200 0.02000 0.02160 0.02350 0.04030 0.04030 0.03520 0.03570 0.01730 0.01050 0.009520 0.02010 0.01910 0.02460 0.02370 0.002790 0.002560 0.001880 0.0001710 7.000 1.130 0.01120 0.02940 0.01980 0.02130 0.01570 0.03400 0.03320 0.03360 0.03400 0.01460 0.007500 0.007660 0.01020 0.009560 0.01600 0.01530 0.002670 0.001190 0.0009740 0.0001660 8.000 0.9340 0.01110 0.02660 0.01960 0.02100 0.01080 0.02750 0.02610 0.03140 0.03170 0.01050 0.007150 0.007560 0.005380 0.005050 0.01040 0.009890 0.002130 0.0008680 0.0008380 0.0001340 10.00 0.6700 0.01100 0.02100 0.01900 0.02000 0.007200 0.01600 0.01400 0.02600 0.02600 0.004000 0.006400 0.006200 0.002900 0.002900 0.004300 0.004100 0.0008600 0.0008200 0.0007600 5.500E-05 15.00 0.3300 0.010000 0.009700 0.01600 0.01600 0.006200 0.003300 0.002900 0.01200 0.01200 0.001600 0.001500 0.001200 0.002300 0.002300 0.0005300 0.0004900 0.0002400 0.0002200 0.0001500 1.500E-05 20.00 0.1800 0.009200 0.003800 0.01200 0.01200 0.003200 0.001900 0.002200 0.004600 0.004400 0.001000 0.0004200 0.0004500 0.001100 0.001000 7.900E-05 7.300E-05 0.0001600 5.200E-05 5.000E-05 9.800E-06 30.00 0.06400 0.007100 0.001100 0.005400 0.004600 0.0003700 0.001400 0.001400 0.0006300 0.0005600 0.0001100 0.0003200 0.0003200 0.0001500 0.0001400 3.300E-06 2.900E-06 1.700E-05 4.000E-05 3.600E-05 1.100E-06 40.00 0.02900 0.005100 0.001000 0.002200 0.001700 0.0002100 0.0006400 0.0005200 1.000E-04 8.700E-05 4.900E-05 0.0001500 0.0001200 2.500E-05 2.100E-05 2.500E-07 2.100E-07 7.300E-06 1.900E-05 1.400E-05 4.400E-07 60.00 0.008800 0.002500 0.0005900 0.0003800 0.0002300 0.0001400 0.0001100 7.000E-05 5.300E-06 4.100E-06 3.400E-05 2.500E-05 1.700E-05 1.300E-06 9.600E-07 4.700E-09 3.700E-09 5.000E-06 3.100E-06 1.900E-06 3.000E-07 100.0 0.001600 0.0005700 0.0001200 2.600E-05 1.100E-05 2.700E-05 7.000E-06 3.000E-06 9.000E-08 5.300E-08 6.500E-06 1.600E-06 6.900E-07 2.100E-08 1.200E-08 2.500E-11 1.700E-11 9.600E-07 2.000E-07 7.900E-08 5.900E-08 #S 68 Er #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 6 2 2 4 2 #UBIND 5.749E+04 9752. 9265. 8358. 2211. 2010. 1816. 1457. 1413. 453.0 366.0 320.0 172.0 169.0 0.000 0.000 64.00 33.00 33.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 11.80 0.01130 0.04020 0.01980 0.02150 0.08930 0.05260 0.05560 0.03620 0.03690 0.1900 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.5170 0.3530 0.3830 1.970 0.05000 11.60 0.01130 0.04020 0.01980 0.02150 0.08930 0.05260 0.05560 0.03620 0.03690 0.1900 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.5160 0.3530 0.3830 1.900 0.1000 11.20 0.01130 0.04020 0.01980 0.02150 0.08930 0.05260 0.05560 0.03620 0.03690 0.1900 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.5110 0.3530 0.3830 1.690 0.1500 10.60 0.01130 0.04020 0.01980 0.02150 0.08930 0.05260 0.05560 0.03620 0.03690 0.1900 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.5030 0.3530 0.3830 1.400 0.2000 9.910 0.01130 0.04020 0.01980 0.02150 0.08920 0.05260 0.05560 0.03620 0.03690 0.1890 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.4920 0.3520 0.3820 1.080 0.3000 8.740 0.01130 0.04020 0.01980 0.02150 0.08900 0.05260 0.05560 0.03620 0.03690 0.1870 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.4620 0.3510 0.3800 0.5280 0.4000 8.000 0.01130 0.04020 0.01980 0.02150 0.08880 0.05260 0.05560 0.03620 0.03690 0.1850 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.4240 0.3470 0.3740 0.2150 0.5000 7.590 0.01130 0.04020 0.01980 0.02150 0.08850 0.05260 0.05560 0.03620 0.03690 0.1810 0.1210 0.1270 0.09960 0.1010 0.1070 0.1100 0.3790 0.3390 0.3630 0.08970 0.6000 7.330 0.01130 0.04010 0.01980 0.02150 0.08810 0.05260 0.05560 0.03620 0.03690 0.1780 0.1210 0.1270 0.09960 0.1010 0.1070 0.1100 0.3310 0.3270 0.3470 0.05590 0.7000 7.100 0.01130 0.04010 0.01980 0.02150 0.08770 0.05260 0.05560 0.03620 0.03690 0.1730 0.1200 0.1270 0.09960 0.1010 0.1070 0.1100 0.2830 0.3110 0.3250 0.05170 0.8000 6.850 0.01130 0.04000 0.01980 0.02150 0.08720 0.05260 0.05560 0.03620 0.03690 0.1690 0.1200 0.1270 0.09960 0.1010 0.1070 0.1100 0.2370 0.2910 0.2990 0.05110 1.000 6.290 0.01130 0.04000 0.01980 0.02150 0.08600 0.05260 0.05550 0.03620 0.03690 0.1580 0.1200 0.1260 0.09950 0.1010 0.1060 0.1090 0.1570 0.2420 0.2390 0.04090 1.200 5.730 0.01130 0.03980 0.01980 0.02150 0.08450 0.05260 0.05550 0.03620 0.03690 0.1450 0.1180 0.1240 0.09930 0.1010 0.1060 0.1080 0.09890 0.1880 0.1760 0.02570 1.400 5.250 0.01130 0.03970 0.01980 0.02150 0.08280 0.05260 0.05550 0.03620 0.03690 0.1320 0.1170 0.1220 0.09900 0.1010 0.1040 0.1060 0.06240 0.1380 0.1220 0.01400 1.600 4.860 0.01130 0.03950 0.01980 0.02150 0.08090 0.05250 0.05540 0.03620 0.03690 0.1180 0.1140 0.1190 0.09850 0.1000 0.1020 0.1040 0.04290 0.09540 0.08000 0.007140 1.800 4.560 0.01130 0.03940 0.01980 0.02150 0.07880 0.05240 0.05530 0.03620 0.03690 0.1040 0.1110 0.1150 0.09760 0.09930 0.09930 0.1010 0.03440 0.06330 0.05050 0.003820 2.000 4.320 0.01130 0.03920 0.01980 0.02150 0.07660 0.05230 0.05520 0.03620 0.03690 0.09110 0.1070 0.1100 0.09640 0.09800 0.09610 0.09720 0.03180 0.04110 0.03160 0.002490 2.400 3.940 0.01130 0.03870 0.01980 0.02150 0.07160 0.05200 0.05480 0.03620 0.03690 0.06730 0.09680 0.09800 0.09260 0.09380 0.08860 0.08890 0.03130 0.01840 0.01490 0.002040 3.000 3.440 0.01130 0.03790 0.01980 0.02150 0.06330 0.05130 0.05390 0.03620 0.03680 0.04080 0.07790 0.07610 0.08330 0.08380 0.07590 0.07540 0.02570 0.01130 0.01120 0.001830 4.000 2.620 0.01120 0.03620 0.01970 0.02140 0.04870 0.04900 0.05100 0.03600 0.03660 0.02070 0.04530 0.04120 0.06110 0.06040 0.05510 0.05410 0.01030 0.01020 0.009420 0.0007780 5.000 1.940 0.01120 0.03410 0.01970 0.02140 0.03520 0.04520 0.04630 0.03550 0.03610 0.01750 0.02230 0.01910 0.03840 0.03730 0.03810 0.03700 0.003670 0.006150 0.004910 0.0002510 6.000 1.470 0.01110 0.03170 0.01960 0.02120 0.02420 0.04010 0.04030 0.03460 0.03510 0.01710 0.01110 0.009870 0.02140 0.02030 0.02560 0.02470 0.002730 0.002770 0.002020 0.0001650 7.000 1.170 0.01100 0.02920 0.01940 0.02100 0.01630 0.03420 0.03340 0.03320 0.03360 0.01480 0.007540 0.007600 0.01110 0.01040 0.01690 0.01630 0.002650 0.001260 0.001000 0.0001610 8.000 0.9560 0.01090 0.02650 0.01920 0.02070 0.01120 0.02800 0.02660 0.03120 0.03150 0.01090 0.007020 0.007450 0.005830 0.005460 0.01110 0.01060 0.002180 0.0008600 0.0008190 0.0001350 10.00 0.6900 0.01090 0.02100 0.01900 0.02000 0.007200 0.01700 0.01500 0.02600 0.02600 0.004400 0.006500 0.006300 0.002900 0.002900 0.004700 0.004500 0.0009400 0.0008000 0.0007600 5.900E-05 15.00 0.3300 0.010000 0.010000 0.01600 0.01600 0.006200 0.003500 0.003000 0.01200 0.01200 0.001600 0.001700 0.001300 0.002300 0.002300 0.0006000 0.0005600 0.0002300 0.0002400 0.0001700 1.400E-05 20.00 0.1800 0.009100 0.004000 0.01200 0.01200 0.003300 0.001900 0.002200 0.004900 0.004600 0.001100 0.0004300 0.0004500 0.001100 0.001100 9.200E-05 8.500E-05 0.0001700 5.300E-05 5.000E-05 1.000E-05 30.00 0.06600 0.007000 0.001100 0.005500 0.004800 0.0004100 0.001500 0.001400 0.0006900 0.0006200 0.0001200 0.0003300 0.0003300 0.0001700 0.0001500 4.000E-06 3.500E-06 1.900E-05 4.000E-05 3.700E-05 1.200E-06 40.00 0.03000 0.005100 0.0009800 0.002300 0.001700 0.0002100 0.0006700 0.0005600 0.0001100 9.700E-05 4.900E-05 0.0001600 0.0001300 2.800E-05 2.400E-05 3.000E-07 2.600E-07 7.200E-06 1.900E-05 1.500E-05 4.300E-07 60.00 0.009200 0.002500 0.0006000 0.0004100 0.0002500 0.0001400 0.0001200 7.600E-05 6.100E-06 4.600E-06 3.500E-05 2.800E-05 1.800E-05 1.500E-06 1.100E-06 5.800E-09 4.600E-09 5.100E-06 3.400E-06 2.000E-06 3.100E-07 100.0 0.001700 0.0006000 0.0001200 2.900E-05 1.200E-05 2.900E-05 7.800E-06 3.300E-06 1.000E-07 6.100E-08 7.000E-06 1.800E-06 7.700E-07 2.400E-08 1.400E-08 3.200E-11 2.100E-11 1.000E-06 2.200E-07 8.700E-08 6.200E-08 #S 69 Tm #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 7 2 2 4 2 #UBIND 5.939E+04 1.012E+04 9617. 8648. 2305. 2088. 1883. 1513. 1466. 470.0 382.0 333.0 180.0 176.0 0.000 0.000 51.00 30.00 30.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 11.70 0.01110 0.03950 0.01940 0.02110 0.08760 0.05160 0.05450 0.03550 0.03620 0.1860 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.5090 0.3470 0.3780 1.950 0.05000 11.50 0.01110 0.03950 0.01940 0.02110 0.08760 0.05160 0.05450 0.03550 0.03620 0.1860 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.5070 0.3470 0.3780 1.880 0.1000 11.10 0.01110 0.03950 0.01940 0.02110 0.08750 0.05160 0.05450 0.03550 0.03620 0.1860 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.5030 0.3470 0.3780 1.680 0.1500 10.50 0.01110 0.03950 0.01940 0.02110 0.08750 0.05160 0.05450 0.03550 0.03620 0.1860 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.4950 0.3470 0.3770 1.390 0.2000 9.880 0.01110 0.03950 0.01940 0.02110 0.08740 0.05160 0.05450 0.03550 0.03620 0.1850 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.4850 0.3470 0.3770 1.080 0.3000 8.720 0.01110 0.03950 0.01940 0.02110 0.08730 0.05160 0.05450 0.03550 0.03620 0.1830 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.4560 0.3450 0.3750 0.5370 0.4000 7.980 0.01110 0.03940 0.01940 0.02110 0.08710 0.05160 0.05450 0.03550 0.03620 0.1810 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.4190 0.3410 0.3690 0.2220 0.5000 7.580 0.01110 0.03940 0.01940 0.02110 0.08680 0.05160 0.05450 0.03550 0.03620 0.1780 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.3770 0.3340 0.3590 0.09190 0.6000 7.320 0.01110 0.03940 0.01940 0.02110 0.08640 0.05160 0.05450 0.03550 0.03620 0.1750 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.3310 0.3230 0.3440 0.05540 0.7000 7.090 0.01110 0.03940 0.01940 0.02110 0.08600 0.05160 0.05450 0.03550 0.03620 0.1710 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.2840 0.3080 0.3230 0.05030 0.8000 6.850 0.01110 0.03930 0.01940 0.02110 0.08550 0.05150 0.05450 0.03550 0.03620 0.1660 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.2390 0.2890 0.2980 0.04990 1.000 6.310 0.01110 0.03920 0.01940 0.02110 0.08440 0.05150 0.05450 0.03550 0.03620 0.1550 0.1170 0.1240 0.09740 0.09930 0.1040 0.1070 0.1600 0.2430 0.2400 0.04080 1.200 5.770 0.01110 0.03910 0.01940 0.02110 0.08300 0.05150 0.05450 0.03550 0.03620 0.1440 0.1160 0.1220 0.09730 0.09920 0.1030 0.1060 0.1020 0.1900 0.1790 0.02620 1.400 5.300 0.01110 0.03900 0.01940 0.02110 0.08140 0.05150 0.05440 0.03550 0.03620 0.1310 0.1140 0.1200 0.09700 0.09890 0.1020 0.1040 0.06460 0.1410 0.1250 0.01460 1.600 4.910 0.01110 0.03880 0.01940 0.02110 0.07960 0.05140 0.05440 0.03550 0.03620 0.1180 0.1120 0.1170 0.09650 0.09830 0.09980 0.1020 0.04390 0.09880 0.08310 0.007530 1.800 4.600 0.01110 0.03870 0.01940 0.02110 0.07760 0.05140 0.05430 0.03550 0.03620 0.1050 0.1090 0.1140 0.09570 0.09750 0.09740 0.09880 0.03440 0.06640 0.05300 0.004010 2.000 4.360 0.01110 0.03850 0.01940 0.02110 0.07550 0.05130 0.05420 0.03550 0.03620 0.09170 0.1060 0.1090 0.09460 0.09630 0.09440 0.09550 0.03120 0.04350 0.03340 0.002530 2.400 3.980 0.01110 0.03800 0.01940 0.02110 0.07070 0.05100 0.05380 0.03550 0.03620 0.06850 0.09610 0.09760 0.09120 0.09250 0.08750 0.08790 0.03060 0.01940 0.01530 0.001960 3.000 3.490 0.01110 0.03730 0.01940 0.02110 0.06280 0.05030 0.05300 0.03550 0.03610 0.04200 0.07830 0.07670 0.08260 0.08320 0.07550 0.07520 0.02590 0.01100 0.01090 0.001800 4.000 2.690 0.01100 0.03560 0.01940 0.02110 0.04880 0.04820 0.05030 0.03530 0.03600 0.02110 0.04680 0.04260 0.06170 0.06110 0.05560 0.05470 0.01100 0.01010 0.009430 0.0008130 5.000 2.000 0.01100 0.03370 0.01930 0.02100 0.03570 0.04470 0.04600 0.03490 0.03550 0.01720 0.02360 0.02020 0.03970 0.03860 0.03900 0.03800 0.003870 0.006390 0.005140 0.0002640 6.000 1.520 0.01090 0.03140 0.01920 0.02090 0.02480 0.04000 0.04020 0.03410 0.03460 0.01690 0.01180 0.01030 0.02270 0.02160 0.02660 0.02570 0.002670 0.002980 0.002170 0.0001600 7.000 1.200 0.01080 0.02900 0.01910 0.02070 0.01680 0.03430 0.03370 0.03280 0.03320 0.01490 0.007640 0.007570 0.01200 0.01120 0.01780 0.01710 0.002610 0.001350 0.001040 0.0001560 8.000 0.9790 0.01070 0.02640 0.01890 0.02040 0.01160 0.02840 0.02700 0.03090 0.03120 0.01130 0.006900 0.007320 0.006320 0.005890 0.01180 0.01130 0.002230 0.0008610 0.0008030 0.0001350 10.00 0.7000 0.01070 0.02100 0.01800 0.02000 0.007200 0.01700 0.01600 0.02600 0.02600 0.004800 0.006500 0.006400 0.002900 0.002900 0.005200 0.004900 0.001000 0.0007900 0.0007500 6.300E-05 15.00 0.3400 0.009800 0.010000 0.01600 0.01600 0.006200 0.003700 0.003200 0.01300 0.01300 0.001600 0.001800 0.001400 0.002400 0.002300 0.0006800 0.0006400 0.0002300 0.0002600 0.0001800 1.400E-05 20.00 0.1900 0.009000 0.004200 0.01200 0.01200 0.003500 0.001900 0.002100 0.005200 0.004900 0.001100 0.0004400 0.0004500 0.001200 0.001100 0.0001100 9.900E-05 0.0001700 5.500E-05 5.000E-05 1.000E-05 30.00 0.06800 0.007000 0.001100 0.005700 0.005000 0.0004400 0.001500 0.001500 0.0007500 0.0006700 0.0001400 0.0003400 0.0003400 0.0001900 0.0001700 4.700E-06 4.200E-06 2.100E-05 4.000E-05 3.800E-05 1.300E-06 40.00 0.03100 0.005200 0.0009700 0.002400 0.001800 0.0002100 0.0007100 0.0005900 0.0001300 0.0001100 4.800E-05 0.0001700 0.0001400 3.200E-05 2.700E-05 3.700E-07 3.200E-07 7.100E-06 2.000E-05 1.600E-05 4.200E-07 60.00 0.009500 0.002600 0.0006200 0.0004400 0.0002700 0.0001500 0.0001300 8.300E-05 6.900E-06 5.200E-06 3.600E-05 3.100E-05 2.000E-05 1.700E-06 1.300E-06 7.100E-09 5.600E-09 5.200E-06 3.700E-06 2.200E-06 3.100E-07 100.0 0.001700 0.0006200 0.0001300 3.200E-05 1.300E-05 3.100E-05 8.700E-06 3.700E-06 1.200E-07 7.100E-08 7.500E-06 2.000E-06 8.600E-07 2.800E-08 1.700E-08 4.000E-11 2.600E-11 1.100E-06 2.400E-07 9.600E-08 6.500E-08 #S 70 Yb #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 2 #UBIND 6.133E+04 1.049E+04 9978. 8943. 2397. 2172. 1949. 1576. 1527. 487.0 399.0 346.0 189.0 185.0 0.000 0.000 53.00 23.00 23.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 11.60 0.01090 0.03880 0.01900 0.02080 0.08590 0.05050 0.05350 0.03480 0.03550 0.1830 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.5010 0.3420 0.3730 1.940 0.05000 11.50 0.01090 0.03880 0.01900 0.02080 0.08590 0.05050 0.05350 0.03480 0.03550 0.1830 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4990 0.3420 0.3730 1.870 0.1000 11.10 0.01090 0.03880 0.01900 0.02080 0.08580 0.05050 0.05350 0.03480 0.03550 0.1820 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4950 0.3420 0.3730 1.670 0.1500 10.50 0.01090 0.03880 0.01900 0.02080 0.08580 0.05050 0.05350 0.03480 0.03550 0.1820 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4880 0.3420 0.3720 1.390 0.2000 9.850 0.01090 0.03870 0.01900 0.02080 0.08570 0.05050 0.05350 0.03480 0.03550 0.1810 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4780 0.3410 0.3720 1.080 0.3000 8.710 0.01090 0.03870 0.01900 0.02080 0.08560 0.05050 0.05350 0.03480 0.03550 0.1800 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4510 0.3400 0.3700 0.5450 0.4000 7.970 0.01090 0.03870 0.01900 0.02080 0.08540 0.05050 0.05350 0.03480 0.03550 0.1780 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4150 0.3360 0.3640 0.2280 0.5000 7.560 0.01090 0.03870 0.01900 0.02080 0.08510 0.05050 0.05350 0.03480 0.03550 0.1750 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.3740 0.3300 0.3550 0.09430 0.6000 7.310 0.01090 0.03870 0.01900 0.02080 0.08480 0.05050 0.05350 0.03480 0.03550 0.1720 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.3300 0.3200 0.3400 0.05510 0.7000 7.090 0.01090 0.03860 0.01900 0.02080 0.08440 0.05050 0.05350 0.03480 0.03550 0.1680 0.1150 0.1220 0.09540 0.09740 0.1020 0.1050 0.2850 0.3050 0.3210 0.04890 0.8000 6.860 0.01090 0.03860 0.01900 0.02080 0.08390 0.05050 0.05350 0.03480 0.03550 0.1630 0.1150 0.1220 0.09540 0.09740 0.1020 0.1040 0.2410 0.2870 0.2970 0.04860 1.000 6.340 0.01090 0.03850 0.01900 0.02080 0.08280 0.05050 0.05350 0.03480 0.03550 0.1530 0.1150 0.1210 0.09540 0.09730 0.1020 0.1040 0.1630 0.2430 0.2400 0.04060 1.200 5.810 0.01090 0.03840 0.01900 0.02080 0.08150 0.05050 0.05350 0.03480 0.03550 0.1420 0.1140 0.1200 0.09530 0.09720 0.1010 0.1030 0.1050 0.1920 0.1810 0.02660 1.400 5.340 0.01090 0.03830 0.01900 0.02080 0.08000 0.05050 0.05350 0.03480 0.03550 0.1300 0.1120 0.1180 0.09500 0.09690 0.09960 0.1020 0.06690 0.1440 0.1280 0.01510 1.600 4.950 0.01090 0.03810 0.01900 0.02080 0.07830 0.05040 0.05340 0.03480 0.03550 0.1170 0.1100 0.1160 0.09460 0.09640 0.09780 0.09950 0.04500 0.1020 0.08610 0.007930 1.800 4.650 0.01090 0.03800 0.01900 0.02080 0.07650 0.05040 0.05330 0.03480 0.03550 0.1050 0.1080 0.1120 0.09390 0.09570 0.09550 0.09690 0.03460 0.06950 0.05550 0.004210 2.000 4.400 0.01090 0.03780 0.01900 0.02080 0.07440 0.05030 0.05320 0.03480 0.03550 0.09230 0.1040 0.1080 0.09290 0.09460 0.09280 0.09390 0.03080 0.04590 0.03520 0.002580 2.400 4.030 0.01090 0.03740 0.01900 0.02080 0.06990 0.05000 0.05290 0.03480 0.03550 0.06960 0.09540 0.09710 0.08980 0.09110 0.08630 0.08680 0.02990 0.02050 0.01580 0.001880 3.000 3.540 0.01090 0.03670 0.01900 0.02080 0.06240 0.04940 0.05210 0.03480 0.03550 0.04320 0.07850 0.07720 0.08190 0.08260 0.07500 0.07480 0.02600 0.01090 0.01060 0.001760 4.000 2.750 0.01080 0.03510 0.01900 0.02070 0.04890 0.04750 0.04960 0.03470 0.03530 0.02150 0.04810 0.04400 0.06230 0.06170 0.05590 0.05520 0.01160 0.009990 0.009410 0.0008460 5.000 2.060 0.01080 0.03320 0.01890 0.02070 0.03610 0.04420 0.04560 0.03430 0.03490 0.01690 0.02490 0.02130 0.04100 0.03980 0.03970 0.03890 0.004090 0.006600 0.005350 0.0002780 6.000 1.570 0.01070 0.03110 0.01890 0.02060 0.02540 0.03980 0.04020 0.03350 0.03410 0.01670 0.01240 0.01070 0.02390 0.02280 0.02740 0.02670 0.002630 0.003200 0.002320 0.0001560 7.000 1.230 0.01060 0.02870 0.01870 0.02040 0.01740 0.03450 0.03390 0.03230 0.03280 0.01510 0.007780 0.007580 0.01290 0.01200 0.01860 0.01800 0.002570 0.001440 0.001080 0.0001500 8.000 1.000 0.01050 0.02630 0.01860 0.02010 0.01200 0.02870 0.02750 0.03060 0.03100 0.01170 0.006800 0.007200 0.006850 0.006370 0.01250 0.01200 0.002250 0.0008710 0.0007920 0.0001340 10.00 0.7100 0.010000 0.02100 0.01800 0.01900 0.007200 0.01800 0.01600 0.02600 0.02600 0.005100 0.006500 0.006500 0.003000 0.002900 0.005600 0.005300 0.001100 0.0007700 0.0007400 6.600E-05 15.00 0.3500 0.009700 0.01100 0.01600 0.01600 0.006200 0.003900 0.003300 0.01300 0.01300 0.001500 0.002000 0.001500 0.002400 0.002400 0.0007700 0.0007200 0.0002300 0.0002800 0.0002000 1.300E-05 20.00 0.1900 0.008900 0.004400 0.01200 0.01200 0.003700 0.001900 0.002100 0.005500 0.005200 0.001200 0.0004600 0.0004500 0.001300 0.001200 0.0001200 0.0001100 0.0001800 5.800E-05 5.000E-05 1.000E-05 30.00 0.07100 0.007000 0.001100 0.005800 0.005100 0.0004900 0.001500 0.001500 0.0008200 0.0007300 0.0001500 0.0003400 0.0003500 0.0002100 0.0001800 5.600E-06 5.000E-06 2.400E-05 4.000E-05 3.800E-05 1.400E-06 40.00 0.03200 0.005200 0.0009500 0.002500 0.001900 0.0002000 0.0007400 0.0006200 0.0001400 0.0001200 4.800E-05 0.0001800 0.0001500 3.600E-05 3.000E-05 4.400E-07 3.800E-07 7.000E-06 2.100E-05 1.700E-05 4.100E-07 60.00 0.009900 0.002600 0.0006300 0.0004800 0.0002900 0.0001500 0.0001400 9.000E-05 7.800E-06 5.900E-06 3.600E-05 3.300E-05 2.200E-05 1.900E-06 1.400E-06 8.700E-09 6.900E-09 5.300E-06 4.000E-06 2.400E-06 3.100E-07 100.0 0.001800 0.0006500 0.0001400 3.600E-05 1.400E-05 3.300E-05 9.700E-06 4.100E-06 1.400E-07 8.100E-08 8.000E-06 2.300E-06 9.600E-07 3.300E-08 1.900E-08 4.900E-11 3.200E-11 1.200E-06 2.700E-07 1.100E-07 6.800E-08 #S 71 Lu #N 23 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 1 2 #UBIND 6.331E+04 1.087E+04 1.035E+04 9244. 2491. 2264. 2024. 1640. 1589. 507.0 412.0 359.0 206.0 196.0 7.000 7.000 57.00 28.00 28.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 0.000 11.50 0.01070 0.03810 0.01870 0.02050 0.08420 0.04950 0.05260 0.03420 0.03490 0.1790 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4800 0.3270 0.3540 0.4890 1.770 0.05000 11.30 0.01070 0.03810 0.01870 0.02050 0.08420 0.04950 0.05260 0.03420 0.03490 0.1790 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4780 0.3270 0.3540 0.4890 1.720 0.1000 11.00 0.01070 0.03810 0.01870 0.02050 0.08420 0.04950 0.05260 0.03420 0.03490 0.1780 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4740 0.3270 0.3540 0.4890 1.560 0.1500 10.60 0.01070 0.03810 0.01870 0.02050 0.08410 0.04950 0.05260 0.03420 0.03490 0.1780 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4680 0.3270 0.3540 0.4890 1.330 0.2000 10.00 0.01070 0.03800 0.01870 0.02050 0.08410 0.04950 0.05260 0.03420 0.03490 0.1770 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4590 0.3270 0.3540 0.4880 1.070 0.3000 8.990 0.01070 0.03800 0.01870 0.02050 0.08400 0.04950 0.05260 0.03420 0.03490 0.1760 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4350 0.3260 0.3520 0.4820 0.5940 0.4000 8.240 0.01070 0.03800 0.01870 0.02050 0.08380 0.04950 0.05260 0.03420 0.03490 0.1740 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4030 0.3230 0.3480 0.4650 0.2760 0.5000 7.780 0.01070 0.03800 0.01870 0.02050 0.08350 0.04950 0.05260 0.03420 0.03490 0.1710 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.3660 0.3170 0.3400 0.4330 0.1210 0.6000 7.470 0.01070 0.03800 0.01870 0.02050 0.08320 0.04950 0.05260 0.03420 0.03490 0.1680 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.3260 0.3090 0.3280 0.3890 0.06470 0.7000 7.210 0.01070 0.03790 0.01870 0.02050 0.08280 0.04950 0.05260 0.03420 0.03490 0.1650 0.1130 0.1200 0.09310 0.09500 0.09520 0.09670 0.2840 0.2970 0.3120 0.3400 0.05130 0.8000 6.960 0.01070 0.03790 0.01870 0.02050 0.08240 0.04950 0.05260 0.03420 0.03490 0.1600 0.1130 0.1200 0.09310 0.09500 0.09520 0.09670 0.2430 0.2810 0.2920 0.2910 0.05020 1.000 6.410 0.01070 0.03780 0.01870 0.02050 0.08140 0.04950 0.05260 0.03420 0.03490 0.1510 0.1120 0.1190 0.09300 0.09500 0.09500 0.09650 0.1690 0.2420 0.2420 0.2030 0.04560 1.200 5.850 0.01070 0.03770 0.01870 0.02050 0.08010 0.04950 0.05250 0.03420 0.03490 0.1410 0.1110 0.1180 0.09290 0.09490 0.09470 0.09610 0.1120 0.1960 0.1880 0.1360 0.03300 1.400 5.360 0.01070 0.03760 0.01870 0.02050 0.07870 0.04950 0.05250 0.03420 0.03490 0.1290 0.1100 0.1160 0.09270 0.09460 0.09400 0.09540 0.07240 0.1500 0.1370 0.08950 0.02020 1.600 4.950 0.01070 0.03750 0.01870 0.02050 0.07710 0.04940 0.05250 0.03420 0.03490 0.1170 0.1080 0.1140 0.09240 0.09420 0.09300 0.09420 0.04870 0.1100 0.09520 0.05780 0.01130 1.800 4.630 0.01070 0.03730 0.01870 0.02050 0.07530 0.04940 0.05240 0.03420 0.03490 0.1050 0.1060 0.1100 0.09180 0.09360 0.09150 0.09270 0.03650 0.07670 0.06320 0.03690 0.006090 2.000 4.380 0.01070 0.03720 0.01870 0.02050 0.07340 0.04930 0.05230 0.03420 0.03490 0.09280 0.1030 0.1060 0.09090 0.09260 0.08960 0.09060 0.03150 0.05190 0.04090 0.02330 0.003560 2.400 4.000 0.01070 0.03680 0.01870 0.02050 0.06910 0.04910 0.05200 0.03420 0.03490 0.07070 0.09450 0.09650 0.08820 0.08960 0.08460 0.08540 0.03000 0.02350 0.01830 0.009210 0.002230 3.000 3.540 0.01070 0.03610 0.01870 0.02040 0.06190 0.04850 0.05130 0.03420 0.03480 0.04450 0.07880 0.07770 0.08110 0.08190 0.07520 0.07550 0.02700 0.01150 0.01110 0.002820 0.002120 4.000 2.790 0.01060 0.03460 0.01860 0.02040 0.04900 0.04670 0.04900 0.03400 0.03470 0.02200 0.04950 0.04540 0.06290 0.06240 0.05780 0.05750 0.01300 0.01040 0.01010 0.001770 0.001110 5.000 2.110 0.01060 0.03280 0.01860 0.02040 0.03660 0.04370 0.04520 0.03370 0.03430 0.01670 0.02630 0.02250 0.04230 0.04120 0.04200 0.04150 0.004610 0.007230 0.006050 0.001610 0.0003750 6.000 1.610 0.01050 0.03080 0.01850 0.02020 0.02600 0.03960 0.04000 0.03300 0.03360 0.01650 0.01330 0.01130 0.02530 0.02410 0.02950 0.02900 0.002720 0.003660 0.002720 0.001130 0.0001900 7.000 1.260 0.01040 0.02850 0.01840 0.02010 0.01790 0.03450 0.03410 0.03190 0.03240 0.01520 0.008010 0.007650 0.01390 0.01300 0.02030 0.01980 0.002630 0.001660 0.001240 0.0006640 0.0001780 8.000 1.030 0.01040 0.02610 0.01820 0.01980 0.01240 0.02900 0.02780 0.03030 0.03070 0.01210 0.006750 0.007120 0.007480 0.006930 0.01380 0.01340 0.002380 0.0009470 0.0008470 0.0003560 0.0001640 10.00 0.7300 0.010000 0.02100 0.01800 0.01900 0.007300 0.01900 0.01700 0.02600 0.02600 0.005600 0.006500 0.006500 0.003100 0.003000 0.006300 0.006100 0.001200 0.0008000 0.0007900 0.0001200 8.600E-05 15.00 0.3600 0.009500 0.01100 0.01500 0.01600 0.006200 0.004200 0.003500 0.01400 0.01300 0.001500 0.002200 0.001700 0.002400 0.002400 0.0009000 0.0008500 0.0002300 0.0003200 0.0002300 7.900E-05 1.600E-05 20.00 0.1900 0.008800 0.004600 0.01200 0.01200 0.003800 0.001900 0.002100 0.005700 0.005400 0.001200 0.0004900 0.0004600 0.001300 0.001300 0.0001500 0.0001400 0.0001900 6.500E-05 5.500E-05 4.600E-05 1.300E-05 30.00 0.07300 0.007000 0.001100 0.006000 0.005300 0.0005300 0.001500 0.001500 0.0008900 0.0008000 0.0001700 0.0003400 0.0003500 0.0002300 0.0002000 6.900E-06 6.200E-06 2.800E-05 4.200E-05 4.100E-05 7.900E-06 1.900E-06 40.00 0.03300 0.005200 0.0009400 0.002600 0.002000 0.0002000 0.0007800 0.0006500 0.0001600 0.0001300 4.800E-05 0.0001900 0.0001600 4.000E-05 3.400E-05 5.600E-07 4.800E-07 7.300E-06 2.300E-05 1.900E-05 1.400E-06 4.900E-07 60.00 0.010000 0.002700 0.0006400 0.0005100 0.0003100 0.0001500 0.0001500 9.700E-05 8.900E-06 6.700E-06 3.700E-05 3.600E-05 2.400E-05 2.200E-06 1.600E-06 1.100E-08 8.900E-09 5.700E-06 4.500E-06 2.800E-06 7.700E-08 3.800E-07 100.0 0.001900 0.0006800 0.0001500 3.900E-05 1.600E-05 3.500E-05 1.100E-05 4.500E-06 1.600E-07 9.300E-08 8.500E-06 2.500E-06 1.100E-06 3.800E-08 2.200E-08 6.400E-11 4.200E-11 1.300E-06 3.200E-07 1.300E-07 1.300E-09 8.700E-08 #S 72 Hf #N 23 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 2 2 #UBIND 6.535E+04 1.127E+04 1.074E+04 9561. 2601. 2365. 2108. 1716. 1662. 538.0 437.0 380.0 224.0 214.0 19.00 17.00 65.00 38.00 31.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 0.000 11.40 0.01050 0.03740 0.01830 0.02010 0.08260 0.04860 0.05170 0.03360 0.03430 0.1750 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4610 0.3140 0.3380 0.4230 1.680 0.05000 11.30 0.01050 0.03740 0.01830 0.02010 0.08260 0.04860 0.05170 0.03360 0.03430 0.1750 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4590 0.3140 0.3380 0.4230 1.630 0.1000 11.00 0.01050 0.03740 0.01830 0.02010 0.08260 0.04860 0.05170 0.03360 0.03430 0.1740 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4560 0.3140 0.3380 0.4230 1.500 0.1500 10.60 0.01050 0.03740 0.01830 0.02010 0.08260 0.04860 0.05170 0.03360 0.03430 0.1740 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4500 0.3140 0.3380 0.4230 1.300 0.2000 10.10 0.01050 0.03740 0.01830 0.02010 0.08250 0.04860 0.05170 0.03360 0.03430 0.1740 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4420 0.3140 0.3380 0.4230 1.070 0.3000 9.140 0.01050 0.03740 0.01830 0.02010 0.08240 0.04860 0.05170 0.03360 0.03430 0.1720 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4200 0.3130 0.3360 0.4200 0.6210 0.4000 8.410 0.01050 0.03730 0.01830 0.02010 0.08220 0.04860 0.05170 0.03360 0.03430 0.1700 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.3920 0.3110 0.3330 0.4120 0.3080 0.5000 7.940 0.01050 0.03730 0.01830 0.02010 0.08190 0.04860 0.05170 0.03360 0.03430 0.1680 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.3580 0.3060 0.3270 0.3960 0.1410 0.6000 7.620 0.01050 0.03730 0.01830 0.02010 0.08160 0.04860 0.05170 0.03360 0.03430 0.1650 0.1100 0.1170 0.09080 0.09270 0.08980 0.09070 0.3210 0.2990 0.3170 0.3710 0.07270 0.7000 7.350 0.01050 0.03730 0.01830 0.02010 0.08130 0.04860 0.05170 0.03360 0.03430 0.1610 0.1100 0.1170 0.09080 0.09270 0.08980 0.09070 0.2830 0.2880 0.3030 0.3390 0.05260 0.8000 7.090 0.01050 0.03720 0.01830 0.02010 0.08090 0.04860 0.05170 0.03360 0.03430 0.1580 0.1100 0.1170 0.09070 0.09270 0.08980 0.09070 0.2450 0.2750 0.2860 0.3020 0.04960 1.000 6.530 0.01050 0.03720 0.01830 0.02010 0.07990 0.04860 0.05160 0.03360 0.03430 0.1490 0.1100 0.1170 0.09070 0.09270 0.08970 0.09060 0.1740 0.2400 0.2420 0.2290 0.04720 1.200 5.960 0.01050 0.03710 0.01830 0.02010 0.07880 0.04850 0.05160 0.03360 0.03430 0.1390 0.1090 0.1150 0.09060 0.09260 0.08950 0.09040 0.1180 0.1980 0.1930 0.1650 0.03660 1.400 5.430 0.01050 0.03700 0.01830 0.02010 0.07740 0.04850 0.05160 0.03360 0.03430 0.1280 0.1080 0.1140 0.09050 0.09240 0.08910 0.09010 0.07760 0.1560 0.1450 0.1150 0.02400 1.600 4.990 0.01050 0.03680 0.01830 0.02010 0.07590 0.04850 0.05150 0.03360 0.03430 0.1160 0.1060 0.1120 0.09020 0.09200 0.08850 0.08940 0.05240 0.1160 0.1030 0.07790 0.01410 1.800 4.640 0.01050 0.03670 0.01830 0.02010 0.07420 0.04840 0.05150 0.03360 0.03430 0.1050 0.1040 0.1090 0.08970 0.09150 0.08750 0.08840 0.03850 0.08340 0.07040 0.05190 0.007850 2.000 4.360 0.01050 0.03650 0.01830 0.02010 0.07240 0.04840 0.05140 0.03360 0.03430 0.09320 0.1010 0.1050 0.08890 0.09070 0.08620 0.08700 0.03230 0.05780 0.04670 0.03400 0.004530 2.400 3.970 0.01050 0.03620 0.01830 0.02010 0.06830 0.04820 0.05110 0.03360 0.03420 0.07170 0.09350 0.09580 0.08650 0.08800 0.08240 0.08320 0.02990 0.02680 0.02100 0.01420 0.002470 3.000 3.530 0.01050 0.03550 0.01830 0.02010 0.06140 0.04760 0.05040 0.03350 0.03420 0.04580 0.07880 0.07810 0.08020 0.08110 0.07470 0.07520 0.02780 0.01210 0.01160 0.004240 0.002310 4.000 2.820 0.01040 0.03410 0.01830 0.02010 0.04900 0.04600 0.04830 0.03340 0.03410 0.02260 0.05080 0.04680 0.06340 0.06300 0.05910 0.05910 0.01440 0.01060 0.01060 0.002240 0.001340 5.000 2.160 0.01040 0.03240 0.01830 0.02000 0.03690 0.04320 0.04470 0.03310 0.03380 0.01660 0.02770 0.02370 0.04360 0.04250 0.04400 0.04370 0.005220 0.007840 0.006750 0.002100 0.0004700 6.000 1.650 0.01030 0.03040 0.01820 0.01990 0.02650 0.03930 0.03990 0.03250 0.03310 0.01620 0.01410 0.01190 0.02670 0.02550 0.03150 0.03110 0.002840 0.004160 0.003150 0.001540 0.0002170 7.000 1.300 0.01030 0.02830 0.01810 0.01980 0.01850 0.03460 0.03420 0.03150 0.03200 0.01520 0.008310 0.007770 0.01500 0.01410 0.02200 0.02160 0.002680 0.001920 0.001420 0.0009370 0.0001950 8.000 1.050 0.01020 0.02600 0.01790 0.01960 0.01290 0.02930 0.02820 0.03000 0.03040 0.01250 0.006720 0.007040 0.008170 0.007550 0.01520 0.01480 0.002490 0.001040 0.0009090 0.0005150 0.0001850 10.00 0.7400 0.010000 0.02100 0.01700 0.01900 0.007400 0.01900 0.01700 0.02600 0.02600 0.006000 0.006500 0.006600 0.003200 0.003100 0.007100 0.006800 0.001400 0.0008200 0.0008300 0.0001600 1.000E-04 15.00 0.3700 0.009400 0.01100 0.01500 0.01600 0.006100 0.004500 0.003700 0.01400 0.01400 0.001500 0.002400 0.001800 0.002500 0.002400 0.001100 0.001000 0.0002400 0.0003600 0.0002700 1.000E-04 1.700E-05 20.00 0.2000 0.008700 0.004800 0.01200 0.01200 0.004000 0.001900 0.002100 0.006000 0.005700 0.001200 0.0005200 0.0004700 0.001400 0.001300 0.0001800 0.0001700 0.0002000 7.300E-05 6.000E-05 6.300E-05 1.500E-05 30.00 0.07500 0.006900 0.001100 0.006200 0.005400 0.0005900 0.001500 0.001600 0.0009700 0.0008600 0.0001900 0.0003500 0.0003600 0.0002500 0.0002200 8.500E-06 7.700E-06 3.200E-05 4.400E-05 4.500E-05 1.100E-05 2.400E-06 40.00 0.03400 0.005200 0.0009200 0.002700 0.002100 0.0002000 0.0008100 0.0006900 0.0001700 0.0001500 4.900E-05 0.0002000 0.0001700 4.500E-05 3.800E-05 7.000E-07 6.000E-07 7.700E-06 2.500E-05 2.100E-05 2.100E-06 5.500E-07 60.00 0.01100 0.002700 0.0006500 0.0005500 0.0003300 0.0001600 0.0001600 0.0001100 1.000E-05 7.500E-06 3.800E-05 4.000E-05 2.600E-05 2.500E-06 1.900E-06 1.400E-08 1.100E-08 6.000E-06 5.200E-06 3.200E-06 1.100E-07 4.300E-07 100.0 0.002000 0.0007100 0.0001500 4.300E-05 1.700E-05 3.700E-05 1.200E-05 5.000E-06 1.800E-07 1.100E-07 9.100E-06 2.800E-06 1.200E-06 4.400E-08 2.600E-08 8.200E-11 5.400E-11 1.400E-06 3.700E-07 1.500E-07 2.000E-09 1.000E-07 #S 73 Ta #N 23 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 3 2 #UBIND 6.741E+04 1.168E+04 1.114E+04 9881. 2705. 2466. 2191. 1790. 1732. 563.0 462.0 402.0 239.0 227.0 25.00 23.00 68.00 42.00 34.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 0.000 11.30 0.01030 0.03670 0.01800 0.01980 0.08110 0.04760 0.05080 0.03300 0.03370 0.1710 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4430 0.3020 0.3240 0.3820 1.610 0.05000 11.20 0.01030 0.03670 0.01800 0.01980 0.08110 0.04760 0.05080 0.03300 0.03370 0.1710 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4420 0.3020 0.3240 0.3820 1.560 0.1000 10.90 0.01030 0.03670 0.01800 0.01980 0.08100 0.04760 0.05080 0.03300 0.03370 0.1710 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4390 0.3020 0.3240 0.3820 1.440 0.1500 10.60 0.01030 0.03670 0.01800 0.01980 0.08100 0.04760 0.05080 0.03300 0.03370 0.1700 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4340 0.3020 0.3240 0.3820 1.270 0.2000 10.10 0.01030 0.03670 0.01800 0.01980 0.08100 0.04760 0.05080 0.03300 0.03370 0.1700 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4270 0.3020 0.3240 0.3820 1.060 0.3000 9.240 0.01030 0.03670 0.01800 0.01980 0.08080 0.04760 0.05080 0.03300 0.03370 0.1680 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4070 0.3010 0.3230 0.3810 0.6400 0.4000 8.540 0.01030 0.03670 0.01800 0.01980 0.08060 0.04760 0.05080 0.03300 0.03370 0.1670 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.3810 0.2990 0.3200 0.3760 0.3330 0.5000 8.070 0.01030 0.03670 0.01800 0.01980 0.08040 0.04760 0.05080 0.03300 0.03370 0.1640 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.3500 0.2950 0.3150 0.3670 0.1590 0.6000 7.740 0.01030 0.03660 0.01800 0.01980 0.08010 0.04760 0.05080 0.03300 0.03370 0.1620 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.3160 0.2890 0.3060 0.3500 0.08060 0.7000 7.480 0.01030 0.03660 0.01800 0.01980 0.07980 0.04760 0.05080 0.03300 0.03370 0.1580 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.2810 0.2800 0.2950 0.3280 0.05420 0.8000 7.220 0.01030 0.03660 0.01800 0.01980 0.07940 0.04760 0.05080 0.03300 0.03370 0.1550 0.1080 0.1150 0.08850 0.09050 0.08520 0.08580 0.2450 0.2680 0.2800 0.3010 0.04870 1.000 6.670 0.01030 0.03650 0.01800 0.01980 0.07850 0.04760 0.05070 0.03300 0.03370 0.1470 0.1070 0.1140 0.08850 0.09040 0.08520 0.08580 0.1780 0.2370 0.2410 0.2410 0.04720 1.200 6.080 0.01030 0.03640 0.01800 0.01980 0.07740 0.04760 0.05070 0.03300 0.03370 0.1370 0.1070 0.1130 0.08840 0.09040 0.08510 0.08570 0.1230 0.2000 0.1960 0.1820 0.03880 1.400 5.530 0.01030 0.03630 0.01800 0.01980 0.07610 0.04760 0.05070 0.03300 0.03370 0.1270 0.1050 0.1120 0.08820 0.09020 0.08480 0.08550 0.08270 0.1600 0.1510 0.1330 0.02690 1.600 5.060 0.01030 0.03620 0.01800 0.01980 0.07470 0.04760 0.05070 0.03300 0.03370 0.1160 0.1040 0.1100 0.08800 0.08990 0.08440 0.08510 0.05610 0.1220 0.1100 0.09410 0.01660 1.800 4.680 0.01030 0.03610 0.01800 0.01980 0.07310 0.04750 0.05060 0.03300 0.03370 0.1050 0.1020 0.1070 0.08760 0.08940 0.08370 0.08440 0.04080 0.08960 0.07730 0.06510 0.009610 2.000 4.370 0.01030 0.03590 0.01800 0.01980 0.07130 0.04750 0.05050 0.03300 0.03370 0.09350 0.09930 0.1040 0.08690 0.08870 0.08280 0.08350 0.03330 0.06350 0.05250 0.04410 0.005560 2.400 3.940 0.01030 0.03560 0.01800 0.01980 0.06750 0.04730 0.05030 0.03290 0.03360 0.07270 0.09250 0.09510 0.08490 0.08640 0.08000 0.08070 0.02980 0.03040 0.02390 0.01950 0.002710 3.000 3.510 0.01030 0.03490 0.01800 0.01980 0.06090 0.04680 0.04960 0.03290 0.03360 0.04710 0.07880 0.07840 0.07920 0.08020 0.07370 0.07430 0.02840 0.01300 0.01210 0.005870 0.002420 4.000 2.840 0.01030 0.03360 0.01800 0.01980 0.04900 0.04530 0.04760 0.03280 0.03350 0.02320 0.05200 0.04820 0.06370 0.06350 0.05990 0.06010 0.01580 0.01080 0.01110 0.002600 0.001540 5.000 2.200 0.01020 0.03200 0.01790 0.01970 0.03730 0.04270 0.04430 0.03260 0.03320 0.01650 0.02920 0.02500 0.04490 0.04380 0.04560 0.04550 0.005910 0.008410 0.007450 0.002490 0.0005700 6.000 1.700 0.01020 0.03010 0.01790 0.01960 0.02710 0.03910 0.03980 0.03200 0.03260 0.01600 0.01510 0.01260 0.02810 0.02690 0.03330 0.03300 0.002990 0.004680 0.003630 0.001910 0.0002450 7.000 1.330 0.01010 0.02800 0.01780 0.01950 0.01900 0.03450 0.03430 0.03110 0.03160 0.01520 0.008670 0.007940 0.01620 0.01510 0.02360 0.02330 0.002730 0.002200 0.001630 0.001210 0.0002080 8.000 1.080 0.010000 0.02590 0.01760 0.01930 0.01330 0.02950 0.02850 0.02970 0.03010 0.01280 0.006730 0.006980 0.008910 0.008220 0.01650 0.01620 0.002590 0.001150 0.0009810 0.0006800 0.0002010 10.00 0.7600 0.009800 0.02100 0.01700 0.01900 0.007500 0.02000 0.01800 0.02600 0.02600 0.006400 0.006400 0.006600 0.003400 0.003200 0.007900 0.007600 0.001500 0.0008400 0.0008700 0.0002100 0.0001200 15.00 0.3700 0.009300 0.01100 0.01500 0.01600 0.006100 0.004800 0.003900 0.01400 0.01400 0.001500 0.002500 0.002000 0.002500 0.002500 0.001200 0.001200 0.0002500 0.0004000 0.0003000 0.0001200 1.900E-05 20.00 0.2000 0.008600 0.005000 0.01200 0.01200 0.004100 0.001900 0.002000 0.006300 0.006000 0.001300 0.0005600 0.0004900 0.001500 0.001400 0.0002100 0.0002000 0.0002100 8.300E-05 6.600E-05 7.800E-05 1.600E-05 30.00 0.07700 0.006900 0.001100 0.006300 0.005500 0.0006500 0.001600 0.001600 0.001000 0.0009300 0.0002200 0.0003500 0.0003700 0.0002700 0.0002400 1.000E-05 9.300E-06 3.800E-05 4.600E-05 4.800E-05 1.500E-05 2.900E-06 40.00 0.03500 0.005200 0.0009000 0.002900 0.002200 0.0002000 0.0008500 0.0007200 0.0001900 0.0001600 4.900E-05 0.0002100 0.0001800 5.000E-05 4.200E-05 8.600E-07 7.500E-07 8.100E-06 2.800E-05 2.300E-05 2.700E-06 6.100E-07 60.00 0.01100 0.002800 0.0006600 0.0005900 0.0003600 0.0001600 0.0001800 0.0001100 1.100E-05 8.400E-06 3.900E-05 4.300E-05 2.800E-05 2.900E-06 2.100E-06 1.800E-08 1.400E-08 6.300E-06 5.800E-06 3.600E-06 1.600E-07 4.800E-07 100.0 0.002100 0.0007400 0.0001600 4.800E-05 1.900E-05 4.000E-05 1.300E-05 5.500E-06 2.100E-07 1.200E-07 9.700E-06 3.200E-06 1.300E-06 5.200E-08 3.000E-08 1.000E-10 6.900E-11 1.600E-06 4.300E-07 1.700E-07 2.800E-09 1.200E-07 #S 74 W #N 23 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 2 #UBIND 6.952E+04 1.210E+04 1.154E+04 1.020E+04 2817. 2572. 2278. 1869. 1807. 592.0 489.0 423.0 255.0 243.0 34.00 32.00 74.00 44.00 34.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 0.000 11.20 0.01010 0.03610 0.01770 0.01950 0.07960 0.04670 0.04990 0.03240 0.03310 0.1670 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.4270 0.2910 0.3110 0.3520 1.550 0.05000 11.10 0.01010 0.03610 0.01770 0.01950 0.07960 0.04670 0.04990 0.03240 0.03310 0.1670 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.4260 0.2910 0.3110 0.3520 1.510 0.1000 10.90 0.01010 0.03610 0.01770 0.01950 0.07950 0.04670 0.04990 0.03240 0.03310 0.1670 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.4230 0.2910 0.3110 0.3520 1.400 0.1500 10.50 0.01010 0.03610 0.01770 0.01950 0.07950 0.04670 0.04990 0.03240 0.03310 0.1660 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.4180 0.2910 0.3110 0.3520 1.240 0.2000 10.10 0.01010 0.03610 0.01770 0.01950 0.07950 0.04670 0.04990 0.03240 0.03310 0.1660 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.4120 0.2910 0.3110 0.3520 1.040 0.3000 9.320 0.01010 0.03600 0.01770 0.01950 0.07930 0.04670 0.04990 0.03240 0.03310 0.1650 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.3940 0.2900 0.3100 0.3510 0.6540 0.4000 8.640 0.01010 0.03600 0.01770 0.01950 0.07920 0.04670 0.04990 0.03240 0.03310 0.1630 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.3710 0.2890 0.3080 0.3490 0.3540 0.5000 8.170 0.01010 0.03600 0.01770 0.01950 0.07890 0.04670 0.04990 0.03240 0.03310 0.1610 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.3420 0.2850 0.3030 0.3420 0.1740 0.6000 7.850 0.01010 0.03600 0.01770 0.01950 0.07870 0.04670 0.04990 0.03240 0.03310 0.1580 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.3110 0.2800 0.2960 0.3310 0.08850 0.7000 7.590 0.01010 0.03600 0.01770 0.01950 0.07840 0.04670 0.04990 0.03240 0.03310 0.1550 0.1050 0.1120 0.08630 0.08830 0.08130 0.08170 0.2780 0.2720 0.2860 0.3150 0.05620 0.8000 7.340 0.01010 0.03590 0.01770 0.01950 0.07800 0.04670 0.04990 0.03240 0.03310 0.1520 0.1050 0.1120 0.08630 0.08830 0.08130 0.08170 0.2450 0.2620 0.2730 0.2950 0.04780 1.000 6.800 0.01010 0.03590 0.01770 0.01950 0.07710 0.04670 0.04990 0.03240 0.03310 0.1440 0.1050 0.1120 0.08630 0.08830 0.08130 0.08170 0.1820 0.2340 0.2390 0.2450 0.04650 1.200 6.220 0.01010 0.03580 0.01770 0.01950 0.07610 0.04670 0.04990 0.03240 0.03310 0.1350 0.1040 0.1110 0.08620 0.08820 0.08120 0.08160 0.1280 0.2000 0.1980 0.1940 0.04000 1.400 5.650 0.01010 0.03570 0.01770 0.01950 0.07490 0.04670 0.04980 0.03240 0.03310 0.1250 0.1030 0.1100 0.08610 0.08810 0.08110 0.08150 0.08750 0.1630 0.1560 0.1470 0.02910 1.600 5.150 0.01010 0.03560 0.01770 0.01950 0.07350 0.04670 0.04980 0.03240 0.03310 0.1150 0.1020 0.1080 0.08590 0.08780 0.08080 0.08120 0.05990 0.1270 0.1170 0.1080 0.01880 1.800 4.740 0.01010 0.03540 0.01760 0.01950 0.07200 0.04660 0.04970 0.03240 0.03310 0.1040 0.1000 0.1050 0.08550 0.08740 0.08030 0.08080 0.04320 0.09540 0.08370 0.07690 0.01130 2.000 4.400 0.01010 0.03530 0.01760 0.01950 0.07030 0.04660 0.04970 0.03240 0.03310 0.09370 0.09770 0.1020 0.08500 0.08680 0.07960 0.08010 0.03450 0.06910 0.05810 0.05370 0.006660 2.400 3.940 0.01010 0.03500 0.01760 0.01950 0.06670 0.04640 0.04940 0.03240 0.03310 0.07350 0.09140 0.09430 0.08320 0.08480 0.07750 0.07810 0.02970 0.03410 0.02710 0.02500 0.002990 3.000 3.490 0.01010 0.03440 0.01760 0.01950 0.06040 0.04600 0.04890 0.03230 0.03300 0.04830 0.07870 0.07860 0.07820 0.07920 0.07240 0.07300 0.02870 0.01400 0.01280 0.007770 0.002480 4.000 2.860 0.01010 0.03310 0.01760 0.01950 0.04890 0.04460 0.04700 0.03230 0.03290 0.02390 0.05310 0.04940 0.06390 0.06380 0.06030 0.06060 0.01710 0.01090 0.01140 0.002930 0.001720 5.000 2.240 0.010000 0.03160 0.01760 0.01940 0.03760 0.04220 0.04390 0.03200 0.03270 0.01650 0.03060 0.02630 0.04600 0.04500 0.04690 0.04700 0.006680 0.008940 0.008110 0.002800 0.0006770 6.000 1.740 0.009980 0.02980 0.01750 0.01940 0.02760 0.03880 0.03960 0.03150 0.03210 0.01580 0.01600 0.01330 0.02950 0.02820 0.03490 0.03470 0.003170 0.005220 0.004130 0.002250 0.0002750 7.000 1.360 0.009920 0.02780 0.01740 0.01920 0.01950 0.03450 0.03440 0.03070 0.03120 0.01520 0.009090 0.008150 0.01730 0.01620 0.02520 0.02490 0.002760 0.002530 0.001870 0.001470 0.0002170 8.000 1.100 0.009850 0.02570 0.01730 0.01900 0.01370 0.02970 0.02880 0.02940 0.02980 0.01300 0.006780 0.006940 0.009690 0.008930 0.01780 0.01750 0.002680 0.001280 0.001060 0.0008540 0.0002130 10.00 0.7700 0.009700 0.02100 0.01700 0.01800 0.007700 0.02000 0.01800 0.02600 0.02600 0.006800 0.006400 0.006700 0.003600 0.003400 0.008700 0.008400 0.001600 0.0008500 0.0009000 0.0002700 0.0001400 15.00 0.3800 0.009100 0.01200 0.01500 0.01600 0.006000 0.005100 0.004100 0.01500 0.01400 0.001500 0.002700 0.002100 0.002500 0.002500 0.001400 0.001300 0.0002600 0.0004400 0.0003500 0.0001400 2.100E-05 20.00 0.2100 0.008500 0.005300 0.01200 0.01200 0.004200 0.001900 0.002000 0.006600 0.006200 0.001300 0.0006000 0.0005100 0.001500 0.001500 0.0002500 0.0002300 0.0002200 9.500E-05 7.300E-05 9.300E-05 1.700E-05 30.00 0.07900 0.006800 0.001200 0.006400 0.005700 0.0007100 0.001600 0.001600 0.001100 0.001000 0.0002400 0.0003500 0.0003800 0.0003000 0.0002700 1.200E-05 1.100E-05 4.300E-05 4.700E-05 5.100E-05 1.900E-05 3.400E-06 40.00 0.03700 0.005200 0.0008900 0.003000 0.002300 0.0002000 0.0008800 0.0007500 0.0002100 0.0001800 5.100E-05 0.0002100 0.0001900 5.600E-05 4.700E-05 1.100E-06 9.200E-07 8.600E-06 3.000E-05 2.500E-05 3.500E-06 6.700E-07 60.00 0.01100 0.002800 0.0006700 0.0006300 0.0003800 0.0001600 0.0001900 0.0001200 1.300E-05 9.400E-06 4.000E-05 4.700E-05 3.000E-05 3.300E-06 2.400E-06 2.200E-08 1.800E-08 6.600E-06 6.500E-06 4.100E-06 2.000E-07 5.100E-07 100.0 0.002200 0.0007700 0.0001700 5.300E-05 2.000E-05 4.200E-05 1.500E-05 6.000E-06 2.400E-07 1.400E-07 1.000E-05 3.600E-06 1.500E-06 6.000E-08 3.400E-08 1.300E-10 8.700E-11 1.700E-06 4.900E-07 2.000E-07 3.700E-09 1.300E-07 #S 75 Re #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 1 2 #UBIND 7.168E+04 1.253E+04 1.196E+04 1.053E+04 2932. 2682. 2367. 1949. 1883. 625.0 518.0 445.0 274.0 260.0 43.00 40.00 83.00 46.00 35.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 11.10 0.009970 0.03540 0.01730 0.01920 0.07810 0.04580 0.04910 0.03180 0.03250 0.1630 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.4120 0.2790 0.3000 0.3270 0.3460 1.490 0.05000 11.00 0.009970 0.03540 0.01730 0.01920 0.07810 0.04580 0.04910 0.03180 0.03250 0.1630 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.4110 0.2790 0.3000 0.3270 0.3460 1.460 0.1000 10.80 0.009970 0.03540 0.01730 0.01920 0.07810 0.04580 0.04910 0.03180 0.03250 0.1630 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.4080 0.2790 0.3000 0.3270 0.3460 1.360 0.1500 10.50 0.009970 0.03540 0.01730 0.01920 0.07800 0.04580 0.04910 0.03180 0.03250 0.1630 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.4040 0.2790 0.3000 0.3270 0.3460 1.210 0.2000 10.20 0.009970 0.03540 0.01730 0.01920 0.07800 0.04580 0.04910 0.03180 0.03250 0.1620 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.3980 0.2790 0.3000 0.3270 0.3460 1.030 0.3000 9.390 0.009970 0.03540 0.01730 0.01920 0.07790 0.04580 0.04910 0.03180 0.03250 0.1610 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.3820 0.2790 0.2990 0.3270 0.3460 0.6660 0.4000 8.740 0.009970 0.03540 0.01730 0.01920 0.07770 0.04580 0.04910 0.03180 0.03250 0.1600 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.3600 0.2770 0.2970 0.3250 0.3430 0.3740 0.5000 8.270 0.009970 0.03540 0.01730 0.01920 0.07750 0.04580 0.04910 0.03180 0.03250 0.1580 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.3350 0.2740 0.2930 0.3210 0.3370 0.1910 0.6000 7.950 0.009970 0.03540 0.01730 0.01920 0.07730 0.04580 0.04910 0.03180 0.03250 0.1550 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.3060 0.2700 0.2870 0.3130 0.3270 0.09780 0.7000 7.690 0.009970 0.03530 0.01730 0.01920 0.07700 0.04580 0.04910 0.03180 0.03250 0.1520 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.2750 0.2630 0.2790 0.3020 0.3120 0.05930 0.8000 7.450 0.009970 0.03530 0.01730 0.01920 0.07660 0.04580 0.04910 0.03180 0.03250 0.1490 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.2440 0.2550 0.2670 0.2860 0.2920 0.04760 1.000 6.930 0.009970 0.03530 0.01730 0.01920 0.07580 0.04580 0.04900 0.03180 0.03250 0.1420 0.1030 0.1090 0.08420 0.08620 0.07760 0.07820 0.1840 0.2310 0.2370 0.2460 0.2450 0.04550 1.200 6.350 0.009960 0.03520 0.01730 0.01920 0.07480 0.04580 0.04900 0.03180 0.03250 0.1330 0.1020 0.1090 0.08410 0.08610 0.07760 0.07820 0.1320 0.2000 0.1990 0.2010 0.1950 0.04090 1.400 5.780 0.009960 0.03510 0.01730 0.01920 0.07370 0.04580 0.04900 0.03180 0.03250 0.1240 0.1010 0.1080 0.08400 0.08600 0.07750 0.07810 0.09210 0.1660 0.1600 0.1570 0.1490 0.03120 1.600 5.250 0.009960 0.03500 0.01730 0.01920 0.07240 0.04580 0.04900 0.03180 0.03250 0.1140 0.09990 0.1060 0.08380 0.08580 0.07730 0.07790 0.06370 0.1320 0.1220 0.1190 0.1100 0.02110 1.800 4.810 0.009960 0.03480 0.01730 0.01920 0.07090 0.04570 0.04890 0.03180 0.03250 0.1040 0.09820 0.1040 0.08350 0.08540 0.07700 0.07760 0.04580 0.1010 0.08950 0.08780 0.07950 0.01320 2.000 4.450 0.009950 0.03470 0.01730 0.01920 0.06940 0.04570 0.04880 0.03180 0.03250 0.09390 0.09600 0.1010 0.08310 0.08490 0.07650 0.07710 0.03590 0.07510 0.06340 0.06320 0.05610 0.007920 2.400 3.940 0.009940 0.03440 0.01730 0.01920 0.06580 0.04550 0.04860 0.03180 0.03250 0.07430 0.09030 0.09340 0.08150 0.08310 0.07500 0.07560 0.02970 0.03840 0.03040 0.03100 0.02660 0.003370 3.000 3.470 0.009930 0.03380 0.01730 0.01920 0.05990 0.04510 0.04810 0.03180 0.03250 0.04950 0.07850 0.07870 0.07710 0.07820 0.07090 0.07150 0.02890 0.01550 0.01360 0.01010 0.008380 0.002530 4.000 2.860 0.009900 0.03260 0.01730 0.01920 0.04890 0.04390 0.04640 0.03170 0.03240 0.02470 0.05410 0.05060 0.06400 0.06400 0.06050 0.06080 0.01850 0.01100 0.01160 0.003290 0.003080 0.001910 5.000 2.280 0.009860 0.03110 0.01730 0.01910 0.03790 0.04170 0.04350 0.03150 0.03220 0.01650 0.03190 0.02760 0.04700 0.04610 0.04810 0.04810 0.007540 0.009490 0.008720 0.003090 0.002940 0.0008000 6.000 1.770 0.009810 0.02940 0.01720 0.01910 0.02800 0.03850 0.03940 0.03100 0.03160 0.01550 0.01710 0.01410 0.03080 0.02960 0.03640 0.03620 0.003410 0.005840 0.004640 0.002590 0.002380 0.0003140 7.000 1.400 0.009750 0.02760 0.01710 0.01890 0.02010 0.03440 0.03440 0.03020 0.03080 0.01510 0.009560 0.008410 0.01850 0.01740 0.02670 0.02640 0.002800 0.002910 0.002130 0.001760 0.001560 0.0002280 8.000 1.130 0.009680 0.02550 0.01700 0.01880 0.01420 0.02990 0.02900 0.02910 0.02950 0.01320 0.006860 0.006930 0.01050 0.009690 0.01910 0.01880 0.002750 0.001460 0.001160 0.001050 0.0009050 0.0002240 10.00 0.7900 0.009500 0.02100 0.01700 0.01800 0.007900 0.02100 0.01900 0.02600 0.02600 0.007300 0.006300 0.006700 0.003800 0.003600 0.009500 0.009200 0.001800 0.0008800 0.0009300 0.0003300 0.0002900 0.0001500 15.00 0.3900 0.009000 0.01200 0.01500 0.01600 0.005900 0.005400 0.004300 0.01500 0.01500 0.001500 0.002900 0.002300 0.002500 0.002500 0.001600 0.001500 0.0002800 0.0004900 0.0003900 0.0001600 0.0001500 2.200E-05 20.00 0.2100 0.008300 0.005500 0.01200 0.01200 0.004300 0.001900 0.002000 0.006900 0.006500 0.001300 0.0006600 0.0005300 0.001600 0.001500 0.0002900 0.0002700 0.0002300 0.0001100 8.100E-05 0.0001100 9.900E-05 1.900E-05 30.00 0.08200 0.006800 0.001200 0.006600 0.005800 0.0007800 0.001600 0.001600 0.001200 0.001100 0.0002700 0.0003500 0.0003800 0.0003300 0.0002900 1.500E-05 1.300E-05 4.900E-05 4.900E-05 5.300E-05 2.300E-05 1.900E-05 4.000E-06 40.00 0.03800 0.005300 0.0008700 0.003100 0.002400 0.0002000 0.0009100 0.0007900 0.0002300 0.0001900 5.300E-05 0.0002200 0.0002000 6.300E-05 5.200E-05 1.300E-06 1.100E-06 9.200E-06 3.200E-05 2.800E-05 4.400E-06 3.400E-06 7.400E-07 60.00 0.01200 0.002900 0.0006800 0.0006800 0.0004100 0.0001600 0.0002000 0.0001300 1.400E-05 1.100E-05 4.000E-05 5.100E-05 3.300E-05 3.700E-06 2.800E-06 2.700E-08 2.200E-08 6.900E-06 7.400E-06 4.700E-06 2.600E-07 1.800E-07 5.500E-07 100.0 0.002400 0.0008000 0.0001800 5.800E-05 2.200E-05 4.400E-05 1.600E-05 6.600E-06 2.700E-07 1.600E-07 1.100E-05 4.000E-06 1.600E-06 6.900E-08 3.900E-08 1.700E-10 1.100E-10 1.900E-06 5.700E-07 2.300E-07 4.800E-09 2.600E-09 1.500E-07 #S 76 Os #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 2 2 #UBIND 7.387E+04 1.297E+04 1.238E+04 1.087E+04 3049. 2792. 2458. 2031. 1960. 655.0 547.0 469.0 290.0 273.0 54.00 51.00 84.00 58.00 46.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 11.00 0.009800 0.03480 0.01700 0.01890 0.07670 0.04500 0.04820 0.03120 0.03200 0.1600 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3970 0.2680 0.2900 0.3070 0.3240 1.440 0.05000 11.00 0.009800 0.03480 0.01700 0.01890 0.07670 0.04500 0.04820 0.03120 0.03200 0.1600 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3970 0.2680 0.2900 0.3070 0.3240 1.410 0.1000 10.80 0.009800 0.03480 0.01700 0.01890 0.07670 0.04500 0.04820 0.03120 0.03200 0.1600 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3940 0.2680 0.2900 0.3070 0.3240 1.320 0.1500 10.50 0.009800 0.03480 0.01700 0.01890 0.07660 0.04500 0.04820 0.03120 0.03200 0.1590 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3900 0.2680 0.2900 0.3070 0.3240 1.190 0.2000 10.20 0.009800 0.03480 0.01700 0.01890 0.07660 0.04500 0.04820 0.03120 0.03200 0.1590 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3850 0.2680 0.2900 0.3070 0.3240 1.020 0.3000 9.440 0.009790 0.03480 0.01700 0.01890 0.07650 0.04500 0.04820 0.03120 0.03200 0.1580 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3700 0.2680 0.2890 0.3070 0.3240 0.6760 0.4000 8.820 0.009790 0.03480 0.01700 0.01890 0.07630 0.04500 0.04820 0.03120 0.03200 0.1560 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3510 0.2670 0.2870 0.3060 0.3220 0.3920 0.5000 8.360 0.009790 0.03480 0.01700 0.01890 0.07610 0.04500 0.04820 0.03120 0.03200 0.1540 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3270 0.2640 0.2840 0.3030 0.3180 0.2070 0.6000 8.030 0.009790 0.03480 0.01700 0.01890 0.07590 0.04500 0.04820 0.03120 0.03200 0.1520 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3010 0.2610 0.2790 0.2980 0.3110 0.1070 0.7000 7.780 0.009790 0.03470 0.01700 0.01890 0.07560 0.04500 0.04820 0.03120 0.03200 0.1500 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.2720 0.2550 0.2710 0.2890 0.2990 0.06290 0.8000 7.550 0.009790 0.03470 0.01700 0.01890 0.07530 0.04500 0.04820 0.03120 0.03200 0.1470 0.1010 0.1080 0.08220 0.08410 0.07440 0.07510 0.2430 0.2470 0.2610 0.2770 0.2840 0.04770 1.000 7.050 0.009790 0.03460 0.01700 0.01890 0.07450 0.04500 0.04820 0.03120 0.03200 0.1400 0.1000 0.1070 0.08220 0.08410 0.07440 0.07510 0.1860 0.2270 0.2340 0.2440 0.2450 0.04430 1.200 6.490 0.009790 0.03460 0.01700 0.01890 0.07360 0.04500 0.04820 0.03120 0.03200 0.1320 0.09980 0.1070 0.08210 0.08410 0.07440 0.07510 0.1360 0.1990 0.2000 0.2050 0.2010 0.04120 1.400 5.910 0.009790 0.03450 0.01700 0.01890 0.07250 0.04500 0.04820 0.03120 0.03200 0.1230 0.09900 0.1050 0.08210 0.08400 0.07430 0.07500 0.09640 0.1680 0.1630 0.1650 0.1580 0.03280 1.600 5.370 0.009780 0.03440 0.01700 0.01890 0.07130 0.04490 0.04820 0.03120 0.03200 0.1130 0.09790 0.1040 0.08190 0.08380 0.07420 0.07490 0.06750 0.1370 0.1270 0.1280 0.1200 0.02310 1.800 4.900 0.009780 0.03430 0.01700 0.01890 0.06990 0.04490 0.04810 0.03120 0.03200 0.1040 0.09640 0.1020 0.08160 0.08350 0.07400 0.07470 0.04850 0.1070 0.09480 0.09730 0.08940 0.01490 2.000 4.510 0.009780 0.03410 0.01700 0.01890 0.06840 0.04490 0.04810 0.03120 0.03200 0.09390 0.09440 0.09930 0.08120 0.08300 0.07370 0.07430 0.03740 0.08070 0.06850 0.07190 0.06470 0.009210 2.400 3.950 0.009770 0.03380 0.01700 0.01890 0.06500 0.04470 0.04790 0.03120 0.03200 0.07500 0.08920 0.09240 0.07990 0.08150 0.07250 0.07320 0.02970 0.04280 0.03380 0.03700 0.03220 0.003800 3.000 3.450 0.009760 0.03330 0.01700 0.01890 0.05930 0.04440 0.04740 0.03120 0.03190 0.05060 0.07820 0.07860 0.07600 0.07710 0.06930 0.06990 0.02890 0.01710 0.01450 0.01260 0.01060 0.002550 4.000 2.870 0.009730 0.03210 0.01700 0.01890 0.04880 0.04320 0.04580 0.03120 0.03190 0.02550 0.05500 0.05170 0.06400 0.06410 0.06030 0.06060 0.01970 0.01110 0.01170 0.003680 0.003410 0.002060 5.000 2.300 0.009690 0.03070 0.01700 0.01890 0.03810 0.04110 0.04300 0.03100 0.03160 0.01660 0.03320 0.02880 0.04790 0.04710 0.04900 0.04900 0.008460 0.009970 0.009280 0.003320 0.003190 0.0009290 6.000 1.810 0.009640 0.02910 0.01690 0.01880 0.02850 0.03810 0.03920 0.03050 0.03120 0.01530 0.01810 0.01490 0.03210 0.03090 0.03770 0.03750 0.003700 0.006450 0.005180 0.002900 0.002690 0.0003590 7.000 1.430 0.009580 0.02730 0.01680 0.01870 0.02060 0.03430 0.03440 0.02980 0.03040 0.01500 0.01010 0.008720 0.01970 0.01850 0.02810 0.02770 0.002850 0.003340 0.002420 0.002050 0.001840 0.0002380 8.000 1.160 0.009520 0.02540 0.01670 0.01850 0.01460 0.03000 0.02930 0.02880 0.02920 0.01340 0.006990 0.006930 0.01140 0.01050 0.02040 0.02000 0.002810 0.001660 0.001270 0.001260 0.001090 0.0002330 10.00 0.8100 0.009400 0.02100 0.01600 0.01800 0.008000 0.02100 0.01900 0.02600 0.02600 0.007700 0.006200 0.006600 0.004000 0.003800 0.010000 0.010000 0.001900 0.0009000 0.0009500 0.0004100 0.0003500 0.0001700 15.00 0.4000 0.008900 0.01200 0.01500 0.01600 0.005800 0.005800 0.004600 0.01500 0.01500 0.001500 0.003100 0.002500 0.002500 0.002500 0.001800 0.001700 0.0002900 0.0005500 0.0004300 0.0001700 0.0001700 2.400E-05 20.00 0.2200 0.008200 0.005700 0.01200 0.01200 0.004400 0.001900 0.002000 0.007200 0.006800 0.001300 0.0007200 0.0005600 0.001700 0.001600 0.0003400 0.0003100 0.0002400 0.0001300 9.000E-05 0.0001200 0.0001100 2.000E-05 30.00 0.08400 0.006800 0.001300 0.006700 0.005900 0.0008500 0.001600 0.001700 0.001300 0.001200 0.0002900 0.0003500 0.0003900 0.0003500 0.0003200 1.800E-05 1.600E-05 5.600E-05 5.100E-05 5.600E-05 2.700E-05 2.300E-05 4.700E-06 40.00 0.03900 0.005300 0.0008600 0.003200 0.002500 0.0002100 0.0009400 0.0008200 0.0002500 0.0002100 5.500E-05 0.0002300 0.0002100 7.000E-05 5.800E-05 1.500E-06 1.300E-06 9.900E-06 3.500E-05 3.000E-05 5.400E-06 4.200E-06 8.100E-07 60.00 0.01200 0.002900 0.0006900 0.0007200 0.0004300 0.0001600 0.0002200 0.0001400 1.600E-05 1.200E-05 4.100E-05 5.500E-05 3.600E-05 4.200E-06 3.100E-06 3.400E-08 2.700E-08 7.100E-06 8.300E-06 5.200E-06 3.300E-07 2.300E-07 5.800E-07 100.0 0.002500 0.0008300 0.0001900 6.300E-05 2.400E-05 4.700E-05 1.800E-05 7.200E-06 3.100E-07 1.800E-07 1.200E-05 4.400E-06 1.800E-06 8.000E-08 4.500E-08 2.100E-10 1.400E-10 2.100E-06 6.600E-07 2.600E-07 6.100E-09 3.300E-09 1.700E-07 #S 77 Ir #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 3 2 #UBIND 7.611E+04 1.342E+04 1.282E+04 1.122E+04 3174. 2909. 2551. 2116. 2041. 690.0 577.0 495.0 312.0 296.0 64.00 61.00 96.00 63.00 51.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 11.00 0.009620 0.03420 0.01670 0.01870 0.07530 0.04420 0.04750 0.03070 0.03140 0.1560 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3840 0.2580 0.2810 0.2910 0.3060 1.400 0.05000 10.90 0.009620 0.03420 0.01670 0.01870 0.07530 0.04420 0.04750 0.03070 0.03140 0.1560 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3830 0.2580 0.2810 0.2910 0.3060 1.370 0.1000 10.70 0.009620 0.03420 0.01670 0.01870 0.07530 0.04420 0.04750 0.03070 0.03140 0.1560 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3810 0.2580 0.2810 0.2910 0.3060 1.290 0.1500 10.50 0.009620 0.03420 0.01670 0.01870 0.07530 0.04420 0.04750 0.03070 0.03140 0.1560 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3780 0.2580 0.2800 0.2910 0.3060 1.160 0.2000 10.20 0.009620 0.03420 0.01670 0.01870 0.07520 0.04420 0.04750 0.03070 0.03140 0.1560 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3730 0.2580 0.2800 0.2910 0.3060 1.010 0.3000 9.490 0.009620 0.03420 0.01670 0.01870 0.07510 0.04420 0.04750 0.03070 0.03140 0.1550 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3600 0.2580 0.2800 0.2900 0.3060 0.6830 0.4000 8.880 0.009620 0.03420 0.01670 0.01870 0.07500 0.04420 0.04750 0.03070 0.03140 0.1530 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3420 0.2570 0.2780 0.2900 0.3050 0.4070 0.5000 8.430 0.009620 0.03420 0.01670 0.01870 0.07480 0.04420 0.04750 0.03070 0.03140 0.1510 0.09850 0.1060 0.08030 0.08220 0.07160 0.07230 0.3200 0.2550 0.2760 0.2880 0.3020 0.2210 0.6000 8.110 0.009620 0.03420 0.01670 0.01870 0.07460 0.04420 0.04750 0.03070 0.03140 0.1490 0.09850 0.1060 0.08030 0.08220 0.07160 0.07230 0.2950 0.2520 0.2710 0.2830 0.2960 0.1160 0.7000 7.860 0.009620 0.03410 0.01670 0.01870 0.07430 0.04420 0.04750 0.03070 0.03140 0.1470 0.09850 0.1060 0.08030 0.08220 0.07160 0.07230 0.2690 0.2470 0.2640 0.2770 0.2870 0.06700 0.8000 7.640 0.009620 0.03410 0.01670 0.01870 0.07400 0.04420 0.04750 0.03070 0.03140 0.1440 0.09840 0.1050 0.08030 0.08220 0.07160 0.07230 0.2420 0.2410 0.2550 0.2670 0.2750 0.04830 1.000 7.170 0.009620 0.03410 0.01670 0.01870 0.07320 0.04420 0.04740 0.03070 0.03140 0.1370 0.09810 0.1050 0.08030 0.08220 0.07160 0.07230 0.1880 0.2220 0.2310 0.2400 0.2430 0.04290 1.200 6.620 0.009620 0.03400 0.01670 0.01870 0.07240 0.04420 0.04740 0.03070 0.03140 0.1300 0.09770 0.1040 0.08020 0.08210 0.07160 0.07230 0.1400 0.1980 0.2000 0.2070 0.2040 0.04090 1.400 6.040 0.009620 0.03390 0.01670 0.01870 0.07130 0.04410 0.04740 0.03070 0.03140 0.1210 0.09700 0.1030 0.08020 0.08210 0.07150 0.07220 0.1000 0.1700 0.1650 0.1700 0.1650 0.03390 1.600 5.490 0.009610 0.03380 0.01670 0.01870 0.07020 0.04410 0.04740 0.03070 0.03140 0.1120 0.09590 0.1020 0.08000 0.08190 0.07140 0.07220 0.07110 0.1400 0.1310 0.1360 0.1290 0.02470 1.800 5.000 0.009610 0.03370 0.01670 0.01870 0.06880 0.04410 0.04730 0.03070 0.03140 0.1030 0.09460 0.1000 0.07980 0.08170 0.07130 0.07200 0.05130 0.1110 0.09960 0.1050 0.09800 0.01660 2.000 4.590 0.009610 0.03360 0.01670 0.01870 0.06740 0.04400 0.04730 0.03070 0.03140 0.09380 0.09280 0.09780 0.07950 0.08130 0.07100 0.07180 0.03910 0.08580 0.07330 0.07980 0.07270 0.01050 2.400 3.980 0.009600 0.03330 0.01670 0.01870 0.06420 0.04390 0.04710 0.03070 0.03140 0.07560 0.08800 0.09150 0.07830 0.07990 0.07020 0.07090 0.02980 0.04720 0.03720 0.04300 0.03780 0.004310 3.000 3.440 0.009590 0.03280 0.01670 0.01870 0.05880 0.04360 0.04660 0.03070 0.03140 0.05170 0.07780 0.07850 0.07480 0.07600 0.06760 0.06820 0.02880 0.01900 0.01550 0.01540 0.01300 0.002570 4.000 2.860 0.009560 0.03170 0.01670 0.01860 0.04870 0.04250 0.04510 0.03060 0.03140 0.02630 0.05580 0.05270 0.06390 0.06410 0.06000 0.06030 0.02080 0.01120 0.01180 0.004130 0.003780 0.002180 5.000 2.330 0.009520 0.03030 0.01670 0.01860 0.03830 0.04060 0.04260 0.03050 0.03120 0.01670 0.03450 0.03010 0.04870 0.04790 0.04960 0.04960 0.009430 0.01040 0.009780 0.003500 0.003400 0.001060 6.000 1.850 0.009480 0.02880 0.01660 0.01850 0.02890 0.03780 0.03890 0.03010 0.03070 0.01510 0.01920 0.01580 0.03330 0.03210 0.03890 0.03870 0.004040 0.007050 0.005720 0.003170 0.002980 0.0004110 7.000 1.460 0.009420 0.02700 0.01650 0.01840 0.02100 0.03420 0.03440 0.02940 0.03000 0.01490 0.01070 0.009080 0.02080 0.01960 0.02940 0.02900 0.002900 0.003790 0.002740 0.002330 0.002110 0.0002490 8.000 1.180 0.009360 0.02520 0.01640 0.01830 0.01510 0.03010 0.02940 0.02850 0.02890 0.01360 0.007170 0.006970 0.01220 0.01130 0.02160 0.02120 0.002850 0.001900 0.001400 0.001470 0.001290 0.0002390 10.00 0.8200 0.009200 0.02100 0.01600 0.01800 0.008200 0.02100 0.02000 0.02600 0.02600 0.008100 0.006100 0.006600 0.004300 0.004000 0.01100 0.01100 0.002100 0.0009300 0.0009700 0.0004900 0.0004200 0.0001800 15.00 0.4100 0.008700 0.01200 0.01500 0.01600 0.005700 0.006100 0.004800 0.01500 0.01500 0.001500 0.003300 0.002700 0.002500 0.002500 0.002000 0.001900 0.0003100 0.0006000 0.0004700 0.0001900 0.0001800 2.700E-05 20.00 0.2300 0.008100 0.005900 0.01200 0.01200 0.004500 0.002000 0.002000 0.007400 0.007000 0.001300 0.0007900 0.0006000 0.001700 0.001700 0.0003900 0.0003600 0.0002500 0.0001400 1.000E-04 0.0001400 0.0001300 2.100E-05 30.00 0.08600 0.006700 0.001300 0.006800 0.006100 0.0009200 0.001600 0.001700 0.001400 0.001200 0.0003200 0.0003500 0.0003900 0.0003800 0.0003400 2.100E-05 1.900E-05 6.400E-05 5.200E-05 5.800E-05 3.200E-05 2.700E-05 5.300E-06 40.00 0.04000 0.005300 0.0008400 0.003300 0.002600 0.0002100 0.0009700 0.0008500 0.0002800 0.0002300 5.800E-05 0.0002400 0.0002200 7.700E-05 6.400E-05 1.900E-06 1.600E-06 1.100E-05 3.700E-05 3.300E-05 6.500E-06 5.100E-06 9.000E-07 60.00 0.01300 0.002900 0.0006900 0.0007700 0.0004600 0.0001600 0.0002300 0.0001500 1.800E-05 1.300E-05 4.100E-05 5.900E-05 3.800E-05 4.800E-06 3.500E-06 4.100E-08 3.300E-08 7.400E-06 9.300E-06 5.800E-06 4.000E-07 2.800E-07 6.100E-07 100.0 0.002600 0.0008600 0.0002000 6.900E-05 2.600E-05 4.900E-05 2.000E-05 7.900E-06 3.500E-07 2.000E-07 1.200E-05 4.900E-06 2.000E-06 9.200E-08 5.200E-08 2.600E-10 1.700E-10 2.200E-06 7.600E-07 3.000E-07 7.600E-09 4.100E-09 1.800E-07 #S 78 Pt #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 5 1 #UBIND 7.840E+04 1.388E+04 1.327E+04 1.156E+04 3298. 3027. 2646. 2202. 2121. 724.0 608.0 520.0 332.0 315.0 75.00 71.00 102.0 66.00 52.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 10.10 0.009460 0.03360 0.01640 0.01840 0.07400 0.04340 0.04670 0.03020 0.03090 0.1530 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3750 0.2500 0.2740 0.2870 0.3040 1.450 0.05000 10.00 0.009460 0.03360 0.01640 0.01840 0.07400 0.04340 0.04670 0.03020 0.03090 0.1530 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3740 0.2500 0.2740 0.2870 0.3040 1.410 0.1000 9.930 0.009460 0.03360 0.01640 0.01840 0.07390 0.04340 0.04670 0.03020 0.03090 0.1530 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3720 0.2500 0.2740 0.2870 0.3040 1.320 0.1500 9.790 0.009460 0.03360 0.01640 0.01840 0.07390 0.04340 0.04670 0.03020 0.03090 0.1530 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3690 0.2500 0.2740 0.2870 0.3040 1.190 0.2000 9.620 0.009460 0.03360 0.01640 0.01840 0.07390 0.04340 0.04670 0.03020 0.03090 0.1520 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3650 0.2500 0.2740 0.2870 0.3040 1.030 0.3000 9.240 0.009460 0.03360 0.01640 0.01840 0.07380 0.04340 0.04670 0.03020 0.03090 0.1510 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3520 0.2500 0.2730 0.2860 0.3030 0.6820 0.4000 8.900 0.009460 0.03360 0.01640 0.01840 0.07360 0.04340 0.04670 0.03020 0.03090 0.1500 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3350 0.2490 0.2720 0.2860 0.3020 0.3980 0.5000 8.630 0.009460 0.03360 0.01640 0.01840 0.07350 0.04340 0.04670 0.03020 0.03090 0.1480 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3150 0.2470 0.2690 0.2830 0.2990 0.2110 0.6000 8.410 0.009460 0.03360 0.01640 0.01840 0.07330 0.04340 0.04670 0.03020 0.03090 0.1460 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.2910 0.2450 0.2650 0.2790 0.2930 0.1080 0.7000 8.200 0.009450 0.03360 0.01640 0.01840 0.07300 0.04340 0.04670 0.03020 0.03090 0.1440 0.09630 0.1030 0.07840 0.08030 0.06900 0.06980 0.2660 0.2400 0.2590 0.2730 0.2840 0.06120 0.8000 7.980 0.009450 0.03350 0.01640 0.01840 0.07270 0.04340 0.04670 0.03020 0.03090 0.1410 0.09620 0.1030 0.07840 0.08030 0.06900 0.06980 0.2400 0.2350 0.2510 0.2630 0.2720 0.04370 1.000 7.470 0.009450 0.03350 0.01640 0.01840 0.07200 0.04340 0.04670 0.03020 0.03090 0.1350 0.09600 0.1030 0.07840 0.08030 0.06900 0.06980 0.1890 0.2180 0.2280 0.2370 0.2390 0.03880 1.200 6.880 0.009450 0.03340 0.01640 0.01840 0.07120 0.04330 0.04670 0.03020 0.03090 0.1280 0.09560 0.1020 0.07840 0.08030 0.06900 0.06980 0.1420 0.1960 0.1990 0.2040 0.2020 0.03700 1.400 6.260 0.009450 0.03330 0.01640 0.01840 0.07020 0.04330 0.04660 0.03020 0.03090 0.1200 0.09500 0.1020 0.07830 0.08020 0.06900 0.06980 0.1030 0.1700 0.1660 0.1700 0.1640 0.03070 1.600 5.670 0.009450 0.03320 0.01640 0.01840 0.06910 0.04330 0.04660 0.03020 0.03090 0.1120 0.09400 0.1000 0.07820 0.08010 0.06890 0.06970 0.07390 0.1420 0.1330 0.1370 0.1290 0.02270 1.800 5.140 0.009440 0.03310 0.01640 0.01840 0.06780 0.04330 0.04660 0.03020 0.03090 0.1030 0.09280 0.09850 0.07800 0.07980 0.06880 0.06960 0.05340 0.1150 0.1030 0.1070 0.09950 0.01540 2.000 4.700 0.009440 0.03300 0.01640 0.01840 0.06650 0.04320 0.04650 0.03020 0.03090 0.09370 0.09120 0.09630 0.07770 0.07950 0.06860 0.06940 0.04050 0.08990 0.07690 0.08240 0.07490 0.009940 2.400 4.030 0.009430 0.03270 0.01640 0.01840 0.06340 0.04310 0.04640 0.03020 0.03090 0.07610 0.08670 0.09040 0.07670 0.07830 0.06790 0.06870 0.02980 0.05100 0.04000 0.04580 0.04020 0.004120 3.000 3.430 0.009420 0.03220 0.01640 0.01840 0.05820 0.04280 0.04590 0.03020 0.03090 0.05270 0.07730 0.07830 0.07360 0.07480 0.06590 0.06660 0.02840 0.02080 0.01640 0.01720 0.01450 0.002290 4.000 2.860 0.009390 0.03120 0.01640 0.01840 0.04850 0.04180 0.04450 0.03010 0.03090 0.02710 0.05650 0.05360 0.06370 0.06400 0.05940 0.05970 0.02160 0.01120 0.01170 0.004390 0.003920 0.002000 5.000 2.350 0.009360 0.02990 0.01640 0.01830 0.03850 0.04010 0.04210 0.03000 0.03070 0.01690 0.03580 0.03130 0.04940 0.04870 0.05000 0.05000 0.01030 0.01060 0.01010 0.003460 0.003330 0.001040 6.000 1.880 0.009320 0.02840 0.01630 0.01830 0.02930 0.03740 0.03870 0.02960 0.03030 0.01490 0.02030 0.01670 0.03450 0.03330 0.03990 0.03970 0.004390 0.007580 0.006170 0.003230 0.003020 0.0004130 7.000 1.500 0.009270 0.02680 0.01630 0.01820 0.02150 0.03410 0.03440 0.02900 0.02960 0.01480 0.01130 0.009480 0.02200 0.02080 0.03060 0.03020 0.002940 0.004240 0.003050 0.002460 0.002220 0.0002320 8.000 1.210 0.009210 0.02500 0.01620 0.01800 0.01550 0.03020 0.02960 0.02810 0.02860 0.01360 0.007380 0.007030 0.01310 0.01220 0.02280 0.02240 0.002850 0.002150 0.001530 0.001610 0.001400 0.0002150 10.00 0.8400 0.009100 0.02100 0.01600 0.01800 0.008500 0.02200 0.02000 0.02500 0.02600 0.008500 0.006100 0.006600 0.004700 0.004300 0.01200 0.01200 0.002200 0.0009600 0.0009700 0.0005500 0.0004600 0.0001700 15.00 0.4100 0.008600 0.01200 0.01400 0.01500 0.005600 0.006500 0.005100 0.01600 0.01500 0.001600 0.003500 0.002800 0.002500 0.002500 0.002300 0.002100 0.0003400 0.0006400 0.0005100 0.0001900 0.0001800 2.600E-05 20.00 0.2300 0.008000 0.006100 0.01200 0.01200 0.004600 0.002000 0.002000 0.007700 0.007300 0.001400 0.0008700 0.0006400 0.001800 0.001700 0.0004400 0.0004100 0.0002500 0.0001600 0.0001100 0.0001500 0.0001300 1.900E-05 30.00 0.08900 0.006700 0.001400 0.006900 0.006200 0.001000 0.001500 0.001700 0.001500 0.001300 0.0003600 0.0003400 0.0004000 0.0004200 0.0003700 2.500E-05 2.200E-05 7.100E-05 5.300E-05 5.900E-05 3.500E-05 3.000E-05 5.400E-06 40.00 0.04100 0.005300 0.0008300 0.003500 0.002700 0.0002200 0.001000 0.0008800 0.0003000 0.0002500 6.200E-05 0.0002500 0.0002300 8.500E-05 7.100E-05 2.200E-06 1.900E-06 1.200E-05 4.000E-05 3.500E-05 7.300E-06 5.700E-06 8.900E-07 60.00 0.01300 0.003000 0.0007000 0.0008200 0.0004900 0.0001700 0.0002500 0.0001600 2.000E-05 1.400E-05 4.100E-05 6.400E-05 4.100E-05 5.400E-06 4.000E-06 5.000E-08 3.900E-08 7.500E-06 1.000E-05 6.400E-06 4.600E-07 3.200E-07 5.600E-07 100.0 0.002700 0.0008900 0.0002100 7.500E-05 2.800E-05 5.100E-05 2.200E-05 8.600E-06 3.900E-07 2.200E-07 1.300E-05 5.400E-06 2.200E-06 1.100E-07 6.000E-08 3.100E-10 2.000E-10 2.400E-06 8.600E-07 3.300E-07 8.900E-09 4.700E-09 1.800E-07 #S 79 Au #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 1 #UBIND 8.072E+04 1.435E+04 1.373E+04 1.192E+04 3425. 3150. 2743. 2291. 2206. 759.0 644.0 546.0 352.0 334.0 88.00 84.00 108.0 72.00 54.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 10.00 0.009290 0.03310 0.01610 0.01810 0.07270 0.04260 0.04590 0.02970 0.03040 0.1500 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3630 0.2420 0.2660 0.2730 0.2880 1.410 0.05000 9.990 0.009290 0.03310 0.01610 0.01810 0.07270 0.04260 0.04590 0.02970 0.03040 0.1500 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3630 0.2420 0.2660 0.2730 0.2880 1.380 0.1000 9.900 0.009290 0.03310 0.01610 0.01810 0.07260 0.04260 0.04590 0.02970 0.03040 0.1500 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3610 0.2420 0.2660 0.2730 0.2880 1.300 0.1500 9.770 0.009290 0.03310 0.01610 0.01810 0.07260 0.04260 0.04590 0.02970 0.03040 0.1490 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3580 0.2420 0.2660 0.2730 0.2880 1.170 0.2000 9.610 0.009290 0.03310 0.01610 0.01810 0.07260 0.04260 0.04590 0.02970 0.03040 0.1490 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3540 0.2420 0.2660 0.2730 0.2880 1.020 0.3000 9.250 0.009290 0.03310 0.01610 0.01810 0.07250 0.04260 0.04590 0.02970 0.03040 0.1480 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3420 0.2410 0.2650 0.2720 0.2880 0.6880 0.4000 8.920 0.009290 0.03300 0.01610 0.01810 0.07240 0.04260 0.04590 0.02970 0.03040 0.1470 0.09430 0.1010 0.07670 0.07850 0.06660 0.06750 0.3270 0.2410 0.2640 0.2720 0.2870 0.4100 0.5000 8.660 0.009290 0.03300 0.01610 0.01810 0.07220 0.04260 0.04590 0.02970 0.03040 0.1450 0.09430 0.1010 0.07670 0.07850 0.06660 0.06750 0.3080 0.2390 0.2620 0.2700 0.2850 0.2230 0.6000 8.450 0.009290 0.03300 0.01610 0.01810 0.07200 0.04260 0.04590 0.02970 0.03040 0.1430 0.09430 0.1010 0.07670 0.07850 0.06660 0.06750 0.2860 0.2370 0.2580 0.2670 0.2810 0.1160 0.7000 8.250 0.009290 0.03300 0.01610 0.01810 0.07170 0.04260 0.04590 0.02970 0.03040 0.1410 0.09420 0.1010 0.07670 0.07850 0.06660 0.06750 0.2630 0.2330 0.2530 0.2620 0.2730 0.06480 0.8000 8.050 0.009290 0.03300 0.01610 0.01810 0.07150 0.04260 0.04590 0.02970 0.03040 0.1390 0.09420 0.1010 0.07670 0.07850 0.06660 0.06750 0.2390 0.2280 0.2450 0.2540 0.2630 0.04430 1.000 7.570 0.009290 0.03290 0.01610 0.01810 0.07080 0.04260 0.04590 0.02970 0.03040 0.1330 0.09400 0.1010 0.07670 0.07850 0.06660 0.06750 0.1900 0.2140 0.2250 0.2320 0.2360 0.03740 1.200 7.000 0.009290 0.03290 0.01610 0.01810 0.07000 0.04260 0.04590 0.02970 0.03040 0.1260 0.09360 0.1000 0.07660 0.07840 0.06660 0.06750 0.1450 0.1940 0.1980 0.2040 0.2030 0.03630 1.400 6.400 0.009290 0.03280 0.01610 0.01810 0.06910 0.04260 0.04590 0.02970 0.03040 0.1190 0.09300 0.09960 0.07660 0.07840 0.06660 0.06750 0.1070 0.1700 0.1680 0.1730 0.1690 0.03110 1.600 5.800 0.009280 0.03270 0.01610 0.01810 0.06800 0.04250 0.04590 0.02970 0.03040 0.1110 0.09220 0.09850 0.07650 0.07830 0.06660 0.06740 0.07730 0.1440 0.1360 0.1420 0.1360 0.02370 1.800 5.270 0.009280 0.03260 0.01610 0.01810 0.06680 0.04250 0.04580 0.02970 0.03040 0.1020 0.09100 0.09690 0.07630 0.07810 0.06650 0.06740 0.05620 0.1180 0.1070 0.1140 0.1060 0.01660 2.000 4.800 0.009280 0.03250 0.01610 0.01810 0.06550 0.04250 0.04580 0.02970 0.03040 0.09350 0.08960 0.09480 0.07610 0.07780 0.06640 0.06720 0.04240 0.09420 0.08120 0.08900 0.08170 0.01100 2.400 4.080 0.009270 0.03220 0.01610 0.01810 0.06270 0.04240 0.04560 0.02970 0.03040 0.07650 0.08550 0.08940 0.07520 0.07680 0.06590 0.06670 0.03020 0.05520 0.04350 0.05140 0.04560 0.004620 3.000 3.440 0.009260 0.03170 0.01610 0.01810 0.05770 0.04210 0.04520 0.02970 0.03040 0.05370 0.07670 0.07810 0.07240 0.07370 0.06420 0.06490 0.02810 0.02300 0.01780 0.02030 0.01720 0.002310 4.000 2.850 0.009230 0.03080 0.01610 0.01810 0.04830 0.04120 0.04390 0.02960 0.03040 0.02800 0.05710 0.05450 0.06340 0.06380 0.05870 0.05900 0.02250 0.01130 0.01170 0.005010 0.004400 0.002060 5.000 2.360 0.009200 0.02950 0.01610 0.01810 0.03870 0.03950 0.04170 0.02950 0.03020 0.01710 0.03690 0.03250 0.05000 0.04940 0.05030 0.05030 0.01130 0.01080 0.01050 0.003600 0.003460 0.001150 6.000 1.910 0.009160 0.02810 0.01600 0.01800 0.02970 0.03710 0.03840 0.02920 0.02990 0.01470 0.02140 0.01760 0.03550 0.03440 0.04070 0.04050 0.004850 0.008120 0.006700 0.003440 0.003230 0.0004700 7.000 1.530 0.009110 0.02650 0.01600 0.01790 0.02200 0.03390 0.03440 0.02860 0.02920 0.01460 0.01200 0.009920 0.02310 0.02190 0.03170 0.03130 0.003030 0.004740 0.003420 0.002720 0.002460 0.0002450 8.000 1.230 0.009060 0.02480 0.01590 0.01780 0.01590 0.03020 0.02970 0.02780 0.02830 0.01370 0.007650 0.007130 0.01410 0.01310 0.02390 0.02350 0.002870 0.002440 0.001710 0.001830 0.001610 0.0002180 10.00 0.8600 0.008900 0.02100 0.01590 0.01700 0.008700 0.02200 0.02000 0.02500 0.02600 0.008900 0.006000 0.006500 0.005000 0.004600 0.01300 0.01300 0.002300 0.001000 0.0009900 0.0006500 0.0005500 0.0001800 15.00 0.4200 0.008500 0.01300 0.01400 0.01500 0.005600 0.006900 0.005300 0.01600 0.01600 0.001600 0.003700 0.003000 0.002500 0.002500 0.002500 0.002400 0.0003700 0.0006900 0.0005600 0.0002000 0.0001900 2.900E-05 20.00 0.2400 0.007900 0.006300 0.01200 0.01200 0.004600 0.002100 0.002000 0.008000 0.007600 0.001400 0.0009500 0.0006900 0.001900 0.001800 0.0005000 0.0004700 0.0002600 0.0001900 0.0001200 0.0001600 0.0001500 1.900E-05 30.00 0.09100 0.006600 0.001400 0.007000 0.006300 0.001100 0.001500 0.001700 0.001600 0.001400 0.0003900 0.0003400 0.0004000 0.0004500 0.0004000 2.900E-05 2.600E-05 7.900E-05 5.400E-05 6.100E-05 4.100E-05 3.400E-05 6.000E-06 40.00 0.04200 0.005300 0.0008200 0.003600 0.002800 0.0002300 0.001000 0.0009200 0.0003300 0.0002700 6.700E-05 0.0002600 0.0002400 9.400E-05 7.900E-05 2.600E-06 2.300E-06 1.300E-05 4.200E-05 3.700E-05 8.600E-06 6.700E-06 9.800E-07 60.00 0.01300 0.003000 0.0007000 0.0008700 0.0005200 0.0001700 0.0002700 0.0001700 2.200E-05 1.600E-05 4.100E-05 6.800E-05 4.500E-05 6.100E-06 4.500E-06 6.000E-08 4.700E-08 7.700E-06 1.100E-05 7.000E-06 5.500E-07 3.800E-07 5.800E-07 100.0 0.002800 0.0009200 0.0002200 8.200E-05 3.100E-05 5.400E-05 2.400E-05 9.400E-06 4.500E-07 2.500E-07 1.400E-05 6.000E-06 2.400E-06 1.200E-07 6.800E-08 3.800E-10 2.500E-10 2.600E-06 9.900E-07 3.800E-07 1.100E-08 5.700E-09 1.900E-07 #S 80 Hg #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 #UBIND 8.310E+04 1.484E+04 1.421E+04 1.228E+04 3562. 3279. 2847. 2385. 2295. 800.0 677.0 571.0 379.0 360.0 104.0 100.0 120.0 81.00 58.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 10.80 0.009130 0.03250 0.01580 0.01790 0.07140 0.04180 0.04520 0.02920 0.03000 0.1470 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3500 0.2330 0.2560 0.2520 0.2650 1.290 0.05000 10.80 0.009130 0.03250 0.01580 0.01790 0.07140 0.04180 0.04520 0.02920 0.03000 0.1470 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3490 0.2330 0.2560 0.2520 0.2650 1.270 0.1000 10.60 0.009130 0.03250 0.01580 0.01790 0.07140 0.04180 0.04520 0.02920 0.03000 0.1460 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3470 0.2330 0.2560 0.2520 0.2650 1.210 0.1500 10.40 0.009130 0.03250 0.01580 0.01790 0.07140 0.04180 0.04520 0.02920 0.03000 0.1460 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3450 0.2330 0.2560 0.2520 0.2650 1.100 0.2000 10.20 0.009130 0.03250 0.01580 0.01790 0.07130 0.04180 0.04520 0.02920 0.03000 0.1460 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3410 0.2330 0.2560 0.2520 0.2650 0.9760 0.3000 9.570 0.009130 0.03250 0.01580 0.01790 0.07120 0.04180 0.04520 0.02920 0.03000 0.1450 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3310 0.2320 0.2550 0.2520 0.2650 0.6960 0.4000 9.020 0.009130 0.03250 0.01580 0.01790 0.07110 0.04180 0.04520 0.02920 0.03000 0.1440 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3170 0.2320 0.2540 0.2520 0.2640 0.4430 0.5000 8.600 0.009130 0.03250 0.01580 0.01790 0.07090 0.04180 0.04520 0.02920 0.03000 0.1420 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.2990 0.2310 0.2530 0.2510 0.2630 0.2580 0.6000 8.280 0.009130 0.03250 0.01580 0.01790 0.07080 0.04180 0.04520 0.02920 0.03000 0.1410 0.09230 0.09940 0.07500 0.07680 0.06450 0.06530 0.2800 0.2290 0.2490 0.2490 0.2610 0.1430 0.7000 8.050 0.009130 0.03240 0.01580 0.01790 0.07050 0.04180 0.04520 0.02920 0.03000 0.1390 0.09220 0.09940 0.07500 0.07680 0.06450 0.06530 0.2590 0.2260 0.2450 0.2460 0.2560 0.08120 0.8000 7.850 0.009130 0.03240 0.01580 0.01790 0.07030 0.04180 0.04520 0.02920 0.03000 0.1360 0.09220 0.09930 0.07500 0.07680 0.06450 0.06530 0.2360 0.2210 0.2390 0.2410 0.2500 0.05260 1.000 7.450 0.009130 0.03240 0.01580 0.01790 0.06960 0.04180 0.04520 0.02920 0.03000 0.1310 0.09200 0.09900 0.07500 0.07680 0.06450 0.06530 0.1910 0.2090 0.2210 0.2250 0.2310 0.03910 1.200 6.970 0.009130 0.03230 0.01580 0.01790 0.06890 0.04180 0.04520 0.02920 0.03000 0.1240 0.09160 0.09850 0.07490 0.07670 0.06450 0.06530 0.1480 0.1920 0.1970 0.2040 0.2050 0.03860 1.400 6.430 0.009130 0.03220 0.01580 0.01790 0.06800 0.04180 0.04520 0.02920 0.03000 0.1170 0.09110 0.09780 0.07490 0.07670 0.06450 0.06530 0.1110 0.1700 0.1690 0.1780 0.1760 0.03490 1.600 5.880 0.009120 0.03210 0.01580 0.01790 0.06700 0.04180 0.04520 0.02920 0.03000 0.1100 0.09040 0.09670 0.07480 0.07660 0.06440 0.06530 0.08130 0.1460 0.1400 0.1500 0.1460 0.02810 1.800 5.350 0.009120 0.03200 0.01580 0.01790 0.06590 0.04180 0.04510 0.02920 0.03000 0.1010 0.08930 0.09530 0.07470 0.07640 0.06440 0.06520 0.05960 0.1220 0.1120 0.1230 0.1170 0.02070 2.000 4.880 0.009120 0.03190 0.01580 0.01790 0.06460 0.04170 0.04510 0.02920 0.03000 0.09320 0.08800 0.09340 0.07450 0.07620 0.06430 0.06510 0.04490 0.09860 0.08620 0.09890 0.09250 0.01420 2.400 4.140 0.009110 0.03170 0.01580 0.01790 0.06190 0.04160 0.04490 0.02920 0.03000 0.07680 0.08430 0.08840 0.07370 0.07530 0.06390 0.06470 0.03090 0.05970 0.04770 0.05980 0.05400 0.006160 3.000 3.450 0.009100 0.03120 0.01580 0.01790 0.05710 0.04140 0.04460 0.02920 0.02990 0.05460 0.07610 0.07770 0.07120 0.07250 0.06260 0.06330 0.02800 0.02560 0.01960 0.02500 0.02150 0.002700 4.000 2.850 0.009080 0.03030 0.01580 0.01780 0.04820 0.04050 0.04340 0.02920 0.02990 0.02880 0.05760 0.05520 0.06300 0.06350 0.05790 0.05830 0.02340 0.01160 0.01190 0.006060 0.005300 0.002390 5.000 2.380 0.009040 0.02910 0.01580 0.01780 0.03880 0.03900 0.04120 0.02900 0.02970 0.01740 0.03800 0.03360 0.05040 0.05000 0.05030 0.05040 0.01250 0.01110 0.01090 0.003900 0.003790 0.001440 6.000 1.930 0.009000 0.02780 0.01580 0.01780 0.03000 0.03670 0.03820 0.02870 0.02940 0.01460 0.02250 0.01850 0.03650 0.03550 0.04140 0.04120 0.005410 0.008680 0.007310 0.003790 0.003620 0.0006090 7.000 1.560 0.008960 0.02630 0.01570 0.01770 0.02240 0.03370 0.03430 0.02820 0.02890 0.01440 0.01270 0.01040 0.02420 0.02300 0.03270 0.03230 0.003170 0.005280 0.003860 0.003110 0.002860 0.0002980 8.000 1.260 0.008910 0.02460 0.01560 0.01750 0.01640 0.03020 0.02990 0.02750 0.02800 0.01370 0.007960 0.007250 0.01500 0.01400 0.02500 0.02460 0.002910 0.002790 0.001920 0.002170 0.001930 0.0002490 10.00 0.8700 0.008800 0.02100 0.01500 0.01700 0.008900 0.02200 0.02100 0.02500 0.02500 0.009300 0.005900 0.006500 0.005400 0.005000 0.01400 0.01300 0.002500 0.001100 0.001000 0.0008000 0.0006800 0.0002200 15.00 0.4300 0.008400 0.01300 0.01400 0.01500 0.005500 0.007200 0.005600 0.01600 0.01600 0.001700 0.003800 0.003200 0.002500 0.002500 0.002800 0.002600 0.0004100 0.0007400 0.0006100 0.0002200 0.0002100 3.600E-05 20.00 0.2400 0.007800 0.006500 0.01200 0.01200 0.004700 0.002200 0.002100 0.008200 0.007800 0.001400 0.001000 0.0007400 0.001900 0.001900 0.0005700 0.0005300 0.0002600 0.0002100 0.0001400 0.0001800 0.0001700 2.200E-05 30.00 0.09300 0.006600 0.001500 0.007100 0.006400 0.001200 0.001500 0.001700 0.001700 0.001500 0.0004200 0.0003400 0.0004000 0.0004800 0.0004300 3.300E-05 3.000E-05 8.900E-05 5.600E-05 6.400E-05 4.900E-05 4.100E-05 7.600E-06 40.00 0.04400 0.005200 0.0008100 0.003700 0.002900 0.0002400 0.001100 0.0009500 0.0003500 0.0003000 7.200E-05 0.0002600 0.0002400 1.000E-04 8.700E-05 3.100E-06 2.700E-06 1.500E-05 4.400E-05 4.000E-05 1.000E-05 8.300E-06 1.200E-06 60.00 0.01400 0.003100 0.0007000 0.0009200 0.0005500 0.0001600 0.0002800 0.0001800 2.400E-05 1.800E-05 4.100E-05 7.300E-05 4.800E-05 6.900E-06 5.100E-06 7.200E-08 5.700E-08 7.900E-06 1.300E-05 7.900E-06 6.900E-07 4.800E-07 6.700E-07 100.0 0.002900 0.0009500 0.0002300 9.000E-05 3.300E-05 5.600E-05 2.600E-05 1.000E-05 5.000E-07 2.800E-07 1.400E-05 6.600E-06 2.600E-06 1.400E-07 7.800E-08 4.700E-10 3.000E-10 2.800E-06 1.100E-06 4.300E-07 1.400E-08 7.400E-09 2.400E-07 #S 81 Tl #N 25 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 1 #UBIND 8.553E+04 1.535E+04 1.470E+04 1.266E+04 3704. 3416. 2957. 2485. 2390. 846.0 722.0 609.0 407.0 386.0 123.0 119.0 137.0 100.0 76.00 15.00 12.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 0.000 11.10 0.008980 0.03200 0.01550 0.01760 0.07020 0.04110 0.04450 0.02870 0.02950 0.1440 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3370 0.2240 0.2460 0.2350 0.2440 1.150 0.9130 0.05000 11.00 0.008980 0.03200 0.01550 0.01760 0.07010 0.04110 0.04450 0.02870 0.02950 0.1440 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3370 0.2240 0.2460 0.2350 0.2440 1.130 0.9130 0.1000 10.90 0.008980 0.03200 0.01550 0.01760 0.07010 0.04110 0.04450 0.02870 0.02950 0.1430 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3350 0.2240 0.2460 0.2350 0.2440 1.080 0.9100 0.1500 10.80 0.008980 0.03200 0.01550 0.01760 0.07010 0.04110 0.04450 0.02870 0.02950 0.1430 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3330 0.2240 0.2460 0.2350 0.2440 1.010 0.8990 0.2000 10.60 0.008980 0.03200 0.01550 0.01760 0.07010 0.04110 0.04450 0.02870 0.02950 0.1430 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3290 0.2240 0.2460 0.2350 0.2440 0.9130 0.8740 0.3000 9.980 0.008980 0.03200 0.01550 0.01760 0.07000 0.04110 0.04450 0.02870 0.02950 0.1420 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3200 0.2230 0.2460 0.2350 0.2440 0.6930 0.7710 0.4000 9.360 0.008980 0.03190 0.01550 0.01760 0.06990 0.04110 0.04450 0.02870 0.02950 0.1410 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3070 0.2230 0.2450 0.2350 0.2440 0.4770 0.6130 0.5000 8.800 0.008970 0.03190 0.01550 0.01760 0.06970 0.04110 0.04450 0.02870 0.02950 0.1400 0.09030 0.09750 0.07330 0.07510 0.06250 0.06330 0.2920 0.2220 0.2430 0.2340 0.2430 0.3040 0.4440 0.6000 8.340 0.008970 0.03190 0.01550 0.01760 0.06950 0.04110 0.04450 0.02870 0.02950 0.1380 0.09030 0.09750 0.07330 0.07510 0.06250 0.06330 0.2740 0.2200 0.2410 0.2330 0.2420 0.1820 0.2980 0.7000 8.000 0.008970 0.03190 0.01550 0.01760 0.06930 0.04110 0.04450 0.02870 0.02950 0.1360 0.09030 0.09750 0.07330 0.07510 0.06250 0.06330 0.2540 0.2180 0.2370 0.2310 0.2390 0.1080 0.1890 0.8000 7.730 0.008970 0.03190 0.01550 0.01760 0.06910 0.04110 0.04450 0.02870 0.02950 0.1340 0.09020 0.09740 0.07330 0.07510 0.06250 0.06330 0.2330 0.2140 0.2320 0.2280 0.2350 0.06860 0.1150 1.000 7.310 0.008970 0.03180 0.01550 0.01760 0.06850 0.04110 0.04450 0.02870 0.02950 0.1290 0.09010 0.09710 0.07330 0.07510 0.06250 0.06330 0.1910 0.2040 0.2160 0.2170 0.2230 0.04370 0.04010 1.200 6.890 0.008970 0.03180 0.01550 0.01760 0.06780 0.04110 0.04450 0.02870 0.02950 0.1230 0.08980 0.09670 0.07330 0.07510 0.06250 0.06330 0.1500 0.1880 0.1950 0.2000 0.2030 0.04220 0.01630 1.400 6.420 0.008970 0.03170 0.01550 0.01760 0.06690 0.04110 0.04450 0.02870 0.02950 0.1160 0.08930 0.09600 0.07330 0.07500 0.06250 0.06330 0.1140 0.1690 0.1700 0.1790 0.1800 0.04020 0.01060 1.600 5.920 0.008970 0.03160 0.01550 0.01760 0.06600 0.04100 0.04450 0.02870 0.02950 0.1080 0.08860 0.09500 0.07320 0.07500 0.06250 0.06330 0.08510 0.1480 0.1430 0.1550 0.1530 0.03430 0.010000 1.800 5.430 0.008960 0.03150 0.01550 0.01760 0.06490 0.04100 0.04440 0.02870 0.02950 0.1010 0.08760 0.09370 0.07310 0.07480 0.06240 0.06320 0.06290 0.1250 0.1160 0.1310 0.1270 0.02650 0.009900 2.000 4.960 0.008960 0.03140 0.01550 0.01760 0.06370 0.04100 0.04440 0.02870 0.02950 0.09290 0.08640 0.09200 0.07290 0.07460 0.06230 0.06310 0.04750 0.1030 0.09110 0.1070 0.1030 0.01890 0.009270 2.400 4.200 0.008960 0.03120 0.01550 0.01760 0.06110 0.04090 0.04430 0.02870 0.02950 0.07710 0.08300 0.08730 0.07220 0.07380 0.06200 0.06280 0.03180 0.06430 0.05210 0.06780 0.06290 0.008550 0.006730 3.000 3.470 0.008940 0.03080 0.01550 0.01760 0.05660 0.04070 0.04390 0.02870 0.02950 0.05540 0.07550 0.07730 0.07000 0.07140 0.06100 0.06170 0.02780 0.02850 0.02160 0.02990 0.02650 0.003370 0.003040 4.000 2.840 0.008920 0.02990 0.01550 0.01760 0.04790 0.03990 0.04280 0.02870 0.02940 0.02970 0.05800 0.05580 0.06260 0.06310 0.05700 0.05750 0.02420 0.01190 0.01200 0.007310 0.006450 0.002860 0.0007650 5.000 2.390 0.008890 0.02880 0.01550 0.01760 0.03890 0.03850 0.04080 0.02860 0.02930 0.01770 0.03910 0.03470 0.05080 0.05040 0.05030 0.05040 0.01360 0.01130 0.01130 0.004200 0.004130 0.001860 0.0006000 6.000 1.960 0.008850 0.02750 0.01550 0.01750 0.03030 0.03630 0.03790 0.02830 0.02900 0.01440 0.02360 0.01940 0.03740 0.03640 0.04200 0.04180 0.006030 0.009220 0.007930 0.004100 0.004010 0.0008200 0.0005310 7.000 1.580 0.008810 0.02600 0.01540 0.01740 0.02280 0.03350 0.03420 0.02790 0.02850 0.01420 0.01350 0.01090 0.02530 0.02410 0.03350 0.03320 0.003340 0.005850 0.004340 0.003490 0.003290 0.0003820 0.0003530 8.000 1.290 0.008760 0.02440 0.01540 0.01730 0.01680 0.03020 0.02990 0.02710 0.02770 0.01370 0.008320 0.007410 0.01590 0.01490 0.02600 0.02560 0.002940 0.003170 0.002180 0.002510 0.002280 0.0002970 0.0001940 10.00 0.8900 0.008600 0.02100 0.01500 0.01700 0.009200 0.02300 0.02100 0.02500 0.02500 0.009600 0.005900 0.006400 0.005900 0.005400 0.01500 0.01400 0.002600 0.001100 0.001100 0.0009600 0.0008300 0.0002700 6.400E-05 15.00 0.4400 0.008300 0.01300 0.01400 0.01500 0.005400 0.007600 0.005900 0.01600 0.01600 0.001800 0.004000 0.003400 0.002500 0.002500 0.003000 0.002900 0.0004500 0.0008000 0.0006600 0.0002400 0.0002400 4.800E-05 4.300E-05 20.00 0.2500 0.007700 0.006700 0.01200 0.01200 0.004700 0.002300 0.002100 0.008500 0.008100 0.001300 0.001100 0.0007900 0.002000 0.001900 0.0006400 0.0006000 0.0002700 0.0002400 0.0001600 0.0002100 0.0001900 2.700E-05 1.300E-05 30.00 0.09600 0.006500 0.001600 0.007200 0.006500 0.001300 0.001500 0.001700 0.001800 0.001600 0.0004600 0.0003300 0.0004000 0.0005200 0.0004700 3.900E-05 3.500E-05 9.900E-05 5.700E-05 6.600E-05 5.700E-05 5.000E-05 1.000E-05 3.000E-06 40.00 0.04500 0.005200 0.0008000 0.003800 0.003000 0.0002500 0.001100 0.0009800 0.0003800 0.0003200 7.900E-05 0.0002700 0.0002500 0.0001100 9.500E-05 3.600E-06 3.100E-06 1.600E-05 4.700E-05 4.300E-05 1.300E-05 1.000E-05 1.600E-06 2.500E-06 60.00 0.01400 0.003100 0.0007000 0.0009700 0.0005800 0.0001600 0.0003000 0.0001900 2.700E-05 2.000E-05 4.100E-05 7.800E-05 5.200E-05 7.700E-06 5.700E-06 8.600E-08 6.800E-08 8.100E-06 1.400E-05 8.800E-06 8.500E-07 6.000E-07 8.100E-07 7.400E-07 100.0 0.003100 0.0009800 0.0002400 9.700E-05 3.600E-05 5.900E-05 2.800E-05 1.100E-05 5.700E-07 3.200E-07 1.500E-05 7.300E-06 2.900E-06 1.600E-07 8.800E-08 5.600E-10 3.700E-10 3.000E-06 1.300E-06 4.900E-07 1.700E-08 9.400E-09 3.000E-07 6.800E-08 #S 82 Pb #N 25 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 #UBIND 8.800E+04 1.586E+04 1.520E+04 1.304E+04 3851. 3554. 3066. 2586. 2484. 894.0 764.0 645.0 434.0 412.0 141.0 136.0 148.0 105.0 86.00 20.00 18.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 0.000 11.30 0.008820 0.03140 0.01530 0.01740 0.06890 0.04030 0.04380 0.02830 0.02900 0.1410 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3250 0.2150 0.2370 0.2210 0.2270 1.040 0.8080 0.05000 11.20 0.008820 0.03140 0.01530 0.01740 0.06890 0.04030 0.04380 0.02830 0.02900 0.1410 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3250 0.2150 0.2370 0.2210 0.2270 1.030 0.8080 0.1000 11.20 0.008820 0.03140 0.01530 0.01740 0.06890 0.04030 0.04380 0.02830 0.02900 0.1400 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3230 0.2150 0.2370 0.2210 0.2270 0.9920 0.8060 0.1500 11.00 0.008820 0.03140 0.01530 0.01740 0.06890 0.04030 0.04380 0.02830 0.02900 0.1400 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3210 0.2150 0.2370 0.2210 0.2270 0.9340 0.8000 0.2000 10.80 0.008820 0.03140 0.01530 0.01740 0.06890 0.04030 0.04380 0.02830 0.02900 0.1400 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3180 0.2150 0.2370 0.2210 0.2270 0.8590 0.7850 0.3000 10.30 0.008820 0.03140 0.01530 0.01740 0.06880 0.04030 0.04380 0.02830 0.02900 0.1390 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3100 0.2150 0.2360 0.2210 0.2270 0.6790 0.7190 0.4000 9.700 0.008820 0.03140 0.01530 0.01740 0.06870 0.04030 0.04380 0.02830 0.02900 0.1380 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.2980 0.2140 0.2360 0.2210 0.2270 0.4940 0.6080 0.5000 9.070 0.008820 0.03140 0.01530 0.01740 0.06850 0.04030 0.04380 0.02830 0.02900 0.1370 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.2840 0.2140 0.2350 0.2210 0.2270 0.3340 0.4740 0.6000 8.520 0.008820 0.03140 0.01530 0.01740 0.06840 0.04030 0.04380 0.02830 0.02900 0.1350 0.08840 0.09560 0.07180 0.07350 0.06060 0.06140 0.2670 0.2120 0.2320 0.2200 0.2260 0.2140 0.3450 0.7000 8.070 0.008820 0.03140 0.01530 0.01740 0.06820 0.04030 0.04380 0.02830 0.02900 0.1330 0.08840 0.09560 0.07180 0.07350 0.06060 0.06140 0.2490 0.2100 0.2290 0.2190 0.2250 0.1330 0.2370 0.8000 7.720 0.008820 0.03130 0.01530 0.01740 0.06790 0.04030 0.04380 0.02830 0.02900 0.1310 0.08830 0.09550 0.07180 0.07350 0.06060 0.06140 0.2300 0.2070 0.2250 0.2160 0.2220 0.08480 0.1560 1.000 7.210 0.008820 0.03130 0.01530 0.01740 0.06740 0.04030 0.04380 0.02830 0.02900 0.1260 0.08820 0.09530 0.07180 0.07350 0.06060 0.06140 0.1910 0.1980 0.2110 0.2080 0.2130 0.04850 0.06230 1.200 6.800 0.008820 0.03120 0.01530 0.01740 0.06670 0.04030 0.04380 0.02830 0.02900 0.1210 0.08790 0.09490 0.07170 0.07350 0.06060 0.06140 0.1520 0.1850 0.1930 0.1960 0.1990 0.04420 0.02540 1.400 6.380 0.008820 0.03120 0.01530 0.01740 0.06590 0.04030 0.04380 0.02830 0.02900 0.1140 0.08750 0.09430 0.07170 0.07350 0.06060 0.06140 0.1170 0.1680 0.1700 0.1780 0.1800 0.04340 0.01420 1.600 5.930 0.008810 0.03110 0.01530 0.01740 0.06500 0.04030 0.04380 0.02830 0.02900 0.1070 0.08690 0.09340 0.07170 0.07340 0.06060 0.06140 0.08860 0.1480 0.1450 0.1580 0.1580 0.03890 0.01190 1.800 5.470 0.008810 0.03100 0.01530 0.01740 0.06400 0.04030 0.04380 0.02830 0.02900 0.1000 0.08600 0.09210 0.07160 0.07330 0.06060 0.06140 0.06620 0.1270 0.1200 0.1360 0.1340 0.03150 0.01180 2.000 5.030 0.008810 0.03090 0.01530 0.01740 0.06280 0.04030 0.04370 0.02830 0.02900 0.09250 0.08490 0.09050 0.07140 0.07310 0.06050 0.06130 0.05010 0.1060 0.09560 0.1140 0.1110 0.02360 0.01150 2.400 4.270 0.008800 0.03070 0.01530 0.01740 0.06030 0.04020 0.04360 0.02830 0.02900 0.07730 0.08180 0.08620 0.07080 0.07240 0.06030 0.06110 0.03290 0.06880 0.05650 0.07490 0.07110 0.01130 0.009040 3.000 3.500 0.008790 0.03030 0.01530 0.01740 0.05600 0.04000 0.04330 0.02830 0.02900 0.05620 0.07480 0.07690 0.06890 0.07020 0.05950 0.06020 0.02760 0.03160 0.02390 0.03500 0.03180 0.004120 0.004450 4.000 2.840 0.008770 0.02940 0.01530 0.01740 0.04770 0.03920 0.04220 0.02820 0.02900 0.03060 0.05840 0.05640 0.06210 0.06270 0.05610 0.05660 0.02480 0.01240 0.01220 0.008750 0.007780 0.003250 0.001090 5.000 2.400 0.008740 0.02840 0.01520 0.01730 0.03900 0.03790 0.04030 0.02810 0.02890 0.01810 0.04010 0.03580 0.05110 0.05080 0.05010 0.05020 0.01480 0.01140 0.01160 0.004510 0.004460 0.002270 0.0007570 6.000 1.980 0.008710 0.02710 0.01520 0.01730 0.03060 0.03590 0.03760 0.02790 0.02860 0.01440 0.02460 0.02040 0.03830 0.03730 0.04240 0.04230 0.006730 0.009730 0.008540 0.004350 0.004340 0.001050 0.0006970 7.000 1.610 0.008660 0.02570 0.01520 0.01720 0.02330 0.03330 0.03410 0.02750 0.02810 0.01400 0.01430 0.01150 0.02630 0.02510 0.03430 0.03400 0.003550 0.006440 0.004850 0.003830 0.003690 0.0004760 0.0004890 8.000 1.310 0.008620 0.02420 0.01510 0.01710 0.01720 0.03010 0.03000 0.02680 0.02740 0.01360 0.008730 0.007600 0.01690 0.01580 0.02690 0.02650 0.002980 0.003600 0.002470 0.002850 0.002640 0.0003420 0.0002790 10.00 0.9100 0.008500 0.02100 0.01500 0.01700 0.009500 0.02300 0.02100 0.02500 0.02500 0.009900 0.005800 0.006400 0.006300 0.005800 0.01500 0.01500 0.002700 0.001200 0.001100 0.001100 0.001000 0.0003100 8.800E-05 15.00 0.4500 0.008100 0.01300 0.01400 0.01500 0.005300 0.008000 0.006200 0.01600 0.01600 0.001900 0.004200 0.003500 0.002400 0.002500 0.003300 0.003200 0.0005000 0.0008500 0.0007200 0.0002600 0.0002600 6.000E-05 5.700E-05 20.00 0.2500 0.007600 0.006900 0.01200 0.01200 0.004700 0.002400 0.002100 0.008800 0.008300 0.001300 0.001300 0.0008600 0.002000 0.002000 0.0007200 0.0006700 0.0002700 0.0002800 0.0001800 0.0002300 0.0002200 3.000E-05 1.900E-05 30.00 0.09800 0.006500 0.001600 0.007300 0.006700 0.001400 0.001500 0.001700 0.001900 0.001700 0.0005000 0.0003300 0.0004000 0.0005600 0.0005000 4.500E-05 4.000E-05 0.0001100 5.800E-05 6.900E-05 6.700E-05 5.900E-05 1.200E-05 3.900E-06 40.00 0.04600 0.005200 0.0007900 0.003900 0.003100 0.0002700 0.001100 0.001000 0.0004200 0.0003500 8.600E-05 0.0002700 0.0002600 0.0001200 1.000E-04 4.300E-06 3.700E-06 1.900E-05 5.000E-05 4.600E-05 1.500E-05 1.200E-05 2.100E-06 3.300E-06 60.00 0.01500 0.003100 0.0007000 0.001000 0.0006100 0.0001600 0.0003200 0.0002100 2.900E-05 2.200E-05 4.100E-05 8.300E-05 5.500E-05 8.600E-06 6.300E-06 1.000E-07 8.100E-08 8.300E-06 1.500E-05 9.700E-06 1.000E-06 7.400E-07 9.300E-07 1.000E-06 100.0 0.003200 0.001000 0.0002400 0.0001100 3.800E-05 6.100E-05 3.100E-05 1.200E-05 6.400E-07 3.600E-07 1.600E-05 8.000E-06 3.200E-06 1.800E-07 1.000E-07 6.800E-10 4.400E-10 3.200E-06 1.500E-06 5.500E-07 2.100E-08 1.200E-08 3.600E-07 9.800E-08 #S 83 Bi #N 26 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 1 #UBIND 9.053E+04 1.639E+04 1.571E+04 1.342E+04 3999. 3696. 3177. 2687. 2580. 939.0 805.0 679.0 464.0 441.0 162.0 157.0 160.0 117.0 93.00 27.00 25.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 0.000 11.50 0.008670 0.03090 0.01500 0.01710 0.06780 0.03960 0.04320 0.02780 0.02860 0.1380 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3140 0.2070 0.2280 0.2080 0.2140 0.9550 0.7130 0.8320 0.05000 11.40 0.008670 0.03090 0.01500 0.01710 0.06780 0.03960 0.04320 0.02780 0.02860 0.1380 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3130 0.2070 0.2280 0.2080 0.2140 0.9450 0.7130 0.8320 0.1000 11.40 0.008670 0.03090 0.01500 0.01710 0.06780 0.03960 0.04320 0.02780 0.02860 0.1380 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3120 0.2070 0.2280 0.2080 0.2140 0.9160 0.7120 0.8300 0.1500 11.30 0.008670 0.03090 0.01500 0.01710 0.06770 0.03960 0.04320 0.02780 0.02860 0.1370 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3100 0.2070 0.2280 0.2080 0.2140 0.8700 0.7090 0.8230 0.2000 11.10 0.008670 0.03090 0.01500 0.01710 0.06770 0.03960 0.04320 0.02780 0.02860 0.1370 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3070 0.2070 0.2280 0.2080 0.2140 0.8090 0.7000 0.8070 0.3000 10.60 0.008670 0.03090 0.01500 0.01710 0.06760 0.03960 0.04320 0.02780 0.02860 0.1360 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3000 0.2070 0.2280 0.2080 0.2140 0.6610 0.6600 0.7330 0.4000 10.00 0.008670 0.03090 0.01500 0.01710 0.06750 0.03960 0.04320 0.02780 0.02860 0.1350 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.2890 0.2070 0.2270 0.2080 0.2140 0.5010 0.5860 0.6120 0.5000 9.350 0.008670 0.03090 0.01500 0.01710 0.06740 0.03960 0.04320 0.02780 0.02860 0.1340 0.08660 0.09380 0.07030 0.07200 0.05890 0.05970 0.2760 0.2060 0.2260 0.2080 0.2140 0.3560 0.4870 0.4680 0.6000 8.720 0.008670 0.03090 0.01500 0.01710 0.06720 0.03960 0.04320 0.02780 0.02860 0.1330 0.08660 0.09380 0.07030 0.07200 0.05890 0.05970 0.2610 0.2050 0.2240 0.2080 0.2140 0.2400 0.3810 0.3330 0.7000 8.180 0.008670 0.03080 0.01500 0.01710 0.06700 0.03960 0.04320 0.02780 0.02860 0.1310 0.08650 0.09380 0.07030 0.07200 0.05890 0.05970 0.2450 0.2030 0.2220 0.2070 0.2130 0.1560 0.2820 0.2230 0.8000 7.750 0.008670 0.03080 0.01500 0.01710 0.06680 0.03960 0.04320 0.02780 0.02860 0.1290 0.08650 0.09370 0.07030 0.07200 0.05890 0.05970 0.2270 0.2010 0.2180 0.2050 0.2110 0.1020 0.1990 0.1420 1.000 7.140 0.008670 0.03080 0.01500 0.01710 0.06630 0.03960 0.04320 0.02780 0.02860 0.1240 0.08640 0.09350 0.07030 0.07200 0.05890 0.05970 0.1900 0.1930 0.2060 0.2000 0.2050 0.05440 0.09000 0.05380 1.200 6.710 0.008670 0.03070 0.01500 0.01710 0.06560 0.03960 0.04320 0.02780 0.02860 0.1190 0.08610 0.09310 0.07020 0.07200 0.05890 0.05970 0.1530 0.1810 0.1900 0.1900 0.1940 0.04580 0.03890 0.02240 1.400 6.320 0.008660 0.03070 0.01500 0.01710 0.06490 0.03960 0.04310 0.02780 0.02860 0.1130 0.08570 0.09260 0.07020 0.07200 0.05890 0.05970 0.1200 0.1660 0.1700 0.1760 0.1780 0.04540 0.01970 0.01430 1.600 5.920 0.008660 0.03060 0.01500 0.01710 0.06400 0.03960 0.04310 0.02780 0.02860 0.1060 0.08520 0.09170 0.07020 0.07190 0.05890 0.05960 0.09190 0.1490 0.1470 0.1590 0.1590 0.04250 0.01450 0.01330 1.800 5.500 0.008660 0.03050 0.01500 0.01710 0.06300 0.03960 0.04310 0.02780 0.02860 0.09930 0.08440 0.09060 0.07010 0.07180 0.05890 0.05960 0.06950 0.1290 0.1230 0.1390 0.1390 0.03610 0.01380 0.01310 2.000 5.080 0.008660 0.03040 0.01500 0.01710 0.06200 0.03960 0.04310 0.02780 0.02860 0.09210 0.08340 0.08910 0.06990 0.07160 0.05880 0.05960 0.05280 0.1090 0.09970 0.1200 0.1170 0.02820 0.01370 0.01230 2.400 4.330 0.008650 0.03020 0.01500 0.01710 0.05960 0.03950 0.04290 0.02780 0.02860 0.07750 0.08050 0.08510 0.06940 0.07100 0.05860 0.05940 0.03410 0.07290 0.06090 0.08190 0.07830 0.01430 0.01160 0.008800 3.000 3.530 0.008640 0.02980 0.01500 0.01710 0.05540 0.03930 0.04260 0.02780 0.02860 0.05690 0.07410 0.07640 0.06770 0.06910 0.05800 0.05870 0.02750 0.03470 0.02640 0.04040 0.03700 0.005060 0.006260 0.003770 4.000 2.830 0.008620 0.02900 0.01500 0.01710 0.04750 0.03860 0.04170 0.02780 0.02860 0.03140 0.05860 0.05690 0.06150 0.06220 0.05510 0.05560 0.02530 0.01290 0.01250 0.01050 0.009300 0.003590 0.001540 0.0009810 5.000 2.400 0.008590 0.02800 0.01500 0.01710 0.03900 0.03740 0.03990 0.02770 0.02850 0.01850 0.04100 0.03680 0.05130 0.05110 0.04980 0.05000 0.01590 0.01150 0.01190 0.004870 0.004780 0.002690 0.0009370 0.0008390 6.000 2.000 0.008560 0.02680 0.01490 0.01700 0.03090 0.03550 0.03730 0.02750 0.02820 0.01430 0.02570 0.02130 0.03900 0.03810 0.04270 0.04260 0.007480 0.01020 0.009140 0.004600 0.004610 0.001320 0.0008890 0.0006960 7.000 1.640 0.008520 0.02550 0.01490 0.01700 0.02360 0.03310 0.03400 0.02710 0.02780 0.01380 0.01510 0.01210 0.02730 0.02610 0.03500 0.03480 0.003820 0.007020 0.005390 0.004180 0.004050 0.0005870 0.0006560 0.0004280 8.000 1.340 0.008470 0.02400 0.01480 0.01690 0.01770 0.03010 0.03010 0.02650 0.02710 0.01360 0.009180 0.007820 0.01780 0.01670 0.02770 0.02740 0.003020 0.004050 0.002790 0.003210 0.003000 0.0003890 0.0003890 0.0002210 10.00 0.9300 0.008400 0.02100 0.01480 0.01690 0.009800 0.02300 0.02200 0.02500 0.02500 0.010000 0.005800 0.006300 0.006800 0.006200 0.01600 0.01600 0.002800 0.001300 0.001200 0.001300 0.001200 0.0003600 0.0001200 8.200E-05 15.00 0.4600 0.008000 0.01300 0.01400 0.01500 0.005200 0.008400 0.006500 0.01700 0.01600 0.002000 0.004300 0.003700 0.002400 0.002500 0.003600 0.003500 0.0005600 0.0008900 0.0007800 0.0002800 0.0002800 7.500E-05 7.400E-05 5.500E-05 20.00 0.2600 0.007500 0.007100 0.01200 0.01200 0.004700 0.002500 0.002200 0.009000 0.008600 0.001300 0.001400 0.0009300 0.002100 0.002000 0.0008000 0.0007500 0.0002700 0.0003100 0.0002000 0.0002500 0.0002400 3.400E-05 2.600E-05 1.400E-05 30.00 0.1000 0.006400 0.001700 0.007400 0.006800 0.001500 0.001500 0.001700 0.002000 0.001800 0.0005300 0.0003300 0.0004000 0.0005900 0.0005300 5.100E-05 4.600E-05 0.0001200 6.000E-05 7.100E-05 7.700E-05 6.800E-05 1.500E-05 4.900E-06 4.900E-06 40.00 0.04700 0.005200 0.0007900 0.004000 0.003200 0.0002900 0.001100 0.001000 0.0004500 0.0003700 9.500E-05 0.0002800 0.0002700 0.0001400 0.0001100 5.000E-06 4.300E-06 2.100E-05 5.200E-05 4.900E-05 1.800E-05 1.500E-05 2.600E-06 4.300E-06 3.400E-06 60.00 0.01500 0.003100 0.0007000 0.001100 0.0006400 0.0001600 0.0003400 0.0002200 3.200E-05 2.400E-05 4.100E-05 8.900E-05 5.900E-05 9.600E-06 7.100E-06 1.200E-07 9.600E-08 8.400E-06 1.700E-05 1.100E-05 1.300E-06 9.000E-07 1.000E-06 1.400E-06 7.400E-07 100.0 0.003300 0.001000 0.0002500 0.0001100 4.100E-05 6.400E-05 3.400E-05 1.300E-05 7.100E-07 4.000E-07 1.700E-05 8.800E-06 3.500E-06 2.000E-07 1.100E-07 8.100E-10 5.300E-10 3.500E-06 1.700E-06 6.300E-07 2.600E-08 1.400E-08 4.300E-07 1.400E-07 4.300E-08 #S 84 Po #N 26 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 2 #UBIND 9.311E+04 1.694E+04 1.624E+04 1.381E+04 4149. 3854. 3302. 2798. 2683. 995.0 851.0 705.0 500.0 473.0 184.0 184.0 177.0 132.0 104.0 31.00 31.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 0.000 11.60 0.008520 0.03040 0.01470 0.01690 0.06660 0.03900 0.04250 0.02740 0.02820 0.1350 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.3030 0.2000 0.2200 0.1970 0.2030 0.8870 0.6450 0.7530 0.05000 11.60 0.008520 0.03040 0.01470 0.01690 0.06660 0.03900 0.04250 0.02740 0.02820 0.1350 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.3030 0.2000 0.2200 0.1970 0.2030 0.8780 0.6450 0.7530 0.1000 11.50 0.008520 0.03040 0.01470 0.01690 0.06660 0.03900 0.04250 0.02740 0.02820 0.1350 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.3020 0.2000 0.2200 0.1970 0.2030 0.8550 0.6450 0.7520 0.1500 11.50 0.008520 0.03040 0.01470 0.01690 0.06660 0.03900 0.04250 0.02740 0.02820 0.1350 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.3000 0.2000 0.2200 0.1970 0.2030 0.8170 0.6430 0.7470 0.2000 11.30 0.008520 0.03040 0.01470 0.01690 0.06660 0.03900 0.04250 0.02740 0.02820 0.1340 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.2970 0.2000 0.2200 0.1970 0.2030 0.7670 0.6370 0.7360 0.3000 10.90 0.008520 0.03040 0.01470 0.01690 0.06650 0.03900 0.04250 0.02740 0.02820 0.1340 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.2900 0.2000 0.2200 0.1970 0.2030 0.6410 0.6110 0.6870 0.4000 10.30 0.008520 0.03040 0.01470 0.01690 0.06640 0.03900 0.04250 0.02740 0.02820 0.1330 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.2810 0.1990 0.2190 0.1970 0.2030 0.5020 0.5590 0.5980 0.5000 9.640 0.008520 0.03040 0.01470 0.01690 0.06630 0.03900 0.04250 0.02740 0.02820 0.1320 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.2690 0.1990 0.2180 0.1970 0.2030 0.3710 0.4850 0.4830 0.6000 8.960 0.008520 0.03040 0.01470 0.01690 0.06610 0.03900 0.04250 0.02740 0.02820 0.1300 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.2550 0.1980 0.2170 0.1970 0.2030 0.2600 0.3980 0.3660 0.7000 8.360 0.008520 0.03030 0.01470 0.01690 0.06590 0.03900 0.04250 0.02740 0.02820 0.1290 0.08480 0.09200 0.06880 0.07060 0.05730 0.05800 0.2400 0.1960 0.2150 0.1960 0.2020 0.1760 0.3100 0.2610 0.8000 7.850 0.008520 0.03030 0.01470 0.01690 0.06570 0.03900 0.04250 0.02740 0.02820 0.1270 0.08470 0.09200 0.06880 0.07060 0.05730 0.05800 0.2240 0.1940 0.2110 0.1950 0.2010 0.1180 0.2320 0.1780 1.000 7.120 0.008520 0.03030 0.01470 0.01690 0.06520 0.03900 0.04250 0.02740 0.02820 0.1220 0.08460 0.09180 0.06880 0.07050 0.05730 0.05800 0.1890 0.1880 0.2010 0.1910 0.1960 0.06090 0.1160 0.07470 1.200 6.640 0.008520 0.03020 0.01470 0.01690 0.06460 0.03890 0.04250 0.02740 0.02820 0.1170 0.08440 0.09140 0.06880 0.07050 0.05730 0.05800 0.1550 0.1780 0.1870 0.1840 0.1880 0.04730 0.05380 0.03160 1.400 6.260 0.008520 0.03020 0.01470 0.01690 0.06390 0.03890 0.04250 0.02740 0.02820 0.1110 0.08400 0.09090 0.06880 0.07050 0.05730 0.05800 0.1230 0.1640 0.1690 0.1730 0.1750 0.04640 0.02660 0.01780 1.600 5.890 0.008510 0.03010 0.01470 0.01690 0.06300 0.03890 0.04250 0.02740 0.02820 0.1050 0.08350 0.09010 0.06870 0.07050 0.05730 0.05800 0.09500 0.1480 0.1470 0.1580 0.1590 0.04480 0.01730 0.01500 1.800 5.510 0.008510 0.03000 0.01470 0.01690 0.06210 0.03890 0.04250 0.02740 0.02820 0.09850 0.08280 0.08910 0.06860 0.07040 0.05720 0.05800 0.07260 0.1300 0.1250 0.1420 0.1410 0.03970 0.01530 0.01480 2.000 5.120 0.008510 0.02990 0.01470 0.01690 0.06110 0.03890 0.04240 0.02740 0.02820 0.09160 0.08190 0.08780 0.06850 0.07020 0.05720 0.05800 0.05550 0.1120 0.1030 0.1240 0.1220 0.03220 0.01520 0.01440 2.400 4.390 0.008510 0.02970 0.01470 0.01690 0.05880 0.03880 0.04230 0.02740 0.02820 0.07750 0.07920 0.08400 0.06800 0.06970 0.05710 0.05780 0.03550 0.07670 0.06510 0.08800 0.08460 0.01760 0.01380 0.01110 3.000 3.570 0.008500 0.02940 0.01470 0.01690 0.05480 0.03860 0.04200 0.02740 0.02820 0.05760 0.07330 0.07590 0.06650 0.06800 0.05650 0.05720 0.02750 0.03800 0.02910 0.04570 0.04220 0.006170 0.008150 0.005190 4.000 2.830 0.008480 0.02860 0.01470 0.01690 0.04720 0.03800 0.04110 0.02740 0.02810 0.03230 0.05870 0.05730 0.06100 0.06170 0.05410 0.05460 0.02570 0.01370 0.01280 0.01250 0.01100 0.003860 0.002090 0.001280 5.000 2.400 0.008450 0.02760 0.01470 0.01690 0.03910 0.03690 0.03950 0.02730 0.02800 0.01890 0.04180 0.03770 0.05140 0.05130 0.04940 0.04970 0.01700 0.01150 0.01220 0.005290 0.005120 0.003100 0.001100 0.001000 6.000 2.020 0.008420 0.02650 0.01470 0.01680 0.03110 0.03520 0.03700 0.02710 0.02780 0.01430 0.02670 0.02220 0.03970 0.03890 0.04290 0.04290 0.008290 0.01050 0.009710 0.004810 0.004840 0.001610 0.001060 0.0008720 7.000 1.660 0.008380 0.02520 0.01460 0.01670 0.02400 0.03280 0.03380 0.02670 0.02740 0.01360 0.01600 0.01270 0.02820 0.02710 0.03560 0.03540 0.004130 0.007580 0.005950 0.004490 0.004390 0.0007140 0.0008230 0.0005620 8.000 1.360 0.008340 0.02380 0.01460 0.01660 0.01810 0.03000 0.03010 0.02620 0.02680 0.01350 0.009660 0.008080 0.01870 0.01760 0.02850 0.02820 0.003080 0.004520 0.003140 0.003560 0.003350 0.0004370 0.0005090 0.0002990 10.00 0.9400 0.008200 0.02100 0.01400 0.01600 0.010000 0.02300 0.02200 0.02400 0.02500 0.010000 0.005800 0.006200 0.007300 0.006700 0.01700 0.01700 0.002900 0.001500 0.001200 0.001600 0.001400 0.0004000 0.0001600 1.000E-04 15.00 0.4700 0.007900 0.01300 0.01300 0.01500 0.005100 0.008800 0.006800 0.01700 0.01700 0.002100 0.004400 0.003900 0.002400 0.002500 0.003900 0.003800 0.0006300 0.0009400 0.0008400 0.0003000 0.0003000 9.100E-05 9.000E-05 6.900E-05 20.00 0.2600 0.007400 0.007300 0.01200 0.01200 0.004700 0.002700 0.002200 0.009200 0.008800 0.001300 0.001500 0.001000 0.002100 0.002100 0.0008900 0.0008300 0.0002800 0.0003500 0.0002300 0.0002700 0.0002600 3.700E-05 3.500E-05 1.900E-05 30.00 0.1000 0.006400 0.001800 0.007500 0.006900 0.001600 0.001400 0.001700 0.002100 0.001900 0.0005700 0.0003200 0.0004000 0.0006300 0.0005700 5.800E-05 5.300E-05 0.0001300 6.100E-05 7.300E-05 8.800E-05 7.800E-05 1.800E-05 5.800E-06 5.900E-06 40.00 0.04900 0.005200 0.0007900 0.004100 0.003300 0.0003100 0.001100 0.001100 0.0004800 0.0004000 1.000E-04 0.0002800 0.0002800 0.0001500 0.0001200 5.800E-06 5.000E-06 2.400E-05 5.400E-05 5.300E-05 2.100E-05 1.700E-05 3.200E-06 5.200E-06 4.300E-06 60.00 0.01600 0.003200 0.0006900 0.001100 0.0006700 0.0001600 0.0003500 0.0002300 3.500E-05 2.600E-05 4.100E-05 9.400E-05 6.300E-05 1.100E-05 7.900E-06 1.400E-07 1.100E-07 8.600E-06 1.800E-05 1.200E-05 1.500E-06 1.100E-06 1.100E-06 1.800E-06 9.700E-07 100.0 0.003500 0.001100 0.0002600 0.0001200 4.400E-05 6.700E-05 3.700E-05 1.400E-05 8.000E-07 4.400E-07 1.800E-05 9.700E-06 3.800E-06 2.300E-07 1.300E-07 9.700E-10 6.300E-10 3.700E-06 1.900E-06 7.100E-07 3.200E-08 1.700E-08 5.000E-07 1.800E-07 5.800E-08 #S 85 At #N 26 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 3 #UBIND 9.573E+04 1.749E+04 1.678E+04 1.421E+04 4317. 4008. 3426. 2909. 2787. 1042. 886.0 740.0 533.0 507.0 210.0 210.0 195.0 148.0 115.0 40.00 40.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 0.000 11.70 0.008380 0.02990 0.01450 0.01670 0.06550 0.03830 0.04190 0.02700 0.02780 0.1320 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2930 0.1930 0.2120 0.1880 0.1940 0.8300 0.5930 0.6920 0.05000 11.70 0.008380 0.02990 0.01450 0.01670 0.06550 0.03830 0.04190 0.02700 0.02780 0.1320 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2930 0.1930 0.2120 0.1880 0.1940 0.8230 0.5930 0.6920 0.1000 11.70 0.008380 0.02990 0.01450 0.01670 0.06550 0.03830 0.04190 0.02700 0.02780 0.1320 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2920 0.1930 0.2120 0.1880 0.1940 0.8030 0.5930 0.6920 0.1500 11.60 0.008380 0.02990 0.01450 0.01670 0.06550 0.03830 0.04190 0.02700 0.02780 0.1320 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2900 0.1930 0.2120 0.1880 0.1940 0.7710 0.5910 0.6890 0.2000 11.50 0.008380 0.02990 0.01450 0.01670 0.06550 0.03830 0.04190 0.02700 0.02780 0.1320 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2880 0.1930 0.2120 0.1880 0.1940 0.7290 0.5880 0.6810 0.3000 11.10 0.008380 0.02990 0.01450 0.01670 0.06540 0.03830 0.04190 0.02700 0.02780 0.1310 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2810 0.1930 0.2120 0.1880 0.1940 0.6220 0.5690 0.6460 0.4000 10.60 0.008380 0.02990 0.01450 0.01670 0.06530 0.03830 0.04190 0.02700 0.02780 0.1300 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2730 0.1930 0.2120 0.1880 0.1940 0.5000 0.5320 0.5780 0.5000 9.910 0.008380 0.02990 0.01450 0.01670 0.06520 0.03830 0.04190 0.02700 0.02780 0.1290 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2620 0.1920 0.2110 0.1880 0.1940 0.3800 0.4740 0.4860 0.6000 9.220 0.008380 0.02990 0.01450 0.01670 0.06500 0.03830 0.04190 0.02700 0.02780 0.1280 0.08310 0.09030 0.06740 0.06910 0.05580 0.05650 0.2490 0.1910 0.2100 0.1870 0.1930 0.2760 0.4040 0.3850 0.7000 8.560 0.008380 0.02980 0.01450 0.01670 0.06480 0.03830 0.04190 0.02700 0.02780 0.1260 0.08310 0.09030 0.06740 0.06910 0.05580 0.05650 0.2350 0.1900 0.2080 0.1870 0.1930 0.1930 0.3280 0.2890 0.8000 8.000 0.008380 0.02980 0.01450 0.01670 0.06460 0.03830 0.04190 0.02700 0.02780 0.1240 0.08300 0.09030 0.06740 0.06910 0.05580 0.05650 0.2200 0.1880 0.2050 0.1860 0.1920 0.1320 0.2560 0.2070 1.000 7.150 0.008370 0.02980 0.01450 0.01670 0.06420 0.03830 0.04190 0.02700 0.02780 0.1200 0.08290 0.09010 0.06740 0.06910 0.05580 0.05650 0.1880 0.1830 0.1960 0.1830 0.1880 0.06800 0.1400 0.09540 1.200 6.600 0.008370 0.02970 0.01450 0.01670 0.06360 0.03830 0.04190 0.02700 0.02780 0.1150 0.08270 0.08980 0.06740 0.06910 0.05580 0.05650 0.1550 0.1740 0.1840 0.1780 0.1820 0.04900 0.06950 0.04210 1.400 6.200 0.008370 0.02970 0.01450 0.01670 0.06290 0.03830 0.04190 0.02700 0.02780 0.1100 0.08240 0.08930 0.06740 0.06910 0.05580 0.05650 0.1250 0.1620 0.1670 0.1690 0.1720 0.04680 0.03470 0.02200 1.600 5.850 0.008370 0.02960 0.01450 0.01670 0.06210 0.03830 0.04190 0.02700 0.02780 0.1040 0.08190 0.08860 0.06730 0.06910 0.05570 0.05650 0.09780 0.1470 0.1480 0.1570 0.1580 0.04610 0.02090 0.01660 1.800 5.500 0.008370 0.02950 0.01450 0.01670 0.06120 0.03820 0.04180 0.02700 0.02780 0.09770 0.08130 0.08760 0.06730 0.06900 0.05570 0.05650 0.07550 0.1310 0.1270 0.1420 0.1420 0.04230 0.01680 0.01600 2.000 5.140 0.008370 0.02950 0.01450 0.01670 0.06020 0.03820 0.04180 0.02700 0.02780 0.09110 0.08040 0.08640 0.06720 0.06880 0.05570 0.05640 0.05820 0.1140 0.1060 0.1260 0.1250 0.03570 0.01630 0.01580 2.400 4.440 0.008360 0.02930 0.01450 0.01670 0.05810 0.03820 0.04170 0.02700 0.02780 0.07760 0.07800 0.08290 0.06670 0.06840 0.05560 0.05630 0.03700 0.08020 0.06900 0.09300 0.08990 0.02090 0.01550 0.01320 3.000 3.610 0.008350 0.02890 0.01450 0.01670 0.05430 0.03800 0.04140 0.02700 0.02780 0.05820 0.07250 0.07530 0.06540 0.06680 0.05520 0.05590 0.02750 0.04120 0.03190 0.05090 0.04720 0.007480 0.01010 0.006710 4.000 2.830 0.008330 0.02820 0.01450 0.01670 0.04700 0.03740 0.04060 0.02700 0.02770 0.03310 0.05880 0.05770 0.06030 0.06110 0.05310 0.05370 0.02600 0.01450 0.01320 0.01470 0.01300 0.004090 0.002750 0.001620 5.000 2.410 0.008310 0.02720 0.01450 0.01660 0.03910 0.03640 0.03900 0.02690 0.02760 0.01940 0.04260 0.03860 0.05150 0.05150 0.04900 0.04920 0.01800 0.01160 0.01240 0.005770 0.005520 0.003470 0.001270 0.001150 6.000 2.030 0.008280 0.02620 0.01440 0.01660 0.03140 0.03470 0.03670 0.02670 0.02740 0.01430 0.02770 0.02320 0.04030 0.03960 0.04300 0.04300 0.009150 0.01090 0.01020 0.004980 0.005030 0.001910 0.001210 0.001040 7.000 1.680 0.008240 0.02490 0.01440 0.01650 0.02440 0.03260 0.03370 0.02640 0.02710 0.01340 0.01680 0.01330 0.02910 0.02800 0.03610 0.03590 0.004500 0.008120 0.006530 0.004760 0.004680 0.0008600 0.0009880 0.0007000 8.000 1.390 0.008200 0.02360 0.01430 0.01640 0.01850 0.02990 0.03010 0.02590 0.02650 0.01330 0.01020 0.008370 0.01960 0.01850 0.02920 0.02890 0.003160 0.005020 0.003530 0.003890 0.003690 0.0004910 0.0006390 0.0003850 10.00 0.9600 0.008100 0.02100 0.01400 0.01600 0.010000 0.02400 0.02200 0.02400 0.02500 0.01100 0.005800 0.006200 0.007800 0.007100 0.01800 0.01700 0.002900 0.001600 0.001300 0.001800 0.001600 0.0004300 0.0002000 0.0001300 15.00 0.4700 0.007800 0.01300 0.01300 0.01500 0.005100 0.009100 0.007100 0.01700 0.01700 0.002200 0.004500 0.004000 0.002400 0.002500 0.004300 0.004100 0.0007100 0.0009800 0.0008900 0.0003200 0.0003200 0.0001100 0.0001100 8.400E-05 20.00 0.2700 0.007400 0.007500 0.01200 0.01200 0.004700 0.002800 0.002300 0.009500 0.009100 0.001300 0.001600 0.001100 0.002100 0.002100 0.0009900 0.0009200 0.0002800 0.0003900 0.0002500 0.0002900 0.0002800 4.000E-05 4.300E-05 2.400E-05 30.00 0.1100 0.006300 0.001900 0.007600 0.006900 0.001700 0.001400 0.001700 0.002300 0.002000 0.0006100 0.0003200 0.0004000 0.0006700 0.0006100 6.700E-05 6.000E-05 0.0001400 6.300E-05 7.500E-05 9.900E-05 8.800E-05 2.100E-05 6.700E-06 6.900E-06 40.00 0.05000 0.005200 0.0007900 0.004300 0.003400 0.0003300 0.001100 0.001100 0.0005200 0.0004300 0.0001200 0.0002900 0.0002900 0.0001600 0.0001300 6.600E-06 5.700E-06 2.700E-05 5.600E-05 5.600E-05 2.400E-05 2.000E-05 3.900E-06 6.000E-06 5.200E-06 60.00 0.01600 0.003200 0.0006900 0.001200 0.0007100 0.0001600 0.0003700 0.0002500 3.900E-05 2.800E-05 4.000E-05 1.000E-04 6.700E-05 1.200E-05 8.700E-06 1.700E-07 1.300E-07 8.700E-06 2.000E-05 1.300E-05 1.800E-06 1.300E-06 1.200E-06 2.200E-06 1.200E-06 100.0 0.003600 0.001100 0.0002700 0.0001300 4.800E-05 6.900E-05 4.000E-05 1.500E-05 8.900E-07 4.900E-07 1.800E-05 1.100E-05 4.100E-06 2.600E-07 1.500E-07 1.200E-09 7.400E-10 4.000E-06 2.100E-06 8.000E-07 3.900E-08 2.100E-08 5.600E-07 2.300E-07 7.400E-08 #S 86 Rn #N 26 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 4 #UBIND 9.840E+04 1.805E+04 1.738E+04 1.462E+04 4482. 4159. 3538. 3022. 2892. 1097. 929.0 768.0 567.0 541.0 238.0 238.0 214.0 164.0 127.0 48.00 48.00 26.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 0.000 11.90 0.008230 0.02940 0.01420 0.01650 0.06440 0.03760 0.04130 0.02660 0.02740 0.1300 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2840 0.1870 0.2050 0.1790 0.1850 0.7820 0.5510 0.6440 0.05000 11.80 0.008230 0.02940 0.01420 0.01650 0.06440 0.03760 0.04130 0.02660 0.02740 0.1300 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2830 0.1870 0.2050 0.1790 0.1850 0.7760 0.5510 0.6440 0.1000 11.80 0.008230 0.02940 0.01420 0.01650 0.06440 0.03760 0.04130 0.02660 0.02740 0.1290 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2820 0.1870 0.2050 0.1790 0.1850 0.7590 0.5500 0.6430 0.1500 11.70 0.008230 0.02940 0.01420 0.01650 0.06440 0.03760 0.04130 0.02660 0.02740 0.1290 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2810 0.1870 0.2050 0.1790 0.1850 0.7320 0.5490 0.6410 0.2000 11.60 0.008230 0.02940 0.01420 0.01650 0.06440 0.03760 0.04130 0.02660 0.02740 0.1290 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2790 0.1870 0.2050 0.1790 0.1850 0.6960 0.5470 0.6360 0.3000 11.30 0.008230 0.02940 0.01420 0.01650 0.06430 0.03760 0.04130 0.02660 0.02740 0.1290 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2730 0.1870 0.2050 0.1790 0.1850 0.6020 0.5340 0.6090 0.4000 10.80 0.008230 0.02940 0.01420 0.01650 0.06420 0.03760 0.04130 0.02660 0.02740 0.1280 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2650 0.1860 0.2050 0.1790 0.1850 0.4940 0.5050 0.5570 0.5000 10.20 0.008230 0.02940 0.01420 0.01650 0.06410 0.03760 0.04130 0.02660 0.02740 0.1270 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2550 0.1860 0.2040 0.1790 0.1850 0.3860 0.4610 0.4830 0.6000 9.470 0.008230 0.02940 0.01420 0.01650 0.06400 0.03760 0.04130 0.02660 0.02740 0.1250 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2430 0.1850 0.2030 0.1790 0.1850 0.2880 0.4030 0.3960 0.7000 8.790 0.008230 0.02940 0.01420 0.01650 0.06380 0.03760 0.04130 0.02660 0.02740 0.1240 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2300 0.1840 0.2010 0.1790 0.1850 0.2070 0.3380 0.3080 0.8000 8.170 0.008230 0.02930 0.01420 0.01650 0.06360 0.03760 0.04130 0.02660 0.02740 0.1220 0.08140 0.08860 0.06610 0.06780 0.05430 0.05500 0.2160 0.1830 0.1990 0.1780 0.1840 0.1460 0.2730 0.2300 1.000 7.210 0.008230 0.02930 0.01420 0.01650 0.06310 0.03760 0.04130 0.02660 0.02740 0.1180 0.08120 0.08850 0.06610 0.06780 0.05430 0.05500 0.1860 0.1780 0.1910 0.1760 0.1810 0.07550 0.1600 0.1150 1.200 6.580 0.008230 0.02930 0.01420 0.01650 0.06260 0.03760 0.04130 0.02660 0.02740 0.1140 0.08110 0.08820 0.06600 0.06780 0.05430 0.05500 0.1560 0.1700 0.1800 0.1720 0.1760 0.05120 0.08520 0.05350 1.400 6.160 0.008230 0.02920 0.01420 0.01650 0.06190 0.03760 0.04130 0.02660 0.02740 0.1090 0.08080 0.08770 0.06600 0.06780 0.05430 0.05500 0.1260 0.1590 0.1650 0.1640 0.1680 0.04690 0.04390 0.02710 1.600 5.810 0.008230 0.02910 0.01420 0.01650 0.06120 0.03760 0.04120 0.02660 0.02740 0.1030 0.08030 0.08710 0.06600 0.06770 0.05430 0.05500 0.1000 0.1460 0.1480 0.1550 0.1560 0.04660 0.02520 0.01850 1.800 5.490 0.008230 0.02910 0.01420 0.01650 0.06030 0.03760 0.04120 0.02660 0.02740 0.09680 0.07980 0.08620 0.06590 0.06760 0.05430 0.05500 0.07830 0.1310 0.1290 0.1420 0.1430 0.04410 0.01860 0.01690 2.000 5.150 0.008220 0.02900 0.01420 0.01650 0.05940 0.03760 0.04120 0.02660 0.02740 0.09050 0.07900 0.08510 0.06580 0.06750 0.05430 0.05500 0.06080 0.1150 0.1090 0.1280 0.1270 0.03860 0.01720 0.01680 2.400 4.490 0.008220 0.02880 0.01420 0.01650 0.05730 0.03750 0.04110 0.02660 0.02740 0.07750 0.07680 0.08190 0.06550 0.06710 0.05420 0.05490 0.03860 0.08330 0.07270 0.09720 0.09430 0.02410 0.01670 0.01480 3.000 3.650 0.008210 0.02850 0.01420 0.01640 0.05370 0.03740 0.04090 0.02660 0.02740 0.05880 0.07170 0.07470 0.06430 0.06570 0.05390 0.05450 0.02760 0.04440 0.03480 0.05580 0.05200 0.008990 0.01190 0.008280 4.000 2.840 0.008190 0.02780 0.01420 0.01640 0.04670 0.03680 0.04000 0.02650 0.02730 0.03390 0.05880 0.05800 0.05970 0.06060 0.05210 0.05270 0.02610 0.01550 0.01370 0.01700 0.01510 0.004280 0.003530 0.002030 5.000 2.410 0.008170 0.02690 0.01420 0.01640 0.03910 0.03590 0.03860 0.02650 0.02730 0.01990 0.04330 0.03950 0.05150 0.05150 0.04840 0.04880 0.01900 0.01160 0.01250 0.006350 0.005980 0.003810 0.001440 0.001270 6.000 2.050 0.008140 0.02580 0.01420 0.01640 0.03160 0.03430 0.03640 0.02630 0.02710 0.01440 0.02870 0.02410 0.04090 0.04020 0.04300 0.04300 0.010000 0.01110 0.01070 0.005140 0.005190 0.002230 0.001350 0.001190 7.000 1.710 0.008100 0.02470 0.01410 0.01630 0.02470 0.03230 0.03350 0.02600 0.02670 0.01330 0.01770 0.01400 0.03000 0.02890 0.03650 0.03640 0.004920 0.008640 0.007110 0.004990 0.004940 0.001030 0.001150 0.0008400 8.000 1.410 0.008060 0.02340 0.01410 0.01620 0.01890 0.02970 0.03010 0.02550 0.02620 0.01320 0.01080 0.008690 0.02050 0.01930 0.02990 0.02960 0.003270 0.005520 0.003950 0.004210 0.004010 0.0005530 0.0007750 0.0004780 10.00 0.9800 0.008000 0.02100 0.01400 0.01600 0.01100 0.02400 0.02200 0.02400 0.02400 0.01100 0.005800 0.006200 0.008400 0.007600 0.01900 0.01800 0.003000 0.001800 0.001400 0.002100 0.001800 0.0004600 0.0002500 0.0001500 15.00 0.4800 0.007700 0.01300 0.01300 0.01500 0.005000 0.009500 0.007400 0.01700 0.01700 0.002400 0.004600 0.004200 0.002400 0.002500 0.004600 0.004400 0.0007900 0.001000 0.0009500 0.0003400 0.0003400 0.0001300 0.0001200 9.800E-05 20.00 0.2700 0.007300 0.007700 0.01200 0.01200 0.004700 0.003000 0.002400 0.009700 0.009300 0.001300 0.001700 0.001200 0.002200 0.002100 0.001100 0.001000 0.0002900 0.0004300 0.0002800 0.0003100 0.0003000 4.300E-05 5.300E-05 3.000E-05 30.00 0.1100 0.006300 0.002000 0.007600 0.007000 0.001700 0.001400 0.001700 0.002400 0.002100 0.0006500 0.0003200 0.0004000 0.0007200 0.0006500 7.600E-05 6.800E-05 0.0001600 6.500E-05 7.700E-05 0.0001100 9.900E-05 2.400E-05 7.600E-06 7.800E-06 40.00 0.05100 0.005200 0.0008000 0.004400 0.003400 0.0003600 0.001200 0.001100 0.0005600 0.0004600 0.0001300 0.0002900 0.0003000 0.0001700 0.0001500 7.700E-06 6.600E-06 3.100E-05 5.800E-05 5.900E-05 2.700E-05 2.300E-05 4.700E-06 6.800E-06 6.000E-06 60.00 0.01700 0.003200 0.0006800 0.001300 0.0007400 0.0001500 0.0003900 0.0002600 4.300E-05 3.100E-05 4.000E-05 0.0001100 7.200E-05 1.300E-05 9.700E-06 2.000E-07 1.500E-07 8.800E-06 2.200E-05 1.400E-05 2.100E-06 1.500E-06 1.300E-06 2.600E-06 1.500E-06 100.0 0.003700 0.001100 0.0002800 0.0001500 5.100E-05 7.200E-05 4.300E-05 1.700E-05 9.900E-07 5.500E-07 1.900E-05 1.200E-05 4.500E-06 3.000E-07 1.600E-07 1.400E-09 8.800E-10 4.200E-06 2.400E-06 9.000E-07 4.600E-08 2.500E-08 6.300E-07 2.800E-07 9.100E-08 #S 87 Fr #N 27 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 4 1 #UBIND 1.011E+05 1.864E+04 1.791E+04 1.503E+04 4652. 4327. 3663. 3136. 3000. 1153. 980.0 810.0 603.0 577.0 268.0 268.0 234.0 182.0 140.0 58.00 58.00 34.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 0.000 13.80 0.008090 0.02890 0.01400 0.01620 0.06340 0.03700 0.04070 0.02620 0.02700 0.1270 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2750 0.1810 0.1990 0.1720 0.1770 0.7140 0.4960 0.5710 2.650 0.05000 13.60 0.008090 0.02890 0.01400 0.01620 0.06340 0.03700 0.04070 0.02620 0.02700 0.1270 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2740 0.1810 0.1990 0.1720 0.1770 0.7090 0.4960 0.5710 2.470 0.1000 13.10 0.008090 0.02890 0.01400 0.01620 0.06330 0.03700 0.04070 0.02620 0.02700 0.1270 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2730 0.1810 0.1990 0.1720 0.1770 0.6960 0.4960 0.5700 2.010 0.1500 12.50 0.008090 0.02890 0.01400 0.01620 0.06330 0.03700 0.04070 0.02620 0.02700 0.1270 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2720 0.1810 0.1990 0.1720 0.1770 0.6750 0.4960 0.5690 1.440 0.2000 11.90 0.008090 0.02890 0.01400 0.01620 0.06330 0.03700 0.04070 0.02620 0.02700 0.1270 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2700 0.1810 0.1990 0.1720 0.1770 0.6460 0.4940 0.5660 0.9080 0.3000 11.00 0.008090 0.02890 0.01400 0.01620 0.06320 0.03700 0.04070 0.02620 0.02700 0.1260 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2650 0.1800 0.1980 0.1720 0.1770 0.5720 0.4860 0.5510 0.2740 0.4000 10.40 0.008090 0.02890 0.01400 0.01620 0.06320 0.03700 0.04070 0.02620 0.02700 0.1250 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2580 0.1800 0.1980 0.1720 0.1770 0.4830 0.4680 0.5180 0.09520 0.5000 9.950 0.008090 0.02890 0.01400 0.01620 0.06310 0.03700 0.04070 0.02620 0.02700 0.1240 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2480 0.1800 0.1980 0.1720 0.1770 0.3900 0.4370 0.4680 0.07540 0.6000 9.410 0.008090 0.02890 0.01400 0.01620 0.06290 0.03700 0.04070 0.02620 0.02700 0.1230 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2380 0.1790 0.1970 0.1710 0.1770 0.3020 0.3950 0.4040 0.07320 0.7000 8.820 0.008090 0.02890 0.01400 0.01620 0.06280 0.03700 0.04070 0.02620 0.02700 0.1220 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2260 0.1780 0.1950 0.1710 0.1770 0.2260 0.3450 0.3330 0.06170 0.8000 8.250 0.008090 0.02890 0.01400 0.01620 0.06260 0.03700 0.04070 0.02620 0.02700 0.1200 0.07970 0.08700 0.06480 0.06650 0.05300 0.05370 0.2130 0.1770 0.1930 0.1710 0.1760 0.1650 0.2910 0.2640 0.04580 1.000 7.280 0.008090 0.02880 0.01400 0.01620 0.06210 0.03700 0.04070 0.02620 0.02700 0.1160 0.07960 0.08690 0.06470 0.06650 0.05300 0.05370 0.1850 0.1730 0.1870 0.1690 0.1740 0.08830 0.1870 0.1480 0.02000 1.200 6.600 0.008090 0.02880 0.01400 0.01620 0.06160 0.03700 0.04070 0.02620 0.02700 0.1120 0.07950 0.08660 0.06470 0.06650 0.05300 0.05370 0.1560 0.1660 0.1770 0.1660 0.1700 0.05670 0.1080 0.07480 0.007940 1.400 6.130 0.008090 0.02870 0.01400 0.01620 0.06100 0.03700 0.04070 0.02620 0.02700 0.1070 0.07920 0.08620 0.06470 0.06640 0.05300 0.05370 0.1280 0.1570 0.1630 0.1600 0.1630 0.04880 0.05870 0.03800 0.004030 1.600 5.780 0.008090 0.02870 0.01400 0.01620 0.06030 0.03700 0.04070 0.02620 0.02700 0.1020 0.07880 0.08560 0.06470 0.06640 0.05300 0.05370 0.1030 0.1450 0.1480 0.1520 0.1540 0.04830 0.03310 0.02350 0.003260 1.800 5.460 0.008090 0.02860 0.01400 0.01620 0.05950 0.03700 0.04060 0.02620 0.02700 0.09590 0.07830 0.08480 0.06460 0.06640 0.05300 0.05370 0.08090 0.1310 0.1300 0.1410 0.1420 0.04700 0.02240 0.01950 0.003220 2.000 5.150 0.008080 0.02850 0.01400 0.01620 0.05860 0.03690 0.04060 0.02620 0.02700 0.08990 0.07760 0.08370 0.06460 0.06630 0.05290 0.05360 0.06330 0.1160 0.1110 0.1290 0.1280 0.04270 0.01920 0.01910 0.003090 2.400 4.530 0.008080 0.02840 0.01400 0.01620 0.05660 0.03690 0.04050 0.02620 0.02700 0.07740 0.07550 0.08080 0.06420 0.06590 0.05290 0.05360 0.04020 0.08600 0.07610 0.1010 0.09820 0.02860 0.01870 0.01780 0.002230 3.000 3.690 0.008070 0.02800 0.01400 0.01620 0.05310 0.03670 0.04030 0.02620 0.02700 0.05920 0.07080 0.07410 0.06320 0.06470 0.05260 0.05330 0.02770 0.04750 0.03760 0.06030 0.05670 0.01130 0.01440 0.01090 0.0008930 4.000 2.850 0.008050 0.02740 0.01400 0.01620 0.04640 0.03630 0.03950 0.02620 0.02700 0.03480 0.05880 0.05820 0.05900 0.05990 0.05110 0.05170 0.02620 0.01660 0.01430 0.01960 0.01740 0.004680 0.004730 0.002800 0.0002980 5.000 2.410 0.008030 0.02650 0.01400 0.01620 0.03900 0.03530 0.03820 0.02610 0.02690 0.02040 0.04390 0.04030 0.05140 0.05150 0.04790 0.04830 0.01990 0.01160 0.01260 0.007030 0.006540 0.004300 0.001740 0.001530 0.0002750 6.000 2.060 0.008000 0.02550 0.01390 0.01620 0.03170 0.03390 0.03610 0.02590 0.02670 0.01440 0.02960 0.02500 0.04130 0.04070 0.04290 0.04300 0.01100 0.01130 0.01120 0.005290 0.005350 0.002690 0.001550 0.001460 0.0001760 7.000 1.720 0.007970 0.02440 0.01390 0.01610 0.02500 0.03200 0.03330 0.02570 0.02640 0.01310 0.01850 0.01460 0.03080 0.02970 0.03680 0.03670 0.005400 0.009120 0.007670 0.005190 0.005180 0.001270 0.001380 0.001080 8.370E-05 8.000 1.430 0.007930 0.02320 0.01390 0.01600 0.01920 0.02960 0.03000 0.02520 0.02590 0.01300 0.01130 0.009040 0.02140 0.02020 0.03050 0.03020 0.003400 0.006040 0.004380 0.004500 0.004330 0.0006550 0.0009740 0.0006390 4.200E-05 10.00 1.000 0.007800 0.02100 0.01390 0.01600 0.01100 0.02400 0.02300 0.02400 0.02400 0.01100 0.005900 0.006100 0.009000 0.008200 0.01900 0.01900 0.003000 0.002000 0.001500 0.002300 0.002100 0.0005100 0.0003200 0.0002000 3.200E-05 15.00 0.4900 0.007500 0.01400 0.01300 0.01400 0.004900 0.009900 0.007700 0.01700 0.01700 0.002600 0.004700 0.004300 0.002400 0.002500 0.005000 0.004700 0.0008800 0.001000 0.001000 0.0003700 0.0003600 0.0001600 0.0001400 0.0001200 1.000E-05 20.00 0.2800 0.007200 0.007900 0.01100 0.01200 0.004700 0.003200 0.002400 0.009900 0.009500 0.001300 0.001900 0.001300 0.002200 0.002200 0.001200 0.001100 0.0002900 0.0004800 0.0003200 0.0003200 0.0003200 4.800E-05 6.700E-05 4.000E-05 3.000E-06 30.00 0.1100 0.006200 0.002100 0.007700 0.007100 0.001800 0.001400 0.001700 0.002500 0.002200 0.0006800 0.0003200 0.0004000 0.0007600 0.0006900 8.500E-05 7.700E-05 0.0001700 6.700E-05 7.900E-05 0.0001200 0.0001100 2.800E-05 9.100E-06 9.500E-06 1.700E-06 40.00 0.05300 0.005100 0.0008000 0.004500 0.003500 0.0003900 0.001200 0.001100 0.0006000 0.0005000 0.0001400 0.0002900 0.0003100 0.0001900 0.0001600 8.800E-06 7.600E-06 3.500E-05 6.000E-05 6.300E-05 3.100E-05 2.600E-05 5.800E-06 8.100E-06 7.500E-06 3.600E-07 60.00 0.01700 0.003200 0.0006800 0.001300 0.0007800 0.0001500 0.0004100 0.0002700 4.700E-05 3.400E-05 3.900E-05 0.0001100 7.600E-05 1.500E-05 1.100E-05 2.300E-07 1.800E-07 8.800E-06 2.400E-05 1.600E-05 2.400E-06 1.700E-06 1.400E-06 3.200E-06 1.900E-06 9.000E-08 100.0 0.003900 0.001200 0.0002900 0.0001600 5.500E-05 7.400E-05 4.700E-05 1.800E-05 1.100E-06 6.100E-07 2.000E-05 1.300E-05 4.900E-06 3.400E-07 1.800E-07 1.600E-09 1.000E-09 4.500E-06 2.700E-06 1.000E-06 5.500E-08 3.000E-08 7.400E-07 3.600E-07 1.200E-07 4.600E-08 #S 88 Ra #N 27 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 4 2 #UBIND 1.039E+05 1.924E+04 1.848E+04 1.544E+04 4822. 4490. 3792. 3248. 3105. 1208. 1058. 879.0 636.0 603.0 299.0 299.0 254.0 200.0 153.0 68.00 68.00 44.00 19.00 19.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 0.000 14.90 0.007960 0.02850 0.01370 0.01600 0.06230 0.03640 0.04010 0.02580 0.02660 0.1250 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2660 0.1750 0.1920 0.1650 0.1700 0.6570 0.4560 0.5200 2.190 0.05000 14.70 0.007960 0.02850 0.01370 0.01600 0.06230 0.03640 0.04010 0.02580 0.02660 0.1250 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2660 0.1750 0.1920 0.1650 0.1700 0.6530 0.4560 0.5200 2.080 0.1000 14.10 0.007960 0.02850 0.01370 0.01600 0.06230 0.03640 0.04010 0.02580 0.02660 0.1240 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2650 0.1750 0.1920 0.1650 0.1700 0.6430 0.4560 0.5200 1.790 0.1500 13.30 0.007960 0.02850 0.01370 0.01600 0.06230 0.03640 0.04010 0.02580 0.02660 0.1240 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2640 0.1750 0.1920 0.1650 0.1700 0.6260 0.4550 0.5190 1.400 0.2000 12.40 0.007960 0.02850 0.01370 0.01600 0.06230 0.03640 0.04010 0.02580 0.02660 0.1240 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2620 0.1750 0.1920 0.1650 0.1700 0.6030 0.4540 0.5170 1.000 0.3000 11.00 0.007960 0.02850 0.01370 0.01600 0.06220 0.03640 0.04010 0.02580 0.02660 0.1240 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2570 0.1750 0.1920 0.1650 0.1700 0.5420 0.4490 0.5070 0.4070 0.4000 10.20 0.007960 0.02840 0.01370 0.01600 0.06210 0.03640 0.04010 0.02580 0.02660 0.1230 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2500 0.1750 0.1920 0.1650 0.1700 0.4680 0.4360 0.4840 0.1510 0.5000 9.750 0.007960 0.02840 0.01370 0.01600 0.06200 0.03640 0.04010 0.02580 0.02660 0.1220 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2420 0.1740 0.1920 0.1650 0.1700 0.3890 0.4140 0.4480 0.08730 0.6000 9.290 0.007960 0.02840 0.01370 0.01600 0.06190 0.03640 0.04010 0.02580 0.02660 0.1210 0.07820 0.08550 0.06350 0.06520 0.05170 0.05240 0.2320 0.1740 0.1910 0.1640 0.1700 0.3110 0.3820 0.3990 0.08200 0.7000 8.810 0.007960 0.02840 0.01370 0.01600 0.06170 0.03640 0.04010 0.02580 0.02660 0.1200 0.07820 0.08550 0.06350 0.06520 0.05170 0.05240 0.2210 0.1730 0.1900 0.1640 0.1690 0.2410 0.3430 0.3410 0.07900 0.8000 8.300 0.007960 0.02840 0.01370 0.01600 0.06160 0.03640 0.04010 0.02580 0.02660 0.1180 0.07820 0.08550 0.06350 0.06520 0.05170 0.05240 0.2090 0.1720 0.1880 0.1640 0.1690 0.1810 0.2980 0.2820 0.06800 1.000 7.350 0.007950 0.02840 0.01370 0.01600 0.06110 0.03640 0.04010 0.02580 0.02660 0.1150 0.07810 0.08530 0.06350 0.06520 0.05170 0.05240 0.1830 0.1680 0.1820 0.1630 0.1680 0.1010 0.2060 0.1720 0.03790 1.200 6.620 0.007950 0.02830 0.01370 0.01600 0.06060 0.03640 0.04010 0.02580 0.02660 0.1100 0.07790 0.08510 0.06350 0.06520 0.05170 0.05240 0.1550 0.1620 0.1730 0.1600 0.1640 0.06300 0.1280 0.09460 0.01690 1.400 6.110 0.007950 0.02830 0.01370 0.01600 0.06000 0.03640 0.04010 0.02580 0.02660 0.1060 0.07770 0.08470 0.06350 0.06520 0.05170 0.05240 0.1290 0.1540 0.1610 0.1560 0.1590 0.05100 0.07370 0.04990 0.007730 1.600 5.740 0.007950 0.02820 0.01370 0.01600 0.05940 0.03630 0.04010 0.02580 0.02660 0.1010 0.07730 0.08420 0.06340 0.06520 0.05170 0.05240 0.1050 0.1430 0.1470 0.1490 0.1510 0.04940 0.04220 0.02930 0.005020 1.800 5.430 0.007950 0.02810 0.01370 0.01600 0.05860 0.03630 0.04010 0.02580 0.02660 0.09500 0.07680 0.08340 0.06340 0.06510 0.05170 0.05240 0.08330 0.1310 0.1300 0.1400 0.1410 0.04890 0.02700 0.02220 0.004640 2.000 5.140 0.007950 0.02810 0.01370 0.01600 0.05780 0.03630 0.04000 0.02580 0.02660 0.08930 0.07620 0.08250 0.06330 0.06500 0.05170 0.05240 0.06580 0.1170 0.1130 0.1290 0.1290 0.04590 0.02140 0.02080 0.004600 2.400 4.550 0.007940 0.02790 0.01370 0.01600 0.05590 0.03630 0.03990 0.02580 0.02660 0.07730 0.07430 0.07970 0.06310 0.06470 0.05160 0.05230 0.04190 0.08840 0.07910 0.1030 0.1010 0.03300 0.02000 0.02010 0.003680 3.000 3.730 0.007930 0.02760 0.01370 0.01600 0.05250 0.03610 0.03970 0.02580 0.02660 0.05970 0.07000 0.07350 0.06210 0.06360 0.05140 0.05210 0.02800 0.05060 0.04040 0.06460 0.06120 0.01400 0.01680 0.01350 0.001620 4.000 2.860 0.007920 0.02700 0.01370 0.01600 0.04610 0.03570 0.03900 0.02580 0.02660 0.03550 0.05870 0.05840 0.05830 0.05930 0.05020 0.05070 0.02610 0.01790 0.01500 0.02230 0.01990 0.005110 0.006100 0.003680 0.0004640 5.000 2.410 0.007900 0.02620 0.01370 0.01600 0.03900 0.03480 0.03770 0.02570 0.02650 0.02090 0.04450 0.04100 0.05130 0.05150 0.04730 0.04770 0.02070 0.01170 0.01260 0.007820 0.007210 0.004760 0.002090 0.001770 0.0004250 6.000 2.060 0.007870 0.02520 0.01370 0.01600 0.03190 0.03350 0.03580 0.02560 0.02630 0.01460 0.03050 0.02590 0.04180 0.04120 0.04280 0.04290 0.01190 0.01140 0.01150 0.005440 0.005490 0.003180 0.001730 0.001700 0.0002930 7.000 1.740 0.007840 0.02410 0.01370 0.01590 0.02530 0.03170 0.03320 0.02530 0.02610 0.01300 0.01940 0.01530 0.03150 0.03050 0.03710 0.03700 0.005930 0.009550 0.008220 0.005350 0.005380 0.001570 0.001600 0.001320 0.0001450 8.000 1.450 0.007800 0.02300 0.01360 0.01580 0.01960 0.02940 0.03000 0.02490 0.02560 0.01290 0.01200 0.009420 0.02220 0.02100 0.03100 0.03080 0.003570 0.006550 0.004840 0.004780 0.004630 0.0007790 0.001180 0.0008100 7.040E-05 10.00 1.000 0.007700 0.02000 0.01300 0.01580 0.01100 0.02400 0.02300 0.02400 0.02400 0.01100 0.006000 0.006100 0.009600 0.008700 0.02000 0.02000 0.003100 0.002300 0.001600 0.002600 0.002300 0.0005500 0.0004100 0.0002500 4.800E-05 15.00 0.5000 0.007400 0.01400 0.01300 0.01400 0.004900 0.010000 0.008000 0.01700 0.01700 0.002800 0.004800 0.004400 0.002400 0.002500 0.005300 0.005100 0.0009800 0.001100 0.001100 0.0003900 0.0003800 0.0001900 0.0001600 0.0001500 1.700E-05 20.00 0.2900 0.007100 0.008100 0.01100 0.01200 0.004700 0.003300 0.002500 0.010000 0.009700 0.001300 0.002000 0.001400 0.002200 0.002200 0.001300 0.001200 0.0003000 0.0005300 0.0003500 0.0003400 0.0003400 5.300E-05 8.200E-05 5.000E-05 4.600E-06 30.00 0.1100 0.006100 0.002200 0.007700 0.007200 0.001900 0.001300 0.001700 0.002600 0.002300 0.0007200 0.0003200 0.0004000 0.0008000 0.0007300 9.600E-05 8.600E-05 0.0001800 7.000E-05 8.100E-05 0.0001400 0.0001200 3.300E-05 1.100E-05 1.100E-05 2.800E-06 40.00 0.05400 0.005100 0.0008200 0.004600 0.003600 0.0004300 0.001200 0.001200 0.0006400 0.0005300 0.0001600 0.0002900 0.0003100 0.0002000 0.0001700 1.000E-05 8.600E-06 3.900E-05 6.100E-05 6.600E-05 3.500E-05 2.900E-05 7.100E-06 9.200E-06 9.000E-06 6.200E-07 60.00 0.01800 0.003300 0.0006700 0.001400 0.0008200 0.0001500 0.0004300 0.0002900 5.100E-05 3.700E-05 3.900E-05 0.0001200 8.100E-05 1.600E-05 1.200E-05 2.700E-07 2.100E-07 8.900E-06 2.500E-05 1.700E-05 2.800E-06 2.000E-06 1.600E-06 3.800E-06 2.400E-06 1.400E-07 100.0 0.004000 0.001200 0.0003000 0.0001700 5.800E-05 7.600E-05 5.100E-05 1.900E-05 1.200E-06 6.800E-07 2.100E-05 1.400E-05 5.300E-06 3.800E-07 2.100E-07 1.900E-09 1.200E-09 4.800E-06 3.000E-06 1.100E-06 6.500E-08 3.500E-08 8.400E-07 4.500E-07 1.500E-07 7.300E-08 #S 89 Ac #N 28 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 4 1 2 #UBIND 1.068E+05 1.984E+04 1.908E+04 1.587E+04 5002. 4656. 3909. 3370. 3219. 1269. 1080. 890.0 675.0 639.0 319.0 319.0 272.0 215.0 167.0 80.00 80.00 0.000 0.000 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 0.000 14.70 0.007820 0.02800 0.01350 0.01580 0.06130 0.03580 0.03950 0.02540 0.02620 0.1220 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2580 0.1690 0.1870 0.1580 0.1630 0.6170 0.4290 0.4840 0.6080 1.990 0.05000 14.50 0.007820 0.02800 0.01350 0.01580 0.06130 0.03580 0.03950 0.02540 0.02620 0.1220 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2580 0.1690 0.1870 0.1580 0.1630 0.6140 0.4290 0.4840 0.6080 1.910 0.1000 14.00 0.007820 0.02800 0.01350 0.01580 0.06130 0.03580 0.03950 0.02540 0.02620 0.1220 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2570 0.1690 0.1870 0.1580 0.1630 0.6060 0.4290 0.4840 0.6080 1.680 0.1500 13.40 0.007820 0.02800 0.01350 0.01580 0.06130 0.03580 0.03950 0.02540 0.02620 0.1220 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2560 0.1690 0.1870 0.1580 0.1630 0.5910 0.4290 0.4840 0.6070 1.360 0.2000 12.60 0.007820 0.02800 0.01350 0.01580 0.06130 0.03580 0.03950 0.02540 0.02620 0.1220 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2540 0.1690 0.1870 0.1580 0.1630 0.5720 0.4280 0.4820 0.6060 1.020 0.3000 11.40 0.007820 0.02800 0.01350 0.01580 0.06120 0.03580 0.03950 0.02540 0.02620 0.1210 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2500 0.1690 0.1860 0.1580 0.1630 0.5200 0.4240 0.4750 0.5920 0.4730 0.4000 10.50 0.007820 0.02800 0.01350 0.01580 0.06110 0.03580 0.03950 0.02540 0.02620 0.1210 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2440 0.1690 0.1860 0.1580 0.1630 0.4550 0.4140 0.4580 0.5550 0.1900 0.5000 9.980 0.007820 0.02800 0.01350 0.01580 0.06100 0.03580 0.03950 0.02540 0.02620 0.1200 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2360 0.1690 0.1860 0.1580 0.1630 0.3840 0.3960 0.4300 0.4930 0.09730 0.6000 9.500 0.007820 0.02800 0.01350 0.01580 0.06090 0.03580 0.03950 0.02540 0.02620 0.1190 0.07670 0.08400 0.06230 0.06400 0.05050 0.05120 0.2270 0.1690 0.1850 0.1580 0.1630 0.3140 0.3700 0.3900 0.4160 0.08210 0.7000 9.000 0.007820 0.02800 0.01350 0.01580 0.06080 0.03580 0.03950 0.02540 0.02620 0.1170 0.07670 0.08400 0.06230 0.06400 0.05050 0.05120 0.2170 0.1680 0.1840 0.1580 0.1630 0.2490 0.3370 0.3430 0.3360 0.08130 0.8000 8.470 0.007820 0.02790 0.01350 0.01580 0.06060 0.03580 0.03950 0.02540 0.02620 0.1160 0.07660 0.08400 0.06230 0.06400 0.05050 0.05120 0.2050 0.1670 0.1820 0.1580 0.1630 0.1920 0.2990 0.2910 0.2610 0.07520 1.000 7.480 0.007820 0.02790 0.01350 0.01580 0.06020 0.03580 0.03950 0.02540 0.02620 0.1130 0.07660 0.08390 0.06230 0.06400 0.05050 0.05120 0.1810 0.1640 0.1770 0.1570 0.1610 0.1110 0.2160 0.1900 0.1460 0.04860 1.200 6.690 0.007820 0.02790 0.01350 0.01580 0.05970 0.03570 0.03950 0.02540 0.02620 0.1090 0.07640 0.08360 0.06230 0.06400 0.05050 0.05120 0.1550 0.1580 0.1700 0.1550 0.1590 0.06860 0.1420 0.1110 0.07580 0.02430 1.400 6.120 0.007820 0.02780 0.01350 0.01580 0.05910 0.03570 0.03950 0.02540 0.02620 0.1040 0.07620 0.08330 0.06230 0.06400 0.05050 0.05120 0.1300 0.1510 0.1590 0.1510 0.1550 0.05280 0.08570 0.06120 0.03740 0.01130 1.600 5.720 0.007820 0.02780 0.01350 0.01580 0.05850 0.03570 0.03950 0.02540 0.02620 0.09940 0.07590 0.08280 0.06220 0.06390 0.05050 0.05120 0.1060 0.1420 0.1460 0.1450 0.1480 0.04970 0.05040 0.03540 0.01840 0.006440 1.800 5.400 0.007810 0.02770 0.01350 0.01580 0.05780 0.03570 0.03950 0.02540 0.02620 0.09410 0.07540 0.08210 0.06220 0.06390 0.05050 0.05120 0.08550 0.1300 0.1310 0.1380 0.1400 0.04950 0.03160 0.02490 0.009940 0.005330 2.000 5.120 0.007810 0.02760 0.01350 0.01580 0.05700 0.03570 0.03950 0.02540 0.02620 0.08860 0.07480 0.08120 0.06210 0.06380 0.05050 0.05110 0.06810 0.1180 0.1140 0.1280 0.1290 0.04760 0.02340 0.02200 0.006720 0.005270 2.400 4.570 0.007810 0.02750 0.01350 0.01580 0.05510 0.03570 0.03940 0.02540 0.02620 0.07710 0.07310 0.07860 0.06190 0.06350 0.05040 0.05110 0.04370 0.09050 0.08190 0.1050 0.1040 0.03650 0.02060 0.02150 0.005650 0.004600 3.000 3.770 0.007800 0.02720 0.01350 0.01580 0.05200 0.03550 0.03920 0.02540 0.02620 0.06010 0.06910 0.07280 0.06110 0.06260 0.05020 0.05090 0.02840 0.05350 0.04330 0.06850 0.06530 0.01670 0.01830 0.01580 0.005140 0.002260 4.000 2.870 0.007780 0.02660 0.01350 0.01580 0.04580 0.03510 0.03850 0.02540 0.02620 0.03630 0.05850 0.05850 0.05760 0.05870 0.04920 0.04980 0.02590 0.01930 0.01570 0.02510 0.02260 0.005520 0.007440 0.004660 0.002360 0.0005890 5.000 2.410 0.007760 0.02580 0.01350 0.01580 0.03890 0.03430 0.03730 0.02530 0.02610 0.02150 0.04500 0.04170 0.05110 0.05140 0.04670 0.04710 0.02140 0.01180 0.01270 0.008760 0.007990 0.005110 0.002450 0.001990 0.0007500 0.0005140 6.000 2.070 0.007740 0.02490 0.01350 0.01580 0.03210 0.03310 0.03550 0.02520 0.02600 0.01470 0.03140 0.02670 0.04210 0.04160 0.04260 0.04280 0.01290 0.01150 0.01180 0.005620 0.005650 0.003630 0.001850 0.001890 0.0003540 0.0003810 7.000 1.760 0.007710 0.02390 0.01340 0.01570 0.02560 0.03140 0.03300 0.02500 0.02570 0.01280 0.02030 0.01600 0.03220 0.03120 0.03730 0.03720 0.006520 0.009930 0.008760 0.005490 0.005550 0.001870 0.001760 0.001540 0.0003230 0.0001980 8.000 1.470 0.007670 0.02270 0.01340 0.01560 0.02000 0.02930 0.03000 0.02460 0.02530 0.01270 0.01260 0.009820 0.02310 0.02190 0.03150 0.03130 0.003770 0.007060 0.005310 0.005030 0.004910 0.0009130 0.001350 0.0009790 0.0003070 9.480E-05 10.00 1.000 0.007600 0.02000 0.01300 0.01500 0.01200 0.02400 0.02300 0.02300 0.02400 0.01100 0.006100 0.006100 0.010000 0.009300 0.02100 0.02000 0.003100 0.002500 0.001700 0.002900 0.002600 0.0005900 0.0005000 0.0003000 0.0001800 5.800E-05 15.00 0.5100 0.007300 0.01400 0.01300 0.01400 0.004900 0.01100 0.008300 0.01700 0.01700 0.003000 0.004800 0.004600 0.002400 0.002500 0.005700 0.005400 0.001100 0.001100 0.001100 0.0004200 0.0004100 0.0002300 0.0001800 0.0001700 2.600E-05 2.300E-05 20.00 0.2900 0.007000 0.008200 0.01100 0.01200 0.004600 0.003500 0.002600 0.010000 0.009900 0.001200 0.002100 0.001500 0.002200 0.002200 0.001400 0.001300 0.0003000 0.0005700 0.0003900 0.0003600 0.0003600 5.800E-05 9.600E-05 6.100E-05 2.100E-05 5.700E-06 30.00 0.1200 0.006100 0.002300 0.007800 0.007300 0.002000 0.001300 0.001700 0.002800 0.002500 0.0007500 0.0003300 0.0004000 0.0008400 0.0007700 0.0001100 9.700E-05 0.0001900 7.300E-05 8.300E-05 0.0001500 0.0001400 3.700E-05 1.200E-05 1.200E-05 9.200E-06 3.600E-06 40.00 0.05500 0.005100 0.0008300 0.004700 0.003700 0.0004600 0.001200 0.001200 0.0006800 0.0005700 0.0001700 0.0002900 0.0003200 0.0002200 0.0001800 1.100E-05 9.800E-06 4.400E-05 6.300E-05 6.900E-05 4.000E-05 3.300E-05 8.500E-06 1.000E-05 1.000E-05 2.500E-06 8.400E-07 60.00 0.01800 0.003300 0.0006600 0.001500 0.0008500 0.0001500 0.0004500 0.0003000 5.600E-05 4.000E-05 3.800E-05 0.0001200 8.500E-05 1.800E-05 1.300E-05 3.100E-07 2.400E-07 8.900E-06 2.700E-05 1.900E-05 3.200E-06 2.300E-06 1.700E-06 4.400E-06 2.800E-06 2.000E-07 1.700E-07 100.0 0.004200 0.001200 0.0003100 0.0001800 6.200E-05 7.900E-05 5.500E-05 2.100E-05 1.400E-06 7.500E-07 2.100E-05 1.500E-05 5.800E-06 4.200E-07 2.300E-07 2.200E-09 1.400E-09 5.000E-06 3.300E-06 1.300E-06 7.600E-08 4.100E-08 9.400E-07 5.300E-07 1.900E-07 4.700E-09 9.300E-08 #S 90 Th #N 28 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 4 2 2 #UBIND 1.097E+05 2.047E+04 1.969E+04 1.630E+04 5182. 4831. 4046. 3491. 3332. 1330. 1168. 968.0 714.0 677.0 344.0 335.0 290.0 229.0 182.0 95.00 88.00 60.00 49.00 43.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 0.000 14.50 0.007690 0.02750 0.01330 0.01560 0.06030 0.03520 0.03900 0.02500 0.02590 0.1200 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2510 0.1640 0.1810 0.1520 0.1570 0.5850 0.4080 0.4560 0.5350 1.860 0.05000 14.40 0.007690 0.02750 0.01330 0.01560 0.06030 0.03520 0.03900 0.02500 0.02590 0.1200 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2500 0.1640 0.1810 0.1520 0.1570 0.5820 0.4080 0.4560 0.5350 1.790 0.1000 14.00 0.007690 0.02750 0.01330 0.01560 0.06030 0.03520 0.03900 0.02500 0.02590 0.1200 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2500 0.1640 0.1810 0.1520 0.1570 0.5750 0.4080 0.4560 0.5350 1.600 0.1500 13.40 0.007690 0.02750 0.01330 0.01560 0.06030 0.03520 0.03900 0.02500 0.02590 0.1200 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2490 0.1640 0.1810 0.1520 0.1570 0.5620 0.4070 0.4560 0.5350 1.330 0.2000 12.80 0.007690 0.02750 0.01330 0.01560 0.06030 0.03520 0.03900 0.02500 0.02590 0.1190 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2470 0.1640 0.1810 0.1520 0.1570 0.5450 0.4070 0.4550 0.5340 1.030 0.3000 11.60 0.007690 0.02750 0.01330 0.01560 0.06020 0.03520 0.03900 0.02500 0.02590 0.1190 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2430 0.1640 0.1810 0.1520 0.1570 0.5000 0.4030 0.4490 0.5280 0.5170 0.4000 10.80 0.007690 0.02750 0.01330 0.01560 0.06010 0.03520 0.03900 0.02500 0.02590 0.1180 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2370 0.1640 0.1810 0.1520 0.1570 0.4430 0.3950 0.4360 0.5080 0.2220 0.5000 10.20 0.007690 0.02750 0.01330 0.01560 0.06000 0.03520 0.03900 0.02500 0.02590 0.1180 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2300 0.1640 0.1810 0.1520 0.1570 0.3790 0.3810 0.4130 0.4710 0.1080 0.6000 9.700 0.007690 0.02750 0.01330 0.01560 0.05990 0.03520 0.03900 0.02500 0.02590 0.1170 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2220 0.1640 0.1800 0.1520 0.1570 0.3150 0.3590 0.3810 0.4180 0.08160 0.7000 9.210 0.007690 0.02750 0.01330 0.01560 0.05980 0.03520 0.03900 0.02500 0.02590 0.1150 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2120 0.1630 0.1790 0.1520 0.1570 0.2540 0.3310 0.3400 0.3560 0.07990 0.8000 8.690 0.007690 0.02750 0.01330 0.01560 0.05960 0.03520 0.03900 0.02500 0.02590 0.1140 0.07520 0.08250 0.06110 0.06280 0.04930 0.05000 0.2020 0.1620 0.1780 0.1520 0.1570 0.2000 0.2970 0.2950 0.2930 0.07710 1.000 7.660 0.007690 0.02750 0.01330 0.01560 0.05920 0.03520 0.03900 0.02500 0.02590 0.1110 0.07510 0.08240 0.06110 0.06280 0.04930 0.05000 0.1790 0.1590 0.1730 0.1510 0.1560 0.1190 0.2230 0.2020 0.1820 0.05570 1.200 6.790 0.007690 0.02740 0.01330 0.01560 0.05880 0.03520 0.03900 0.02500 0.02590 0.1070 0.07500 0.08220 0.06110 0.06280 0.04930 0.05000 0.1540 0.1550 0.1660 0.1500 0.1540 0.07400 0.1520 0.1250 0.1040 0.03080 1.400 6.160 0.007680 0.02740 0.01330 0.01560 0.05820 0.03510 0.03900 0.02500 0.02590 0.1030 0.07480 0.08190 0.06110 0.06280 0.04930 0.05000 0.1300 0.1480 0.1570 0.1470 0.1500 0.05480 0.09640 0.07190 0.05570 0.01500 1.600 5.710 0.007680 0.02730 0.01330 0.01560 0.05760 0.03510 0.03900 0.02500 0.02590 0.09820 0.07450 0.08140 0.06110 0.06280 0.04930 0.05000 0.1080 0.1400 0.1450 0.1420 0.1450 0.04980 0.05840 0.04180 0.02890 0.008020 1.800 5.380 0.007680 0.02730 0.01330 0.01560 0.05690 0.03510 0.03890 0.02500 0.02590 0.09320 0.07410 0.08080 0.06100 0.06270 0.04930 0.05000 0.08750 0.1290 0.1310 0.1350 0.1380 0.04940 0.03640 0.02790 0.01550 0.005910 2.000 5.100 0.007680 0.02720 0.01330 0.01560 0.05620 0.03510 0.03890 0.02500 0.02590 0.08790 0.07350 0.08000 0.06100 0.06270 0.04930 0.05000 0.07020 0.1180 0.1150 0.1270 0.1280 0.04840 0.02570 0.02320 0.009530 0.005620 2.400 4.580 0.007680 0.02700 0.01330 0.01560 0.05440 0.03510 0.03880 0.02500 0.02590 0.07690 0.07190 0.07760 0.06080 0.06240 0.04930 0.05000 0.04540 0.09220 0.08440 0.1070 0.1060 0.03930 0.02090 0.02230 0.006810 0.005220 3.000 3.810 0.007670 0.02680 0.01330 0.01560 0.05140 0.03500 0.03870 0.02500 0.02590 0.06040 0.06820 0.07210 0.06000 0.06150 0.04910 0.04980 0.02880 0.05630 0.04600 0.07200 0.06900 0.01950 0.01940 0.01770 0.006460 0.002880 4.000 2.890 0.007650 0.02620 0.01330 0.01560 0.04540 0.03460 0.03800 0.02500 0.02580 0.03700 0.05830 0.05850 0.05690 0.05800 0.04820 0.04880 0.02570 0.02080 0.01660 0.02800 0.02530 0.005980 0.008790 0.005730 0.003350 0.0007160 5.000 2.410 0.007630 0.02540 0.01320 0.01560 0.03890 0.03380 0.03690 0.02500 0.02580 0.02200 0.04540 0.04230 0.05090 0.05120 0.04600 0.04650 0.02200 0.01190 0.01270 0.009840 0.008880 0.005370 0.002860 0.002210 0.001120 0.0005780 6.000 2.080 0.007610 0.02460 0.01320 0.01560 0.03220 0.03270 0.03510 0.02490 0.02570 0.01490 0.03220 0.02760 0.04240 0.04200 0.04230 0.04250 0.01380 0.01150 0.01210 0.005840 0.005810 0.004060 0.001940 0.002040 0.0004800 0.0004580 7.000 1.770 0.007580 0.02360 0.01320 0.01550 0.02590 0.03110 0.03280 0.02470 0.02540 0.01270 0.02110 0.01670 0.03290 0.03190 0.03740 0.03740 0.007140 0.01030 0.009260 0.005600 0.005690 0.002190 0.001880 0.001730 0.0004120 0.0002510 8.000 1.490 0.007550 0.02250 0.01320 0.01540 0.02030 0.02910 0.02990 0.02430 0.02500 0.01250 0.01330 0.01030 0.02380 0.02270 0.03190 0.03170 0.004010 0.007550 0.005800 0.005250 0.005170 0.001070 0.001510 0.001150 0.0004000 0.0001200 10.00 1.100 0.007500 0.02000 0.01300 0.01500 0.01200 0.02400 0.02300 0.02300 0.02400 0.01100 0.006300 0.006100 0.01100 0.009800 0.02100 0.02100 0.003100 0.002800 0.001900 0.003100 0.002900 0.0006200 0.0005900 0.0003600 0.0002500 6.600E-05 15.00 0.5200 0.007200 0.01400 0.01200 0.01400 0.004900 0.01100 0.008600 0.01700 0.01700 0.003200 0.004900 0.004700 0.002400 0.002500 0.006100 0.005800 0.001200 0.001100 0.001100 0.0004600 0.0004400 0.0002600 0.0001900 0.0001900 3.600E-05 2.800E-05 20.00 0.3000 0.006900 0.008400 0.01100 0.01200 0.004600 0.003700 0.002700 0.01100 0.010000 0.001200 0.002300 0.001600 0.002200 0.002300 0.001600 0.001500 0.0003100 0.0006200 0.0004300 0.0003700 0.0003700 6.300E-05 0.0001100 7.200E-05 2.800E-05 6.700E-06 30.00 0.1200 0.006000 0.002400 0.007800 0.007400 0.002100 0.001300 0.001700 0.002900 0.002600 0.0007900 0.0003300 0.0003900 0.0008900 0.0008100 0.0001200 0.0001100 0.0002000 7.800E-05 8.500E-05 0.0001600 0.0001500 4.100E-05 1.300E-05 1.400E-05 1.300E-05 4.400E-06 40.00 0.05700 0.005100 0.0008500 0.004700 0.003800 0.0005000 0.001200 0.001200 0.0007300 0.0006000 0.0001900 0.0002900 0.0003300 0.0002400 0.0002000 1.300E-05 1.100E-05 5.000E-05 6.400E-05 7.200E-05 4.500E-05 3.700E-05 1.000E-05 1.100E-05 1.200E-05 3.500E-06 1.100E-06 60.00 0.01900 0.003300 0.0006500 0.001500 0.0008900 0.0001400 0.0004700 0.0003200 6.100E-05 4.400E-05 3.800E-05 0.0001300 9.000E-05 2.000E-05 1.400E-05 3.600E-07 2.800E-07 9.000E-06 2.900E-05 2.000E-05 3.700E-06 2.700E-06 1.800E-06 5.000E-06 3.300E-06 2.900E-07 1.900E-07 100.0 0.004300 0.001300 0.0003200 0.0002000 6.700E-05 8.100E-05 5.900E-05 2.200E-05 1.500E-06 8.300E-07 2.200E-05 1.600E-05 6.200E-06 4.800E-07 2.600E-07 2.600E-09 1.700E-09 5.300E-06 3.700E-06 1.400E-06 8.900E-08 4.800E-08 1.000E-06 6.200E-07 2.200E-07 6.900E-09 1.100E-07 #S 91 Pa #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 2 4 1 2 #UBIND 1.126E+05 2.111E+04 2.031E+04 1.673E+04 5367. 5001. 4174. 3611. 3442. 1387. 1224. 1007. 743.0 708.0 371.0 360.0 310.0 223.0 223.0 94.00 94.00 0.000 0.000 0.000 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 14.40 0.007560 0.02710 0.01300 0.01540 0.05930 0.03460 0.03840 0.02470 0.02550 0.1180 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2450 0.1610 0.1770 0.1490 0.1530 0.2070 0.5800 0.4010 0.4590 0.5580 1.930 0.05000 14.30 0.007560 0.02710 0.01300 0.01540 0.05930 0.03460 0.03840 0.02470 0.02550 0.1180 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2440 0.1610 0.1770 0.1490 0.1530 0.2070 0.5780 0.4010 0.4590 0.5580 1.860 0.1000 13.80 0.007560 0.02710 0.01300 0.01540 0.05930 0.03460 0.03840 0.02470 0.02550 0.1180 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2440 0.1610 0.1770 0.1490 0.1530 0.2070 0.5700 0.4010 0.4590 0.5580 1.650 0.1500 13.20 0.007560 0.02710 0.01300 0.01540 0.05930 0.03460 0.03840 0.02470 0.02550 0.1170 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2430 0.1610 0.1770 0.1490 0.1530 0.2070 0.5580 0.4010 0.4590 0.5580 1.360 0.2000 12.50 0.007560 0.02710 0.01300 0.01540 0.05930 0.03460 0.03840 0.02470 0.02550 0.1170 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2410 0.1610 0.1770 0.1490 0.1530 0.2070 0.5420 0.4000 0.4580 0.5570 1.040 0.3000 11.30 0.007560 0.02710 0.01300 0.01540 0.05920 0.03460 0.03840 0.02470 0.02550 0.1170 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2380 0.1610 0.1770 0.1490 0.1530 0.2070 0.4970 0.3970 0.4520 0.5480 0.5010 0.4000 10.50 0.007560 0.02710 0.01300 0.01540 0.05920 0.03460 0.03840 0.02470 0.02550 0.1160 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2320 0.1600 0.1770 0.1490 0.1530 0.2070 0.4420 0.3900 0.4380 0.5230 0.2070 0.5000 10.00 0.007560 0.02710 0.01300 0.01540 0.05910 0.03460 0.03840 0.02470 0.02550 0.1150 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2250 0.1600 0.1770 0.1490 0.1530 0.2060 0.3800 0.3760 0.4150 0.4770 0.09860 0.6000 9.570 0.007560 0.02710 0.01300 0.01540 0.05900 0.03460 0.03840 0.02470 0.02550 0.1140 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2180 0.1600 0.1760 0.1490 0.1530 0.2060 0.3170 0.3560 0.3820 0.4170 0.07560 0.7000 9.130 0.007560 0.02710 0.01300 0.01540 0.05880 0.03460 0.03840 0.02470 0.02550 0.1130 0.07370 0.08120 0.06000 0.06170 0.04820 0.04890 0.2090 0.1590 0.1750 0.1490 0.1530 0.2040 0.2570 0.3290 0.3410 0.3490 0.07440 0.8000 8.670 0.007560 0.02700 0.01300 0.01540 0.05870 0.03460 0.03840 0.02470 0.02550 0.1120 0.07370 0.08110 0.06000 0.06170 0.04820 0.04890 0.1990 0.1590 0.1740 0.1490 0.1530 0.2020 0.2030 0.2970 0.2950 0.2830 0.07120 1.000 7.740 0.007560 0.02700 0.01300 0.01540 0.05830 0.03460 0.03840 0.02470 0.02550 0.1090 0.07370 0.08100 0.06000 0.06170 0.04820 0.04890 0.1770 0.1560 0.1700 0.1480 0.1520 0.1940 0.1220 0.2260 0.2020 0.1710 0.05030 1.200 6.950 0.007550 0.02700 0.01300 0.01540 0.05790 0.03460 0.03840 0.02470 0.02550 0.1060 0.07360 0.08080 0.06000 0.06170 0.04820 0.04890 0.1540 0.1520 0.1640 0.1470 0.1500 0.1820 0.07480 0.1560 0.1250 0.09610 0.02740 1.400 6.350 0.007550 0.02690 0.01300 0.01540 0.05740 0.03460 0.03840 0.02470 0.02550 0.1020 0.07340 0.08060 0.06000 0.06170 0.04820 0.04890 0.1310 0.1460 0.1550 0.1440 0.1470 0.1670 0.05420 0.1000 0.07180 0.05110 0.01330 1.600 5.900 0.007550 0.02690 0.01300 0.01540 0.05680 0.03460 0.03840 0.02470 0.02550 0.09710 0.07310 0.08010 0.06000 0.06170 0.04820 0.04890 0.1090 0.1380 0.1430 0.1400 0.1430 0.1500 0.04830 0.06130 0.04140 0.02640 0.006980 1.800 5.560 0.007550 0.02680 0.01300 0.01540 0.05610 0.03450 0.03840 0.02470 0.02550 0.09230 0.07270 0.07950 0.05990 0.06160 0.04820 0.04890 0.08900 0.1280 0.1300 0.1340 0.1360 0.1330 0.04770 0.03810 0.02710 0.01410 0.005050 2.000 5.260 0.007550 0.02680 0.01300 0.01540 0.05540 0.03450 0.03840 0.02470 0.02550 0.08720 0.07220 0.07870 0.05990 0.06160 0.04820 0.04890 0.07200 0.1170 0.1160 0.1260 0.1270 0.1160 0.04700 0.02630 0.02200 0.008600 0.004750 2.400 4.700 0.007550 0.02660 0.01300 0.01540 0.05370 0.03450 0.03830 0.02470 0.02550 0.07660 0.07080 0.07650 0.05970 0.06130 0.04820 0.04890 0.04690 0.09330 0.08620 0.1070 0.1060 0.08520 0.03920 0.02020 0.02090 0.005940 0.004470 3.000 3.900 0.007540 0.02640 0.01300 0.01540 0.05080 0.03440 0.03810 0.02470 0.02550 0.06070 0.06730 0.07140 0.05900 0.06050 0.04810 0.04870 0.02920 0.05840 0.04820 0.07370 0.07120 0.05000 0.02040 0.01920 0.01700 0.005670 0.002570 4.000 2.920 0.007520 0.02580 0.01300 0.01540 0.04510 0.03400 0.03760 0.02470 0.02550 0.03770 0.05810 0.05850 0.05620 0.05730 0.04730 0.04790 0.02540 0.02210 0.01740 0.02990 0.02740 0.01810 0.006060 0.009390 0.005870 0.003120 0.0006440 5.000 2.410 0.007500 0.02510 0.01300 0.01540 0.03880 0.03340 0.03650 0.02460 0.02540 0.02260 0.04580 0.04290 0.05060 0.05100 0.04540 0.04580 0.02240 0.01210 0.01260 0.01060 0.009650 0.005990 0.005230 0.003100 0.002160 0.001090 0.0004890 6.000 2.080 0.007480 0.02430 0.01300 0.01540 0.03230 0.03230 0.03480 0.02450 0.02530 0.01510 0.03300 0.02840 0.04260 0.04230 0.04200 0.04230 0.01460 0.01140 0.01220 0.005920 0.005890 0.002280 0.004160 0.001920 0.001920 0.0004500 0.0004060 7.000 1.780 0.007450 0.02330 0.01300 0.01530 0.02610 0.03080 0.03260 0.02430 0.02510 0.01270 0.02200 0.01740 0.03340 0.03250 0.03740 0.03750 0.007770 0.01040 0.009650 0.005550 0.005700 0.001420 0.002360 0.001880 0.001690 0.0003650 0.0002350 8.000 1.510 0.007420 0.02230 0.01290 0.01530 0.02060 0.02890 0.02980 0.02400 0.02470 0.01230 0.01400 0.01070 0.02460 0.02340 0.03220 0.03210 0.004280 0.007950 0.006220 0.005300 0.005290 0.001320 0.001160 0.001570 0.001160 0.0003580 0.0001140 10.00 1.100 0.007300 0.02000 0.01290 0.01500 0.01200 0.02400 0.02300 0.02300 0.02300 0.01100 0.006500 0.006100 0.01100 0.010000 0.02200 0.02200 0.003100 0.003100 0.002100 0.003300 0.003100 0.001200 0.0006100 0.0006600 0.0003700 0.0002400 5.700E-05 15.00 0.5300 0.007100 0.01400 0.01200 0.01400 0.004800 0.01100 0.008900 0.01700 0.01700 0.003400 0.004900 0.004800 0.002400 0.002500 0.006400 0.006200 0.001300 0.001100 0.001200 0.0004900 0.0004600 0.0004900 0.0002800 0.0001900 0.0001800 3.400E-05 2.700E-05 20.00 0.3000 0.006800 0.008500 0.01100 0.01200 0.004500 0.004000 0.002800 0.01100 0.010000 0.001200 0.002400 0.001700 0.002200 0.002300 0.001700 0.001600 0.0003200 0.0006600 0.0004600 0.0003700 0.0003800 0.0001400 6.500E-05 0.0001200 7.400E-05 2.500E-05 6.000E-06 30.00 0.1200 0.006000 0.002500 0.007800 0.007400 0.002200 0.001300 0.001600 0.003000 0.002700 0.0008200 0.0003400 0.0003900 0.0009300 0.0008500 0.0001400 0.0001200 0.0002100 8.200E-05 8.600E-05 0.0001700 0.0001600 1.100E-05 4.200E-05 1.400E-05 1.300E-05 1.200E-05 3.900E-06 40.00 0.05800 0.005000 0.0008700 0.004800 0.003900 0.0005500 0.001200 0.001200 0.0007700 0.0006400 0.0002100 0.0002900 0.0003300 0.0002600 0.0002100 1.500E-05 1.300E-05 5.600E-05 6.400E-05 7.400E-05 4.900E-05 4.100E-05 1.200E-06 1.100E-05 1.100E-05 1.100E-05 3.400E-06 1.000E-06 60.00 0.01900 0.003300 0.0006400 0.001600 0.0009300 0.0001400 0.0004900 0.0003300 6.600E-05 4.800E-05 3.700E-05 0.0001400 9.500E-05 2.200E-05 1.600E-05 4.200E-07 3.200E-07 9.000E-06 3.100E-05 2.200E-05 4.200E-06 3.000E-06 3.500E-08 1.800E-06 5.200E-06 3.300E-06 2.900E-07 1.600E-07 100.0 0.004500 0.001300 0.0003300 0.0002100 7.100E-05 8.300E-05 6.400E-05 2.400E-05 1.700E-06 9.100E-07 2.300E-05 1.800E-05 6.700E-06 5.300E-07 2.900E-07 3.000E-09 1.900E-09 5.500E-06 4.000E-06 1.500E-06 1.000E-07 5.500E-08 2.600E-10 1.100E-06 6.800E-07 2.300E-07 7.000E-09 9.900E-08 #S 92 U #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 3 2 2 4 1 2 #UBIND 1.156E+05 2.176E+04 2.095E+04 1.717E+04 5548. 5181. 4304. 3728. 3552. 1442. 1273. 1045. 780.0 738.0 392.0 381.0 324.0 260.0 195.0 105.0 96.00 0.000 71.00 43.00 33.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 14.30 0.007430 0.02670 0.01280 0.01520 0.05840 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2380 0.1570 0.1730 0.1450 0.1490 0.1930 0.5650 0.3900 0.4500 0.5430 1.910 0.05000 14.20 0.007430 0.02670 0.01280 0.01520 0.05840 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2380 0.1570 0.1730 0.1450 0.1490 0.1930 0.5630 0.3900 0.4500 0.5430 1.840 0.1000 13.70 0.007430 0.02670 0.01280 0.01520 0.05840 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2380 0.1570 0.1730 0.1450 0.1490 0.1930 0.5560 0.3900 0.4500 0.5430 1.640 0.1500 13.20 0.007430 0.02670 0.01280 0.01520 0.05840 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2370 0.1570 0.1730 0.1450 0.1490 0.1930 0.5450 0.3900 0.4500 0.5430 1.350 0.2000 12.50 0.007430 0.02670 0.01280 0.01520 0.05830 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2360 0.1570 0.1730 0.1450 0.1490 0.1930 0.5290 0.3890 0.4490 0.5420 1.040 0.3000 11.30 0.007430 0.02670 0.01280 0.01520 0.05830 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2320 0.1570 0.1730 0.1450 0.1490 0.1930 0.4880 0.3860 0.4430 0.5350 0.5130 0.4000 10.50 0.007430 0.02670 0.01280 0.01520 0.05820 0.03400 0.03790 0.02430 0.02520 0.1140 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2270 0.1560 0.1730 0.1450 0.1490 0.1930 0.4360 0.3800 0.4310 0.5120 0.2140 0.5000 9.990 0.007430 0.02660 0.01280 0.01520 0.05810 0.03400 0.03790 0.02430 0.02520 0.1130 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2210 0.1560 0.1720 0.1450 0.1490 0.1930 0.3770 0.3680 0.4090 0.4710 0.09980 0.6000 9.580 0.007430 0.02660 0.01280 0.01520 0.05800 0.03400 0.03790 0.02430 0.02520 0.1120 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2130 0.1560 0.1720 0.1450 0.1490 0.1920 0.3170 0.3490 0.3790 0.4150 0.07330 0.7000 9.170 0.007430 0.02660 0.01280 0.01520 0.05790 0.03400 0.03790 0.02430 0.02520 0.1110 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2050 0.1550 0.1710 0.1450 0.1490 0.1920 0.2600 0.3250 0.3400 0.3510 0.07130 0.8000 8.730 0.007430 0.02660 0.01280 0.01520 0.05780 0.03400 0.03790 0.02430 0.02520 0.1100 0.07230 0.07980 0.05890 0.06060 0.04720 0.04790 0.1950 0.1550 0.1700 0.1450 0.1490 0.1900 0.2070 0.2960 0.2970 0.2880 0.06910 1.000 7.840 0.007430 0.02660 0.01280 0.01520 0.05740 0.03400 0.03790 0.02430 0.02520 0.1070 0.07230 0.07970 0.05890 0.06060 0.04720 0.04790 0.1750 0.1520 0.1660 0.1450 0.1480 0.1850 0.1260 0.2290 0.2070 0.1790 0.05060 1.200 7.060 0.007430 0.02650 0.01280 0.01520 0.05700 0.03400 0.03790 0.02430 0.02520 0.1040 0.07220 0.07950 0.05890 0.06060 0.04720 0.04790 0.1530 0.1490 0.1610 0.1430 0.1470 0.1760 0.07780 0.1620 0.1300 0.1030 0.02850 1.400 6.450 0.007420 0.02650 0.01280 0.01520 0.05650 0.03400 0.03790 0.02430 0.02520 0.1000 0.07200 0.07920 0.05890 0.06060 0.04720 0.04790 0.1310 0.1430 0.1520 0.1410 0.1440 0.1640 0.05510 0.1060 0.07630 0.05630 0.01410 1.600 6.000 0.007420 0.02650 0.01280 0.01520 0.05590 0.03400 0.03790 0.02430 0.02520 0.09590 0.07180 0.07880 0.05890 0.06060 0.04720 0.04790 0.1100 0.1360 0.1420 0.1370 0.1400 0.1500 0.04770 0.06640 0.04420 0.02980 0.007300 1.800 5.640 0.007420 0.02640 0.01280 0.01520 0.05530 0.03400 0.03790 0.02430 0.02520 0.09130 0.07140 0.07830 0.05890 0.06050 0.04720 0.04790 0.09060 0.1270 0.1300 0.1320 0.1340 0.1350 0.04670 0.04140 0.02820 0.01600 0.004970 2.000 5.340 0.007420 0.02630 0.01280 0.01520 0.05460 0.03400 0.03790 0.02430 0.02520 0.08650 0.07100 0.07760 0.05880 0.06050 0.04720 0.04790 0.07370 0.1170 0.1160 0.1250 0.1260 0.1200 0.04630 0.02790 0.02200 0.009490 0.004520 2.400 4.780 0.007420 0.02620 0.01280 0.01520 0.05300 0.03390 0.03780 0.02430 0.02520 0.07640 0.06960 0.07550 0.05860 0.06030 0.04720 0.04780 0.04850 0.09440 0.08790 0.1070 0.1070 0.09070 0.04000 0.02010 0.02040 0.005950 0.004340 3.000 3.970 0.007410 0.02600 0.01280 0.01520 0.05030 0.03380 0.03760 0.02430 0.02520 0.06090 0.06640 0.07070 0.05800 0.05960 0.04700 0.04770 0.02980 0.06050 0.05040 0.07570 0.07350 0.05560 0.02200 0.01920 0.01730 0.005680 0.002670 4.000 2.950 0.007400 0.02540 0.01280 0.01520 0.04480 0.03350 0.03710 0.02430 0.02520 0.03840 0.05780 0.05850 0.05550 0.05660 0.04640 0.04700 0.02510 0.02360 0.01840 0.03220 0.02970 0.02140 0.006400 0.01030 0.006430 0.003390 0.0006790 5.000 2.420 0.007380 0.02480 0.01280 0.01520 0.03860 0.03290 0.03610 0.02430 0.02510 0.02320 0.04610 0.04340 0.05030 0.05080 0.04470 0.04520 0.02270 0.01230 0.01260 0.01170 0.01060 0.007440 0.005220 0.003480 0.002250 0.001250 0.0004710 6.000 2.080 0.007360 0.02400 0.01280 0.01520 0.03240 0.03190 0.03450 0.02420 0.02500 0.01540 0.03370 0.02910 0.04280 0.04250 0.04170 0.04200 0.01540 0.01130 0.01230 0.006120 0.006040 0.002790 0.004360 0.001970 0.001910 0.0004940 0.0004100 7.000 1.790 0.007330 0.02310 0.01280 0.01510 0.02640 0.03050 0.03240 0.02400 0.02480 0.01260 0.02280 0.01810 0.03400 0.03310 0.03740 0.03750 0.008430 0.01060 0.010000 0.005560 0.005720 0.001600 0.002590 0.001900 0.001730 0.0003730 0.0002500 8.000 1.520 0.007300 0.02210 0.01270 0.01510 0.02090 0.02870 0.02970 0.02370 0.02440 0.01220 0.01460 0.01120 0.02530 0.02420 0.03250 0.03240 0.004590 0.008340 0.006670 0.005390 0.005420 0.001430 0.001300 0.001650 0.001240 0.0003670 0.0001240 10.00 1.100 0.007200 0.02000 0.01270 0.01500 0.01300 0.02400 0.02300 0.02300 0.02300 0.01100 0.006700 0.006200 0.01200 0.01100 0.02200 0.02200 0.003100 0.003400 0.002200 0.003500 0.003300 0.001400 0.0006200 0.0007400 0.0004100 0.0002600 5.600E-05 15.00 0.5400 0.007000 0.01400 0.01200 0.01400 0.004800 0.01200 0.009200 0.01700 0.01700 0.003600 0.004900 0.004900 0.002500 0.002500 0.006800 0.006500 0.001400 0.001100 0.001200 0.0005300 0.0004900 0.0005700 0.0003100 0.0001900 0.0001900 3.800E-05 2.800E-05 20.00 0.3100 0.006700 0.008700 0.01100 0.01200 0.004500 0.004200 0.002900 0.01100 0.01100 0.001200 0.002600 0.001800 0.002200 0.002300 0.001800 0.001700 0.0003400 0.0007100 0.0005000 0.0003800 0.0003900 0.0001700 6.900E-05 0.0001300 8.000E-05 2.600E-05 6.200E-06 30.00 0.1200 0.005900 0.002600 0.007900 0.007500 0.002300 0.001300 0.001600 0.003100 0.002800 0.0008500 0.0003500 0.0003900 0.0009800 0.0008900 0.0001500 0.0001300 0.0002200 8.800E-05 8.700E-05 0.0001900 0.0001700 1.400E-05 4.500E-05 1.500E-05 1.300E-05 1.300E-05 4.000E-06 40.00 0.06000 0.005000 0.0008900 0.004900 0.004000 0.0005900 0.001200 0.001200 0.0008200 0.0006800 0.0002300 0.0002900 0.0003400 0.0002700 0.0002300 1.700E-05 1.400E-05 6.200E-05 6.400E-05 7.700E-05 5.400E-05 4.500E-05 1.600E-06 1.200E-05 1.100E-05 1.200E-05 3.800E-06 1.100E-06 60.00 0.02000 0.003300 0.0006300 0.001700 0.0009700 0.0001400 0.0005100 0.0003500 7.200E-05 5.100E-05 3.700E-05 0.0001400 1.000E-04 2.400E-05 1.700E-05 4.800E-07 3.700E-07 9.100E-06 3.300E-05 2.300E-05 4.700E-06 3.400E-06 4.500E-08 1.800E-06 5.600E-06 3.500E-06 3.300E-07 1.600E-07 100.0 0.004600 0.001300 0.0003300 0.0002300 7.600E-05 8.500E-05 6.900E-05 2.600E-05 1.900E-06 1.000E-06 2.300E-05 1.900E-05 7.300E-06 6.000E-07 3.200E-07 3.500E-09 2.200E-09 5.700E-06 4.400E-06 1.700E-06 1.200E-07 6.300E-08 3.400E-10 1.100E-06 7.600E-07 2.600E-07 8.100E-09 1.000E-07 #S 93 Np #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 4 2 2 4 1 2 #UBIND 1.187E+05 2.242E+04 2.160E+04 1.761E+04 5722. 5366. 4435. 3850. 3664. 1501. 1328. 1087. 817.0 773.0 415.0 404.0 338.0 283.0 206.0 109.0 101.0 0.000 0.000 0.000 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 14.20 0.007300 0.02620 0.01260 0.01500 0.05750 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2330 0.1530 0.1690 0.1420 0.1450 0.1820 0.5520 0.3800 0.4420 0.5320 1.890 0.05000 14.10 0.007300 0.02620 0.01260 0.01500 0.05750 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2330 0.1530 0.1690 0.1420 0.1450 0.1820 0.5500 0.3800 0.4420 0.5320 1.820 0.1000 13.70 0.007300 0.02620 0.01260 0.01500 0.05740 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2320 0.1530 0.1690 0.1420 0.1450 0.1820 0.5430 0.3800 0.4420 0.5320 1.630 0.1500 13.10 0.007300 0.02620 0.01260 0.01500 0.05740 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2310 0.1530 0.1690 0.1420 0.1450 0.1820 0.5330 0.3790 0.4420 0.5320 1.350 0.2000 12.40 0.007300 0.02620 0.01260 0.01500 0.05740 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2300 0.1530 0.1690 0.1420 0.1450 0.1820 0.5180 0.3790 0.4410 0.5310 1.050 0.3000 11.30 0.007300 0.02620 0.01260 0.01500 0.05740 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2270 0.1530 0.1690 0.1420 0.1450 0.1820 0.4800 0.3760 0.4360 0.5240 0.5240 0.4000 10.50 0.007300 0.02620 0.01260 0.01500 0.05730 0.03340 0.03740 0.02400 0.02480 0.1120 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2220 0.1530 0.1690 0.1420 0.1450 0.1820 0.4300 0.3700 0.4240 0.5030 0.2210 0.5000 9.980 0.007300 0.02620 0.01260 0.01500 0.05720 0.03340 0.03740 0.02400 0.02480 0.1110 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2160 0.1530 0.1690 0.1420 0.1450 0.1820 0.3750 0.3600 0.4040 0.4650 0.1010 0.6000 9.590 0.007300 0.02620 0.01260 0.01500 0.05710 0.03340 0.03740 0.02400 0.02480 0.1100 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2090 0.1520 0.1680 0.1420 0.1450 0.1820 0.3180 0.3430 0.3750 0.4120 0.07140 0.7000 9.200 0.007300 0.02620 0.01260 0.01500 0.05700 0.03340 0.03740 0.02400 0.02480 0.1090 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2010 0.1520 0.1670 0.1420 0.1450 0.1810 0.2620 0.3210 0.3390 0.3520 0.06860 0.8000 8.780 0.007300 0.02620 0.01260 0.01500 0.05690 0.03340 0.03740 0.02400 0.02480 0.1080 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.1920 0.1510 0.1660 0.1420 0.1450 0.1800 0.2110 0.2940 0.2980 0.2910 0.06710 1.000 7.920 0.007300 0.02620 0.01260 0.01500 0.05650 0.03340 0.03740 0.02400 0.02480 0.1060 0.07090 0.07840 0.05790 0.05960 0.04620 0.04690 0.1730 0.1490 0.1630 0.1410 0.1450 0.1760 0.1310 0.2310 0.2110 0.1850 0.05070 1.200 7.160 0.007300 0.02610 0.01260 0.01500 0.05610 0.03340 0.03740 0.02400 0.02480 0.1020 0.07080 0.07820 0.05790 0.05950 0.04620 0.04690 0.1520 0.1460 0.1580 0.1400 0.1430 0.1700 0.08080 0.1670 0.1350 0.1090 0.02960 1.400 6.550 0.007300 0.02610 0.01260 0.01500 0.05570 0.03340 0.03740 0.02400 0.02480 0.09880 0.07070 0.07800 0.05790 0.05950 0.04620 0.04690 0.1310 0.1410 0.1500 0.1380 0.1410 0.1600 0.05610 0.1120 0.08040 0.06080 0.01500 1.600 6.090 0.007300 0.02600 0.01260 0.01500 0.05510 0.03340 0.03740 0.02400 0.02480 0.09480 0.07050 0.07760 0.05780 0.05950 0.04620 0.04690 0.1110 0.1340 0.1410 0.1350 0.1370 0.1490 0.04730 0.07130 0.04680 0.03290 0.007660 1.800 5.720 0.007300 0.02600 0.01260 0.01500 0.05450 0.03340 0.03740 0.02400 0.02480 0.09040 0.07010 0.07710 0.05780 0.05950 0.04620 0.04690 0.09190 0.1260 0.1290 0.1300 0.1320 0.1360 0.04560 0.04480 0.02940 0.01780 0.004960 2.000 5.420 0.007290 0.02590 0.01260 0.01500 0.05390 0.03340 0.03740 0.02400 0.02480 0.08580 0.06970 0.07640 0.05780 0.05940 0.04620 0.04690 0.07540 0.1170 0.1170 0.1240 0.1250 0.1220 0.04550 0.02980 0.02220 0.01040 0.004330 2.400 4.860 0.007290 0.02580 0.01260 0.01500 0.05230 0.03340 0.03730 0.02400 0.02480 0.07600 0.06850 0.07450 0.05760 0.05930 0.04620 0.04690 0.05000 0.09520 0.08930 0.1070 0.1070 0.09480 0.04040 0.02000 0.01980 0.005960 0.004190 3.000 4.040 0.007280 0.02560 0.01260 0.01500 0.04970 0.03330 0.03710 0.02400 0.02480 0.06110 0.06550 0.07000 0.05710 0.05860 0.04610 0.04680 0.03040 0.06250 0.05260 0.07750 0.07560 0.06040 0.02350 0.01910 0.01750 0.005610 0.002750 4.000 2.990 0.007270 0.02500 0.01260 0.01500 0.04440 0.03300 0.03660 0.02400 0.02480 0.03910 0.05750 0.05840 0.05480 0.05590 0.04550 0.04620 0.02480 0.02520 0.01930 0.03440 0.03190 0.02460 0.006800 0.01110 0.006950 0.003590 0.0007200 5.000 2.420 0.007250 0.02440 0.01260 0.01500 0.03850 0.03240 0.03570 0.02400 0.02480 0.02380 0.04640 0.04390 0.05000 0.05050 0.04400 0.04450 0.02290 0.01260 0.01260 0.01280 0.01160 0.008940 0.005170 0.003890 0.002370 0.001400 0.0004530 6.000 2.080 0.007230 0.02360 0.01260 0.01500 0.03240 0.03150 0.03420 0.02390 0.02470 0.01570 0.03430 0.02990 0.04290 0.04270 0.04130 0.04160 0.01610 0.01120 0.01230 0.006360 0.006230 0.003360 0.004520 0.002020 0.001880 0.0005410 0.0004110 7.000 1.800 0.007210 0.02280 0.01250 0.01490 0.02660 0.03010 0.03210 0.02370 0.02450 0.01260 0.02360 0.01880 0.03440 0.03370 0.03740 0.03750 0.009110 0.01070 0.01030 0.005550 0.005730 0.001790 0.002820 0.001910 0.001760 0.0003780 0.0002640 8.000 1.540 0.007180 0.02180 0.01250 0.01490 0.02120 0.02840 0.02960 0.02340 0.02420 0.01200 0.01540 0.01170 0.02600 0.02490 0.03270 0.03260 0.004940 0.008690 0.007100 0.005450 0.005520 0.001510 0.001450 0.001720 0.001310 0.0003710 0.0001350 10.00 1.100 0.007100 0.02000 0.01200 0.01490 0.01300 0.02400 0.02400 0.02200 0.02300 0.01100 0.006900 0.006200 0.01300 0.01200 0.02300 0.02300 0.003100 0.003800 0.002400 0.003800 0.003500 0.001500 0.0006300 0.0008200 0.0004500 0.0002800 5.500E-05 15.00 0.5500 0.006900 0.01400 0.01200 0.01400 0.004900 0.01200 0.009400 0.01700 0.01700 0.003900 0.004900 0.004900 0.002500 0.002500 0.007200 0.006900 0.001500 0.001100 0.001200 0.0005700 0.0005200 0.0006500 0.0003400 0.0001900 0.0001900 4.200E-05 3.000E-05 20.00 0.3100 0.006600 0.008800 0.01100 0.01200 0.004400 0.004400 0.003000 0.01100 0.01100 0.001300 0.002700 0.001900 0.002200 0.002300 0.002000 0.001900 0.0003500 0.0007500 0.0005400 0.0003800 0.0004000 0.0002000 7.400E-05 0.0001400 8.600E-05 2.600E-05 6.400E-06 30.00 0.1300 0.005800 0.002800 0.007900 0.007600 0.002400 0.001200 0.001600 0.003300 0.002900 0.0008800 0.0003700 0.0003900 0.001000 0.0009300 0.0001700 0.0001500 0.0002300 9.400E-05 8.900E-05 0.0002000 0.0001800 1.700E-05 4.700E-05 1.700E-05 1.300E-05 1.400E-05 4.100E-06 40.00 0.06100 0.005000 0.0009200 0.005000 0.004100 0.0006400 0.001200 0.001300 0.0008700 0.0007200 0.0002500 0.0002800 0.0003400 0.0002900 0.0002500 1.900E-05 1.600E-05 6.800E-05 6.400E-05 7.900E-05 5.800E-05 4.900E-05 1.900E-06 1.400E-05 1.100E-05 1.200E-05 4.100E-06 1.200E-06 60.00 0.02000 0.003300 0.0006200 0.001700 0.001000 0.0001400 0.0005300 0.0003600 7.800E-05 5.600E-05 3.700E-05 0.0001500 0.0001100 2.600E-05 1.900E-05 5.500E-07 4.300E-07 9.200E-06 3.400E-05 2.500E-05 5.200E-06 3.800E-06 5.600E-08 1.800E-06 6.000E-06 3.800E-06 3.700E-07 1.600E-07 100.0 0.004800 0.001300 0.0003400 0.0002400 8.100E-05 8.700E-05 7.400E-05 2.700E-05 2.100E-06 1.100E-06 2.400E-05 2.100E-05 7.900E-06 6.700E-07 3.600E-07 4.100E-09 2.600E-09 6.000E-06 4.900E-06 1.800E-06 1.300E-07 7.100E-08 4.300E-10 1.200E-06 8.500E-07 2.800E-07 9.300E-09 1.000E-07 #S 94 Pu #N 28 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 2 2 4 2 #UBIND 1.218E+05 2.310E+04 2.227E+04 1.806E+04 5933. 5546. 4562. 3973. 3778. 1558. 1377. 1120. 849.0 801.0 422.0 422.0 352.0 279.0 212.0 116.0 105.0 0.000 0.000 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 0.000 14.20 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2280 0.1500 0.1660 0.1390 0.1420 0.1830 0.5500 0.3760 0.4490 1.990 0.05000 14.00 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2270 0.1500 0.1660 0.1390 0.1420 0.1830 0.5480 0.3760 0.4490 1.910 0.1000 13.60 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2270 0.1500 0.1660 0.1390 0.1420 0.1830 0.5420 0.3760 0.4490 1.690 0.1500 12.90 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2260 0.1500 0.1660 0.1390 0.1420 0.1830 0.5310 0.3750 0.4490 1.390 0.2000 12.20 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2250 0.1500 0.1660 0.1390 0.1420 0.1830 0.5170 0.3750 0.4480 1.050 0.3000 11.00 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2220 0.1500 0.1660 0.1390 0.1420 0.1830 0.4790 0.3730 0.4420 0.4990 0.4000 10.20 0.007180 0.02580 0.01240 0.01490 0.05640 0.03290 0.03690 0.02370 0.02450 0.1100 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2170 0.1490 0.1660 0.1390 0.1420 0.1830 0.4300 0.3670 0.4300 0.1980 0.5000 9.770 0.007180 0.02580 0.01240 0.01490 0.05630 0.03290 0.03690 0.02370 0.02450 0.1090 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2120 0.1490 0.1650 0.1390 0.1420 0.1830 0.3760 0.3570 0.4090 0.08890 0.6000 9.440 0.007180 0.02580 0.01240 0.01490 0.05620 0.03290 0.03690 0.02370 0.02450 0.1090 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2050 0.1490 0.1650 0.1390 0.1420 0.1820 0.3190 0.3410 0.3780 0.06470 0.7000 9.100 0.007180 0.02580 0.01240 0.01490 0.05610 0.03290 0.03690 0.02370 0.02450 0.1080 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.1980 0.1490 0.1640 0.1390 0.1420 0.1810 0.2640 0.3200 0.3400 0.06310 0.8000 8.740 0.007180 0.02580 0.01240 0.01490 0.05600 0.03290 0.03690 0.02370 0.02450 0.1070 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.1900 0.1480 0.1630 0.1390 0.1420 0.1800 0.2130 0.2940 0.2970 0.06100 1.000 7.980 0.007180 0.02570 0.01240 0.01490 0.05570 0.03290 0.03690 0.02370 0.02450 0.1040 0.06960 0.07710 0.05690 0.05850 0.04520 0.04600 0.1710 0.1460 0.1600 0.1390 0.1420 0.1750 0.1320 0.2330 0.2090 0.04440 1.200 7.280 0.007170 0.02570 0.01240 0.01490 0.05530 0.03290 0.03690 0.02370 0.02450 0.1010 0.06950 0.07700 0.05690 0.05850 0.04520 0.04600 0.1510 0.1430 0.1550 0.1380 0.1410 0.1680 0.08140 0.1690 0.1330 0.02510 1.400 6.710 0.007170 0.02570 0.01240 0.01490 0.05480 0.03290 0.03690 0.02370 0.02450 0.09750 0.06940 0.07670 0.05690 0.05850 0.04520 0.04600 0.1310 0.1380 0.1480 0.1360 0.1380 0.1580 0.05570 0.1150 0.07880 0.01250 1.600 6.260 0.007170 0.02560 0.01240 0.01490 0.05430 0.03290 0.03690 0.02370 0.02450 0.09360 0.06920 0.07640 0.05680 0.05850 0.04520 0.04590 0.1110 0.1320 0.1390 0.1330 0.1350 0.1460 0.04600 0.07350 0.04550 0.006350 1.800 5.900 0.007170 0.02560 0.01240 0.01490 0.05370 0.03290 0.03690 0.02370 0.02450 0.08940 0.06890 0.07590 0.05680 0.05850 0.04520 0.04590 0.09310 0.1250 0.1290 0.1280 0.1300 0.1330 0.04400 0.04630 0.02820 0.004090 2.000 5.580 0.007170 0.02550 0.01240 0.01490 0.05310 0.03290 0.03690 0.02370 0.02450 0.08500 0.06850 0.07530 0.05680 0.05840 0.04520 0.04590 0.07680 0.1160 0.1170 0.1220 0.1240 0.1200 0.04390 0.03050 0.02090 0.003550 2.400 4.990 0.007170 0.02540 0.01240 0.01480 0.05170 0.03280 0.03680 0.02370 0.02450 0.07570 0.06740 0.07350 0.05660 0.05830 0.04520 0.04590 0.05140 0.09570 0.09050 0.1070 0.1070 0.09390 0.03970 0.01960 0.01840 0.003440 3.000 4.140 0.007160 0.02520 0.01240 0.01480 0.04910 0.03270 0.03670 0.02370 0.02450 0.06130 0.06470 0.06930 0.05620 0.05770 0.04510 0.04580 0.03090 0.06420 0.05440 0.07850 0.07700 0.06070 0.02400 0.01850 0.01640 0.002330 4.000 3.040 0.007150 0.02470 0.01240 0.01480 0.04410 0.03250 0.03620 0.02370 0.02450 0.03970 0.05710 0.05830 0.05400 0.05530 0.04470 0.04530 0.02440 0.02660 0.02030 0.03620 0.03380 0.02570 0.007000 0.01140 0.006880 0.0006310 5.000 2.440 0.007130 0.02410 0.01230 0.01480 0.03840 0.03190 0.03530 0.02360 0.02450 0.02440 0.04660 0.04440 0.04960 0.05020 0.04330 0.04390 0.02300 0.01290 0.01260 0.01370 0.01250 0.009710 0.004980 0.004180 0.002320 0.0003700 6.000 2.090 0.007110 0.02330 0.01230 0.01480 0.03250 0.03110 0.03390 0.02350 0.02440 0.01600 0.03500 0.03060 0.04290 0.04280 0.04090 0.04120 0.01680 0.01110 0.01220 0.006580 0.006400 0.003680 0.004500 0.002030 0.001740 0.0003440 7.000 1.810 0.007090 0.02250 0.01230 0.01480 0.02680 0.02980 0.03190 0.02340 0.02420 0.01260 0.02440 0.01950 0.03490 0.03410 0.03730 0.03740 0.009790 0.01070 0.01060 0.005500 0.005700 0.001850 0.002950 0.001860 0.001660 0.0002320 8.000 1.550 0.007060 0.02160 0.01230 0.01470 0.02150 0.02820 0.02950 0.02310 0.02390 0.01180 0.01610 0.01220 0.02660 0.02550 0.03290 0.03280 0.005320 0.008970 0.007480 0.005430 0.005560 0.001470 0.001560 0.001730 0.001270 0.0001230 10.00 1.100 0.007000 0.02000 0.01200 0.01470 0.01300 0.02400 0.02400 0.02200 0.02300 0.01100 0.007200 0.006300 0.01300 0.01200 0.02400 0.02300 0.003100 0.004100 0.002600 0.003900 0.003700 0.001400 0.0006300 0.0008800 0.0004600 4.700E-05 15.00 0.5600 0.006800 0.01400 0.01200 0.01400 0.004900 0.01200 0.009700 0.01700 0.01700 0.004100 0.004800 0.005000 0.002600 0.002500 0.007600 0.007300 0.001600 0.001100 0.001200 0.0006100 0.0005600 0.0006700 0.0003600 0.0001900 0.0001800 2.700E-05 20.00 0.3200 0.006500 0.009000 0.01100 0.01200 0.004300 0.004600 0.003200 0.01100 0.01100 0.001300 0.002800 0.002000 0.002200 0.002300 0.002200 0.002000 0.0003700 0.0007900 0.0005800 0.0003800 0.0004000 0.0002100 7.700E-05 0.0001400 8.600E-05 5.700E-06 30.00 0.1300 0.005800 0.002900 0.007900 0.007600 0.002500 0.001200 0.001600 0.003400 0.003000 0.0009000 0.0003800 0.0003900 0.001100 0.0009800 0.0001900 0.0001700 0.0002400 1.000E-04 9.000E-05 0.0002100 0.0001900 1.900E-05 4.800E-05 1.800E-05 1.300E-05 3.500E-06 40.00 0.06300 0.004900 0.0009600 0.005100 0.004200 0.0006900 0.001100 0.001300 0.0009300 0.0007700 0.0002700 0.0002800 0.0003500 0.0003100 0.0002600 2.100E-05 1.800E-05 7.500E-05 6.400E-05 8.000E-05 6.300E-05 5.300E-05 2.100E-06 1.500E-05 1.100E-05 1.100E-05 1.100E-06 60.00 0.02100 0.003400 0.0006100 0.001800 0.001100 0.0001400 0.0005500 0.0003800 8.400E-05 6.000E-05 3.700E-05 0.0001500 0.0001100 2.900E-05 2.100E-05 6.300E-07 4.900E-07 9.300E-06 3.600E-05 2.600E-05 5.800E-06 4.200E-06 6.400E-08 1.800E-06 6.200E-06 3.700E-06 1.300E-07 100.0 0.005000 0.001400 0.0003500 0.0002600 8.600E-05 8.900E-05 7.900E-05 2.900E-05 2.300E-06 1.200E-06 2.500E-05 2.200E-05 8.500E-06 7.400E-07 4.000E-07 4.700E-09 3.000E-09 6.200E-06 5.300E-06 2.000E-06 1.500E-07 8.000E-08 5.000E-10 1.200E-06 9.100E-07 2.800E-07 8.800E-08 #S 95 Am #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 1 2 2 4 2 #UBIND 1.250E+05 2.377E+04 2.294E+04 1.850E+04 6120. 5710. 4667. 4092. 3887. 1617. 1412. 1136. 883.0 832.0 464.0 449.0 351.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 14.10 0.007050 0.02540 0.01210 0.01470 0.05570 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2220 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.5380 0.3670 0.4410 1.970 0.05000 13.90 0.007050 0.02540 0.01210 0.01470 0.05570 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2220 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.5360 0.3670 0.4410 1.890 0.1000 13.50 0.007050 0.02540 0.01210 0.01470 0.05560 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2220 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.5300 0.3670 0.4410 1.680 0.1500 12.90 0.007050 0.02540 0.01210 0.01470 0.05560 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2210 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.5200 0.3670 0.4410 1.380 0.2000 12.20 0.007050 0.02540 0.01210 0.01470 0.05560 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2200 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.5070 0.3660 0.4400 1.060 0.3000 11.00 0.007050 0.02540 0.01210 0.01470 0.05560 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2170 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.4710 0.3640 0.4350 0.5100 0.4000 10.20 0.007050 0.02540 0.01210 0.01470 0.05550 0.03240 0.03640 0.02330 0.02420 0.1080 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2130 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.4250 0.3590 0.4230 0.2060 0.5000 9.760 0.007050 0.02540 0.01210 0.01470 0.05540 0.03240 0.03640 0.02330 0.02420 0.1070 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2080 0.1460 0.1620 0.1360 0.1390 0.1740 0.1860 0.3730 0.3500 0.4030 0.09070 0.6000 9.440 0.007050 0.02540 0.01210 0.01470 0.05540 0.03240 0.03640 0.02330 0.02420 0.1070 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2010 0.1460 0.1620 0.1360 0.1390 0.1730 0.1860 0.3190 0.3360 0.3750 0.06320 0.7000 9.120 0.007050 0.02540 0.01210 0.01470 0.05520 0.03240 0.03640 0.02330 0.02420 0.1060 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.1940 0.1450 0.1610 0.1360 0.1390 0.1730 0.1850 0.2660 0.3160 0.3390 0.06090 0.8000 8.770 0.007050 0.02540 0.01210 0.01470 0.05510 0.03240 0.03640 0.02330 0.02420 0.1050 0.06840 0.07590 0.05590 0.05760 0.04430 0.04500 0.1870 0.1450 0.1600 0.1360 0.1390 0.1720 0.1830 0.2160 0.2920 0.2980 0.05940 1.000 8.040 0.007050 0.02530 0.01210 0.01470 0.05480 0.03230 0.03640 0.02330 0.02420 0.1020 0.06840 0.07590 0.05590 0.05760 0.04430 0.04500 0.1690 0.1430 0.1570 0.1350 0.1390 0.1680 0.1770 0.1360 0.2340 0.2130 0.04460 1.200 7.360 0.007050 0.02530 0.01210 0.01470 0.05450 0.03230 0.03640 0.02330 0.02420 0.09940 0.06830 0.07570 0.05590 0.05760 0.04430 0.04500 0.1500 0.1400 0.1530 0.1340 0.1380 0.1630 0.1680 0.08460 0.1740 0.1380 0.02600 1.400 6.790 0.007050 0.02530 0.01210 0.01470 0.05400 0.03230 0.03640 0.02330 0.02420 0.09610 0.06810 0.07550 0.05590 0.05760 0.04430 0.04500 0.1310 0.1360 0.1460 0.1330 0.1360 0.1540 0.1570 0.05710 0.1200 0.08290 0.01330 1.600 6.340 0.007050 0.02520 0.01210 0.01470 0.05350 0.03230 0.03640 0.02330 0.02420 0.09250 0.06800 0.07520 0.05590 0.05750 0.04430 0.04500 0.1120 0.1300 0.1380 0.1300 0.1330 0.1440 0.1440 0.04600 0.07810 0.04840 0.006720 1.800 5.970 0.007050 0.02520 0.01210 0.01470 0.05300 0.03230 0.03640 0.02330 0.02420 0.08850 0.06770 0.07480 0.05580 0.05750 0.04430 0.04500 0.09420 0.1230 0.1280 0.1260 0.1280 0.1330 0.1310 0.04320 0.04970 0.02970 0.004140 2.000 5.650 0.007050 0.02510 0.01210 0.01470 0.05240 0.03230 0.03640 0.02330 0.02420 0.08430 0.06730 0.07420 0.05580 0.05750 0.04430 0.04500 0.07820 0.1150 0.1170 0.1210 0.1220 0.1210 0.1170 0.04300 0.03260 0.02130 0.003430 2.400 5.070 0.007040 0.02500 0.01210 0.01470 0.05100 0.03230 0.03630 0.02330 0.02420 0.07530 0.06630 0.07250 0.05570 0.05730 0.04430 0.04500 0.05290 0.09620 0.09160 0.1070 0.1070 0.09680 0.09170 0.03980 0.01970 0.01800 0.003320 3.000 4.220 0.007040 0.02480 0.01210 0.01470 0.04860 0.03220 0.03620 0.02330 0.02420 0.06140 0.06380 0.06850 0.05530 0.05680 0.04430 0.04500 0.03160 0.06600 0.05630 0.08000 0.07850 0.06460 0.05960 0.02530 0.01820 0.01650 0.002370 4.000 3.090 0.007030 0.02430 0.01210 0.01470 0.04370 0.03200 0.03570 0.02330 0.02420 0.04030 0.05670 0.05820 0.05330 0.05460 0.04380 0.04450 0.02410 0.02830 0.02130 0.03840 0.03590 0.02870 0.02570 0.007530 0.01200 0.007380 0.0006750 5.000 2.450 0.007010 0.02370 0.01210 0.01460 0.03820 0.03150 0.03490 0.02330 0.02420 0.02500 0.04670 0.04470 0.04920 0.04990 0.04270 0.04330 0.02300 0.01340 0.01260 0.01500 0.01360 0.01130 0.009910 0.004940 0.004630 0.002470 0.0003590 6.000 2.090 0.006990 0.02300 0.01210 0.01460 0.03250 0.03060 0.03350 0.02320 0.02410 0.01630 0.03550 0.03130 0.04300 0.04290 0.04050 0.04080 0.01740 0.01100 0.01220 0.006940 0.006650 0.004370 0.003790 0.004590 0.002110 0.001730 0.0003400 7.000 1.820 0.006970 0.02220 0.01210 0.01460 0.02690 0.02950 0.03170 0.02310 0.02390 0.01260 0.02510 0.02020 0.03520 0.03460 0.03710 0.03730 0.01050 0.01080 0.01080 0.005520 0.005690 0.002100 0.001880 0.003160 0.001850 0.001670 0.0002420 8.000 1.560 0.006940 0.02140 0.01210 0.01450 0.02180 0.02800 0.02940 0.02290 0.02360 0.01170 0.01680 0.01270 0.02720 0.02620 0.03300 0.03300 0.005740 0.009250 0.007870 0.005460 0.005600 0.001560 0.001460 0.001720 0.001760 0.001330 0.0001330 10.00 1.100 0.006900 0.01900 0.01200 0.01400 0.01400 0.02400 0.02400 0.02200 0.02300 0.01100 0.007500 0.006400 0.01400 0.01300 0.02400 0.02400 0.003100 0.004400 0.002900 0.004100 0.003900 0.001500 0.001400 0.0006500 0.0009600 0.0005100 4.700E-05 15.00 0.5700 0.006700 0.01400 0.01200 0.01300 0.004900 0.01300 0.010000 0.01700 0.01700 0.004400 0.004800 0.005100 0.002600 0.002600 0.008000 0.007700 0.001800 0.001100 0.001300 0.0006600 0.0006000 0.0007500 0.0006800 0.0003800 0.0001900 0.0001800 2.800E-05 20.00 0.3300 0.006400 0.009100 0.01100 0.01200 0.004300 0.004900 0.003300 0.01100 0.01100 0.001300 0.002900 0.002100 0.002200 0.002300 0.002300 0.002200 0.0004000 0.0008300 0.0006200 0.0003900 0.0004100 0.0002400 0.0002100 8.300E-05 0.0001500 9.200E-05 6.100E-06 30.00 0.1300 0.005700 0.003000 0.007900 0.007700 0.002600 0.001200 0.001600 0.003500 0.003200 0.0009300 0.0004100 0.0003900 0.001100 0.001000 0.0002100 0.0001800 0.0002500 0.0001100 9.200E-05 0.0002200 0.0002100 2.200E-05 1.900E-05 4.900E-05 2.000E-05 1.300E-05 3.500E-06 40.00 0.06400 0.004900 0.0009900 0.005200 0.004200 0.0007500 0.001100 0.001300 0.0009800 0.0008100 0.0002900 0.0002800 0.0003500 0.0003300 0.0002800 2.400E-05 2.000E-05 8.300E-05 6.400E-05 8.200E-05 6.900E-05 5.800E-05 2.600E-06 2.100E-06 1.700E-05 1.100E-05 1.200E-05 1.200E-06 60.00 0.02100 0.003400 0.0006000 0.001900 0.001100 0.0001300 0.0005700 0.0004000 9.100E-05 6.500E-05 3.700E-05 0.0001600 0.0001200 3.100E-05 2.300E-05 7.200E-07 5.500E-07 9.400E-06 3.800E-05 2.800E-05 6.400E-06 4.700E-06 7.800E-08 5.600E-08 1.800E-06 6.500E-06 4.000E-06 1.300E-07 100.0 0.005100 0.001400 0.0003600 0.0002800 9.100E-05 9.100E-05 8.500E-05 3.100E-05 2.500E-06 1.300E-06 2.500E-05 2.400E-05 9.100E-06 8.300E-07 4.400E-07 5.500E-09 3.400E-09 6.400E-06 5.800E-06 2.200E-06 1.700E-07 9.100E-08 6.100E-10 3.600E-10 1.200E-06 1.000E-06 3.100E-07 8.900E-08 #S 96 Cm #N 30 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 1 2 2 4 1 2 #UBIND 1.282E+05 2.446E+04 2.378E+04 1.893E+04 6288. 5895. 4797. 4227. 3971. 1643. 1440. 1154. 883.0 832.0 464.0 449.0 382.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 Shell_28 0.000 13.90 0.006930 0.02500 0.01190 0.01450 0.05480 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2170 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.5160 0.3530 0.4210 0.5160 1.830 0.05000 13.80 0.006930 0.02500 0.01190 0.01450 0.05480 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2170 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.5150 0.3530 0.4210 0.5160 1.760 0.1000 13.40 0.006930 0.02500 0.01190 0.01450 0.05480 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2160 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.5090 0.3530 0.4210 0.5160 1.590 0.1500 12.90 0.006930 0.02500 0.01190 0.01450 0.05480 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2160 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.5010 0.3530 0.4210 0.5160 1.340 0.2000 12.30 0.006930 0.02500 0.01190 0.01450 0.05480 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2150 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.4890 0.3530 0.4200 0.5150 1.060 0.3000 11.20 0.006930 0.02500 0.01190 0.01450 0.05470 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2120 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.4560 0.3510 0.4160 0.5090 0.5550 0.4000 10.40 0.006930 0.02500 0.01190 0.01450 0.05470 0.03180 0.03590 0.02300 0.02390 0.1060 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2080 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.4150 0.3470 0.4070 0.4900 0.2460 0.5000 9.950 0.006930 0.02500 0.01190 0.01450 0.05460 0.03180 0.03590 0.02300 0.02390 0.1060 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2030 0.1420 0.1580 0.1320 0.1360 0.1590 0.1660 0.3670 0.3390 0.3900 0.4550 0.1090 0.6000 9.580 0.006930 0.02500 0.01190 0.01450 0.05450 0.03180 0.03590 0.02300 0.02390 0.1050 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.1970 0.1420 0.1580 0.1320 0.1360 0.1590 0.1660 0.3170 0.3260 0.3660 0.4060 0.06840 0.7000 9.240 0.006930 0.02500 0.01190 0.01450 0.05440 0.03180 0.03590 0.02300 0.02390 0.1040 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.1910 0.1420 0.1570 0.1320 0.1360 0.1590 0.1660 0.2670 0.3090 0.3350 0.3500 0.06200 0.8000 8.870 0.006930 0.02500 0.01190 0.01450 0.05430 0.03180 0.03590 0.02300 0.02390 0.1030 0.06720 0.07470 0.05490 0.05660 0.04350 0.04420 0.1830 0.1410 0.1570 0.1320 0.1350 0.1590 0.1650 0.2210 0.2880 0.2990 0.2940 0.06160 1.000 8.110 0.006930 0.02490 0.01190 0.01450 0.05400 0.03180 0.03590 0.02300 0.02390 0.1010 0.06710 0.07470 0.05490 0.05660 0.04350 0.04420 0.1670 0.1400 0.1540 0.1320 0.1350 0.1570 0.1630 0.1430 0.2350 0.2200 0.1930 0.05070 1.200 7.390 0.006930 0.02490 0.01190 0.01450 0.05360 0.03180 0.03590 0.02300 0.02390 0.09800 0.06700 0.07460 0.05490 0.05660 0.04350 0.04420 0.1490 0.1370 0.1500 0.1310 0.1340 0.1540 0.1580 0.08990 0.1790 0.1480 0.1180 0.03230 1.400 6.800 0.006930 0.02490 0.01190 0.01450 0.05320 0.03180 0.03590 0.02300 0.02390 0.09480 0.06690 0.07440 0.05490 0.05660 0.04350 0.04420 0.1310 0.1330 0.1440 0.1290 0.1330 0.1480 0.1510 0.06030 0.1270 0.09210 0.06940 0.01760 1.600 6.330 0.006930 0.02480 0.01190 0.01450 0.05280 0.03180 0.03590 0.02300 0.02390 0.09130 0.06680 0.07410 0.05490 0.05660 0.04350 0.04420 0.1120 0.1280 0.1360 0.1270 0.1300 0.1410 0.1430 0.04710 0.08510 0.05500 0.03940 0.009090 1.800 5.960 0.006930 0.02480 0.01190 0.01450 0.05220 0.03180 0.03590 0.02300 0.02390 0.08750 0.06650 0.07370 0.05490 0.05660 0.04350 0.04420 0.09530 0.1220 0.1270 0.1240 0.1260 0.1330 0.1330 0.04320 0.05520 0.03380 0.02200 0.005270 2.000 5.640 0.006930 0.02470 0.01190 0.01450 0.05170 0.03180 0.03590 0.02300 0.02390 0.08350 0.06620 0.07310 0.05490 0.05650 0.04350 0.04420 0.07960 0.1140 0.1160 0.1190 0.1210 0.1230 0.1220 0.04270 0.03630 0.02340 0.01270 0.004000 2.400 5.080 0.006920 0.02460 0.01190 0.01450 0.05030 0.03180 0.03580 0.02300 0.02390 0.07490 0.06520 0.07150 0.05480 0.05640 0.04350 0.04420 0.05440 0.09660 0.09260 0.1060 0.1070 0.1020 0.09930 0.04060 0.02060 0.01840 0.005890 0.003750 3.000 4.270 0.006920 0.02440 0.01190 0.01450 0.04800 0.03170 0.03570 0.02300 0.02390 0.06140 0.06290 0.06780 0.05440 0.05590 0.04340 0.04410 0.03250 0.06760 0.05820 0.08150 0.08010 0.07080 0.06780 0.02730 0.01830 0.01730 0.004940 0.002900 4.000 3.140 0.006910 0.02400 0.01190 0.01450 0.04330 0.03150 0.03530 0.02300 0.02390 0.04080 0.05630 0.05800 0.05260 0.05390 0.04310 0.04370 0.02380 0.03000 0.02250 0.04080 0.03820 0.03340 0.03110 0.008420 0.01300 0.008400 0.003760 0.0008810 5.000 2.470 0.006890 0.02340 0.01190 0.01450 0.03810 0.03100 0.03450 0.02300 0.02380 0.02560 0.04680 0.04510 0.04880 0.04960 0.04200 0.04260 0.02300 0.01400 0.01280 0.01650 0.01480 0.01380 0.01250 0.005030 0.005300 0.002820 0.001730 0.0004160 6.000 2.100 0.006870 0.02270 0.01190 0.01450 0.03250 0.03020 0.03320 0.02290 0.02380 0.01660 0.03610 0.03190 0.04290 0.04300 0.04000 0.04040 0.01800 0.01090 0.01220 0.007410 0.007000 0.005430 0.004890 0.004770 0.002290 0.001820 0.0006680 0.0003980 7.000 1.820 0.006850 0.02200 0.01190 0.01440 0.02710 0.02920 0.03150 0.02280 0.02360 0.01270 0.02580 0.02090 0.03560 0.03500 0.03700 0.03720 0.01120 0.01070 0.01100 0.005590 0.005720 0.002510 0.002320 0.003450 0.001890 0.001770 0.0003760 0.0002980 8.000 1.580 0.006830 0.02110 0.01190 0.01440 0.02210 0.02770 0.02920 0.02260 0.02330 0.01160 0.01750 0.01320 0.02780 0.02680 0.03310 0.03310 0.006210 0.009490 0.008260 0.005500 0.005660 0.001750 0.001690 0.001950 0.001830 0.001460 0.0003460 0.0001700 10.00 1.200 0.006800 0.01900 0.01190 0.01400 0.01400 0.02400 0.02400 0.02200 0.02200 0.01100 0.007800 0.006500 0.01500 0.01300 0.02400 0.02400 0.003100 0.004800 0.003100 0.004300 0.004100 0.001600 0.001600 0.0006900 0.001100 0.0005800 0.0002900 5.700E-05 15.00 0.5800 0.006600 0.01400 0.01100 0.01300 0.005000 0.01300 0.010000 0.01700 0.01700 0.004700 0.004800 0.005100 0.002700 0.002600 0.008400 0.008100 0.001900 0.001100 0.001300 0.0007300 0.0006500 0.0008700 0.0008200 0.0004200 0.0002000 0.0001900 5.100E-05 3.500E-05 20.00 0.3300 0.006300 0.009200 0.01100 0.01200 0.004200 0.005100 0.003400 0.01200 0.01100 0.001300 0.003100 0.002200 0.002200 0.002300 0.002500 0.002300 0.0004300 0.0008700 0.0006700 0.0003900 0.0004100 0.0002900 0.0002600 9.300E-05 0.0001600 1.000E-04 2.500E-05 7.700E-06 30.00 0.1300 0.005600 0.003100 0.007900 0.007700 0.002700 0.001200 0.001600 0.003700 0.003300 0.0009500 0.0004300 0.0003900 0.001100 0.001100 0.0002300 0.0002000 0.0002600 0.0001200 9.500E-05 0.0002300 0.0002200 2.700E-05 2.400E-05 5.200E-05 2.300E-05 1.400E-05 1.500E-05 4.200E-06 40.00 0.06600 0.004900 0.001000 0.005200 0.004300 0.0008100 0.001100 0.001300 0.001000 0.0008600 0.0003200 0.0002700 0.0003500 0.0003600 0.0003000 2.700E-05 2.300E-05 9.100E-05 6.400E-05 8.400E-05 7.500E-05 6.300E-05 3.200E-06 2.700E-06 1.900E-05 1.100E-05 1.200E-05 5.000E-06 1.500E-06 60.00 0.02200 0.003400 0.0005900 0.001900 0.001100 0.0001300 0.0005800 0.0004100 9.800E-05 7.000E-05 3.700E-05 0.0001600 0.0001200 3.400E-05 2.500E-05 8.200E-07 6.300E-07 9.700E-06 4.000E-05 3.000E-05 7.200E-06 5.200E-06 1.000E-07 7.400E-08 1.900E-06 7.000E-06 4.400E-06 4.800E-07 1.600E-07 100.0 0.005300 0.001400 0.0003600 0.0003000 9.600E-05 9.200E-05 9.100E-05 3.300E-05 2.700E-06 1.500E-06 2.600E-05 2.600E-05 9.800E-06 9.200E-07 4.900E-07 6.300E-09 3.900E-09 6.600E-06 6.300E-06 2.400E-06 1.900E-07 1.000E-07 7.900E-10 4.900E-10 1.300E-06 1.100E-06 3.500E-07 1.300E-08 1.100E-07 #S 97 Bk #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 3 2 2 4 2 #UBIND 1.316E+05 2.527E+04 2.438E+04 1.945E+04 6556. 6147. 4977. 4366. 4132. 1755. 1554. 1235. 883.0 832.0 464.0 449.0 398.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.90 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2130 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.5150 0.3510 0.4270 1.930 0.05000 13.70 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2120 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.5130 0.3510 0.4270 1.860 0.1000 13.30 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2120 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.5080 0.3510 0.4270 1.660 0.1500 12.70 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2110 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.4990 0.3510 0.4270 1.380 0.2000 12.10 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2100 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.4880 0.3510 0.4260 1.070 0.3000 10.90 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2080 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.4560 0.3490 0.4220 0.5300 0.4000 10.20 0.006810 0.02460 0.01170 0.01430 0.05380 0.03130 0.03550 0.02270 0.02360 0.1040 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2040 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.4150 0.3450 0.4120 0.2200 0.5000 9.730 0.006810 0.02460 0.01170 0.01430 0.05370 0.03130 0.03550 0.02270 0.02360 0.1040 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2000 0.1390 0.1560 0.1290 0.1330 0.1600 0.1690 0.3680 0.3370 0.3940 0.09480 0.6000 9.420 0.006810 0.02460 0.01170 0.01430 0.05370 0.03130 0.03550 0.02270 0.02360 0.1030 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.1940 0.1390 0.1550 0.1290 0.1330 0.1590 0.1690 0.3180 0.3250 0.3690 0.06090 0.7000 9.130 0.006810 0.02460 0.01170 0.01430 0.05360 0.03130 0.03550 0.02270 0.02360 0.1020 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.1880 0.1390 0.1550 0.1290 0.1330 0.1590 0.1680 0.2690 0.3090 0.3360 0.05680 0.8000 8.820 0.006810 0.02460 0.01170 0.01430 0.05340 0.03130 0.03550 0.02270 0.02360 0.1010 0.06590 0.07360 0.05400 0.05570 0.04270 0.04340 0.1810 0.1380 0.1540 0.1290 0.1330 0.1590 0.1670 0.2220 0.2880 0.2990 0.05610 1.000 8.140 0.006810 0.02450 0.01170 0.01430 0.05320 0.03130 0.03550 0.02270 0.02360 0.09910 0.06590 0.07350 0.05400 0.05570 0.04270 0.04340 0.1650 0.1370 0.1520 0.1290 0.1330 0.1570 0.1640 0.1440 0.2360 0.2190 0.04450 1.200 7.490 0.006810 0.02450 0.01170 0.01430 0.05280 0.03130 0.03550 0.02270 0.02360 0.09650 0.06580 0.07340 0.05400 0.05570 0.04270 0.04340 0.1480 0.1340 0.1480 0.1280 0.1320 0.1530 0.1580 0.09080 0.1800 0.1460 0.02750 1.400 6.940 0.006810 0.02450 0.01170 0.01430 0.05240 0.03130 0.03550 0.02270 0.02360 0.09350 0.06570 0.07320 0.05400 0.05570 0.04270 0.04340 0.1300 0.1310 0.1420 0.1270 0.1310 0.1470 0.1500 0.06030 0.1290 0.09050 0.01470 1.600 6.480 0.006810 0.02440 0.01170 0.01430 0.05200 0.03130 0.03550 0.02270 0.02360 0.09020 0.06560 0.07300 0.05400 0.05570 0.04270 0.04340 0.1130 0.1260 0.1350 0.1250 0.1280 0.1400 0.1410 0.04640 0.08680 0.05390 0.007510 1.800 6.120 0.006810 0.02440 0.01170 0.01430 0.05150 0.03130 0.03540 0.02270 0.02360 0.08660 0.06540 0.07260 0.05400 0.05570 0.04270 0.04340 0.09610 0.1210 0.1260 0.1220 0.1250 0.1310 0.1310 0.04180 0.05660 0.03280 0.004340 2.000 5.800 0.006810 0.02430 0.01170 0.01430 0.05090 0.03130 0.03540 0.02270 0.02360 0.08270 0.06510 0.07210 0.05400 0.05560 0.04270 0.04340 0.08080 0.1130 0.1160 0.1180 0.1200 0.1210 0.1190 0.04120 0.03700 0.02240 0.003280 2.400 5.220 0.006800 0.02420 0.01170 0.01430 0.04970 0.03130 0.03540 0.02270 0.02360 0.07440 0.06420 0.07060 0.05390 0.05550 0.04260 0.04330 0.05570 0.09680 0.09330 0.1060 0.1070 0.1000 0.09700 0.03960 0.02030 0.01720 0.003060 3.000 4.380 0.006800 0.02400 0.01170 0.01430 0.04740 0.03120 0.03530 0.02270 0.02360 0.06150 0.06200 0.06710 0.05350 0.05510 0.04260 0.04330 0.03320 0.06900 0.05970 0.08230 0.08090 0.07060 0.06660 0.02750 0.01750 0.01630 0.002410 4.000 3.200 0.006790 0.02360 0.01170 0.01430 0.04300 0.03100 0.03490 0.02270 0.02360 0.04140 0.05580 0.05780 0.05190 0.05320 0.04230 0.04300 0.02350 0.03160 0.02360 0.04250 0.03970 0.03430 0.03130 0.008770 0.01300 0.008270 0.0007700 5.000 2.500 0.006770 0.02310 0.01170 0.01430 0.03790 0.03050 0.03410 0.02270 0.02360 0.02620 0.04690 0.04540 0.04840 0.04920 0.04140 0.04200 0.02290 0.01450 0.01290 0.01770 0.01580 0.01460 0.01310 0.004870 0.005570 0.002820 0.0003430 6.000 2.110 0.006760 0.02240 0.01170 0.01430 0.03250 0.02980 0.03290 0.02260 0.02350 0.01700 0.03650 0.03250 0.04290 0.04300 0.03960 0.04000 0.01850 0.01090 0.01200 0.007840 0.007280 0.005900 0.005200 0.004680 0.002340 0.001710 0.0003270 7.000 1.830 0.006740 0.02170 0.01170 0.01420 0.02720 0.02880 0.03120 0.02250 0.02330 0.01280 0.02650 0.02160 0.03590 0.03530 0.03680 0.03700 0.01180 0.01070 0.01110 0.005610 0.005690 0.002680 0.002400 0.003530 0.001820 0.001670 0.0002550 8.000 1.590 0.006710 0.02090 0.01170 0.01420 0.02230 0.02750 0.02910 0.02230 0.02310 0.01150 0.01820 0.01370 0.02830 0.02730 0.03310 0.03310 0.006690 0.009670 0.008570 0.005470 0.005620 0.001760 0.001650 0.002070 0.001780 0.001420 0.0001520 10.00 1.200 0.006700 0.01900 0.01170 0.01400 0.01400 0.02400 0.02400 0.02200 0.02200 0.01100 0.008200 0.006600 0.01500 0.01400 0.02500 0.02400 0.003200 0.005200 0.003400 0.004500 0.004300 0.001600 0.001500 0.0007000 0.001100 0.0006000 5.000E-05 15.00 0.5900 0.006500 0.01400 0.01100 0.01300 0.005000 0.01300 0.01100 0.01700 0.01700 0.004900 0.004700 0.005200 0.002800 0.002700 0.008800 0.008400 0.002000 0.001100 0.001300 0.0007900 0.0006900 0.0009000 0.0008300 0.0004300 0.0001900 0.0001800 3.000E-05 20.00 0.3400 0.006200 0.009400 0.010000 0.01200 0.004100 0.005400 0.003600 0.01200 0.01100 0.001400 0.003200 0.002300 0.002200 0.002300 0.002700 0.002500 0.0004600 0.0009000 0.0007100 0.0003900 0.0004100 0.0003000 0.0002700 1.000E-04 0.0001600 1.000E-04 6.900E-06 30.00 0.1400 0.005600 0.003200 0.007900 0.007800 0.002700 0.001200 0.001500 0.003800 0.003400 0.0009600 0.0004600 0.0004000 0.001200 0.001100 0.0002500 0.0002200 0.0002600 0.0001300 9.800E-05 0.0002500 0.0002300 3.000E-05 2.600E-05 5.200E-05 2.400E-05 1.400E-05 3.600E-06 40.00 0.06700 0.004800 0.001100 0.005300 0.004400 0.0008600 0.001100 0.001300 0.001100 0.0009000 0.0003400 0.0002700 0.0003600 0.0003800 0.0003200 3.000E-05 2.500E-05 9.900E-05 6.300E-05 8.500E-05 8.000E-05 6.800E-05 3.600E-06 2.900E-06 2.000E-05 1.100E-05 1.200E-05 1.400E-06 60.00 0.02200 0.003400 0.0005800 0.002000 0.001200 0.0001300 0.0006000 0.0004300 0.0001100 7.500E-05 3.800E-05 0.0001700 0.0001300 3.700E-05 2.700E-05 9.300E-07 7.100E-07 1.000E-05 4.100E-05 3.100E-05 7.900E-06 5.700E-06 1.100E-07 8.200E-08 2.000E-06 7.100E-06 4.400E-06 1.300E-07 100.0 0.005400 0.001500 0.0003700 0.0003200 1.000E-04 9.400E-05 9.700E-05 3.600E-05 3.000E-06 1.600E-06 2.600E-05 2.800E-05 1.100E-05 1.000E-06 5.400E-07 7.200E-09 4.500E-09 6.800E-06 6.900E-06 2.600E-06 2.200E-07 1.100E-07 9.100E-10 5.400E-10 1.300E-06 1.200E-06 3.600E-07 9.100E-08 #S 98 Cf #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 4 2 2 4 2 #UBIND 1.360E+05 2.611E+04 2.529E+04 1.993E+04 6754. 6359. 5109. 4497. 4253. 1791. 1616. 1279. 883.0 832.0 464.0 449.0 419.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.80 0.006700 0.02420 0.01150 0.01410 0.05310 0.03080 0.03500 0.02240 0.02330 0.1040 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2080 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.5050 0.3440 0.4210 1.910 0.05000 13.70 0.006700 0.02420 0.01150 0.01410 0.05310 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2080 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.5030 0.3440 0.4210 1.840 0.1000 13.30 0.006700 0.02420 0.01150 0.01410 0.05310 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2070 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.4980 0.3440 0.4210 1.650 0.1500 12.70 0.006700 0.02420 0.01150 0.01410 0.05310 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2070 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.4900 0.3440 0.4210 1.370 0.2000 12.10 0.006700 0.02420 0.01150 0.01410 0.05310 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2060 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.4790 0.3440 0.4200 1.070 0.3000 10.90 0.006700 0.02420 0.01150 0.01410 0.05300 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2030 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.4490 0.3420 0.4160 0.5400 0.4000 10.20 0.006700 0.02420 0.01150 0.01410 0.05300 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2000 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.4100 0.3380 0.4060 0.2280 0.5000 9.720 0.006700 0.02420 0.01150 0.01410 0.05290 0.03080 0.03500 0.02240 0.02330 0.1020 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.1960 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.3650 0.3310 0.3900 0.09720 0.6000 9.420 0.006700 0.02420 0.01150 0.01410 0.05280 0.03080 0.03500 0.02240 0.02330 0.1010 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.1910 0.1360 0.1520 0.1270 0.1310 0.1540 0.1620 0.3170 0.3200 0.3660 0.06010 0.7000 9.140 0.006700 0.02420 0.01150 0.01410 0.05270 0.03080 0.03500 0.02240 0.02330 0.1010 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.1850 0.1360 0.1520 0.1270 0.1310 0.1530 0.1620 0.2700 0.3050 0.3350 0.05490 0.8000 8.840 0.006690 0.02420 0.01150 0.01410 0.05260 0.03080 0.03500 0.02240 0.02330 0.09970 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.1780 0.1350 0.1510 0.1260 0.1310 0.1530 0.1610 0.2250 0.2850 0.2990 0.05450 1.000 8.190 0.006690 0.02410 0.01150 0.01410 0.05240 0.03080 0.03500 0.02240 0.02330 0.09760 0.06470 0.07240 0.05310 0.05480 0.04190 0.04260 0.1630 0.1340 0.1490 0.1260 0.1300 0.1510 0.1580 0.1480 0.2370 0.2210 0.04430 1.200 7.550 0.006690 0.02410 0.01150 0.01410 0.05200 0.03080 0.03500 0.02240 0.02330 0.09510 0.06470 0.07230 0.05310 0.05480 0.04190 0.04260 0.1470 0.1320 0.1460 0.1260 0.1300 0.1480 0.1540 0.09390 0.1830 0.1500 0.02810 1.400 7.000 0.006690 0.02410 0.01150 0.01410 0.05170 0.03080 0.03500 0.02240 0.02330 0.09230 0.06460 0.07210 0.05310 0.05480 0.04190 0.04260 0.1300 0.1290 0.1400 0.1240 0.1280 0.1430 0.1470 0.06210 0.1330 0.09400 0.01540 1.600 6.550 0.006690 0.02400 0.01150 0.01410 0.05120 0.03080 0.03500 0.02240 0.02330 0.08910 0.06440 0.07190 0.05310 0.05480 0.04190 0.04260 0.1130 0.1240 0.1340 0.1230 0.1260 0.1370 0.1390 0.04680 0.09100 0.05650 0.007940 1.800 6.180 0.006690 0.02400 0.01150 0.01410 0.05080 0.03080 0.03500 0.02240 0.02330 0.08560 0.06420 0.07150 0.05310 0.05480 0.04190 0.04260 0.09680 0.1190 0.1250 0.1200 0.1230 0.1290 0.1300 0.04140 0.06000 0.03440 0.004490 2.000 5.860 0.006690 0.02400 0.01150 0.01410 0.05020 0.03080 0.03500 0.02240 0.02330 0.08190 0.06390 0.07100 0.05310 0.05480 0.04190 0.04260 0.08190 0.1120 0.1160 0.1160 0.1180 0.1200 0.1190 0.04040 0.03940 0.02310 0.003240 2.400 5.290 0.006690 0.02390 0.01150 0.01410 0.04900 0.03080 0.03490 0.02240 0.02330 0.07400 0.06310 0.06970 0.05300 0.05470 0.04190 0.04260 0.05710 0.09680 0.09400 0.1050 0.1060 0.1010 0.09860 0.03920 0.02080 0.01690 0.002930 3.000 4.450 0.006680 0.02370 0.01150 0.01410 0.04690 0.03070 0.03480 0.02240 0.02330 0.06150 0.06110 0.06640 0.05270 0.05430 0.04180 0.04250 0.03410 0.07030 0.06130 0.08310 0.08180 0.07290 0.06940 0.02840 0.01700 0.01620 0.002410 4.000 3.260 0.006670 0.02330 0.01150 0.01410 0.04260 0.03050 0.03440 0.02240 0.02330 0.04190 0.05540 0.05750 0.05120 0.05250 0.04160 0.04220 0.02330 0.03320 0.02470 0.04440 0.04150 0.03680 0.03390 0.009460 0.01340 0.008670 0.0008190 5.000 2.530 0.006660 0.02270 0.01150 0.01410 0.03770 0.03010 0.03370 0.02240 0.02330 0.02680 0.04690 0.04560 0.04800 0.04880 0.04080 0.04130 0.02270 0.01520 0.01300 0.01910 0.01700 0.01630 0.01460 0.004870 0.006050 0.003020 0.0003390 6.000 2.110 0.006640 0.02210 0.01150 0.01410 0.03250 0.02940 0.03260 0.02230 0.02320 0.01740 0.03700 0.03310 0.04280 0.04300 0.03910 0.03960 0.01890 0.01080 0.01190 0.008370 0.007660 0.006750 0.005970 0.004680 0.002500 0.001710 0.0003180 7.000 1.830 0.006620 0.02140 0.01150 0.01410 0.02740 0.02850 0.03100 0.02220 0.02310 0.01290 0.02720 0.02220 0.03610 0.03560 0.03650 0.03680 0.01250 0.01060 0.01120 0.005680 0.005700 0.003020 0.002710 0.003690 0.001810 0.001650 0.0002590 8.000 1.600 0.006600 0.02070 0.01150 0.01400 0.02250 0.02720 0.02900 0.02200 0.02280 0.01140 0.01890 0.01430 0.02880 0.02780 0.03310 0.03320 0.007200 0.009810 0.008870 0.005460 0.005600 0.001870 0.001750 0.002250 0.001770 0.001440 0.0001610 10.00 1.200 0.006500 0.01900 0.01100 0.01400 0.01400 0.02400 0.02400 0.02100 0.02200 0.01100 0.008600 0.006800 0.01600 0.01500 0.02500 0.02500 0.003200 0.005500 0.003600 0.004600 0.004400 0.001600 0.001600 0.0007400 0.001200 0.0006400 5.200E-05 15.00 0.6000 0.006400 0.01400 0.01100 0.01300 0.005100 0.01300 0.01100 0.01700 0.01700 0.005200 0.004700 0.005200 0.002900 0.002800 0.009200 0.008800 0.002100 0.001100 0.001300 0.0008600 0.0007500 0.0009700 0.0009000 0.0004600 0.0001900 0.0001800 3.100E-05 20.00 0.3400 0.006100 0.009500 0.010000 0.01200 0.004100 0.005600 0.003700 0.01200 0.01200 0.001400 0.003300 0.002500 0.002200 0.002300 0.002800 0.002600 0.0005000 0.0009300 0.0007500 0.0004000 0.0004100 0.0003400 0.0003000 0.0001100 0.0001600 0.0001100 7.400E-06 30.00 0.1400 0.005500 0.003400 0.007900 0.007800 0.002800 0.001200 0.001500 0.004000 0.003500 0.0009800 0.0005000 0.0004000 0.001200 0.001100 0.0002700 0.0002400 0.0002700 0.0001500 1.000E-04 0.0002600 0.0002400 3.400E-05 2.900E-05 5.300E-05 2.700E-05 1.400E-05 3.600E-06 40.00 0.06800 0.004800 0.001100 0.005400 0.004500 0.0009300 0.001100 0.001300 0.001200 0.0009500 0.0003700 0.0002600 0.0003600 0.0004000 0.0003400 3.300E-05 2.800E-05 0.0001100 6.300E-05 8.600E-05 8.700E-05 7.300E-05 4.200E-06 3.400E-06 2.200E-05 1.100E-05 1.200E-05 1.400E-06 60.00 0.02300 0.003400 0.0005700 0.002100 0.001200 0.0001300 0.0006200 0.0004500 0.0001100 8.100E-05 3.900E-05 0.0001700 0.0001300 4.000E-05 2.900E-05 1.100E-06 8.100E-07 1.000E-05 4.300E-05 3.300E-05 8.800E-06 6.300E-06 1.300E-07 9.800E-08 2.100E-06 7.400E-06 4.600E-06 1.400E-07 100.0 0.005600 0.001500 0.0003800 0.0003400 0.0001100 9.500E-05 1.000E-04 3.800E-05 3.300E-06 1.700E-06 2.600E-05 3.000E-05 1.100E-05 1.100E-06 6.000E-07 8.300E-09 5.100E-09 6.900E-06 7.500E-06 2.800E-06 2.400E-07 1.300E-07 1.100E-09 6.500E-10 1.400E-06 1.300E-06 3.900E-07 9.100E-08 #S 99 Es #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 5 2 2 4 2 #UBIND 1.395E+05 2.690E+04 2.602E+04 2.041E+04 6977. 6574. 5252. 4630. 4374. 1868. 1680. 1321. 883.0 832.0 464.0 449.0 435.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.70 0.006580 0.02380 0.01130 0.01400 0.05230 0.03030 0.03460 0.02210 0.02300 0.1020 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.2040 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4950 0.3370 0.4150 1.890 0.05000 13.60 0.006580 0.02380 0.01130 0.01400 0.05230 0.03030 0.03460 0.02210 0.02300 0.1020 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.2030 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4930 0.3370 0.4150 1.820 0.1000 13.20 0.006580 0.02380 0.01130 0.01400 0.05230 0.03030 0.03460 0.02210 0.02300 0.1020 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.2030 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4880 0.3370 0.4150 1.640 0.1500 12.60 0.006580 0.02380 0.01130 0.01400 0.05220 0.03030 0.03460 0.02210 0.02300 0.1020 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.2020 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4810 0.3370 0.4150 1.370 0.2000 12.00 0.006580 0.02380 0.01130 0.01400 0.05220 0.03030 0.03460 0.02210 0.02300 0.1010 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.2020 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4700 0.3370 0.4140 1.070 0.3000 10.90 0.006580 0.02380 0.01130 0.01400 0.05220 0.03030 0.03460 0.02210 0.02300 0.1010 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1990 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4420 0.3360 0.4100 0.5490 0.4000 10.10 0.006580 0.02380 0.01130 0.01400 0.05220 0.03030 0.03460 0.02210 0.02300 0.1010 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1960 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4050 0.3320 0.4020 0.2350 0.5000 9.700 0.006580 0.02380 0.01130 0.01400 0.05210 0.03030 0.03460 0.02210 0.02300 0.1000 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1920 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.3620 0.3260 0.3860 0.09990 0.6000 9.400 0.006580 0.02380 0.01130 0.01400 0.05200 0.03030 0.03460 0.02210 0.02300 0.09960 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1870 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.3170 0.3150 0.3630 0.05960 0.7000 9.140 0.006580 0.02380 0.01130 0.01400 0.05190 0.03030 0.03460 0.02210 0.02300 0.09890 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1820 0.1330 0.1490 0.1240 0.1280 0.1480 0.1560 0.2710 0.3010 0.3330 0.05320 0.8000 8.850 0.006580 0.02380 0.01130 0.01400 0.05180 0.03030 0.03460 0.02210 0.02300 0.09810 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1750 0.1320 0.1490 0.1240 0.1280 0.1480 0.1550 0.2270 0.2830 0.2990 0.05290 1.000 8.220 0.006580 0.02380 0.01130 0.01400 0.05160 0.03030 0.03460 0.02210 0.02300 0.09610 0.06360 0.07130 0.05230 0.05400 0.04110 0.04180 0.1610 0.1310 0.1470 0.1240 0.1280 0.1470 0.1530 0.1510 0.2370 0.2230 0.04400 1.200 7.610 0.006580 0.02370 0.01130 0.01400 0.05130 0.03030 0.03460 0.02210 0.02300 0.09370 0.06350 0.07120 0.05230 0.05400 0.04110 0.04180 0.1460 0.1290 0.1430 0.1230 0.1270 0.1440 0.1490 0.09700 0.1860 0.1530 0.02870 1.400 7.060 0.006580 0.02370 0.01130 0.01400 0.05090 0.03030 0.03460 0.02210 0.02300 0.09100 0.06340 0.07100 0.05230 0.05400 0.04110 0.04180 0.1300 0.1260 0.1390 0.1220 0.1260 0.1400 0.1440 0.06400 0.1370 0.09740 0.01600 1.600 6.620 0.006580 0.02370 0.01130 0.01400 0.05050 0.03030 0.03460 0.02210 0.02300 0.08800 0.06330 0.07080 0.05230 0.05400 0.04110 0.04180 0.1130 0.1220 0.1320 0.1200 0.1240 0.1340 0.1370 0.04740 0.09490 0.05920 0.008380 1.800 6.250 0.006580 0.02360 0.01130 0.01400 0.05000 0.03030 0.03450 0.02210 0.02300 0.08460 0.06310 0.07050 0.05230 0.05390 0.04110 0.04180 0.09750 0.1170 0.1240 0.1180 0.1210 0.1270 0.1280 0.04100 0.06350 0.03600 0.004670 2.000 5.930 0.006570 0.02360 0.01130 0.01400 0.04950 0.03030 0.03450 0.02210 0.02300 0.08110 0.06290 0.07000 0.05220 0.05390 0.04110 0.04180 0.08290 0.1110 0.1150 0.1140 0.1170 0.1190 0.1190 0.03960 0.04190 0.02380 0.003230 2.400 5.360 0.006570 0.02350 0.01130 0.01400 0.04840 0.03030 0.03450 0.02210 0.02300 0.07350 0.06210 0.06880 0.05220 0.05380 0.04110 0.04180 0.05840 0.09680 0.09450 0.1040 0.1060 0.1020 0.09970 0.03880 0.02140 0.01660 0.002810 3.000 4.530 0.006570 0.02330 0.01130 0.01400 0.04630 0.03020 0.03440 0.02210 0.02300 0.06140 0.06020 0.06570 0.05190 0.05350 0.04110 0.04180 0.03490 0.07150 0.06270 0.08380 0.08260 0.07490 0.07170 0.02920 0.01670 0.01590 0.002400 4.000 3.320 0.006560 0.02290 0.01130 0.01400 0.04220 0.03000 0.03400 0.02210 0.02300 0.04230 0.05490 0.05730 0.05050 0.05190 0.04090 0.04150 0.02310 0.03480 0.02590 0.04620 0.04320 0.03910 0.03630 0.01020 0.01360 0.009020 0.0008700 5.000 2.560 0.006540 0.02240 0.01130 0.01400 0.03750 0.02960 0.03330 0.02210 0.02300 0.02730 0.04690 0.04590 0.04750 0.04840 0.04010 0.04070 0.02250 0.01600 0.01320 0.02040 0.01820 0.01790 0.01620 0.004890 0.006530 0.003220 0.0003390 6.000 2.120 0.006530 0.02180 0.01130 0.01390 0.03250 0.02900 0.03230 0.02200 0.02290 0.01780 0.03730 0.03360 0.04270 0.04290 0.03870 0.03910 0.01920 0.01090 0.01180 0.008940 0.008090 0.007630 0.006790 0.004660 0.002680 0.001720 0.0003080 7.000 1.840 0.006510 0.02120 0.01130 0.01390 0.02750 0.02810 0.03080 0.02190 0.02280 0.01300 0.02780 0.02290 0.03630 0.03590 0.03630 0.03650 0.01310 0.01050 0.01130 0.005770 0.005730 0.003400 0.003050 0.003830 0.001800 0.001630 0.0002620 8.000 1.600 0.006490 0.02040 0.01130 0.01390 0.02270 0.02700 0.02880 0.02180 0.02260 0.01130 0.01960 0.01480 0.02920 0.02830 0.03310 0.03320 0.007730 0.009910 0.009150 0.005440 0.005580 0.002000 0.001870 0.002430 0.001760 0.001460 0.0001700 10.00 1.200 0.006400 0.01900 0.01100 0.01390 0.01500 0.02400 0.02400 0.02100 0.02200 0.01100 0.009000 0.006900 0.01600 0.01500 0.02500 0.02500 0.003300 0.005900 0.003900 0.004700 0.004600 0.001700 0.001600 0.0007900 0.001200 0.0006900 5.400E-05 15.00 0.6100 0.006300 0.01400 0.01100 0.01300 0.005200 0.01400 0.01100 0.01700 0.01700 0.005400 0.004600 0.005200 0.003000 0.002800 0.009600 0.009200 0.002200 0.001100 0.001300 0.0009400 0.0008100 0.001000 0.0009700 0.0004800 0.0002000 0.0001800 3.200E-05 20.00 0.3500 0.006000 0.009600 0.010000 0.01200 0.004000 0.005900 0.003900 0.01200 0.01200 0.001500 0.003400 0.002600 0.002200 0.002300 0.003000 0.002800 0.0005500 0.0009600 0.0007900 0.0004000 0.0004200 0.0003700 0.0003400 0.0001200 0.0001700 0.0001100 8.000E-06 30.00 0.1400 0.005400 0.003500 0.007900 0.007900 0.002800 0.001200 0.001500 0.004100 0.003700 0.0009900 0.0005300 0.0004100 0.001300 0.001200 0.0003000 0.0002700 0.0002700 0.0001600 1.000E-04 0.0002700 0.0002500 3.900E-05 3.400E-05 5.400E-05 3.000E-05 1.500E-05 3.500E-06 40.00 0.07000 0.004700 0.001200 0.005400 0.004500 0.0009900 0.001100 0.001300 0.001200 0.001000 0.0004000 0.0002600 0.0003600 0.0004200 0.0003600 3.700E-05 3.100E-05 0.0001200 6.300E-05 8.700E-05 9.300E-05 7.800E-05 4.900E-06 4.000E-06 2.300E-05 1.100E-05 1.200E-05 1.500E-06 60.00 0.02300 0.003400 0.0005600 0.002200 0.001300 0.0001300 0.0006300 0.0004600 0.0001200 8.700E-05 4.000E-05 0.0001800 0.0001400 4.400E-05 3.100E-05 1.200E-06 9.100E-07 1.100E-05 4.400E-05 3.500E-05 9.600E-06 6.900E-06 1.600E-07 1.200E-07 2.200E-06 7.600E-06 4.900E-06 1.400E-07 100.0 0.005800 0.001500 0.0003800 0.0003600 0.0001100 9.600E-05 0.0001100 4.000E-05 3.600E-06 1.900E-06 2.700E-05 3.200E-05 1.200E-05 1.200E-06 6.600E-07 9.500E-09 5.800E-09 7.100E-06 8.100E-06 3.000E-06 2.700E-07 1.400E-07 1.300E-09 7.700E-10 1.400E-06 1.400E-06 4.200E-07 9.100E-08 #S 100 Fm #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 6 2 2 4 2 #UBIND 1.431E+05 2.770E+04 2.681E+04 2.090E+04 7205. 6793. 5397. 4766. 4498. 1937. 1747. 1366. 883.0 832.0 464.0 449.0 454.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.60 0.006460 0.02340 0.01110 0.01380 0.05150 0.02980 0.03410 0.02180 0.02270 0.09990 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1990 0.1300 0.1480 0.1210 0.1260 0.1440 0.1510 0.4850 0.3310 0.4100 1.870 0.05000 13.50 0.006460 0.02340 0.01110 0.01380 0.05150 0.02980 0.03410 0.02180 0.02270 0.09990 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1990 0.1300 0.1480 0.1210 0.1260 0.1440 0.1510 0.4840 0.3310 0.4100 1.810 0.1000 13.10 0.006460 0.02340 0.01110 0.01380 0.05140 0.02980 0.03410 0.02180 0.02270 0.09990 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1990 0.1300 0.1480 0.1210 0.1260 0.1440 0.1510 0.4790 0.3310 0.4100 1.630 0.1500 12.60 0.006460 0.02340 0.01110 0.01380 0.05140 0.02980 0.03410 0.02180 0.02270 0.09980 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1980 0.1300 0.1480 0.1210 0.1260 0.1440 0.1510 0.4720 0.3310 0.4100 1.370 0.2000 12.00 0.006460 0.02340 0.01110 0.01380 0.05140 0.02980 0.03410 0.02180 0.02270 0.09970 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1970 0.1300 0.1480 0.1210 0.1260 0.1440 0.1510 0.4620 0.3310 0.4090 1.070 0.3000 10.90 0.006460 0.02340 0.01110 0.01380 0.05140 0.02980 0.03410 0.02180 0.02270 0.09950 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1950 0.1300 0.1470 0.1210 0.1260 0.1440 0.1510 0.4350 0.3290 0.4050 0.5580 0.4000 10.10 0.006460 0.02340 0.01110 0.01380 0.05130 0.02980 0.03410 0.02180 0.02270 0.09910 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1920 0.1300 0.1470 0.1210 0.1260 0.1440 0.1510 0.4000 0.3260 0.3970 0.2420 0.5000 9.690 0.006460 0.02340 0.01110 0.01380 0.05130 0.02980 0.03410 0.02180 0.02270 0.09860 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1880 0.1300 0.1470 0.1210 0.1260 0.1440 0.1510 0.3590 0.3200 0.3820 0.1030 0.6000 9.390 0.006460 0.02340 0.01110 0.01380 0.05120 0.02980 0.03410 0.02180 0.02270 0.09800 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1840 0.1300 0.1470 0.1210 0.1260 0.1440 0.1510 0.3160 0.3110 0.3600 0.05930 0.7000 9.130 0.006460 0.02340 0.01110 0.01380 0.05110 0.02980 0.03410 0.02180 0.02270 0.09730 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1780 0.1300 0.1470 0.1210 0.1260 0.1440 0.1510 0.2710 0.2970 0.3320 0.05170 0.8000 8.860 0.006460 0.02340 0.01110 0.01380 0.05100 0.02980 0.03410 0.02180 0.02270 0.09650 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1730 0.1290 0.1460 0.1210 0.1260 0.1440 0.1500 0.2290 0.2800 0.2990 0.05130 1.000 8.260 0.006460 0.02340 0.01110 0.01380 0.05080 0.02980 0.03410 0.02180 0.02270 0.09460 0.06250 0.07020 0.05150 0.05320 0.04040 0.04110 0.1590 0.1280 0.1440 0.1210 0.1250 0.1420 0.1490 0.1540 0.2370 0.2250 0.04370 1.200 7.650 0.006460 0.02340 0.01110 0.01380 0.05050 0.02980 0.03410 0.02180 0.02270 0.09230 0.06240 0.07020 0.05150 0.05310 0.04040 0.04110 0.1450 0.1270 0.1410 0.1210 0.1250 0.1400 0.1450 0.1000 0.1880 0.1560 0.02920 1.400 7.120 0.006460 0.02330 0.01110 0.01380 0.05020 0.02980 0.03410 0.02180 0.02270 0.08970 0.06230 0.07000 0.05150 0.05310 0.04040 0.04110 0.1290 0.1240 0.1370 0.1200 0.1240 0.1370 0.1410 0.06600 0.1400 0.1010 0.01670 1.600 6.680 0.006460 0.02330 0.01110 0.01380 0.04980 0.02980 0.03410 0.02180 0.02270 0.08680 0.06220 0.06980 0.05140 0.05310 0.04040 0.04110 0.1130 0.1200 0.1310 0.1180 0.1220 0.1320 0.1340 0.04820 0.09880 0.06170 0.008840 1.800 6.310 0.006460 0.02330 0.01110 0.01380 0.04930 0.02980 0.03410 0.02180 0.02270 0.08370 0.06200 0.06950 0.05140 0.05310 0.04040 0.04110 0.09810 0.1160 0.1230 0.1160 0.1190 0.1260 0.1270 0.04080 0.06690 0.03770 0.004880 2.000 5.990 0.006460 0.02320 0.01110 0.01380 0.04880 0.02980 0.03410 0.02180 0.02270 0.08030 0.06180 0.06910 0.05140 0.05310 0.04040 0.04110 0.08380 0.1100 0.1150 0.1130 0.1150 0.1180 0.1180 0.03880 0.04450 0.02460 0.003250 2.400 5.430 0.006460 0.02310 0.01110 0.01380 0.04770 0.02980 0.03410 0.02180 0.02270 0.07300 0.06110 0.06790 0.05130 0.05300 0.04040 0.04110 0.05960 0.09660 0.09500 0.1040 0.1050 0.1020 0.1000 0.03830 0.02220 0.01640 0.002700 3.000 4.610 0.006450 0.02290 0.01110 0.01380 0.04580 0.02970 0.03400 0.02180 0.02270 0.06140 0.05940 0.06500 0.05110 0.05270 0.04040 0.04100 0.03580 0.07250 0.06410 0.08430 0.08320 0.07650 0.07360 0.02990 0.01630 0.01570 0.002380 4.000 3.390 0.006440 0.02260 0.01110 0.01380 0.04180 0.02950 0.03360 0.02180 0.02270 0.04270 0.05440 0.05700 0.04990 0.05120 0.04020 0.04080 0.02300 0.03640 0.02700 0.04790 0.04490 0.04130 0.03860 0.01100 0.01390 0.009340 0.0009210 5.000 2.590 0.006430 0.02210 0.01110 0.01380 0.03730 0.02920 0.03300 0.02180 0.02270 0.02790 0.04680 0.04600 0.04710 0.04800 0.03950 0.04010 0.02230 0.01680 0.01350 0.02180 0.01940 0.01960 0.01780 0.004950 0.007010 0.003430 0.0003420 6.000 2.140 0.006420 0.02160 0.01110 0.01380 0.03240 0.02860 0.03200 0.02180 0.02260 0.01820 0.03770 0.03410 0.04250 0.04280 0.03820 0.03870 0.01950 0.01090 0.01170 0.009570 0.008560 0.008560 0.007640 0.004620 0.002890 0.001730 0.0002980 7.000 1.850 0.006400 0.02090 0.01110 0.01380 0.02760 0.02780 0.03050 0.02170 0.02250 0.01320 0.02840 0.02350 0.03650 0.03610 0.03600 0.03630 0.01370 0.01040 0.01130 0.005890 0.005780 0.003820 0.003420 0.003940 0.001810 0.001610 0.0002630 8.000 1.610 0.006380 0.02020 0.01110 0.01370 0.02290 0.02670 0.02870 0.02150 0.02230 0.01120 0.02020 0.01540 0.02970 0.02880 0.03300 0.03310 0.008270 0.009970 0.009400 0.005410 0.005550 0.002150 0.002000 0.002600 0.001740 0.001480 0.0001770 10.00 1.200 0.006300 0.01900 0.01100 0.01370 0.01500 0.02400 0.02400 0.02100 0.02200 0.01100 0.009400 0.007100 0.01700 0.01600 0.02600 0.02500 0.003400 0.006200 0.004200 0.004800 0.004700 0.001700 0.001600 0.0008500 0.001300 0.0007300 5.700E-05 15.00 0.6200 0.006200 0.01400 0.01100 0.01300 0.005300 0.01400 0.01100 0.01700 0.01700 0.005700 0.004500 0.005200 0.003100 0.002900 0.010000 0.009600 0.002300 0.001100 0.001300 0.001000 0.0008800 0.001100 0.001000 0.0005000 0.0002100 0.0001800 3.200E-05 20.00 0.3600 0.005900 0.009700 0.010000 0.01200 0.003900 0.006200 0.004000 0.01200 0.01200 0.001600 0.003500 0.002700 0.002200 0.002300 0.003200 0.003000 0.0006000 0.0009800 0.0008200 0.0004000 0.0004200 0.0004100 0.0003700 0.0001300 0.0001700 0.0001200 8.600E-06 30.00 0.1500 0.005400 0.003600 0.007900 0.007900 0.002900 0.001300 0.001500 0.004200 0.003800 0.0009900 0.0005800 0.0004100 0.001300 0.001200 0.0003200 0.0002900 0.0002700 0.0001800 0.0001100 0.0002800 0.0002600 4.400E-05 3.800E-05 5.500E-05 3.300E-05 1.500E-05 3.500E-06 40.00 0.07200 0.004700 0.001200 0.005500 0.004600 0.001100 0.001100 0.001300 0.001300 0.001100 0.0004200 0.0002600 0.0003600 0.0004500 0.0003800 4.100E-05 3.500E-05 0.0001200 6.300E-05 8.800E-05 1.000E-04 8.400E-05 5.600E-06 4.600E-06 2.500E-05 1.100E-05 1.200E-05 1.600E-06 60.00 0.02400 0.003400 0.0005400 0.002200 0.001300 0.0001400 0.0006500 0.0004800 0.0001300 9.300E-05 4.200E-05 0.0001800 0.0001500 4.700E-05 3.400E-05 1.300E-06 1.000E-06 1.200E-05 4.600E-05 3.700E-05 1.100E-05 7.500E-06 1.900E-07 1.400E-07 2.300E-06 7.900E-06 5.100E-06 1.500E-07 100.0 0.006000 0.001500 0.0003900 0.0003800 0.0001200 9.700E-05 0.0001200 4.300E-05 4.000E-06 2.100E-06 2.700E-05 3.400E-05 1.300E-05 1.400E-06 7.300E-07 1.100E-08 6.700E-09 7.200E-06 8.700E-06 3.300E-06 3.100E-07 1.600E-07 1.500E-09 9.100E-10 1.400E-06 1.500E-06 4.500E-07 9.100E-08 #S 101 Md #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 7 2 2 4 2 #UBIND 1.468E+05 2.853E+04 2.761E+04 2.139E+04 7441. 7019. 5546. 4903. 4622. 2010. 1814. 1410. 883.0 832.0 464.0 449.0 472.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.60 0.006350 0.02310 0.01090 0.01370 0.05070 0.02930 0.03370 0.02160 0.02250 0.09820 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1950 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4760 0.3250 0.4050 1.850 0.05000 13.40 0.006350 0.02310 0.01090 0.01370 0.05070 0.02930 0.03370 0.02160 0.02250 0.09820 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1950 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4740 0.3250 0.4050 1.790 0.1000 13.10 0.006350 0.02310 0.01090 0.01370 0.05070 0.02930 0.03370 0.02160 0.02250 0.09820 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1950 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4700 0.3250 0.4050 1.610 0.1500 12.50 0.006350 0.02310 0.01090 0.01370 0.05060 0.02930 0.03370 0.02160 0.02250 0.09810 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1940 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4640 0.3250 0.4050 1.360 0.2000 12.00 0.006350 0.02310 0.01090 0.01370 0.05060 0.02930 0.03370 0.02160 0.02250 0.09800 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1930 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4540 0.3240 0.4040 1.080 0.3000 10.90 0.006350 0.02300 0.01090 0.01370 0.05060 0.02930 0.03370 0.02160 0.02250 0.09780 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1910 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4280 0.3230 0.4010 0.5670 0.4000 10.10 0.006350 0.02300 0.01090 0.01370 0.05060 0.02930 0.03370 0.02160 0.02250 0.09740 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1890 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.3950 0.3200 0.3930 0.2490 0.5000 9.670 0.006350 0.02300 0.01090 0.01370 0.05050 0.02930 0.03370 0.02160 0.02250 0.09690 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1850 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.3560 0.3150 0.3780 0.1060 0.6000 9.380 0.006350 0.02300 0.01090 0.01370 0.05040 0.02930 0.03370 0.02160 0.02250 0.09640 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1810 0.1270 0.1440 0.1190 0.1230 0.1400 0.1460 0.3140 0.3060 0.3570 0.05930 0.7000 9.130 0.006350 0.02300 0.01090 0.01370 0.05030 0.02930 0.03370 0.02160 0.02250 0.09570 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1760 0.1270 0.1440 0.1190 0.1230 0.1400 0.1460 0.2720 0.2940 0.3300 0.05020 0.8000 8.870 0.006350 0.02300 0.01090 0.01370 0.05030 0.02930 0.03370 0.02160 0.02250 0.09490 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1700 0.1270 0.1440 0.1190 0.1230 0.1390 0.1460 0.2300 0.2780 0.2980 0.04980 1.000 8.280 0.006350 0.02300 0.01090 0.01370 0.05000 0.02930 0.03370 0.02160 0.02250 0.09310 0.06140 0.06920 0.05060 0.05240 0.03970 0.04040 0.1570 0.1260 0.1420 0.1190 0.1230 0.1390 0.1440 0.1570 0.2370 0.2270 0.04330 1.200 7.700 0.006350 0.02300 0.01090 0.01370 0.04970 0.02930 0.03370 0.02160 0.02250 0.09100 0.06130 0.06910 0.05060 0.05230 0.03970 0.04040 0.1430 0.1240 0.1390 0.1180 0.1230 0.1370 0.1420 0.1030 0.1900 0.1590 0.02960 1.400 7.180 0.006350 0.02300 0.01090 0.01370 0.04940 0.02930 0.03370 0.02160 0.02250 0.08850 0.06130 0.06900 0.05060 0.05230 0.03970 0.04040 0.1280 0.1220 0.1350 0.1170 0.1220 0.1340 0.1370 0.06810 0.1430 0.1040 0.01730 1.600 6.740 0.006350 0.02290 0.01090 0.01370 0.04910 0.02930 0.03370 0.02160 0.02250 0.08580 0.06110 0.06880 0.05060 0.05230 0.03970 0.04040 0.1130 0.1190 0.1290 0.1160 0.1200 0.1290 0.1320 0.04910 0.1020 0.06420 0.009320 1.800 6.370 0.006350 0.02290 0.01090 0.01370 0.04860 0.02930 0.03370 0.02160 0.02250 0.08270 0.06100 0.06850 0.05060 0.05230 0.03970 0.04040 0.09860 0.1140 0.1220 0.1140 0.1170 0.1240 0.1250 0.04070 0.07030 0.03940 0.005120 2.000 6.050 0.006350 0.02280 0.01090 0.01370 0.04820 0.02930 0.03370 0.02160 0.02250 0.07950 0.06080 0.06810 0.05060 0.05230 0.03970 0.04040 0.08470 0.1090 0.1140 0.1110 0.1140 0.1170 0.1180 0.03820 0.04720 0.02550 0.003300 2.400 5.490 0.006340 0.02280 0.01090 0.01370 0.04710 0.02930 0.03360 0.02160 0.02250 0.07250 0.06010 0.06700 0.05050 0.05220 0.03970 0.04040 0.06080 0.09630 0.09530 0.1030 0.1040 0.1020 0.1010 0.03770 0.02320 0.01620 0.002590 3.000 4.680 0.006340 0.02260 0.01090 0.01370 0.04520 0.02920 0.03350 0.02160 0.02250 0.06130 0.05850 0.06420 0.05030 0.05190 0.03970 0.04030 0.03670 0.07340 0.06530 0.08460 0.08370 0.07780 0.07530 0.03050 0.01600 0.01540 0.002350 4.000 3.460 0.006330 0.02220 0.01090 0.01370 0.04150 0.02910 0.03320 0.02150 0.02250 0.04310 0.05380 0.05670 0.04920 0.05060 0.03950 0.04010 0.02290 0.03800 0.02820 0.04950 0.04640 0.04330 0.04070 0.01180 0.01400 0.009630 0.0009710 5.000 2.630 0.006320 0.02180 0.01090 0.01360 0.03710 0.02880 0.03260 0.02150 0.02240 0.02850 0.04670 0.04620 0.04660 0.04760 0.03890 0.03950 0.02200 0.01770 0.01380 0.02320 0.02060 0.02120 0.01940 0.005040 0.007480 0.003650 0.0003480 6.000 2.150 0.006300 0.02130 0.01090 0.01360 0.03240 0.02820 0.03160 0.02150 0.02240 0.01860 0.03800 0.03460 0.04240 0.04270 0.03770 0.03820 0.01970 0.01110 0.01160 0.01020 0.009070 0.009520 0.008530 0.004570 0.003130 0.001750 0.0002890 7.000 1.850 0.006290 0.02060 0.01090 0.01360 0.02770 0.02740 0.03030 0.02140 0.02230 0.01330 0.02900 0.02410 0.03660 0.03630 0.03570 0.03600 0.01430 0.01030 0.01130 0.006050 0.005860 0.004280 0.003820 0.004030 0.001830 0.001580 0.0002630 8.000 1.620 0.006270 0.02000 0.01090 0.01360 0.02310 0.02640 0.02850 0.02120 0.02210 0.01120 0.02090 0.01590 0.03000 0.02920 0.03290 0.03310 0.008830 0.009990 0.009620 0.005390 0.005510 0.002330 0.002150 0.002770 0.001720 0.001480 0.0001850 10.00 1.200 0.006200 0.01800 0.01090 0.01300 0.01500 0.02400 0.02400 0.02100 0.02100 0.01100 0.009900 0.007300 0.01800 0.01600 0.02600 0.02600 0.003500 0.006600 0.004500 0.004900 0.004800 0.001700 0.001700 0.0009200 0.001400 0.0007800 6.100E-05 15.00 0.6300 0.006100 0.01400 0.01090 0.01300 0.005400 0.01400 0.01200 0.01700 0.01700 0.005900 0.004500 0.005200 0.003300 0.003000 0.010000 0.010000 0.002400 0.001200 0.001300 0.001100 0.0009500 0.001200 0.001100 0.0005100 0.0002100 0.0001700 3.200E-05 20.00 0.3600 0.005900 0.009800 0.009900 0.01100 0.003900 0.006400 0.004200 0.01200 0.01200 0.001600 0.003600 0.002800 0.002100 0.002300 0.003400 0.003200 0.0006500 0.001000 0.0008600 0.0004100 0.0004200 0.0004500 0.0004000 0.0001500 0.0001700 0.0001200 9.300E-06 30.00 0.1500 0.005300 0.003800 0.007900 0.007900 0.003000 0.001300 0.001500 0.004400 0.003900 0.001000 0.0006200 0.0004200 0.001300 0.001300 0.0003500 0.0003200 0.0002800 0.0002000 0.0001100 0.0002900 0.0002700 5.000E-05 4.300E-05 5.500E-05 3.600E-05 1.600E-05 3.400E-06 40.00 0.07300 0.004700 0.001300 0.005500 0.004700 0.001100 0.001000 0.001300 0.001300 0.001100 0.0004500 0.0002500 0.0003600 0.0004700 0.0004000 4.500E-05 3.800E-05 0.0001300 6.300E-05 8.900E-05 0.0001100 8.900E-05 6.400E-06 5.200E-06 2.700E-05 1.100E-05 1.200E-05 1.700E-06 60.00 0.02400 0.003400 0.0005300 0.002300 0.001400 0.0001400 0.0006600 0.0005000 0.0001400 1.000E-04 4.400E-05 0.0001900 0.0001500 5.100E-05 3.700E-05 1.500E-06 1.200E-06 1.200E-05 4.700E-05 3.800E-05 1.200E-05 8.200E-06 2.200E-07 1.600E-07 2.500E-06 8.100E-06 5.300E-06 1.600E-07 100.0 0.006100 0.001600 0.0003900 0.0004100 0.0001300 9.700E-05 0.0001300 4.600E-05 4.300E-06 2.300E-06 2.700E-05 3.600E-05 1.400E-05 1.500E-06 8.000E-07 1.200E-08 7.600E-09 7.300E-06 9.400E-06 3.500E-06 3.400E-07 1.800E-07 1.800E-09 1.100E-09 1.500E-06 1.600E-06 4.800E-07 9.100E-08 #S 102 No #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 8 2 2 4 2 #UBIND 1.505E+05 2.938E+04 2.844E+04 2.188E+04 7675. 7245. 5688. 5037. 4741. 2078. 1876. 1448. 883.0 832.0 464.0 449.0 484.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.50 0.006240 0.02270 0.01070 0.01350 0.04990 0.02890 0.03330 0.02130 0.02220 0.09660 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1910 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4670 0.3190 0.4000 1.840 0.05000 13.40 0.006240 0.02270 0.01070 0.01350 0.04990 0.02890 0.03330 0.02130 0.02220 0.09650 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1910 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4660 0.3190 0.4000 1.770 0.1000 13.00 0.006240 0.02270 0.01070 0.01350 0.04990 0.02890 0.03330 0.02130 0.02220 0.09650 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1910 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4620 0.3190 0.4000 1.600 0.1500 12.50 0.006240 0.02270 0.01070 0.01350 0.04990 0.02890 0.03330 0.02130 0.02220 0.09640 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1900 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4550 0.3190 0.4000 1.360 0.2000 11.90 0.006240 0.02270 0.01070 0.01350 0.04990 0.02890 0.03330 0.02130 0.02220 0.09640 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1900 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4460 0.3180 0.3990 1.080 0.3000 10.80 0.006240 0.02270 0.01070 0.01350 0.04980 0.02890 0.03330 0.02130 0.02220 0.09610 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1880 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4220 0.3170 0.3960 0.5750 0.4000 10.10 0.006240 0.02270 0.01070 0.01350 0.04980 0.02890 0.03330 0.02130 0.02220 0.09580 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1850 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.3900 0.3150 0.3880 0.2570 0.5000 9.660 0.006240 0.02270 0.01070 0.01350 0.04970 0.02890 0.03330 0.02130 0.02220 0.09530 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1820 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.3530 0.3090 0.3750 0.1090 0.6000 9.370 0.006240 0.02270 0.01070 0.01350 0.04970 0.02890 0.03330 0.02130 0.02220 0.09480 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1770 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.3130 0.3010 0.3550 0.05950 0.7000 9.120 0.006240 0.02270 0.01070 0.01350 0.04960 0.02890 0.03330 0.02130 0.02220 0.09410 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1730 0.1240 0.1420 0.1160 0.1210 0.1360 0.1420 0.2720 0.2900 0.3290 0.04900 0.8000 8.870 0.006240 0.02270 0.01070 0.01350 0.04950 0.02890 0.03330 0.02130 0.02220 0.09340 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1670 0.1240 0.1410 0.1160 0.1210 0.1360 0.1420 0.2320 0.2750 0.2980 0.04840 1.000 8.310 0.006240 0.02260 0.01070 0.01350 0.04930 0.02890 0.03330 0.02130 0.02220 0.09170 0.06030 0.06820 0.04990 0.05160 0.03900 0.03970 0.1550 0.1230 0.1400 0.1160 0.1210 0.1350 0.1400 0.1600 0.2370 0.2290 0.04290 1.200 7.740 0.006240 0.02260 0.01070 0.01350 0.04900 0.02890 0.03330 0.02130 0.02220 0.08970 0.06030 0.06810 0.04990 0.05160 0.03900 0.03970 0.1420 0.1220 0.1370 0.1160 0.1200 0.1330 0.1380 0.1060 0.1920 0.1620 0.03000 1.400 7.230 0.006240 0.02260 0.01070 0.01350 0.04870 0.02890 0.03330 0.02130 0.02220 0.08730 0.06020 0.06800 0.04990 0.05160 0.03900 0.03970 0.1280 0.1200 0.1330 0.1150 0.1190 0.1310 0.1340 0.07030 0.1460 0.1070 0.01790 1.600 6.790 0.006240 0.02260 0.01070 0.01350 0.04830 0.02880 0.03330 0.02130 0.02220 0.08470 0.06010 0.06780 0.04990 0.05160 0.03900 0.03970 0.1130 0.1170 0.1280 0.1140 0.1180 0.1270 0.1300 0.05010 0.1060 0.06670 0.009810 1.800 6.430 0.006240 0.02250 0.01070 0.01350 0.04790 0.02880 0.03330 0.02130 0.02220 0.08180 0.05990 0.06750 0.04980 0.05160 0.03900 0.03970 0.09900 0.1130 0.1210 0.1120 0.1160 0.1220 0.1240 0.04070 0.07370 0.04110 0.005380 2.000 6.110 0.006230 0.02250 0.01070 0.01350 0.04750 0.02880 0.03330 0.02130 0.02220 0.07870 0.05970 0.06720 0.04980 0.05150 0.03900 0.03970 0.08550 0.1080 0.1140 0.1100 0.1130 0.1160 0.1170 0.03760 0.05000 0.02640 0.003370 2.400 5.560 0.006230 0.02240 0.01070 0.01350 0.04650 0.02880 0.03320 0.02130 0.02220 0.07200 0.05920 0.06610 0.04980 0.05150 0.03900 0.03970 0.06190 0.09590 0.09560 0.1020 0.1040 0.1020 0.1010 0.03700 0.02430 0.01610 0.002500 3.000 4.760 0.006230 0.02220 0.01070 0.01350 0.04470 0.02880 0.03310 0.02130 0.02220 0.06120 0.05770 0.06350 0.04960 0.05120 0.03900 0.03960 0.03770 0.07410 0.06650 0.08490 0.08420 0.07890 0.07670 0.03090 0.01570 0.01510 0.002310 4.000 3.520 0.006220 0.02190 0.01070 0.01350 0.04110 0.02860 0.03280 0.02130 0.02220 0.04350 0.05330 0.05640 0.04850 0.05000 0.03890 0.03950 0.02280 0.03950 0.02940 0.05100 0.04790 0.04520 0.04260 0.01260 0.01410 0.009870 0.001020 5.000 2.670 0.006210 0.02150 0.01070 0.01350 0.03690 0.02830 0.03230 0.02130 0.02220 0.02900 0.04660 0.04630 0.04620 0.04720 0.03840 0.03890 0.02170 0.01860 0.01420 0.02460 0.02190 0.02280 0.02090 0.005180 0.007940 0.003870 0.0003570 6.000 2.170 0.006190 0.02100 0.01070 0.01350 0.03230 0.02780 0.03130 0.02120 0.02210 0.01910 0.03820 0.03510 0.04220 0.04260 0.03730 0.03770 0.01990 0.01120 0.01150 0.01100 0.009620 0.01050 0.009440 0.004520 0.003390 0.001780 0.0002790 7.000 1.860 0.006180 0.02040 0.01070 0.01340 0.02770 0.02710 0.03000 0.02110 0.02200 0.01350 0.02950 0.02470 0.03670 0.03650 0.03540 0.03570 0.01480 0.01020 0.01130 0.006230 0.005960 0.004770 0.004260 0.004110 0.001870 0.001550 0.0002610 8.000 1.630 0.006160 0.01970 0.01070 0.01340 0.02330 0.02610 0.02830 0.02100 0.02180 0.01120 0.02150 0.01640 0.03040 0.02960 0.03280 0.03300 0.009390 0.009970 0.009810 0.005370 0.005480 0.002520 0.002310 0.002930 0.001700 0.001480 0.0001910 10.00 1.200 0.006100 0.01800 0.01070 0.01300 0.01600 0.02300 0.02400 0.02000 0.02100 0.01100 0.010000 0.007500 0.01800 0.01700 0.02600 0.02600 0.003600 0.006900 0.004800 0.005000 0.004900 0.001700 0.001700 0.001000 0.001400 0.0008200 6.500E-05 15.00 0.6400 0.006000 0.01400 0.010000 0.01300 0.005500 0.01400 0.01200 0.01700 0.01700 0.006200 0.004400 0.005200 0.003400 0.003100 0.01100 0.010000 0.002400 0.001200 0.001300 0.001200 0.001000 0.001200 0.001200 0.0005300 0.0002200 0.0001700 3.300E-05 20.00 0.3700 0.005800 0.009900 0.009800 0.01100 0.003800 0.006700 0.004400 0.01200 0.01200 0.001700 0.003600 0.002900 0.002100 0.002200 0.003600 0.003400 0.0007100 0.001000 0.0009000 0.0004100 0.0004200 0.0004900 0.0004400 0.0001600 0.0001700 0.0001300 1.000E-05 30.00 0.1500 0.005200 0.003900 0.007800 0.008000 0.003000 0.001300 0.001500 0.004500 0.004100 0.001000 0.0006800 0.0004300 0.001400 0.001300 0.0003800 0.0003400 0.0002800 0.0002200 0.0001200 0.0003000 0.0002800 5.600E-05 4.800E-05 5.500E-05 4.000E-05 1.700E-05 3.400E-06 40.00 0.07500 0.004600 0.001400 0.005600 0.004800 0.001200 0.001000 0.001400 0.001400 0.001200 0.0004800 0.0002500 0.0003600 0.0005000 0.0004200 5.000E-05 4.200E-05 0.0001400 6.300E-05 8.900E-05 0.0001100 9.500E-05 7.300E-06 6.000E-06 2.900E-05 1.100E-05 1.200E-05 1.800E-06 60.00 0.02500 0.003400 0.0005300 0.002400 0.001400 0.0001400 0.0006800 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xrstools-0.15.0+git20210910+c147919d/XRStools/resources/hdf5dialog.ui000066400000000000000000000032741412732462000242740ustar00rootroot00000000000000 Dialog 0 0 502 308 Dialog Qt::Horizontal QDialogButtonBox::Cancel|QDialogButtonBox::Ok buttonBox buttonBox accepted() Dialog accept() 227 277 157 274 buttonBox rejected() Dialog reject() 295 283 286 274 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/imageview.ui000066400000000000000000000015411412732462000242360ustar00rootroot00000000000000 Form 0 0 548 300 Form xrstools-0.15.0+git20210910+c147919d/XRStools/resources/localfilesdialog.ui000066400000000000000000000064721412732462000255660ustar00rootroot00000000000000 Dialog 0 0 502 308 Dialog F.name Browse.. <html><head/><body><p>This filename is taken as a template. Its numerical prefix will be replace with the numbers found in the spec scan that will be choosed.</p></body></html> scan # spec File Browse.. Qt::Horizontal QDialogButtonBox::Cancel|QDialogButtonBox::Ok FileName_lineEdit BrowseImage_pushButton ScanNumber_spinBox buttonBox buttonBox accepted() Dialog accept() 227 277 157 274 buttonBox rejected() Dialog reject() 295 283 286 274 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/mainwindow.ui000066400000000000000000000137761412732462000244520ustar00rootroot00000000000000 MainWindow 0 0 800 600 ROI DETECTION true 0 Global 0 0 800 28 Remote Local actions View transferts FIle toolBar TopToolBarArea false Load... Select Scan and files... subimage spot detection registration global spot detection Toggle Data/Rois showMasks push mask remotely write mask on file load mask from file remote load write mask oin file write mask on file write masksDict on file load masksDict from file Exit Confirm And Exit load image from hdf5 file load image from hdf5 load maskDict from hdf5 write maskDict to hdf5 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/ramanWidget/000077500000000000000000000000001412732462000241635ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/resources/ramanWidget/MainWindow.ui000066400000000000000000000034241412732462000266010ustar00rootroot00000000000000 MainWindow 0 0 800 600 MainWindow Tab 1 Tab 2 0 0 800 28 File Save Configuration Load Configuration Add Set of Analyzers xrstools-0.15.0+git20210910+c147919d/XRStools/resources/ramanWidget/acquisition.ui000066400000000000000000000066671412732462000270710ustar00rootroot00000000000000 Form 0 0 400 300 Form <html><head/><body><p>Choose the method</p></body></html> sum row pixel <html><head/><body><p>Choose the scan for calibration</p></body></html> 1 100000 include elastic <html><head/><body><p>This loads the data by doing averaging over analyzers and calibration for energy. Then for each analyzer set a new tab will be created where further analysis can be done.</p></body></html> Calibration and Acquisition <html><head/><body><p>This loads the data from a previously saved ixstools extracted spectra</p></body></html> Load from ixstools 0 0 QFrame::StyledPanel QFrame::Raised Output prefix Save Analysis xrstools-0.15.0+git20210910+c147919d/XRStools/resources/ramanWidget/edgesTable.ui000066400000000000000000000036761412732462000265750ustar00rootroot00000000000000 Form 0 0 383 294 Form QFrame::StyledPanel QFrame::Raised Qt::Vertical QFrame::StyledPanel QFrame::Raised true 0 0 306 254 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/ramanWidget/hdf5dialog.ui000066400000000000000000000034001412732462000265250ustar00rootroot00000000000000 Dialog 0 0 502 308 Dialog Qt::Horizontal QDialogButtonBox::Cancel|QDialogButtonBox::Ok buttonBox buttonBox accepted() Dialog accept() 227 277 157 274 buttonBox rejected() Dialog reject() 295 283 286 274 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/ramanWidget/plotContainer.ui000066400000000000000000000170551412732462000273530ustar00rootroot00000000000000 Form 0 0 400 443 Form Estimate Pearson 0 0 0 0 0 0 0 0 0 0 0 0 0 0 A1 (FWHM) A2 (Shape) A3 (Peak Height) C0 (backg. poly.) C1 (back. poly) F ( scaling) true 0 0 A0 (Pearson7 center) 0 0 FIt All Plot Qt::Vertical 20 40 0 0 Qt::Horizontal Qt::Horizontal Qt::Horizontal Qt::Horizontal 40 20 HF core shift 0 0 Save S(q,w) xrstools-0.15.0+git20210910+c147919d/XRStools/resources/ramanWidget/scansTable.ui000066400000000000000000000010011412732462000265710ustar00rootroot00000000000000 Form 0 0 400 300 Form xrstools-0.15.0+git20210910+c147919d/XRStools/resources/ramanWidget/source.ui000066400000000000000000000024261412732462000260260ustar00rootroot00000000000000 Form 0 0 400 300 Form <html><head/><body><p>filename and data group</p><p>containing ROIs description.</p><p>You can righ-click for browsing</p><p>existing hdf5 files</p></body></html> ROIs spec file xrstools-0.15.0+git20210910+c147919d/XRStools/resources/ramanWidget/subsetTable.ui000066400000000000000000000010011412732462000267670ustar00rootroot00000000000000 Form 0 0 400 300 Form xrstools-0.15.0+git20210910+c147919d/XRStools/resources/roiNmaSelectionGui/000077500000000000000000000000001412732462000254615ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/resources/roiNmaSelectionGui/NNMF_control.ui000066400000000000000000000055461412732462000303300ustar00rootroot00000000000000 Form 0 0 300 80 300 80 Form 0 20 191 21 0 0 NNMA for N. of comp. = 190 20 113 20 0 0 <html><head/><body><p>Given a number of components, the push button calculates and plot them,</p><p>and set the represented signal percentage in the input entry for the PCA by percentage button </p></body></html> 10 50 171 21 0 0 NNMA versus NofComps 190 50 113 20 0 0 <html><head/><body><p>Given a percentage, the push button calculates components till the percentage is reached. Then the components are plotted and the input entry for the PCA by N. of components is updated.</p></body></html> xrstools-0.15.0+git20210910+c147919d/XRStools/resources/roiNmaSelectionGui/globalcontrol.ui000066400000000000000000000045111412732462000306620ustar00rootroot00000000000000 Form 0 0 890 46 Form 230 10 90 28 Calc. Comps 60 10 51 32 10 10 41 20 N.ofC. 120 10 62 20 Choosed 180 10 41 32 340 10 71 20 Threshold 420 10 61 32 490 10 90 28 Threshold xrstools-0.15.0+git20210910+c147919d/XRStools/resources/roiNmaSelectionGui/hdf5dialog.ui000066400000000000000000000032741412732462000300340ustar00rootroot00000000000000 Dialog 0 0 502 308 Dialog Qt::Horizontal QDialogButtonBox::Cancel|QDialogButtonBox::Ok buttonBox buttonBox accepted() Dialog accept() 227 277 157 274 buttonBox rejected() Dialog reject() 295 283 286 274 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/roiNmaSelectionGui/imageview.ui000066400000000000000000000015411412732462000277760ustar00rootroot00000000000000 Form 0 0 548 300 Form xrstools-0.15.0+git20210910+c147919d/XRStools/resources/roiNmaSelectionGui/localfilesdialog.ui000066400000000000000000000073171412732462000313250ustar00rootroot00000000000000 Dialog 0 0 502 308 Dialog ROI address Browse.. <html><head/><body><p>Here you must select the ROI address of a previous spatial selection (roiSelectionWidget)</p></body></html> <html><head/><body><p>Put here one or several scan numbers separated by a comma.</p></body></html> scans <html><head/><body><p>The directory of the experiment.(must contain a file named hydra) </p></body></html> Exp. Dir. Browse.. Qt::Horizontal QDialogButtonBox::Cancel|QDialogButtonBox::Ok FileName_lineEdit BrowseImage_pushButton buttonBox buttonBox accepted() Dialog accept() 227 277 157 274 buttonBox rejected() Dialog reject() 295 283 286 274 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/roiNmaSelectionGui/mainwindow.ui000066400000000000000000000135271412732462000302040ustar00rootroot00000000000000 MainWindow 0 0 800 600 ROI SPECTRAL DETECTION true 0 Global 0 0 800 28 Load Data Manage Masks Push toolBar TopToolBarArea false false Load... Select Scan and files... false subimage spot detection registration global spot detection false Toggle Data/Rois showMasks false push mask remotely false write mask on file load mask from file remote load write mask oin file write mask on file write masksDict on file load masksDict from file Exit Confirm And Exit load image from hdf5 file load image from hdf5 load maskDict from hdf5 Push Remotely load maskDict from hdf5 load spectral maskDict from hdf5 write spectral maskDict to hdf5 xrstools-0.15.0+git20210910+c147919d/XRStools/resources/roiNmaSelectionGui/pickup_choice.ui000066400000000000000000000054511412732462000306320ustar00rootroot00000000000000 Form 0 0 179 278 167 99 Form 0 40 121 18 Choose by point 0 70 111 18 111 0 Choose by line 0 100 121 18 Choose by column 0 10 161 18 No action on left click 40 140 75 23 Threshold 20 170 113 20 0 190 151 18 Visualize only from roi 40 230 75 23 ---RESET--- xrstools-0.15.0+git20210910+c147919d/XRStools/resources/roiNmaSelectionGui/spotdetectioncontrol.ui000066400000000000000000000066351412732462000323170ustar00rootroot00000000000000 detectionDockWidget 0 0 511 605 DockWidget Annotate relabelise Reset Automatic Detection Hough Detection threshold Qt::Vertical 20 40 1 0 -99 Inflate spotregistrationcontrol.ui000066400000000000000000000041641412732462000327670ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/resources/roiNmaSelectionGui detectionDockWidget 0 0 511 605 DockWidget Global registration registration true 3 4 false false xrstools-0.15.0+git20210910+c147919d/XRStools/resources/setup.py000066400000000000000000000032141412732462000234330ustar00rootroot00000000000000# coding: utf-8 # /*########################################################################## # Copyright (C) 2016-2018 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # ############################################################################*/ __authors__ = ["V. Valls"] __license__ = "MIT" __date__ = "16/05/2017" from numpy.distutils.misc_util import Configuration def configuration(parent_package='', top_path=None): config = Configuration('resources', parent_package, top_path) return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration=configuration) xrstools-0.15.0+git20210910+c147919d/XRStools/resources/spotdetectioncontrol.ui000066400000000000000000000066351412732462000265570ustar00rootroot00000000000000 detectionDockWidget 0 0 511 605 DockWidget Annotate relabelise Reset Automatic Detection Hough Detection threshold Qt::Vertical 20 40 1 0 -99 Inflate xrstools-0.15.0+git20210910+c147919d/XRStools/resources/spotregistrationcontrol.ui000066400000000000000000000041641412732462000273060ustar00rootroot00000000000000 detectionDockWidget 0 0 511 605 DockWidget Global registration registration true 3 4 false false xrstools-0.15.0+git20210910+c147919d/XRStools/rixs_read.py000066400000000000000000000337641412732462000222560ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range from six.moves import zip from six.moves import input #!/usr/bin/python # Filename: rixs_read.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" from . import xrs_rois, xrs_scans, xrs_utilities, math_functions, xrs_fileIO import sys import os import numpy as np import matplotlib.pyplot as plt from scipy import optimize # try to import the fast PyMCA file parsers try: import PyMca5.PyMcaIO.EdfFile as EdfIO import PyMca5.PyMcaIO.specfilewrapper as SpecIO use_PyMca = True except: use_PyMca = False if not use_PyMca: try: import PyMca.EdfFile as EdfIO import PyMca.specfilewrapper as SpecIO use_PyMca = True except: use_PyMca = False print( " >>>>>>>> use_PyMca " , use_PyMca) __metaclass__ = type # new style classes class read_id20: """ Main class for handling raw data from XES experiments on ESRF's ID20. This class is used to read scans from SPEC files and the according EDF-files, it provides access to all tools from the xrs_rois module for defining ROIs, it can be used to integrate scans, sum them up, stitch them together, and define the energy loss scale. INPUT: absFilename = path and filename of the SPEC-file energyColumn = name (string) of the counter for the energy as defined in the SPEC session (counter mnemonic) monitorColumn = name (string) of the counter for the monitor signals as defined in the SPEC session (counter mnemonic) edfName = name/prefix (string) of the EDF-files (default is the same as the SPEC-file name) single_image = boolean switch, 'True' (default) if all 6 detectors are merged in a single image, 'False' if two detector images per point exist. """ def __init__(self,absFilename,energyColumn='Anal Energy',monitorColumn='kaprixs',edfName=None, edf_shape=(255, 1295), EinCoor=[0,0] ): self.scans = {} # dictionary of scans try: self.path = os.path.split(absFilename)[0] + '/' self.filename = os.path.split(absFilename)[1] except IOError: print('file does not exist.') if not edfName: self.edfName = os.path.split(absFilename)[1] else: self.edfName = edfName self.scannumbers = [] self.EDF_PREFIX = 'edf/' self.EDF_POSTFIX = '.edf' self.DET_PIXEL_NUMx = edf_shape[1] self.DET_PIXEL_NUMy = edf_shape[0] self.DET_PIXEL_NUM = edf_shape[0] # which column in the SPEC file to be used for the energy and monitor self.encolumn = energyColumn.lower() # name of energy motor scanned self.monicolumn = monitorColumn.lower() # name of the monitor counter self.EinCoor = EinCoor # incident energy coordinate # compensation factor self.comp_factor = 0.0 self.pixel_size = 0.055 # pixel size in mm # here are the attributes of the old rawdata class self.energy = [] # common energy scale for all analyzers self.energy2 = [] # analyzer energy self.signals = [] # signals for all analyzers self.errors = [] # poisson errors self.groups = {} # dictionary of groups (such as 2 'elastic', or 5 'edge1', etc.) self.tth = [] # list of scattering angles (one for each ROI) self.resolution = [] # list of FWHM of the elastic lines for each analyzer self.signals_orig = [] # signals for all analyzers before interpolation # ROI object self.roi_obj = [] # an instance of the roi_object class from the xrs_rois module (new) def set_roiObj(self,roiobj): self.roi_obj = roiobj def readscan(self,scannumber): """ Returns the data, motors, counter-names, and edf-files from the SPEC file defined when the xrs_read object was initiated. There should be an alternative that uses the PyMca module if installed. INPUT: scannumber = number of the scan to be loaded fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) """ # load SPEC-file print( 'Parsing EDF- and SPEC-files of scan No. %s' % scannumber) fn = self.path + self.filename if use_PyMca == True: data, motors, counters = xrs_fileIO.PyMcaSpecRead(fn,scannumber) else: data, motors, counters = xrs_fileIO.SpecRead(fn,scannumber) # load EDF-files edfmats = xrs_fileIO.ReadEdfImages(counters['ccdno'], self.DET_PIXEL_NUMx, self.DET_PIXEL_NUMy, self.path, self.EDF_PREFIX, self.edfName, self.EDF_POSTFIX) # add the scannumber to self.scannumbers, if not already present if not scannumber in self.scannumbers: self.scannumbers.extend([scannumber]) return data, motors, counters, edfmats #--------- def SumDirect(self,scannumbers): Sum=None for number in scannumbers: data, motors, counters, edfmats = readscan(number, self.path, self.filename, self.DET_PIXEL_NUMx, self.DET_PIXEL_NUMy, self.EDF_PREFIX, self.EDF_POSTFIX, self.edfName) if Sum is None: Sum = np.zeros(edfmats[0].shape ,"f") Sum[:] += edfmats.sum(axis=0) return Sum def loadscan(self, scannumbers, scantype='generic', direct=True): """ Loads the files belonging to scan No. "scannumber" and puts it into an instance of the xrs_scan-class 'scan'. The default scantype is 'generic', later the scans will be grouped (and added) based on the scantype. INPUT: scannumbers = integer or list of scannumbers that should be loaded scantype = string describing the scan to be loaded (e.g. 'edge1' or 'K-edge') """ # make sure scannumbers are iterable if not isinstance(scannumbers,list): scannums = [] scannums.append(scannumbers) else: scannums = scannumbers # load scan/scans for number in scannums: scanname = 'Scan%03d' % number data, motors, counters, edfmats = readscan(number, self.path, self.filename, self.DET_PIXEL_NUMx, self.DET_PIXEL_NUMy, self.EDF_PREFIX, self.EDF_POSTFIX, self.edfName) # create an instance of "scan" class for every scan onescan = xrs_scans.scan(edfmats,number,counters[self.encolumn],counters[self.monicolumn],counters,motors,data,scantype) # assign one dictionary entry to each scan self.scans[scanname] = onescan if not number in self.scannumbers: self.scannumbers.extend([number]) # save the incident energy self.scans[scanname].Ein = motors[self.EinCoor[0]][self.EinCoor[1]] if direct: energy = self.scans[scanname].energy * 1e3 # energy in eV self.scans[scanname].signals = np.zeros((len(energy),self.roi_obj.number_of_rois)) self.scans[scanname].errors = np.zeros((len(energy),self.roi_obj.number_of_rois)) for key,col in zip(self.roi_obj.red_rois, list(range(self.roi_obj.number_of_rois)) ): (pos,M) = self.roi_obj.red_rois[key] S = M.shape x = np.arange(S[0])*self.pixel_size inset = (slice(pos[0] , pos[0]+(S[0]) ), slice( pos[1] , pos[1]+(S[1]) ) ) A = self.scans[scanname].edfmats[:,inset[0],inset[1]] y = np.squeeze(np.sum(A,2)) meanmon = np.mean(self.scans[scanname].monitor) yn = np.zeros_like(y) for jj in range(len(self.scans[scanname].monitor)): yn[jj,:]=y[jj,:]/self.scans[scanname].monitor[jj]*meanmon meanii = len(list(range(yn.shape[1])))//2 yc = np.zeros_like(y) for ii in range(yn.shape[1]): sort = np.argsort(energy) yc[:,ii] = np.interp(energy[sort]+(ii-meanii)*self.comp_factor*self.pixel_size, energy[sort],yn[sort,ii],left=float('nan'),right=float('nan')) self.scans[scanname].signals[:,col] = xrs_utilities.nonzeroavg(yc) print('Deleting EDF-files of scan No. %03d'%number) self.scans[scanname].edfmats = np.array([]) def deletescan(self,scannumbers): """ Deletes scans from the class. INPUT: scannumbers = integer or list of integers (SPEC scan numbers) to delete """ # make sure scannumbers are iterable numbers = [] if not isinstance(scannumbers,list): numbers.append(scannumbers) else: numbers = scannumbers # delete scans for number in numbers: scanname = 'Scan%03d' % number del(self.scans[scanname]) self.scannumbers.remove(number) def SumDirect(self,scannumbers): Sum=None for number in scannumbers: data, motors, counters, edfmats = self.readscan(number) print( edfmats.shape) if Sum is None: Sum = np.zeros(edfmats[0].shape ,"f") Sum[:] += edfmats.sum(axis=0) return Sum def getCompensationFactor(self,scannumber,roiNumber): if not self.roi_obj: print('Please set a ROI object first.') return else: data, motors, counters, edfmats = readscan(scannumber, self.path, self.filename, self.DET_PIXEL_NUMx, self.DET_PIXEL_NUMy, self.EDF_PREFIX, self.EDF_POSTFIX, self.edfName) plt.cla() plt.ion() el_positions = np.zeros(len(edfmats)) for ii in range(len(edfmats)): key = list(self.roi_obj.red_rois.keys())[roiNumber] (pos,M) = self.roi_obj.red_rois[key] S = M.shape x = np.arange(S[0])*self.pixel_size+self.pixel_size inset = (slice(pos[0] , pos[0]+(S[0]) ), slice( pos[1] , pos[1]+(S[1]) ) ) y = np.sum( edfmats[ii,inset[0],inset[1]] ,axis=1) try: popt = optimize.curve_fit(math_functions.gauss_forcurvefit, x, y)[0] g = math_functions.gauss_forcurvefit(x,popt[0],popt[1],popt[2]) el_positions[ii] = (popt[1]) except: g = np.zeros_like(x) el_positions[ii] = 0.0 plt.cla() plt.plot(x,y,x,g) plt.draw() #print S, len(el_positions), len(edfmats), y.shape[0] #center = np.interp(range(len(edfmats)),range(y.shape[0]),el_positions) # fit the slope plt.cla() sort = np.argsort(np.array(counters[self.encolumn])*1e3) energy = (np.array(counters[self.encolumn])*1e3)[sort] center = el_positions[sort] plt.plot(center,energy) plt.draw() input('Zoom in and press enter to continue') limits = plt.axis() inds = np.where(np.logical_and(center >= limits[0], center <= limits[1]))[0] fact = np.polyfit( center[inds], energy[inds],1) plt.plot(center,energy,center,np.polyval(fact,center)) self.comp_factor = comp_factor = fact[0] def readscan(scannumber, path, filename, DET_PIXEL_NUMx, DET_PIXEL_NUMy, EDF_PREFIX, EDF_POSTFIX, edfName): """ Returns the data, motors, counter-names, and edf-files from the SPEC file defined when the xrs_read object was initiated. There should be an alternative that uses the PyMca module if installed. INPUT: scannumber = number of the scan to be loaded fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) """ # load SPEC-file print( 'Parsing EDF- and SPEC-files of scan No. %s' % scannumber) fn = path + filename #if use_PyMca == True: # data, motors, counters = xrs_fileIO.PyMcaSpecRead(fn,scannumber) #else: data, motors, counters = xrs_fileIO.SpecRead(fn,scannumber) # load EDF-files edfmats = xrs_fileIO.ReadEdfImages(counters['ccdno'], DET_PIXEL_NUMx, DET_PIXEL_NUMy, path, EDF_PREFIX, edfName, EDF_POSTFIX) return data, motors, counters, edfmats xrstools-0.15.0+git20210910+c147919d/XRStools/roiNmaSelectionGui/000077500000000000000000000000001412732462000234475ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/roiNmaSelectionGui/__init__.py000066400000000000000000000001641412732462000255610ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function xrstools-0.15.0+git20210910+c147919d/XRStools/roiNmaSelectionGui/graphsScrollWidget.py000066400000000000000000000607271412732462000276440ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from PyMca5.PyMcaGui import MaskImageWidget as sole_MaskImageWidget # from PyQt4 import Qt, QtCore, QtGui from silx.gui import qt as Qt from silx.gui import qt as QtCore from silx.gui import qt as QtGui import os import numpy as np import string from PyMca5.PyMcaGraph.Plot import Plot from six.moves import range from silx.gui.plot.PlotWindow import Plot1D , Plot2D from silx.gui.plot import PlotWidget, actions, items from silx.gui.plot.MaskToolsWidget import MaskToolsDockWidget from silx.gui.plot.Profile import ProfileToolBar from silx.gui.plot.PlotTools import PositionInfo from silx.gui import qt from sklearn.decomposition import NMF from . import nnma import time from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] if my_relativ_path[0]=="/": my_relativ_path = my_relativ_path[1:] # # from PyMca5.PyMcaGraph.backends.OpenGLBackend import OpenGLBackend # Plot.defaultBackend = OpenGLBackend # Plot.defaultBackend = OpenGLBackend # # DA FARE # # Lanciare con selezione default componente 0 OK # # Inizializzare la maschera a zero OK # renderla persistente OK # # Usare i radio button per verificare nella gestione degli eventi OK # # Vedere Che le componenti siano solo sulla roi iniziale OK # # Mettere a zero i dati iniziali con mask ioniziale OK # # Bloccare la selezione dove la maschera iniziale e' 1 OK # # Automatizzare tutto con scelte prestabilite : usare prima comp, trheshold a una frazione OK # # Slider Trasparenza OK # Slider dimensione OK # # Automatizzazione globale con scelte prestabilite ( bisognera conservare creati in liste) OK # # Salvare ROI # Salvare separatamente stato con selezione N comp, numero di comp maschera threshold # Rilettura class MyPlot1D(Plot1D): def __init__(self, parent=None): super(MyPlot1D, self).__init__(parent) # , backend = "gl") self.shint = 400 self.setSizePolicy( Qt.QSizePolicy.Fixed, Qt.QSizePolicy.Fixed ) # ou maximum def sizeHint(self ) : return Qt.QSize( self.shint, self.shint) def setSizeHint(self, val ) : self.shint = val self.updateGeometry() class MyPlot(PlotWidget): def __init__(self, parent=None): super(MyPlot, self).__init__(parent) # , backend = "gl") self.resetZoomAction = actions.control.ResetZoomAction(self) self.addAction(self.resetZoomAction) self.colormapAction = actions.control.ColormapAction(self) self.addAction(self.colormapAction) toolbar = qt.QToolBar('Plot', self) toolbar.addAction(self.resetZoomAction) toolbar.addAction(self.colormapAction) self.addToolBar(toolbar) self.profile = ProfileToolBar(plot=self) self.addToolBar(self.profile) #self.maskToolsDockWidget = MaskToolsDockWidget( # plot=self, name='Mask') #self.maskToolsDockWidget.hide() #self.addDockWidget(qt.Qt.BottomDockWidgetArea, self.maskToolsDockWidget) posInfo = [ ('X', lambda x, y: x), ('Y', lambda x, y: y), ('Data', self._getImageValue)] self.positionWidget = PositionInfo(plot=self, converters=posInfo) self.statusBar().addWidget(self.positionWidget) self.getDefaultColormap().setName('temperature') # 'temperature'....viridis self.transp = 100 self.shint = 400 self.setSizePolicy( Qt.QSizePolicy.Fixed, Qt.QSizePolicy.Fixed ) # ou maximum #def getSelectionMask(self): # return self.maskToolsDockWidget.getSelectionMask() def sizeHint(self ) : return Qt.QSize( self.shint, self.shint) def setSizeHint(self, val ) : self.shint = val self.updateGeometry() def reset_SelectionMask(self, transp=100): self.transp=transp self.setSelectionMask( self.getSelectionMask() ) def setSelectionMask(self, mask): #return bool(self.maskToolsDockWidget.setSelectionMask(mask)) image = np.zeros(mask.shape + (4,), dtype=np.uint8) image[:, :, :3] = (255, 0, 0) # rgb color image[:, :, 3][mask == 1] = self.transp # transparency self.addImage(image, legend='mask', z=2, replace=False, info=mask) def getSelectionMask(self,): #return bool(self.maskToolsDockWidget.setSelectionMask(mask)) return self.getImage(legend='mask').getInfo() def _getImageValue(self, x, y): """Get status bar value of top most image at position (x, y) :param float x: X position in plot coordinates :param float y: Y position in plot coordinates :return: The value at that point or '-' """ value = '-' valueZ = -float('inf') mask = 0 maskZ = -float('inf') mask=" " for image in self.getAllImages(): data = image.getData(copy=False) isMask = image.getLegend() == 'mask' if isMask: data = image.getInfo() # zIndex = maskZ #else: zIndex = valueZ if image.getZValue() >= zIndex: # This image is over the previous one ox, oy = image.getOrigin() sx, sy = image.getScale() row, col = (y - oy) / sy, (x - ox) / sx if row >= 0 and col >= 0: # Test positive before cast otherwise issue with int(-0.5) = 0 row, col = int(row), int(col) if (row < data.shape[0] and col < data.shape[1]): v, z = data[row, col], image.getZValue() if not isMask: value = v valueZ = z else: mask = v maskZ = z if maskZ > valueZ and mask > 0: return value, "Masked" return value # @ui.UILoadable class pickup_choice(QtGui.QWidget): def __init__(self, parent): super( pickup_choice, self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "pickup_choice.ui" ), self) self.choosebypointvalue.setChecked(True) self.visualizefromroi.setChecked(True) # @ui.UILoadable class NNMF_control(QtGui.QWidget): def __init__(self, parent,data_tobeanalyzed): super(NNMF_control , self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "NNMF_control.ui" ), self) self.data_tobeanalyzed = data_tobeanalyzed self.pushButton_byNofComps.clicked.connect(self.onNNMF_button_clik) self.pushButton_percentage.clicked.connect(self.onNNMF_prec_button_clik) self. lineEdit_percentage.hide() key , data, image , (corner, roi_mask) = data_tobeanalyzed self.data=data self.image=image self.roi_mask = roi_mask def threshold(self):# , val=None): print("THRESHOLD") # if val is None: val = float(str( self.pickup_choice_obj.threshold_value.text())) self.threshold_byval(val) def threshold_byval(self, val): mask = np.less(val*self.scp.max() ,self.scp ) self.plot_comp_spatial.setSelectionMask( mask ) self.pickup_choice_obj.threshold_value.setText( str(val) ) def reset(self, val): mask = np.less(self.scp.max()+1 ,self.scp ) self.plot_comp_spatial.setSelectionMask( mask ) def onNNMF_button_clik(self): ncomps = int(str(self. lineEdit_byNofComps.text() )) self.NNMF_by_comps(ncomps) def onNNMF_prec_button_clik(self): ncomps = int(str(self. lineEdit_byNofComps.text() )) self.NNMF_by_percentage(ncomps) ## I segnali corrono sulla dimensione lenta def NNMF_by_comps(self, ncomps): self.cslider.setMinimum(0) self.cslider.setMaximum(ncomps-1) self.cslider.setTickPosition(QtGui.QSlider.TicksBelow) self.cslider.setTickInterval(1) self.lineEdit_byNofComps.setText(str(ncomps) ) key , data, image , (corner, roi_mask) = self.data_tobeanalyzed data = data * roi_mask.flatten() param={} print (" NNMF per ", data.shape, str(data)) start=time.time() #X,Y,obj,count,converged = nnma.FastHALS(data, ncomps, eps=1e-5, verbose=0, # maxcount=1000, **param) # X,Y,obj,count,converged = nnma.ALS(np.array(data), ncomps, eps=1e-5, verbose=0, # maxcount=1000, **param) # print (" OK " ) # assert(converged) # print("obj = %E count=%5d converged=%d TIME=%.2f secs" % \ # (obj,count, converged, time.time()-start)) model = NMF(n_components=ncomps, init='random', random_state=0) X = model.fit_transform((data*self.roi_mask.flatten()).T) Y = model.components_ contributed_area = X.sum(axis=0) * Y.sum(axis=-1) order = np.argsort( contributed_area )[::-1] X=X[:,order] Y=Y[order,:] contributed_area = contributed_area[order] print ("contributed_area ", contributed_area) self.weightplot.addCurve(x=np.arange( contributed_area.shape[0])+1, y=contributed_area, legend="Component weigth", replace=True) # self.weightplot.addCurve(x=np.arange( contributed_area.shape[0])+1, y=contributed_area[::-1], legend="Component weigth", replace=False, axis="left") self.X = X self.Y = Y for icomp in range( Y.shape[0] ): c = Y[icomp,:] self.compsplot.addCurve(x=np.arange( c.shape[0])+1, y=c, legend="Component #%d"%icomp, replace=(icomp==0)) # return if(0): distances=[] for i in range(1,ncomps+1): # Xp,Yp,obj,count,converged = nnma.FastHALS(data, i, eps=1e-5, verbose=0, # maxcount=1000, **param) # Xp,Yp,obj,count,converged = nnma.ALS(np.array(data), i, eps=1e-5, verbose=0, # maxcount=1000, **param) model = NMF(n_components=i, init='random', random_state=0) Xp = model.fit_transform(data.T) Yp = model.components_ dist = np.abs( data.T - np.dot( Xp,Yp ) ).sum() distances.append(dist) self.plot_comp_spatial.addCurve(x=np.arange( contributed_area.shape[0])+1, y=distances, legend="distance", replace=False, yaxis="right") for where in ["left", "right"]: ax=self.weightplot.getYAxis(axis=where) vmin,vmax = ax.getLimits() ax.setLimits(0,vmax) self.cslider.setValue(0) def onComponentSelected(self, previous, legend): if legend==previous: return if legend is None: return key , data, image , (corner, roi_mask) = self.data_tobeanalyzed print(" SELEZIONATO ", previous, legend) icomp = int(''.join(filter(str.isdigit, str(legend) ))) self.cslider.setValue(icomp) def NNMF_by_percentage(self, ncomps): key , data, image , (corner, roi_mask) = self.data_tobeanalyzed data = data * roi_mask.flatten() model = NMF(n_components=ncomps, init='random', random_state=0) distances=[] for i in range(1,ncomps+1): model = NMF(n_components=i, init='random', random_state=0) Xp = model.fit_transform(data.T) Yp = model.components_ dist = np.abs( data.T - np.dot( Xp,Yp ) ).sum() distances.append(dist) distances=np.array(distances) self.weightplot.addCurve(x=np.arange( distances.shape[0])+1, y=distances, legend="distance", replace=False, axis="right") for where in ["left", "right"]: ax=self.weightplot.getYAxis(axis=where) vmin,vmax = ax.getLimits() ax.setLimits(0,vmax) def cslider_valuechange(self, icomp): key , data, image , (corner, roi_mask) = self.data_tobeanalyzed scp = self.X[:,icomp] scp.shape = roi_mask.shape oldmask = self.plot_comp_spatial .getImage(legend='mask') if oldmask is not None: oldmask = oldmask.getInfo() self.plot_comp_spatial.addImage( scp ) self.scp = scp self.plot_comp_label.setText( key + ": Spatial component #%d"%icomp ) self.compsplot.setActiveCurve("Component #%d"%icomp) if oldmask is not None: self.plot_comp_spatial.setSelectionMask(oldmask) def plotSignalHandler(self, signal): key , data, image , (corner, roi_mask) = self.data_tobeanalyzed if self.pickup_choice_obj.visualizefromroi.isChecked(): data = data*self.roi_mask.flatten() if signal["event"]=="mouseMoved": ix=int(signal["x"]) iy=int(signal["y"]) if(ix<0 or ix>= roi_mask.shape[1] ): return if(iy<0 or iy>= roi_mask.shape[0] ): return if self.pickup_choice_obj.choose_byline.isChecked(): ipos = iy*self.roi_mask.shape[1]+np.arange( self.roi_mask.shape[1]) spectra = (self.data[:,ipos]).sum(axis=-1) elif self.pickup_choice_obj.choose_bycolumn.isChecked(): ipos = np.arange( self.roi_mask.shape[0])*self.roi_mask.shape[1]+ix spectra = (self.data[:,ipos]).sum(axis=-1) else: ipos = iy*self.roi_mask.shape[1]+ix spectra = self.data[:,ipos] self.plot_spectra.addCurve( x = np.arange( spectra.shape[0]) , y= spectra , legend = "pixel spectra", replace=True) elif signal["event"]=="mouseClicked" and signal["button"]=="left": if self.pickup_choice_obj.choosebypointvalue.isChecked(): return ix=int(signal["x"]) iy=int(signal["y"]) if(ix<0 or ix>= roi_mask.shape[1] ): return if(iy<0 or iy>= roi_mask.shape[0] ): return if self.pickup_choice_obj.choose_bypoint.isChecked(): mask = self.plot_comp_spatial.getSelectionMask() mask[iy,ix] = (1-mask[iy,ix])*self.roi_mask[iy,ix] self.plot_comp_spatial.setSelectionMask(mask) elif self.pickup_choice_obj.choose_byline.isChecked(): mask = self.plot_comp_spatial.getSelectionMask() if mask[iy,:].sum(): mask[iy,:] = 0 else: mask[iy,:] = self.roi_mask[iy,:] self.plot_comp_spatial.setSelectionMask(mask) elif self.pickup_choice_obj.choose_bycolumn.isChecked(): mask = self.plot_comp_spatial.getSelectionMask() if mask[:,ix].sum(): mask[:,ix] = 0 else: mask[:,ix] = self.roi_mask[:,ix] self.plot_comp_spatial.setSelectionMask(mask) class graphsScrollWidget(QtGui.QScrollArea ): def set_size(self, val): print ( " in set size ", val) # rcount = self.scrolledGrid.rowCount() # ccount = self.scrolledGrid.colCount() # for i in range(rcount): # self.scrolledGrid. count = self.scrolledGrid.count() for i in range(count): item = self.scrolledGrid.itemAt(i) if not ( isinstance(item.widget(), MyPlot) or isinstance(item.widget(), MyPlot1D) ) : continue item.widget().setSizeHint( val) # item.setGeometry( Qt.QRect(0,0,val,val)) def setComps(self): nofcomps = int(str(self.gc.nofcomps.text())) ccomp = int(str(self.gc.choosedcomp.text())) for nnmf in self.nnmf_list : nnmf.NNMF_by_comps( nofcomps ) nnmf.cslider_valuechange( ccomp ) def setThreshold(self): threshold = float(str(self.gc.threshold.text())) for nnmf in self.nnmf_list : nnmf.threshold_byval( threshold ) def set_transp(self, val): for p in self.rois_w_list+self.refined_rois_w_list: p.reset_SelectionMask(val) count = self.scrolledGrid.count() for i in range(count): item = self.scrolledGrid.itemAt(i) if not isinstance(item.widget(), MyPlot): continue print(i, item.widget()) print(i, item.widget().height() ) def setSelectionMaskS(self, masksDict, metadata): for w, tba, nnmf in zip(self.refined_rois_w_list, self.tobeanalyzed_items , self.nnmf_list ): key , data, image , (corner, roi_mask) = tba if key in masksDict: print(" PER \n"*10) print(" KEY ", key) print( "mask ",masksDict[key]) corners, mask = masksDict[key] w.setSelectionMask(mask) if key in metadata: mdata = metadata[key] if "globalThreshold" in mdata : self.gc.threshold.setText(str( mdata[ "globalThreshold"] ) ) if "globalNofComps" in mdata : self.gc.nofcomps.setText(str( mdata["globalNofComps"] )) if "globalChoosedComp" in mdata : self.gc.choosedcomp.setText(str( mdata["globalChoosedComp"]) ) if "threshold" in mdata: nnmf.pickup_choice_obj.threshold_value.setText( str( mdata["threshold"]) ) if "nofcomps" in mdata: nnmf.lineEdit_byNofComps.setText( str( mdata["nofcomps"]) ) if 1: nofcomps = int( str( mdata["nofcomps"])) if nofcomps>0 and nofcomps<100: nnmf.NNMF_by_comps(nofcomps) if "icomp" in mdata: icomp = int( str( mdata["icomp"]) ) nnmf.cslider_valuechange(icomp) # except: # pass # @@@@@ QUA SI PERDE INFORMAZIONE GEOMETRIA INIZIALZE def getSelectionMaskS(self, mask=None): metadata = {} metageo = {} for w, tba, nnmf in zip(self.refined_rois_w_list, self.tobeanalyzed_items , self.nnmf_list ): key , data, image , (corner, roi_mask) = tba if mask is None: mask = np.zeros(image.shape ) wmask = w.getSelectionMask() meta = {} inum = int(''.join(filter(str.isdigit, str(key) )))+1 meta["globalThreshold"]= str(self.gc.threshold.text()) meta["globalNofComps"]= str(self.gc.nofcomps.text()) meta["globalChoosedComp"]= str(self.gc.choosedcomp.text()) meta["threshold"] = str(nnmf.pickup_choice_obj.threshold_value.text()) meta["nofcomps"] = str(nnmf.lineEdit_byNofComps.text() ) meta["icomp"] = nnmf.cslider.value() metadata[key]= meta mask[ corner[0]:corner[0]+roi_mask.shape[0] , corner[1]:corner[1]+roi_mask.shape[1] ] = inum*wmask metageo[inum] = corner, roi_mask.shape return mask, metadata, metageo def __init__(self, parent, tobeanalyzed_items, initial_ncomps=2): QtGui.QScrollArea.__init__(self, parent ) scrolledWidget = QtGui.QWidget() scrolledGrid = QtGui.QGridLayout() self.rois_w_list = [] self.refined_rois_w_list = [] self.nnmf_list = [] self.tobeanalyzed_items = tobeanalyzed_items for irow, (key , data, image , (corner, roi_mask) ) in enumerate(tobeanalyzed_items): icol=0 scrolledGrid.addWidget( QtGui.QLabel( key + ": Commands for components" ), irow*2, icol) tobeanalyzed = (key , data, image , (corner, roi_mask) ) nnmf_ctrl = NNMF_control(None, tobeanalyzed) self.nnmf_list.append(nnmf_ctrl) scrolledGrid.addWidget(nnmf_ctrl , irow*2+1, icol) icol+=1 scrolledGrid.addWidget( QtGui.QLabel( key + ": Region with original selection " ), irow*2, icol); roiplot = MyPlot() roiplot.getYAxis().setInverted(True) self.rois_w_list.append(roiplot) roiplot.addImage( image[ corner[0]:corner[0]+ roi_mask.shape[0], corner[1]:corner[1]+ roi_mask.shape[1] ] ) roiplot.setSelectionMask( roi_mask ) scrolledGrid.addWidget(roiplot ,irow*2+1, icol) icol+=1 scrolledGrid.addWidget( QtGui.QLabel( key + ": Tot. w. per c. & res. dist." ), irow*2, icol); weightplot= MyPlot1D()#control=True) weightplot.getFitAction().setVisible(True) scrolledGrid.addWidget(weightplot , irow*2+1, icol); nnmf_ctrl.weightplot = weightplot icol+=1 scrolledGrid.addWidget( QtGui.QLabel( key + ": Components" ), irow*2, icol); compsplot= MyPlot1D()#control=True) compsplot.getFitAction().setVisible(True) scrolledGrid.addWidget(compsplot , irow*2+1, icol); nnmf_ctrl.compsplot = compsplot compsplot.sigActiveCurveChanged.connect( nnmf_ctrl.onComponentSelected ) icol +=1 cslider = QtGui.QSlider(Qt.Qt.Vertical) scrolledGrid.addWidget(cslider , irow*2+1, icol); cslider.valueChanged.connect(nnmf_ctrl.cslider_valuechange) icol +=1 plot_comp_spatial = MyPlot() self.refined_rois_w_list.append(plot_comp_spatial) plot_comp_spatial.getYAxis(axis="left").setInverted(True) clab = QtGui.QLabel( key + ": Spatial component") scrolledGrid.addWidget( clab, irow*2, icol ) scrolledGrid.addWidget(plot_comp_spatial , irow*2+1, icol); icol +=1 plot_spectra = MyPlot() scrolledGrid.addWidget( plot_spectra, irow*2+1, icol); icol+=1 pickup_choice_obj = pickup_choice(None) scrolledGrid.addWidget( pickup_choice_obj, irow*2+1, icol); plot_comp_spatial.sigPlotSignal.connect( nnmf_ctrl.plotSignalHandler ) nnmf_ctrl.plot_comp_spatial = plot_comp_spatial nnmf_ctrl.plot_comp_label = clab nnmf_ctrl.cslider = cslider nnmf_ctrl.plot_spectra = plot_spectra nnmf_ctrl.pickup_choice_obj = pickup_choice_obj pickup_choice_obj.threshold.clicked.connect( nnmf_ctrl.threshold ) pickup_choice_obj.pushButton_reset.clicked.connect( nnmf_ctrl.reset ) self.nnmf_ctrl = nnmf_ctrl nnmf_ctrl.NNMF_by_comps(initial_ncomps) nnmf_ctrl.cslider_valuechange(0) nnmf_ctrl.threshold_byval( 0.2 ) if(0): scrolledGrid.addWidget( QtGui.QLabel("AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"), 0, 0); for i in range(80): scrolledGrid.addWidget( QtGui.QLabel(" B "), 1, i); scrolledGrid.addWidget( QtGui.QLabel("C"), 2, 0); scrolledGrid.addWidget( QtGui.QLabel("D"), 3, 0); scrolledGrid.addWidget( QtGui.QLabel("E"), 4, 0); myplot= MyPlot1D()#control=True) myplot.getFitAction().setVisible(True) x=np.arange(0,10,0.1) y=np.sin(x) myplot.addCurve(x=x, y=y, legend="pippo", replace=True) scrolledGrid.addWidget(myplot , 5, 0); self.scrolledGrid = scrolledGrid scrolledWidget.setLayout(scrolledGrid) self.setWidget(scrolledWidget) xrstools-0.15.0+git20210910+c147919d/XRStools/roiNmaSelectionGui/localfilesdialog.py000066400000000000000000000075571412732462000273340ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function # from PyQt4 import Qt from silx.gui import qt as Qt import os import os.path from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] if my_relativ_path[0]=="/": my_relativ_path = my_relativ_path[1:] import PyMca5.PyMcaIO.specfilewrapper as specfile import PyMca5.PyMcaGui.HDF5Widget as HDF5Widget class hdf5dialog(Qt.QDialog): def __init__(self, parent=None): super( hdf5dialog, self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "hdf5dialog.ui" ), self) # this will use the ui module to load # the structure specified in localfilesdialog.ui class localfilesdialog(Qt.QDialog): def __init__(self, user_input, parent=None): super( localfilesdialog, self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "localfilesdialog.ui" ), self) self.BrowseSpec_pushButton.clicked.connect(self.__onBrowseSpec) self.BrowseImage_pushButton.clicked.connect(self.__onBrowseFile) self.SpecFileName_lineEdit.textChanged.connect(self.__onChangeSpec) if "sf" in user_input: self.SpecFileName_lineEdit.setText( os.path.dirname(user_input["sf"]) ) if "roi" in user_input: self.FileName_lineEdit.setText( user_input["roi"] ) # self.ScanNumber_spinBox.setMaximum(-1) def __onBrowseSpec(self): filename = Qt.QFileDialog.getExistingDirectory() if isinstance(filename, tuple): filename = filename[0] # getOpenFileName() self.SpecFileName_lineEdit.setText(filename) def __onChangeSpec(self): filename = str(self.SpecFileName_lineEdit.text()) print( filename) try: s=specfile.Specfile(filename) except: s=None # if s is not None: # ns = len(s) # self.ScanNumber_spinBox.setMinimum(0) # self.ScanNumber_spinBox.setMaximum(ns) # else: # self.ScanNumber_spinBox.setMaximum(-1) # s[450].alllabels() # s[450]["ccdno"] # s[424].datacol("ccdno") # s[425].datacol("ccdno") # ls # history def __onBrowseFile(self): filename = Qt.QFileDialog.getOpenFileName(None,'Open hdf5 file with rois',filter="hdf5 (*h5)\nall files ( * )" ) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) print( filename) if len(filename): storage=[None] def mySlot(ddict): name = ddict["name"] storage[0]=name # browse self.__hdf5Dialog = hdf5dialog() self.__hdf5Dialog.setWindowTitle('Select a Group containing roi_definitions by a double click') self.__hdf5Dialog.mainLayout = self.__hdf5Dialog.verticalLayout_2 fileModel = HDF5Widget.FileModel() fileView = HDF5Widget.HDF5Widget(fileModel) hdf5File = fileModel.openFile(filename) shiftsDataset = None fileView.sigHDF5WidgetSignal.connect(mySlot) self.__hdf5Dialog.mainLayout.addWidget(fileView) self.__hdf5Dialog.resize(400, 200) ret = self.__hdf5Dialog.exec_() print( ret) hdf5File.close() if ret: print( " Obtained " ) name = storage[0] self.FileName_lineEdit.setText(filename+":"+name) if __name__=="__main__": app=Qt.QApplication([]) w = localfilesdialog() w.show() app.exec_() xrstools-0.15.0+git20210910+c147919d/XRStools/roiNmaSelectionGui/myMaskImageWidget.py000066400000000000000000000152071412732462000273760ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from PyMca5.PyMcaGui import MaskImageWidget as sole_MaskImageWidget # from PyQt4 import Qt, QtCore from silx.gui import qt as Qt from silx.gui import qt as QtCore import numpy import string #from PyMca5.PyMcaGraph.Plot import Plot from silx.gui.plot import PlotWidget as Plot from six.moves import range # from PyMca5.PyMcaGraph.backends.OpenGLBackend import OpenGLBackend # Plot.defaultBackend = OpenGLBackend # Plot.defaultBackend = OpenGLBackend class MaskImageWidget(sole_MaskImageWidget.MaskImageWidget): changeTagOn=False def _graphSignal(self, ddict, ownsignal = None): if self.changeTagOn: if self.__selectionMask is not None and ddict['event']=="mouseClicked" and ddict['button']=="middle" : x,y = int(ddict["x"]), int(ddict["y"]) y, x = sole_MaskImageWidget.convertToRowAndColumn(x , y, self.__imageData.shape, xScale=self._xScale, yScale=self._yScale, safe=True) id_target = self.__selectionMask[y,x] if id_target and id_target!= self._nRoi: mask_target = (self.__selectionMask == id_target ) mask_swap = (self.__selectionMask== self._nRoi) self.__selectionMask [mask_target] = self._nRoi self.__selectionMask [mask_swap] = id_target emitsignal = True if emitsignal: self.plotImage(update = False) self._emitMaskChangedSignal() return # super(MaskImageWidget, self)._graphSignal(ddict, ownsignal) sole_MaskImageWidget.MaskImageWidget._graphSignal(self, ddict, ownsignal) def dragEnterEvent(self,event): print( dir(event.mimeData())) print( list(event.mimeData().formats())) print( event.mimeData().text()) model = Qt.QStandardItemModel() model.dropMimeData(event.mimeData(), QtCore.Qt.CopyAction, 0,0, Qt.QModelIndex()) print( model) if event.mimeData().hasFormat('application/x-qabstractitemmodeldatalist'): event.acceptProposedAction() bytearray = event.mimeData().data('application/x-qabstractitemmodeldatalist') data_items = decode_data(bytearray) print( data_items) if event.mimeData().hasFormat("text/plain"): print( " OK ") event.acceptProposedAction() def dropEvent(self, e): localpos = self.graph.getWidgetHandle().mapFromGlobal( Qt.QCursor().pos() ) x,y = localpos.x(), localpos.y() x,y = self.graph.pixelToData(x,y ) print( "POSITION ",x,y) mask = self.getSelectionMask() Ct = mask[int(y), int(x)] print( " VALORE MASCHERA ", Ct) if Ct: print( str(e.mimeData().text())) Cc = int( str(e.mimeData().text())) # bytearray = e.mimeData().data('application/x-qabstractitemmodeldatalist') # data = decode_data(bytearray) # print data # Cc = (2-data[0])*4+data[1] + 1 # print Cc zonet = (mask==Ct) zonec = (mask==Cc) mask[zonet]=Cc mask[zonec]=Ct self.setSelectionMask(mask) self.annotateSpots() def annotateSpots(self, a_ids = None, offset=None): self.graph.clearMarkers() mask = self.getSelectionMask().astype("i") nspots = mask.max() for i in range(1,nspots+1): m = (mask==i).astype("f") msum=m.sum() if msum: ny,nx = m.shape px= (m.sum(axis=0)*numpy.arange(nx)).sum()/msum py= (m.sum(axis=1)*numpy.arange(ny)).sum()/msum print( " ################################## ", px, py) extra_info = "" if a_ids is not None: if offset is None: extra_info = "(A%d)"% a_ids[i-1] else: extra_info = "(A[%d],ROI%02d )"% (a_ids[i-1], offset+i-1) # res=self.graph.insertMarker( px, py, "legend"+str(i)+extra_info, "%d"%(i)+extra_info, color='black', selectable=False, draggable=False, searchFeature=True, # xytext = (-20, 0), # textcoords = 'offset points', # ha = 'right', va = 'bottom', # bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.4), # arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0')) # print(dir(self.graph)) if hasattr( self.graph, "insertMarker") : res=self.graph.insertMarker( px, py, "legend"+str(i)+extra_info, "%d"%(i)+extra_info, color='black', selectable=False, draggable=False, searchFeature=True, xytext = (-20, 0), textcoords = 'offset points', ha = 'right', va = 'bottom', bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.4), arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0')) else: res=self.graph.addMarker( px, py, "legend"+str(i)+extra_info, "%d"%(i)+extra_info, color='black', selectable=False, draggable=False, symbol="+") # res=self.graph.addMarker( px, py, "legend"+str(i)+extra_info, "%d"%(i)+extra_info, color='black') def decode_data( bytearray): data = [] item = {} ds = QtCore.QDataStream(bytearray) while not ds.atEnd(): row = ds.readInt32() column = ds.readInt32() return row, column print( row, column) map_items = ds.readInt32() for i in range(map_items): key = ds.readInt32() value = QtCore.QVariant() ds >> value item[QtCore.Qt.ItemDataRole(key)] = value data.append(item) return data xrstools-0.15.0+git20210910+c147919d/XRStools/roiNmaSelectionGui/nnma.py000066400000000000000000000521731412732462000247620ustar00rootroot00000000000000#encoding:latin-1 #*/########################################################################### # Copyright (c) 2009 Uwe Schmitt, uschmitt@mineway.de # # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # * notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # * copyright notice, this list of conditions and the following # * disclaimer in the documentation and/or other materials provided # * with the distribution. Neither the name of the # * nor the names of its contributors may be used to endorse or # * promote products derived from this software without specific # * prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #*/########################################################################### import numpy as np try: import scipy.sparse as sp has_sparse = True is_sparse = lambda A: isinstance(A, sp.spmatrix) except ImportError: has_sparse = False is_sparse = lambda A: False import math __doc__ = """ py_nnma: python modules for nonnegative matrix approximation (NNMA) (c) 2009 Uwe Schmitt, uschmitt@mineway.de NNMA minimizes dist(Y, A X) where: Y >= 0, m x n A >= 0, m x k X >= 0, n x k k < min(m,n) dist(A,B) can be || A - B ||_fro or KL(A,B) This moudule provides the following functions: NMF, NMFKL, SNMF, RRI, ALS, GDCLS, GDCLS_L1, FNMAI, FNMAI_SPARSE, NNSC and FastHALS The common parameters when calling such a function are: input: Y -- the matrix for decomposition, maybe dense from numpy or sparse from scipy.sparse package k -- number of componnets to estimate Astart Xstart -- matrices to start iterations. Maybe None for using random start matrices. eps -- termination swell value maxcount -- max number of iterations to be performed verbose -- if False: produce no output durint interations if integer: give all 'verbose' itetations some output about current state of iterations output: A, X -- result matrices of algorithm obj -- value of objective function of last iteration count -- number of iterations done converged -- flag: indicates if iterations stoped within max number of iterations The following extra parameters exist depending on algorithm: RRI : damping parameter 'psi' (default: 1e-12) SNMF : sparsity parameter 'sparse_par' (default: 0) ALS : regularization parameter 'regul' for stabilizing iterations (default value 0). needed if objective value jitters. GCDLS : 'regul' for l2-smoothness of X (default 0) GDCLS_L1 : 'regul' for l1-smoothness of X (default 0) FNMAI : 'stabil' for stabilizing algorithm (default value 1e-12) 'alpha' for stepsize (default value 0.1) 'tau' for number of inner iterations (default value 2) FNMAI_SPARSE : as FNMAI plus 'regul' for l1-smoothness of X (default 0) NNSC : 'alpha' for stepsize of gradient update of A 'sparse_par' for sparsity ############################################################################# Copyright (c) 2009 Uwe Schmitt, uschmitt@mineway.de All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. Neither the name of the * nor the names of its contributors may be used to endorse or * promote products derived from this software without specific * prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. This module is based on: - Daniel D. Lee and H. Sebastian Seung: "Algorithms for non-negative matrix factorization", in Advances in Neural Information Processing 13 (Proc. NIPS*2000) MIT Press, 2001. "Learning the parts of objects by non-negative matrix factorization", Nature, vol. 401, no. 6755, pp. 788-791, 1999. - A. Cichocki and A-H. Phan: "Fast local algorithms for large scale Nonnegative Matrix and Tensor Factorizations", IEICE Transaction on Fundamentals, in print March 2009. - P. O. Hoyer "Non-negative Matrix Factorization with sparseness constraints", Journal of Machine Learning Research, vol. 5, pp. 1457-1469, 2004. - Dongmin Kim, Suvrit Sra,Inderjit S. Dhillon: "Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem" SIAM Data Mining (SDM), Apr. 2007 - Ngoc-Diep Ho: dissertation from http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-06052008-235205/ """ __license__ = "BSD" # # helper functions for handling sparse and dense matrices from numpy # and scipy.sparse # def divide_sparse_matrix(A, by): assert isinstance(A, sp.spmatrix), "wrong format" A = A.tocoo() A.data /= by[A.row, A.col] return A def divide_matrix(A, by): if is_sparse(A): return divide_sparse_matrix(A, by) elif isinstance(A, np.ndarray): return A / by else: raise TypeError("wrong matrix format %s" % type(A)) def dot(A, B): if is_sparse(A) and is_sparse(B): return (A*B).todense() elif is_sparse(A): return A*B elif is_sparse(B): return (B.transpose() * A.T).T else: return np.dot(A, B) def diff(A, B): E = A - B # if A is sparse E is np.matrix # if A is dense E is np.ndarray # so: convert np.matrix to np.ndarray if needed: if isinstance(E, np.matrix): return E.A return E def flatten(A): if is_sparse(A): return A.todense().flatten().A else: return A.flatten() def frob_norm(A): if is_sparse(A): return math.sqrt( (A.data**2).sum()) else: return np.linalg.norm(A) def transpose(A): if is_sparse(A): return sp.csr_matrix(A.transpose()) else: return A.T def get_scaling_vector(A, p=1.0): if is_sparse(A): dd = ((A**p).tocsc().sum(axis=0).A)**(1.0/p) else: dd = ((A**p).sum(axis=0))**(1.0/p) return dd def coerced(Y): # csr is faster for matrix-vector or matrix-matrix products if is_sparse(Y): if isinstance(Y, sp.csc_matrix): YT = sp.csr_matrix(Y.T) Y = sp.csr_matrix(Y) elif isinstance(Y, sp.csr_matrix): YT = sp.csr_matrix(Y.T) elif isinstance(Y, np.ndarray): YT = Y.T return Y, YT # # building blocks for nnma algorithms ## def GradA(Y, YT, A, X, **param): """ dPhi(Y, A, X) / dA with Phi(Y, A, X) = || Y - A X ||_fro """ XXT = np.dot(X, X.T) return np.dot(A, XXT) - dot(Y, X.T) def GradX(Y, YT ,A, X, **param): """ dPhi(Y, A, X) / dX with Phi(Y, A, X) = || Y - A X ||_fro """ ATA = np.dot(A.T, A) return np.dot(ATA, X) - dot(A.T, Y) def GradA_step(Y, YT, A, X, **param): alpha = param.get("alpha", 1e-3) A = A - alpha * GradA(Y, YT, A, X, **param) #A /= np.sqrt((A*A).sum(axis=0)) A[A<0] = 0 return A def GradX_step(Y, YT, A, X, **param): alpha = param.get("alpha", 1e-6) X = X - alpha * GradX(Y, YT, A, X, **param) X[X<0] = 0 return X def A_mult_update_kl_div(Y, YT, A, X, **param): """ update A for minimization of KL(Y || A X) """ AX = np.dot(A, X) Y_by_AX = divide_matrix(Y, 1e-9+AX) F = dot(Y_by_AX, X.T) / X.sum(axis=1).T return A*F def X_mult_update_kl_div(Y, YT, A, X, **param): """ update V for minimization of KL(Y || A X) """ AX = np.dot(A, X) Y_by_AX = divide_matrix(Y, 1e-9+AX) F = dot(transpose(Y_by_AX), A).T return X* (F.T / A.sum(axis=0)).T def A_mult_update(Y, YT, A, X, **param): """ Lee and Sung multiplicative update """ AXXT = np.dot(A, np.dot(X, X.T)) F = dot(Y, X.T)/(1e-9 + AXXT) return A*F def X_mult_update(Y, YT, A, X, **param): """ Lee and Sung multiplicative update """ ATAX = np.dot(np.dot(A.T, A),X) ATY = dot(YT, A).T F = ATY/(1e-9 + ATAX) return X*F def X_mult_update_nnsc(Y, YT, A, X, **param): """ Lee and Sung multiplicative update """ regul=param.get("sparse_par", 1e-9) ATAX = np.dot(np.dot(A.T, A),X) ATY = dot(YT, A).T F = ATY/(regul + ATAX) return X*F def A_inexact_lsq_update(Y, YT, A, X, **param): """ ALS fixed point update """ regul=param.get("regul", 0.0) XXT = np.dot(X, X.T) YXT = dot(Y, X.T) A = np.dot(YXT, np.linalg.pinv(XXT + regul*np.eye(XXT.shape[0]))) A[A<0] = 0 return A def X_inexact_lsq_update(Y, YT, A, X, **param): """ ALS fixed point update """ regul=param.get("regul", 0.0) ATA = np.dot(A.T, A) ATY = dot(YT, A).T X = np.dot(np.linalg.pinv(ATA + regul*np.eye(ATA.shape[0])), ATY) X[X<0] = 0 return X def X_inexact_lsq_update_l1regul(Y, YT, A, X, **param): """ ALS fixed point update with L1 regularization for X """ regul=param.get("regul", 0.0) ATA = np.dot(A.T, A) ATY = dot(YT, A).T X = np.dot(np.linalg.pinv(ATA+1e-12*np.eye(ATA.shape[0])),ATY-regul) X[X<0] = 0 return X def FNMAI_A_update(Y, YT, A, X, **param): """ FNMAI (Kim et al) update for A """ stabil=param.get("stabil", 1e-12) alpha=param.get("alpha", 0.1) tau=param.get("tau", 2) k = A.shape[1] a = max(1e-9, stabil) for _ in range(tau): G = GradA(Y, YT, A, X) Iplus = (A==0) & (G>0) G[Iplus] = 0 G = np.dot(G, np.linalg.pinv(np.dot(X,X.T)+a*np.eye(k))) G[Iplus] = 0 A -= alpha*G A[A<0] = 0 return A def FNMAI_X_update(Y, YT, A, X, **param): """ FNMAI (Kim et al) update for V """ stabil=param.get("stabil", 1e-12) alpha=param.get("alpha", 0.1) tau=param.get("tau", 2) k = A.shape[1] a = max(1e-9, stabil) for _ in range(tau): G = GradX(Y, YT, A, X) Iplus = (X==0) & (G>0) G[Iplus] = 0 G = np.dot(np.linalg.pinv(np.dot(A.T,A)+a*np.eye(k)), G) G[Iplus] = 0 X -= alpha*G X[X<0] = 0 return X def FastHALS_X_update(Y, YT, A, X, **param): W = dot(YT, A) V = dot(A.T, A) k = A.shape[1] for i in range(k): print (" i ", i) xi = X[i,:] xi += W[:,i]-dot(X.T, V[:,i]) xi[xi<0] = 0 X[i,:] = xi return X def FastHALS_A_update(Y, YT, A, X, **param): P = dot(Y, X.T) Q = dot(X, X.T) k = A.shape[1] for i in range(k): ai = A[:,i] ai = ai*Q[i,i] + P[:,i]-dot(A, Q[:,i]) ai[ai<0] = 0 ai /= (np.linalg.norm(ai)+1.0e-18) A[:,i] = ai return A # # All NNMA algorithms have the same structure which is implemented # in AlgorunnerTemplate # class AlgorunnerTemplate(object): def frob_dist(self, Y, A, X): """ frobenius distance between Y and A X """ return np.linalg.norm(Y - np.dot(A,X)) def kl_divergence(self, Y, A, X): """ kullbach leibler divergence D(Y | A X) """ AXvec = np.dot(A, X).flatten() Yvec = flatten(Y) return (Yvec*np.log(Yvec/AXvec)-Yvec+AXvec).sum() dist = frob_dist # default case def init_factors(self, Y, k, A=None, X=None): """ generate start matrices U, V """ m, n = Y.shape # sample start matrices if A is None: A = np.random.rand(m,k) elif isinstance(A, np.matrix): A = A.A if X is None: X = np.random.rand(k,n) elif isinstance(X, np.matrix): X = X.A # scale A, X with alpha such that || Y - alpha AX ||_fro is # minimized AX = np.dot(A,X).flatten() # alpha = < Y.flatten(), AX.flatten() > / < AX.flatten(),AX.flatten() > if is_sparse(Y): # can we improve this confirming memory usage ???? alpha = np.diag(dot(Y, np.dot(A,X).T)).sum()/np.dot(AX, AX) else: alpha = np.dot(Y.flatten(), AX)/np.dot(AX,AX) A /= math.sqrt(alpha) X /= math.sqrt(alpha) return A, X param_update = None # default, may be overidden by method which # adapts parametes from iteration to iteration def __call__(self, Y, k, A=None, X=None, eps=1e-5, maxcount=1000, verbose=False, **param): """ basic template for NNMA iterations """ m, n = Y.shape if k<1 or k>m or k>n: raise ValueError("number k of components is invalid") Y, YT = coerced(Y) A, X = self.init_factors(Y, k, A, X) count = 0 obj_old = 1e99 param = param.copy() # works for sparse and for dense matrices: # calculate frobenius norm of Y nrm_Y = frob_norm(Y) while True: print ("count", count) A, X = self.update(Y, YT, A, X, **param) if np.any(np.isnan(A)) or np.any(np.isinf(A)) or \ np.any(np.isnan(X)) or np.any(np.isinf(X)): if verbose: print("RESTART") A, X = self.init_factors(Y, k) count = 0 raise " OK " count += 1 # relative distance which is independeant to scaling of A obj = self.dist(Y, A, X) / nrm_Y delta_obj = obj-obj_old if verbose: # each 'verbose' iterations report about actual state if count % verbose == 0: print("count=%6d obj=%E d_obj=%E" %(count, obj, delta_obj)) if count >= maxcount: break # delta_obj should be "almost negative" and small enough: if -eps < delta_obj <= 1e-12: break obj_old = obj if self.param_update is not None: self.param_update(param) if verbose: print("FINISHED:") print("count=%6d obj=%E d_obj=%E" %(count, obj, delta_obj)) return A, X, obj, count, count < maxcount # # Most NNMA algorithms have global updates of U and V which can be # combined with the following base class: # class FactorizedNNMA(AlgorunnerTemplate): def __init__(self, update_A, update_X, param_update = None): self.update_A = update_A self.update_X = update_X self.param_update = param_update def update(self, Y, YT, A, X, **param): A = self.update_A(Y, YT, A, X, **param) X = self.update_X(Y, YT, A, X, **param) return A, X class SNMF_(AlgorunnerTemplate): """ W. Liu, N. Zheng, and X. Lu.: "Non-negative matrix factorization for visual coding". In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP2003), 2003 """ # use kullbach-level distance dist = AlgorunnerTemplate.kl_divergence def update(self, Y, YT, A, X, **param): sparse_par = param.get("sparse_par", 0.0) A /= A.sum(axis=0)+1e-9 AX = np.dot(A, X) Y_by_AX = divide_matrix(Y, 1e-9+AX) X *= dot(Y_by_AX.T, A).T / (1.0 + sparse_par) AX = np.dot(A, X) Y_by_AX = divide_matrix(Y, 1e-9+AX) F = dot(Y_by_AX, X.T) / ( X.T.sum(axis=0) + 1e-9) A *= F return A, X class RRI_(AlgorunnerTemplate): """ Runtime optimisations from Cichocki applied to Damped rank one residual iteration from Ngoc-Diep Ho. """ def update(self, Y, YT, A, X, **param): E = diff(Y, np.dot(A,X)) psi = param.get("psi", 1e-12) for j in range(A.shape[1]): aj = A[:,j] xj = X[j,:] Rt = E + np.outer(aj, xj) xj = np.dot(Rt.T, aj)+psi*xj xj[xj<0]= 0 fac = np.linalg.norm(aj)**2 xj /= fac+psi aj = np.dot(Rt, xj)+psi*aj aj[aj<0]= 0 fac = np.linalg.norm(xj)**2 aj /= fac+psi A[:,j] = aj X[j,:] = xj E = Rt - np.outer(aj, xj) return A, X # # create algorithms objects # SNMF = SNMF_() RRI = RRI_() # classical algorithme with frobenius norm for calculating # objective function NMF = FactorizedNNMA(A_mult_update, X_mult_update) # classical algorithme with kl divergence or calculating # objective function NMFKL = FactorizedNNMA(A_mult_update_kl_div, X_mult_update_kl_div) # Stabilized alternating least sqaures with decreasing regularization # from Cichocki et al. def regul_dec(param): param["regul"] = param.get("regul", 0)* .9 ALS = FactorizedNNMA(A_inexact_lsq_update, X_inexact_lsq_update, regul_dec) # GDCLS from # "Document clustering using nonnegative matrix factorization" # Information Processing and Management # Volume 42 , Issue 2 (March 2006) t # Pages: 373 - 386 , GDCLS = FactorizedNNMA(A_mult_update, X_inexact_lsq_update) #Fast Newton-type Method from Kim et al FNMAI = FactorizedNNMA(FNMAI_A_update, FNMAI_X_update) # own algorithms for approximation of Y ~ A X # replace l2-regularisation when updating X by l1-regularization # for getting spare coordinates GDCLS_L1 = FactorizedNNMA(A_mult_update, X_inexact_lsq_update_l1regul) # replace FNMAI_X_update by l1 regulraized least squares update FNMAI_SPARSE = FactorizedNNMA(FNMAI_A_update, \ X_inexact_lsq_update_l1regul) # Hoyers sparse coding algorithm NNSC = FactorizedNNMA(GradA_step, X_mult_update_nnsc) # FastHALS from Cichocki and Phan FastHALS = FactorizedNNMA(FastHALS_A_update, FastHALS_X_update) if __name__ == "__main__": # test all routines ! param = dict(alpha=.1, tau=2, regul=1e-2, sparse_par=1e-1, psi=1e-3) nc = 10 B = np.random.rand(30,nc) C = np.random.rand(nc,20) A = np.dot(B, C) import sys, time def run(name, routine, verbose=0): print("run %12s" % name,) sys.stdout.flush() start = time.time() X,Y,obj,count,converged = routine(A, 10, eps=5e-5, verbose=verbose, maxcount=1000, **param) print("obj = %E count=%5d converged=%d TIME=%.2f secs" % \ (obj,count, converged, time.time()-start)) print("\nTEST WITH DENSE MATRIX\n") run("NNSC", NNSC, verbose=0) run("FNMAI_SPARSE", FNMAI_SPARSE) run("FNMAI", FNMAI) run("GDCLS_L1", GDCLS_L1) run("GDCLS", GDCLS) run("ALS", ALS) run("NMFKL", NMFKL) run("NMF", NMF) run("RRI", RRI) run("FastHALS", FastHALS) run("SNMF", SNMF) if has_sparse: print("\nTEST WITH SPARSE MATRIX\n") A = sp.csc_matrix(A) run("NNSC", NNSC, verbose=0) run("FNMAI_SPARSE", FNMAI_SPARSE) run("FNMAI", FNMAI) run("GDCLS_L1", GDCLS_L1) run("GDCLS", GDCLS) run("ALS", ALS) run("NMFKL", NMFKL) run("NMF", NMF) run("RRI", RRI) run("FastHALS", FastHALS) run("SNMF", SNMF) xrstools-0.15.0+git20210910+c147919d/XRStools/roiNmaSelectionGui/pippo.py000066400000000000000000000007321412732462000251520ustar00rootroot00000000000000from PyQt4 import QtGui import sys class MyFirstScene(QtGui.QWidget): def __init__(self): QtGui.QWidget.__init__(self) self.scene=QtGui.QGraphicsScene(self) self.scene.addText("Hello, world!") self.view = QtGui.QGraphicsView(self.scene, self) self.layout().addWidget(self.view) self.show() if __name__=="__main__": app=QtGui.QApplication(sys.argv) firstScene = MyFirstScene() sys.exit(app.exec_()) xrstools-0.15.0+git20210910+c147919d/XRStools/roiNmaSelectionGui/roiNmaSelectionWidget.py000066400000000000000000001432161412732462000302670ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function # from PyQt4 import Qt, QtCore, QtGui from silx.gui import qt as Qt from silx.gui import qt as QtCore from silx.gui import qt as QtGui import os import PyMca5.PyMcaIO.EdfFile as edf import PyMca5 import PyMca5.PyMcaIO.specfile as specfile from . import localfilesdialog from . import myMaskImageWidget import numpy import string import six from six.moves import range from six.moves import zip from .. import xrs_rois from .. import xrs_utilities from . import spotdetection from .. import match import pickle import h5py from XRStools import xrs_read from . import graphsScrollWidget from XRStools.installation_dir import installation_dir my_dir = os.path.dirname(os.path.abspath(__file__)) my_relativ_path = my_dir [len( os.path.commonprefix([ installation_dir , my_dir ])):] if my_relativ_path[0]=="/": my_relativ_path = my_relativ_path[1:] FASTDEBUG=False try: import PyTango except: print( " Could not load PyTango") def h5_assign_force(h5group, name, item): if name in h5group: del h5group[name] h5group[name] = item class spotdetectioncontrol(Qt.QDockWidget): def __init__(self, parent,flag, detectionCallBack=None, fatteningCallBack=None, thresholdCallBack=None, annotateMaskCallBack=None, relabeliseMaskCallBack=None, resetMask = None): super( spotdetectioncontrol, self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "spotdetectioncontrol.ui" ), self) self.detectionButton .setToolTip("Run a Canny edge detection. If threshold entry contains a float >0 and <1,\n then all pixel 0 and <1 ,\nthe all pixel =0 and m[1][1]>=0 ): masksDict[ self.labelformat% (m[0]-1) ]=[m[1],m[2]] return masksDict def recomposeGlobalMask(self): offset=0 globalMask = numpy.zeros( self.image.shape).astype("i") metadata = {} metageo={} for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.subregions): globalMask, newmeta, newgeo=mw.getSelectionMaskS(globalMask) metadata.update(newmeta) metageo.update(newgeo) offset += nofrois return offset, globalMask, metadata, metageo def decomposeGlobalMask(self): offset=0 globalMask = self.mws[0].getSelectionMask().astype("i") for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.mws[1:]): localmask = numpy.less(0,globalMask[geo] ) Mask = globalMask[geo] - offset *localmask offset += nofrois mw.setSelectionMask(Mask ) def write_mask_on_file(self): filename = Qt.QFileDialog.getSaveFileName() if isinstance(filename, tuple): filename = filename[0] print( filename) if filename is not None: filename=str(filename) self.recomposeGlobalMask() globalMask = self.mws[0].getSelectionMask().astype("i") ef = edf.EdfFile( filename, "w+") ef.WriteImage( {}, globalMask ) def write_masksDict_on_file(self): filename = Qt.QFileDialog.getSaveFileName() if isinstance(filename, tuple): filename = filename[0] print( filename) if filename is not None: filename=str(filename) masksDict = self.getMasksDict() filename=str(filename) f = open(filename, 'wb') pickle.dump(masksDict , f) f.close() def load_masksDict_from_file(self): filename = Qt.QFileDialog.getOpenFileName() if isinstance(filename, tuple): filename = filename[0] print( filename) if filename is not None: filename=str(filename) f = open(filename, 'rb') masksDict = pickle.load( f) f.close() self.load_masksDict( masksDict) def load_masksDict(self, masksDict, metadata=None): # seguire la traccia inversa di recomposeGlobalMask (subregions ) e getSelectionMaskS in graphsScrollWidget offset=0 globalMask = numpy.zeros( self.image.shape).astype("i") if metadata is None: metadata = {} for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.subregions): print(" SOTTOMASK con metadata : " , metadata) mw.setSelectionMaskS( masksDict, metadata) offset += nofrois def read_mask_from_file(self): filename = Qt.QFileDialog.getOpenFileName() if isinstance(filename, tuple): filename = filename[0] if filename is not None: filename=str(filename) ef = edf.EdfFile( filename, "r") mask = ef.GetData(0) self.recomposeGlobalMask() self.mws[0].setSelectionMask(mask) self.decomposeGlobalMask() def detectionCallBack(self, thr_s="", Hough=False): print( " in detectionCallBack, Hough " , Hough) itab = self.viewsTab.currentIndex() if itab==0: return globalMask = self.mws[0].getSelectionMask().astype("i") roiroiMask = self.roiroiw.getSelectionMask().astype("i") offset=0 for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:itab-1], self.mws[1:itab]): offset += nofrois (name,geo,nofrois), mw = self.names_geos_nofrois[itab-1], self.mws[itab] self.detectSpotsSubMask( name,geo,nofrois, mw , globalMask , roiroiMask, offset,thr_s=thr_s , Hough=Hough ) self.mws[0].setSelectionMask( globalMask ) def get_geo(self): if self.geo_informations is None: subset_infos = xrs_rois.get_geo_informations( (self.image.shape+(self.layout,) ) ) else: subset_infos = self.geo_informations dl = subset_infos["analyser_nIDs"] dl1k = list(dl[list(dl.keys())[0]].keys()) if len(dl1k)==1: for t in imageview.all_layouts: t.setCurrentIndex(1) return subset_infos def getLabelCorrespondance(self): res = [] itab=1 # print " CORRESPONDANCE " # print self.mws[1:] # print self.names_geos_nofrois[:] for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.mws[1:]): subset_infos = self.get_geo() if "3x4" in str(self.layouts[0].currentText() ): a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["3x4"] else: a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["Vertical"] res.extend(list(numpy.array(a_ids) +len(res) )) # print " res " ,res itab+=1 return res def annotateOneMaskCallBack(self): itab = self.viewsTab.currentIndex() if itab>0: subset_infos = self.get_geo() if "3x4" in str(self.layouts[itab].currentText() ): a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["3x4"] else: a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["Vertical"] self.mws[itab].annotateSpots( a_ids, self.get_offset( itab ) ) def annotateAllMasksCallBack(self): itab=1 A_IDS = [] for (name,geo,nofrois) in self.names_geos_nofrois[:] : subset_infos = self.get_geo() if "3x4" in str(self.layouts[0].currentText() ): a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["3x4"] else: a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["Vertical"] A_IDS = A_IDS + a_ids itab+=1 self.mws[0].annotateSpots( A_IDS, None) # self.get_offset(itab) ) def get_offset(self,itab): offset=0 for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:itab-1], self.mws[1:itab]): offset += nofrois return offset def relabeliseAllMasksCallBack(self): offset=0 for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.mws[1:]): self.relabeliseSpots( mw, nofrois, name , geo, offset) def relabeliseOneMaskCallBack(self): itab = self.viewsTab.currentIndex() if itab==0: return offset=0 for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:itab-1], self.mws[1:itab]): offset += nofrois (name,geo,nofrois), mw = self.names_geos_nofrois[itab-1], self.mws[itab] self.relabeliseSpots( mw , nofrois, name, geo, offset) def relabeliseSpots(self,mw, nofrois, name, geo, offset): mask = mw.getSelectionMask( ).astype("i") mask = (mask>0).astype("i") newmask = spotdetection.relabelise(mask,mask, nofrois) self.checkNspots(newmask.max(), nofrois , name) mw.setSelectionMask( newmask ) globalMask = self.mws[0].getSelectionMask().astype("i") globalMask[geo] = newmask self.mws[0].setSelectionMask( globalMask ) def resetOneMask(self): itab = self.viewsTab.currentIndex() if itab==0: return mw = self.mws[itab] mw.graph.clearMarkers() mask = mw.getSelectionMask( ).astype("i") mask[:]=0 mw.setSelectionMask( mask) def resetAllMasks(self): ret = self.warnForGloablChange() print( ret) if ret: for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.mws[1:]): mw.graph.clearMarkers() mask = mw.getSelectionMask( ).astype("i") mask[:]=0 mw.setSelectionMask( mask) def threshold(self,itab,value ): globalMask = self.mws[0].getSelectionMask().astype("i") name,geo,nofrois = self.names_geos_nofrois[itab-1] mw = self.mws[itab] mask = mw.getSelectionMask( ) print( mask.sum()) data = self.image[geo] mask = spotdetection.threshold_mask(mask, data , value ) mw.setSelectionMask(mask ) globalMask[geo] = mask self.mws[0].setSelectionMask( globalMask ) print( mask.sum()) def localThresholdCallBack(self,value): itab = self.viewsTab.currentIndex() if itab==0: return self.threshold(itab,value) def globalThresholdCallBack(self,value): ret = self.warnForGloablChange() print( ret) if ret: for itab in range(1, len(self.mws ) ) : self.threshold(itab,value) def fatten(self, itab, value ) : globalMask = self.mws[0].getSelectionMask().astype("i") name,geo,nofrois = self.names_geos_nofrois[itab-1] mw = self.mws[itab] mask = mw.getSelectionMask( ) if value>0: mask = spotdetection.grow_mask(mask, 1+2*value ) else: mask = spotdetection.shrink_mask(mask, 1-2*value ) mw.setSelectionMask(mask ) globalMask[geo] = mask self.mws[0].setSelectionMask( globalMask ) def fatteningCallBack(self,value): itab = self.viewsTab.currentIndex() if itab==0: return self.fatten(itab,value) def GlobalfatteningCallBack(self,value): print( " in GlobalfatteningCallBack ", value ) ret = self.warnForGloablChange() print( ret) if ret: for itab in range(1, len(self.mws ) ) : self.fatten(itab,value) def checkNspots(self,nspots, nofrois,name ) : if nspots != nofrois: msgBox = Qt.QMessageBox () msgBox.setText("Warning: found %d spots instead of expected %d "%(nspots , nofrois ) ); msgBox.setInformativeText("For detector %s " % name); msgBox.setStandardButtons(Qt.QMessageBox.Ok ); msgBox.setDefaultButton(Qt.QMessageBox.Cancel); ret = msgBox.exec_(); def detectSpotsSubMask( self, name,geo,nofrois, mw , globalMask,roiroiMask, offset ,thr_s="", Hough=False): itab = self.viewsTab.currentIndex() if itab: subset_infos = self.get_geo() if "3x4" in str(self.layouts[itab].currentText() ): a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["3x4"] else: a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["Vertical"] else: a_ids = None submatrix = mw.getImageData() subroiroi = roiroiMask[geo] tval = -1 try: tval = float(thr_s) except: tval=-1 geos = self.get_geo() dl = geos["analyser_nIDs"] dll = dl[list(dl.keys())[0]] ll = dll[list(dll.keys())[0]] nrois = len(ll) mask = spotdetection.get_spots_mask( submatrix,subroiroi, median_size = 5 , tval=tval, nofroi=nrois, Hough=Hough ) self.checkNspots(mask.max(), nofrois , name) mw.setSelectionMask( mask) globalMask[geo]=mask+offset*numpy.less(0,mask) mw.annotateSpots( a_ids , self.get_offset(itab) ) def GlobaldetectionCallBack(self, warn=True, thr_s="", Hough=False): print( " GlobaldetectionCallBack ", warn) if 1 or warn: print( " in Global detectionCallBack" ) ret = self.warnForGloablChange() else: ret = True if ret: offset=0 globalMask = self.mws[0].getSelectionMask().astype("i") roiroiMask = self.roiroiw.getSelectionMask().astype("i") for (name,geo,nofrois), mw in zip(self.names_geos_nofrois, self.mws[1:]): self.detectSpotsSubMask( name,geo,nofrois, mw , globalMask ,roiroiMask, offset ,thr_s=thr_s, Hough=Hough) offset += nofrois self.mws[0].setSelectionMask( globalMask ) def showToggle(self): if self.showIsData : self.showMasks() self.showIsData = not self.showIsData else: self.showDatas() self.showIsData = not self.showIsData def showMasks(self): for (name,geo,nofrois), mw in zip(self.names_geos_nofrois, self.mws[1:]): mask = mw.getSelectionMask().astype("i") mw.setImageData(mask , xScale=(0.0, 1.0), yScale=(0., 1.)) def showDatas(self): Data = self.mws[0].getImageData() for (name,geo,nofrois), mw in zip(self.names_geos_nofrois, self.mws[1:]): d = Data[geo] mask = mw.getSelectionMask().astype("i") if mask.sum(): mm = (d*mask).max() d=numpy.minimum(mm,d ) mw.setImageData(d , xScale=(0.0, 1.0), yScale=(0., 1.)) def warnForGloablChange(self): msgBox = Qt.QMessageBox () msgBox.setText("You are going to recalculate the GLOBAL mask"); msgBox.setInformativeText("This will reset all modifications to local masks. Do you want to proceed?"); msgBox.setStandardButtons(Qt.QMessageBox.Ok | Qt.QMessageBox.Cancel); msgBox.setDefaultButton(Qt.QMessageBox.Cancel); ret = msgBox.exec_(); return ret==Qt.QMessageBox.Ok def CreateSpotDetectionDockWidget(self): w = spotdetectioncontrol(self, QtCore.Qt.Widget,detectionCallBack=self.detectionCallBack, fatteningCallBack=self.fatteningCallBack, thresholdCallBack = self.localThresholdCallBack, annotateMaskCallBack=self.annotateOneMaskCallBack, relabeliseMaskCallBack=self.relabeliseOneMaskCallBack, resetMask = self.resetOneMask ) self.addDockWidget ( QtCore.Qt.LeftDockWidgetArea, w ) # w.setAllowedAreas (QtCore.Qt.AllDockWidgetAreas) w.show() def globregistration(self): print( " in globregistration ") for itab in range(1,len(self.mws)): self.registerTab(itab) def registration(self): itab = self.viewsTab.currentIndex() print( self.layouts[itab].currentText()) self.registerTab(itab) def registerTab(self, itab): if itab>0: name,geo,nofrois = self.names_geos_nofrois[itab-1] self.registerSpots( self.mws[itab], self.layouts[itab].currentText(), name, nofrois ) subset_infos = self.get_geo() if "3x4" in str(self.layouts[itab].currentText() ): a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]]["3x4"] else: a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["Vertical"] self.mws[itab].annotateSpots(a_ids , self.get_offset(itab)) def registerSpots( self, mw, layoutString , name, nofrois) : mask = mw.getSelectionMask().astype("i") newmask = spotdetection.relabelise(mask,mask, nofrois) self.checkNspots(newmask.max(), nofrois , name) mask = newmask nspots = mask.max() spots = [] for i in range(1,nspots+1): zone = (mask==i) mask[zone]=i+100 m = zone.astype("f") msum=m.sum() if msum: ny,nx = m.shape px= (m.sum(axis=0)*numpy.arange(nx)).sum()/msum py= (m.sum(axis=1)*numpy.arange(ny)).sum()/msum spots.append((py,px,i)) self.checkNspots(len(spots), nofrois , name) print( type(str(layoutString))) if "3x4" in str(layoutString): positions = [ [px,py] for (py,px,i) in spots ] print( str( numpy.array(positions))) choices = match.register(numpy.array(positions) ) newspots = [] for (cx,cy),(y,x,i) in zip(choices, spots ): print( x,y, cx, cy) newspots.append((y,x,i, (cy*4+cx)+1 ) ) else: spots.sort() newspots = [] for k,(y,x,i) in enumerate( spots): newspots.append((y,x,i,k+1) ) print( " NEWSPOTS ", newspots) for (y,x,i,k) in newspots: zone = (mask==(i+100)) mask[zone] = k mw.setSelectionMask(mask) def CreateRegistrationWidget(self): w = spotregistrationcontrol(self, QtCore.Qt.Widget,globregistrationCallBack=self.globregistration, registrationCallBack=self.registration ) self.addDockWidget ( QtCore.Qt.LeftDockWidgetArea, w ) w.show() def CreateGlobalSpotDetectionDockWidget(self): w = spotdetectioncontrol(self, QtCore.Qt.Widget,detectionCallBack=self.GlobaldetectionCallBack, fatteningCallBack=self.GlobalfatteningCallBack, thresholdCallBack = self.globalThresholdCallBack, annotateMaskCallBack=self.annotateAllMasksCallBack, relabeliseMaskCallBack=self.relabeliseAllMasksCallBack, resetMask = self.resetAllMasks ) w.detectionButton.setText("GLOBAL detection") w.HoughDetection.setText("GLOBAL Hough") self.addDockWidget ( QtCore.Qt.LeftDockWidgetArea, w ) # w.setAllowedAreas (QtCore.Qt.AllDockWidgetAreas) w.show() def set_roiob(self, roiob): self.roiob=roiob def LoadRemote(self): if self.roiob is None: mb = qt.QMessageBox(); mb.setText("No roi-object has been associated to the roi manager. Cannot Load."); mb.exec_(); return self.showImage(self.roiob) def create_graphsScrollWidget(self, tobeanalyzed = None, nofrois = None, changeTagOn=False, isglobal=False ) : view = imageview(self, isglobal) maskW =graphsScrollWidget.graphsScrollWidget(self, tobeanalyzed) v_layout = QtGui.QVBoxLayout() v_widget = QtGui.QWidget() v_widget.setLayout(v_layout) transp_slider = QtGui.QSlider(Qt.Qt.Horizontal) transp_slider.setMinimum(0) transp_slider.setMaximum(255) h_widget = QtGui.QWidget() h_layout = QtGui.QHBoxLayout() h_widget.setLayout(h_layout) h_widget.layout().addWidget(QtGui.QLabel("Mask Transp. : ")) h_widget.layout().addWidget(transp_slider) gC = globalcontrol(None) h_widget.layout().addWidget(gC) v_widget.layout().addWidget(h_widget) if(0): size_slider = QtGui.QSlider(Qt.Qt.Horizontal) size_slider.setMinimum(200) size_slider.setMaximum(600) h_widget = QtGui.QWidget() h_layout = QtGui.QHBoxLayout() h_widget.setLayout(h_layout) h_widget.layout().addWidget(QtGui.QLabel("Plots Size : ")) h_widget.layout().addWidget(size_slider) v_widget.layout().addWidget(h_widget) view.roiContainerWidget.layout().addWidget(v_widget ) transp_slider.valueChanged.connect(maskW.set_transp) # size_slider.valueChanged.connect(maskW.set_size) maskW.gc = gC gC.pushButton_calccomps.clicked.connect( maskW.setComps) gC.pushButton_threshold.clicked.connect( maskW.setThreshold) view.roiContainerWidget.layout().addWidget(maskW) # view.maskW = maskW transp_slider.setValue (150) # size_slider .setValue (400) return view, maskW def create_viewWidget(self, image = None, nofrois = None, changeTagOn=False, isglobal=False ) : view = imageview(self, isglobal) maskW =myMaskImageWidget.MaskImageWidget(self, aspect=True, profileselection=True, maxNRois=nofrois ) maskW.setY1AxisInverted(1) maskW.setAcceptDrops(True) maskW.setDefaultColormap(2, logflag=True) maskW.changeTagOn = changeTagOn maskW.setImageData(image , xScale=(0.0, 1.0), yScale=(0., 1.)) maskW.setSelectionMask(image*0 , plot=True) view.roiContainerWidget.layout().addWidget(maskW) return view, maskW def showImage(self, image, geo_informations=None, hydra_obj=None, scannums=None, roi_obj=None): self.image=image if geo_informations is None: geo_informations = self.get_geo() self.geo_informations = geo_informations self.image=image subset_infos = geo_informations self.viewsTab.clear() totNofrois = subset_infos["nofrois"] nofrois = totNofrois//len( subset_infos["subgeos"] ) self.mws=[] self.subregions=[] self.layouts=[] view, mw = self.create_viewWidget(image = image, nofrois = totNofrois , changeTagOn = False, isglobal=True ) self.viewsTab.addTab(view, "Global") self.mws.append(mw) self.layouts.append(view.registeringLayoutComboBox) self.names_geos_nofrois = list(zip(subset_infos["subnames"], subset_infos["subgeos"], [nofrois]*len(subset_infos["subgeos"]) )) hydra_obj.load_scan(scannums, direct=False) pw_data = hydra_obj.get_pw_matrices( scannums, method='pixel' ) print(" =============== pw_data ======================= ") print(" INFOS ") print ( subset_infos ) LNR=0 for name,geo,nofr in self.names_geos_nofrois : print (" STO CREANDO PER ", name,geo,nofr ) tobeanalyzed = [] for data, key in zip(pw_data, sorted(roi_obj.red_rois)): roi_num = int(''.join(filter(str.isdigit, str(key) ))) if roi_num>= LNR and roi_num <( LNR + nofrois ) : tobeanalyzed.append( (key , data, image , roi_obj.red_rois[key]) ) print ( key, " e la maschera est ", roi_obj.red_rois[ key ] ) print(" DA PW ", data.shape) view, mw = self.create_graphsScrollWidget( tobeanalyzed, nofrois = nofrois, changeTagOn = True ) self.mws.append(mw) self.viewsTab.addTab(view, name) LNR = LNR + nofr self.subregions.append(mw) # self.layouts.append(view.registeringLayoutComboBox) # view, roiroiw = self.create_viewWidget(image = image, nofrois = 1 , isglobal=True, changeTagOn = False ) # self.viewsTab.addTab(view, "ROI of ROIS") # self.roiroiw = roiroiw # self.layouts.append(view.registeringLayoutComboBox) def update_user_input(self, uinput): self.load_user_input.update(uinput) def LoadLocalOption(self, optional_user_input = None): sf = optional_user_input["sf"] fn = optional_user_input["roi"] ns_s = optional_user_input["ns_s"] self.load_user_input .update( { "sf_spectral":sf, "roi":fn }) self.user_input_signal.emit( self.load_user_input ) if isinstance(ns_s,tuple): ns_s = list(ns_s) self.load_rois(sf, fn , ns_s ) def LoadLocal(self): if not FASTDEBUG: w = localfilesdialog.localfilesdialog(self.load_user_input) result = w.exec_() if not result: return print(" RESULT DIALOG ", result) sf = str(w.SpecFileName_lineEdit.text()) fn = str(w.FileName_lineEdit.text()) l={} g={} exec("ns_s="+ str(w.ScansNumbers_lineEdit.text()), g, l) ns_s = l["ns_s"] # ns = w.ScanNumber_spinBox.value() else: sf = '/data/id20/inhouse/data/run5_17/run7_ihr/' fn = "/mntdirect/_scisoft/users/mirone/WORKS/xrstools_nr/Christoph_scripts/myroi.h5:/datas/ROI" fn = "myroi.h5:/datas/ROI" ns_s = [613,617,621,625] self.load_user_input .update( { "sf_spectral":sf, "roi":fn }) self.user_input_signal.emit( self.load_user_input ) # template = getTemplateName( fn ) if isinstance(ns_s,tuple): ns_s = list(ns_s) self.load_rois(sf, fn , ns_s ) # if FASTDEBUG: # self.GlobaldetectionCallBack(warn=False) def load_rois(self,sf, fn , ns_s ): roiaddress = xrs_utilities.split_hdf5_address(fn) masks={} h5=h5py.File(roiaddress[0],'r') datagroup = h5[roiaddress[1]] newshape, imagesum = xrs_rois.load_rois_fromh5(datagroup,masks, retrieveImage=True) myroi = xrs_rois.roi_object() myroi.load_rois_fromMasksDict(masks, newshape=newshape) # if imagesum is not None: if os.path.isfile(sf): lw = xrs_read.Hydra( os.path.dirname(sf), SPECfname=os.path.basename(sf) ) elif os.path.isdir(sf): lw = xrs_read.Hydra( sf, SPECfname='hydra') else: raise Exception( " Path %s is neither an existing file nor an existing directory "% sf ) lw.set_roiObj(myroi) self.geo_informations=None self.showImage( imagesum ,hydra_obj = lw , scannums=ns_s, roi_obj = myroi) self.roiob = myroi spatial_mask = self.mws[0].getSelectionMask().astype("i") spatial_mask[:]=0 spatial_mask = convert_redmatrix_to_matrix(masks,spatial_mask, offsetX=0, offsetY=0) self.mws[0].setSelectionMask(spatial_mask) # self.decomposeGlobalMask() self.annotateAllMasksCallBack() def write_maskDict_to_hdf5(self): self.write_maskDict_to_hdf5_option() def write_maskDict_to_hdf5_option(self, option=None): if not option : filename = Qt.QFileDialog.getSaveFileName(None,'Open hdf5 file to write spectral rois',filter="hdf5 (*h5)\nall files ( * )") if isinstance(filename, tuple): filename = filename[0] filename = str(filename) else: filename = option self.saveMaskDictOnH5( filename+":/spectral_roi_from_selector/" ) self.load_user_input ["roi_spectral"]= filename+":/spectral_roi_from_selector/" self.user_input_signal.emit( self.load_user_input ) def load_maskDict_from_hdf5(self): filename = Qt.QFileDialog.getOpenFileName(None,'Open hdf5 file with spectral rois',filter="hdf5 (*h5)\nall files ( * )" ) if isinstance(filename, tuple): filename = filename[0] filename=str(filename) print( filename) if len(filename): import PyMca5.PyMcaGui.HDF5Widget as HDF5Widget storage=[None] def mySlot(ddict): name = ddict["name"] storage[0]=name # browse self.__hdf5Dialog = hdf5dialog() self.__hdf5Dialog.setWindowTitle('Select a Group containing roi_definitions by a double click') self.__hdf5Dialog.mainLayout = self.__hdf5Dialog.verticalLayout_2 fileModel = HDF5Widget.FileModel() fileView = HDF5Widget.HDF5Widget(fileModel) hdf5File = fileModel.openFile(filename) shiftsDataset = None fileView.sigHDF5WidgetSignal.connect(mySlot) self.__hdf5Dialog.mainLayout.addWidget(fileView) self.__hdf5Dialog.resize(400, 200) ret = self.__hdf5Dialog.exec_() print( ret) hdf5File.close() if ret: name = storage[0] self.loadMaskDictFromH5( filename+":/"+name ) def loadMaskDictFromH5(self, filename ): self.load_maskDict_from_givenhdf5andgroup(filename) def load_maskDict_from_givenhdf5andgroup(self, filename, gname=None): if gname is None: filename, gname = xrs_utilities.split_hdf5_address(filename ) file= h5py.File(filename,"r") datagroup = file[gname] masks={} metadata={} xrs_rois.load_rois_fromh5(datagroup,masks, metadata=metadata) file.close() print(" CARICO CON METADATA : ", metadata) self.load_masksDict(masks, metadata=metadata) def load_image_from_hdf5(self): print( " load " ) filename = Qt.QFileDialog.getOpenFileName() if isinstance(filename, tuple): filename = filename[0] print( " OK " ) filename=str(filename) print( filename) if len(filename): import PyMca5.PyMcaGui.HDF5Widget as HDF5Widget storage=[None] def mySlot(ddict): name = ddict["name"] storage[0]=name print( " MY SLOT " ) print( name) # browse self.__hdf5Dialog = hdf5dialog() self.__hdf5Dialog.setWindowTitle('Select your data set by a double click') self.__hdf5Dialog.mainLayout = self.__hdf5Dialog.verticalLayout_2 fileModel = HDF5Widget.FileModel() fileView = HDF5Widget.HDF5Widget(fileModel) hdf5File = fileModel.openFile(filename) shiftsDataset = None fileView.sigHDF5WidgetSignal.connect(mySlot) self.__hdf5Dialog.mainLayout.addWidget(fileView) self.__hdf5Dialog.resize(400, 200) # self.__hdf5Dialog.setModal(True) # self.__hdf5Dialog.show() ret = self.__hdf5Dialog.exec_() hdf5File.close() if ret: print( " Obtained " ) name = storage[0] file= h5py.File(filename,"r") image4roi = file[name][:] file.close() self.showImage(image4roi) class hdf5dialog(Qt.QDialog): def __init__(self, parent=None): super( hdf5dialog, self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , my_relativ_path, "hdf5dialog.ui" ), self) def convert_redmatrix_to_matrix( masksDict,mask, offsetX=0, offsetY=0): for key, (pos,M) in six.iteritems(masksDict): num=int("".join([c for c in key if c.isdigit()])) S = M.shape inset = (slice(offsetY+pos[0] , offsetY+pos[0]+S[0] ), slice( offsetX+pos[1] , offsetX+pos[1]+S[1] ) ) M=numpy.less(0,M) mask[ inset ][M>0] = (num+1)*M[M>0] return mask def getTemplateName(name): dirname=os.path.dirname(str(name)) name=os.path.basename(str(name)) ls = len(name) fine = None inizio=None for n in list(range(ls))[::-1]: if fine is None and name[n] in "1234567890": fine = n+1 if name[n] in "1234567890": inizio=n if fine is not None and name[n] not in "1234567890": break print( name) print( inizio) print( fine) name = name[:inizio]+"%" + ("0%d"%(fine-inizio)+"d"+name[fine:]) print( name) if name[:2] in ["h_", "v_"]: return [dirname+"/"+"h_"+name[2:],dirname+"/"+"v_"+name[2:]] else: return [dirname+"/"+name] _cross_data = "\ \x00\x00\x02\x71\ \x89\ \x50\x4e\x47\x0d\x0a\x1a\x0a\x00\x00\x00\x0d\x49\x48\x44\x52\x00\ \x00\x00\x10\x00\x00\x00\x10\x08\x06\x00\x00\x00\x1f\xf3\xff\x61\ \x00\x00\x00\x06\x62\x4b\x47\x44\x00\xff\x00\xff\x00\xff\xa0\xbd\ \xa7\x93\x00\x00\x00\x09\x70\x48\x59\x73\x00\x00\x0d\xd7\x00\x00\ \x0d\xd7\x01\x42\x28\x9b\x78\x00\x00\x00\x07\x74\x49\x4d\x45\x07\ \xde\x07\x10\x0d\x3b\x14\x28\x69\xf4\x66\x00\x00\x01\xfe\x49\x44\ \x41\x54\x38\xcb\x85\x93\xcf\x6a\x14\x41\x10\xc6\x7f\x5d\xdd\x59\ \x77\x3d\x86\x44\x14\xd4\xa0\x41\xfc\x17\xc5\x9b\x20\x1e\x66\x86\ \x3d\xa9\x2f\x10\x04\x4f\x7a\x51\x1f\xc3\x47\x10\x05\x51\x3c\x08\ \x82\x4f\x90\xcb\x30\x33\x37\x11\xd4\x8d\xa2\x17\x49\x02\x1a\x3c\ \x18\x04\x0f\x2e\xe8\x6c\xa6\xbb\x3c\xa4\x27\x4c\x16\xc5\x86\xa6\ \xa1\xaa\xbe\xaf\xbf\xae\xfe\xca\x0c\x87\x49\x8f\x9d\x25\x40\x88\ \xa7\xc6\xfd\xbf\x98\x97\x08\xb6\x31\xe9\x00\x35\x46\x4d\x9e\x57\ \x13\xdf\xc8\x29\xdf\xc8\x25\xef\x65\x2e\xcf\xab\x7a\xba\x0e\xb0\ \xd2\x61\xb4\x80\xcf\xf3\xaa\x6e\xb6\xed\xbd\x34\xc9\x14\x18\x01\ \x25\xca\x66\x9a\x64\x3f\x7c\x23\x57\x22\x91\x6f\x55\x99\xf8\x04\ \x07\x78\x2c\xea\x6b\x59\x03\x8e\xf0\xef\xf5\xb8\xac\x8a\x5b\xc3\ \x61\x32\x00\x42\xab\xc0\xe7\x79\x55\xfb\x5a\x56\x80\x59\xeb\xc2\ \x00\xb8\xb3\x07\x66\xb8\x0c\x9c\x00\x6e\xa6\x49\xb6\x1c\x55\x9b\ \xb6\x39\x92\x26\xd9\x19\x20\x03\xdc\xb1\xa5\xad\xba\xbf\x7f\xfb\ \x51\x4b\x62\x0c\x4b\x22\xe1\x2d\x30\x89\x74\x4f\xe3\x53\x42\x4b\ \x00\x70\x3d\x9e\xbd\xb5\xd5\x83\x1b\x46\x34\x64\xcb\x1f\x1e\x18\ \xd1\x79\x23\x61\x23\x78\x39\x04\x7c\x8e\x35\xfb\xd2\x24\x3b\x07\ \x88\x00\xe2\x7a\x7e\x02\x1c\xdf\x15\x0b\x0b\xbf\xc6\xbd\xd1\xfa\ \xea\x01\x23\xa2\x3f\x83\x97\x59\x60\x7d\xaa\x17\x8b\x80\x0a\x10\ \xfc\xb6\xed\x01\x5b\x9d\xa4\x07\xae\x6d\x7e\x9a\xeb\xe7\x79\x55\ \x97\x55\xf1\x15\x78\x38\x45\x30\x6e\xcd\x23\xaa\x04\xe0\x7d\x4c\ \xd4\xc6\xe8\x61\x6b\xc3\xf7\x10\xe4\x74\x9a\x64\xef\x00\xca\xaa\ \xb8\x0d\x3c\x6b\xd1\xbd\x41\x53\xec\x69\x62\x59\x15\x4f\xe2\xcd\ \xa3\xa2\x2c\xbf\x79\x2f\x17\x50\xde\x00\xe7\xd3\x24\x7b\x1d\x49\ \x6e\x44\xfc\x0b\x0d\xc6\x01\xa1\xf5\x81\x45\x51\x1f\xe4\x22\x4a\ \x05\x7c\x01\x8e\x4e\x49\xfe\x18\x2f\x58\xb4\x2e\xcc\x77\xbf\x71\ \xc7\x89\x06\x71\xd6\xbf\x04\xd2\xbf\x80\x01\xce\x02\x7d\x23\xba\ \x80\xee\xce\x84\xee\xb1\xb1\x62\xac\x75\xe1\x55\x59\x15\x06\xb8\ \x0b\x3c\x07\x72\xe0\x3e\x70\xb5\xac\x8a\x93\x22\x3a\xc6\x60\x76\ \xfd\x35\x1c\x26\xfd\xce\x94\xd1\x25\xb4\x2e\x04\x37\xe3\xc3\xe4\ \xb7\x13\x55\x33\xd3\x99\x81\xb6\xce\xb8\x18\xb4\x31\xd0\xfa\xa0\ \x01\xc4\x37\x22\xbe\x11\xe9\xc6\xa6\xea\xfc\x1f\xdb\x37\xde\x59\ \x25\x68\xce\x04\x00\x00\x00\x00\x49\x45\x4e\x44\xae\x42\x60\x82\ \ " def main(): app=Qt.QApplication([]) w = mainwindow() w.show() app.exec_() if __name__=="__main__": app=Qt.QApplication([]) w = mainwindow() w.show() app.exec_() xrstools-0.15.0+git20210910+c147919d/XRStools/roiNmaSelectionGui/setup.py000066400000000000000000000005031412732462000251570ustar00rootroot00000000000000 import os from numpy.distutils.misc_util import Configuration def configuration(parent_package='', top_path=None): config = Configuration('roiNmaSelectionGui', parent_package, top_path) return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration=configuration) xrstools-0.15.0+git20210910+c147919d/XRStools/roiNmaSelectionGui/spotdetection.py000066400000000000000000000460471412732462000267200ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import scipy.misc from scipy import signal import scipy from scipy.ndimage import maximum_filter import scipy.ndimage.morphology as morph import scipy.ndimage.measurements as meas ## import skimage ## import skimage.transform from numpy import gradient from six.moves import range from six.moves import zip LENMIN=10 def gradX(x): res=np.zeros_like(x) res[:, :-1]=x[:,:-1]-x[:,1:] return res def gradXm(x): res=np.zeros_like(x) res[:,1: ]=-x[:,:-1]+x[:,1:] return res def gradY(x): res=np.zeros_like(x) res[ :-1]=x[:-1]-x[1:] return res def gradYm(x): res=np.zeros_like(x) res[1: ]=-x[:-1]+x[1:] return res def lap(x): res = -( gradX(gradXm(x))+gradXm(gradX(x))+ gradY(gradYm(x)) + gradYm(gradY(x)) )/2.0 return res def Diff(x ,coeffs): print( coeffs.shape) print( x.shape) DY,DX = gradient(x) yy , xy = gradient(DY) dum , xx = gradient(DX) # xx = gradient(gradient(x,axis=1),axis=1) # yy = gradient(gradient(x,axis=0),axis=1) # xy = gradient(gradient(x,axis=0),axis=0) res = xx*coeffs[2] + yy*coeffs[0]+ +2*coeffs[1]*xy return res def shrink(tmp, binning): n_cols = (tmp.shape[0] // binning) n_rows = (tmp.shape[1] // binning) tmp = tmp[:n_rows * binning,:n_cols * binning] tmp.shape = [ n_rows, binning, n_cols, binning ] shrinked = tmp.max(axis=3).max(axis=1) return shrinked def deshrink( large , shrinked, binning ): n_cols = (large.shape[0] // binning) n_rows = (large.shape[1] // binning) tmp = large[:n_rows * binning,:n_cols * binning] tmp.shape = [ n_rows, binning, n_cols, binning ] shrinked=shrinked[:] shrinked.shape=[ n_rows,1 , n_cols, 1 ] tmp[:] = shrinked def ReadFile(name): data=scipy.misc.imread(name)*1.0 data=np.max(data, axis=-1) return data def CercaAnelli(A, lines=False): A=A.astype("d") # Nmarge=5 # A[:Nmarge ,:]=1 # A[:,:Nmarge ]=1 # A[-Nmarge:,:]=1 # A[:,-Nmarge:]=1 Ax = np.concatenate( [A[1:], A[:1] ] )- np.concatenate( [A[-1:], A[:-1] ] ) Ax = 2*Ax + np.concatenate( [Ax[:, 1:], Ax[:, :1] ], axis=1 ) + np.concatenate( [Ax[:,-1:], Ax[:,:-1] ] , axis=1 ) Ay = np.concatenate( [A[:, 1:], A[:, :1] ] , axis=1 ) - np.concatenate( [A[:, -1:], A[:, :-1] ] , axis=1 ) Ay = 2*Ay + np.concatenate( [Ay[ 1:], Ay[ :1] ], axis=0 ) + np.concatenate( [Ay[-1:], Ay[:-1] ] , axis=0 ) if lines: res=Canny_lines(Ax,Ay) else: res=Canny(Ax,Ay) # print len(res) return res def IsMaximum( i,j,slope): totry = [[i-1,j-1],[i,j-1],[i+1,j-1],[i-1,j],[i+1,j],[i-1,j+1],[i,j+1],[i+1,j+1], ] for punto in totry: if( slope[punto[0], punto[1] ]> slope[i,j]): return 0 return 1 def Canny(Ax,Ay): Angles = np.arctan2( Ax, -Ay ) Angles=Angles*180/np.pi Angles = np.floor( Angles/45 + 0.5) slope = Ax*Ax + Ay*Ay Nmarge=6 MaximumRatio=100.0 # maxfits = maximum_filter(slope, size=3) # indici = np.where(slope==maxfits) # ListLocalMaxima=[ [i,j] for i,j in zip(indici[0], indici[1]) ] Ni=Ax.shape[0] Nj=Ax.shape[1] ListLocalMaxima=[] for i in range( Nmarge, Ni-Nmarge): for j in range( Nmarge, Nj-Nmarge): if( IsMaximum( i,j,slope) ): ListLocalMaxima.append( [i,j] ) # print len(ListLocalMaxima) # raise steps={-5:[-1,1], -4:[-1,0],-3:[-1,-1], -2:[0,-1],-1:[1,-1],0:[1,0],1:[1,1],2:[0,1],3:[-1,1],4:[-1,0], 5:[-1,-1]} EdgeList=[] endpoints=[] EdgePoints=np.zeros(Ax.shape ) EdgePoints[:Nmarge ,:]=-1 EdgePoints[:,:Nmarge ]=-1 EdgePoints[-Nmarge:,:]=-1 EdgePoints[:,-Nmarge:]=-1 StartPoints = ListLocalMaxima for starting in StartPoints: EdgePointsTmp = np.zeros(Ax.shape ) s0=starting[0] s1=starting[1] pentevalue= slope[s0, s1 ] if(EdgePoints[ s0, s1 ]==0): Edge=[] Edge.append( [s0,s1] ) prendi=0 traccia=0 while(1): if( slope[s0, s1 ]/pentevalue < 1.0/MaximumRatio): prendi=0 traccia=1 break EdgePointsTmp[ s0, s1 ]=1 direction = Angles[s0,s1] ss = [ steps[direction-1], steps[direction], steps[direction+1]] pttrs = [ [s0+ss[0][0],s1+ss[0][1]], [s0+ss[1][0],s1+ss[1][1]], [s0+ss[2][0],s1+ss[2][1]] ] values = [ [slope[ pttrs[i][0], pttrs[i][1] ],i] for i in range(3) ] imax= max(values)[1] s0=pttrs[imax][0] s1=pttrs[imax][1] if(EdgePoints[ s0, s1 ]): prendi=0 traccia=1 break if( EdgePointsTmp[ s0, s1 ] ): prendi=1 traccia=1 Edge.reverse() newedge = [ ] for p in Edge : if tuple(p)== (s0,s1): break newedge.append(p) Edge = [[s0,s1]]+newedge break else: Edge.append( [s0,s1] ) if(prendi): endpoints.append([s0,s1]) EdgeList.append(Edge) if traccia: for p in Edge: EdgePoints[p[0],p[1]]=1 # values = [ [len(edge),edge] for edge in EdgeList] # edge = max(values)[1] values = [ edge for edge in EdgeList if len(edge)>10 ] # edge = max(values)[1] # return [edge] return values def Canny_lines(Ax,Ay): Angles = np.arctan2( Ax, -Ay ) Angles=Angles*180/np.pi Angles = np.floor( Angles/45 + 0.5) slope = Ax*Ax + Ay*Ay Nmarge=1 MaximumRatio=10000.0 # maxfits = maximum_filter(slope, size=3) # indici = np.where(slope==maxfits) # ListLocalMaxima=[ [i,j] for i,j in zip(indici[0], indici[1]) ] Ni=Ax.shape[0] Nj=Ax.shape[1] ListLocalMaxima=[] for i in range( Nmarge, Ni-Nmarge): for j in range( Nmarge, Nj-Nmarge): if( IsMaximum( i,j,slope) ): ListLocalMaxima.append( [i,j] ) # print len(ListLocalMaxima) # raise steps={-5:np.array([-1,1]), -4:np.array([-1,0]),-3:np.array([-1,-1]), -2:np.array([0,-1]),-1:np.array([1,-1]), 0:np.array([1,0]),1:np.array([1,1]),2:np.array([0,1]),3:np.array([-1,1]),4:np.array([-1,0]), 5:np.array([-1,-1])} EdgeList=[] endpoints=[] EdgePoints=np.zeros(Ax.shape ) EdgePoints[:Nmarge ,:]=-1 EdgePoints[:,:Nmarge ]=-1 EdgePoints[-Nmarge:,:]=-1 EdgePoints[:,-Nmarge:]=-1 StartPoints = ListLocalMaxima stack = [] for starting in StartPoints: s0=starting[0] s1=starting[1] pentevalue= slope[s0, s1 ] stack.append( [ (s0, s1), pentevalue, 1 ] ) while len(stack): starting, pentevalue, direction_fact = stack[-1] stack=stack[:-1] print( " inizio da ", starting) EdgePointsTmp = np.zeros(Ax.shape ) s0=starting[0] s1=starting[1] pentevalue= slope[s0, s1 ] if(EdgePoints[ s0, s1 ]==0): Edge=[] Edge.append( [s0,s1] ) prendi=0 traccia=0 while(1): print( s0,s1) if( slope[s0, s1 ]/pentevalue < 1.0/MaximumRatio): if direction_fact==1 : prendi=0 traccia=0 if len(Edge)>4: stack.append( [ Edge[-4], pentevalue, -1 ]) print( " troppo debole Inverto") else: prendi= len(Edge)>5 traccia=1 print( " troppo debole finisco") break EdgePointsTmp[ s0, s1 ]=1 direction = Angles[s0,s1] ss = np.array([ steps[direction-1], steps[direction], steps[direction+1]])*direction_fact pttrs = [ [s0+ss[0][0],s1+ss[0][1]], [s0+ss[1][0],s1+ss[1][1]], [s0+ss[2][0],s1+ss[2][1]] ] values = [ [slope[ pttrs[i][0], pttrs[i][1] ],i] for i in range(3) ] imax= max(values)[1] s0=pttrs[imax][0] s1=pttrs[imax][1] if(EdgePoints[ s0, s1 ]): print( " scontro vecchio in ", s0, s1) if direction_fact==-1: Edge=Edge[:-10] prendi=1 traccia=1 break else: EdgePointsTmp[:]=0 prendi=0 traccia=0 if len(Edge)>10: stack.append( [ Edge[-4], pentevalue, -1 ]) print( " troppo debole Inverto") break if( EdgePointsTmp[ s0, s1 ] ): print( " scontro nuovo ") prendi=1 traccia=1 Edge.reverse() newedge = [ ] for p in Edge : if tuple(p)== (s0,s1): break newedge.append(p) Edge = [[s0,s1]]+newedge break else: print( " continup ") Edge.append( [s0,s1] ) if(prendi): endpoints.append([s0,s1]) EdgeList.append(Edge) if traccia: for p in Edge: EdgePoints[p[0],p[1]]=1 # values = [ [len(edge),edge] for edge in EdgeList] # edge = max(values)[1] values = [ edge for edge in EdgeList if len(edge)>10 ] # edge = max(values)[1] # return [edge] return values def divergenza( Ax,Ay, edge): if( edge[-1] != edge[0]): edge.append(edge[0]) res=0 for i in range(len(edge)-1): p1=edge[i] p2=edge[i+1] Ax1=Ax[p1[0],p1[1] ] Ax2=Ax[p2[0],p2[1] ] Ay1=Ay[p1[0],p1[1] ] Ay2=Ay[p2[0],p2[1] ] Dx = p2[0]-p1[0] Dy = p2[1]-p1[1] res=res+ ( Dx*(Ay1+Ay2) -Dy*(Ax1+Ax2) )*0.5 return res def threshold_mask(mask, image, value ) : n=mask.max() npix=3 image = signal.medfilt2d(image, kernel_size=3) for i in range(1,n+1): icount=0 tmp_mask_old = 0 while(icount<100): tmp_mask = np.equal( i, mask ) if not np.any(tmp_mask): continue mask[:]=mask*(1-tmp_mask) massimo = (image*tmp_mask).max() print( " MASSIMO " , massimo) tmp_mask_2 = morph.grey_dilation(tmp_mask, footprint=np.ones([npix,npix]), structure=np.zeros([npix,npix])) print( " Npunti , value " , tmp_mask.sum(), tmp_mask_2.sum() , value) tmp_mask = np.less( value*massimo, image )*tmp_mask_2 print( " Npunti " , tmp_mask.sum()) mask[:]+=tmp_mask*i if ( tmp_mask^tmp_mask_old).sum()==0: break tmp_mask_old = tmp_mask print( " ================ ") print( mask.sum()) icount+=1 return mask def grow_mask(input, npix ) : output = morph.grey_dilation(input, footprint=np.ones([npix,npix]), structure=np.zeros([npix,npix])) return output def shrink_mask(input, npix ) : output = morph.grey_erosion(input, footprint=np.ones([npix,npix]), structure=np.zeros([npix,npix])) return output def get_spots_mask( A, rrA, median_size=5, nofroi=12, give_borders=False, tval=-1, Hough=False ) : if not Hough: res=get_spots_mask_Normal( A, rrA, median_size=median_size, nofroi=nofroi, give_borders=False, tval=-1 ) return res else: res=get_spots_mask_Lines( A, rrA, median_size=median_size, nofroi=nofroi, give_borders=False, tval=-1 ) return res def get_spots_mask_Normal( A, rrA, median_size=None, nofroi=12, give_borders=False, tval=-1, Hough=False ) : print( " qui Hough " , Hough) A = A*(1-rrA) A = signal.medfilt2d(A, kernel_size=median_size) mmax = A.max() A[Aamed/100] newmask = np.zeros(filled.shape,"i") i=1 for l in labs: newmask[np.equal(labels,l)]=i i+=1 return newmask def get_spots_mask_Lines( A,rrA, median_size=None, nofroi=12, give_borders=False, tval=-1 ): A=A*(1-rrA) thematrix = np.zeros( [A.shape[0]+4, A.shape[1]+4 ] , A.dtype) thematrix[2:-2 ,2:-2 ] = A thematrix=scipy.ndimage.filters.gaussian_filter(thematrix, 3.0) Nrows , Ncols = thematrix.shape submatrix = np.array(thematrix) mask, diff_coeffs = get_spots_mask_Lines_slave( submatrix , nofroi=nofroi, give_diff_coeffs=True , tval=tval) # ###################### # nspots = mask.max() # if nspots!=12: # print "WARNING: LESS SPOTS WERE FOUND : " , nspots # else: # print "GOOD!: FOUND : " , nspots, " SPOTS " # for l in range(1,nspots+1) : # maskzone = np.equal(mask,l) # submatrix[maskzone] = submatrix.max() # import pylab # f1=pylab.figure() # from matplotlib.colors import LogNorm # pylab.imshow(submatrix, norm=LogNorm(vmin=0.01)) # pylab.show() # ################################# ############################################################## submatrix = np.array(thematrix) for iter in range( (median_size*2)**2): submatrix = submatrix + Diff(submatrix,diff_coeffs )*0.5*0.2 newsub=np.zeros_like(submatrix) newsub[2:-2 , 2:-2 ] = submatrix [2:-2 , 2:-2 ] submatrix=newsub mask = get_spots_mask_Lines_slave( submatrix , nofroi=nofroi, give_diff_coeffs=False , tval=tval ) # ###################### # nspots = mask.max() # if nspots!=12: # print "WARNING: LESS SPOTS WERE FOUND : " , nspots # else: # print "GOOD!: FOUND : " , nspots, " SPOTS " # for l in range(1,nspots+1) : # maskzone = np.equal(mask,l) # submatrix[maskzone] = submatrix.max() # import pylab # f1=pylab.figure() # from matplotlib.colors import LogNorm # pylab.imshow(submatrix, norm=LogNorm(vmin=0.01)) # pylab.show() # ################################# nspots = mask.max() if nspots!= nofroi: print( "WARNING: LESS SPOTS WERE FOUND : " , nspots) else: print( "GOOD!: FOUND : " , nspots, " SPOTS " ) return mask[2:-2,2:-2] def get_spots_mask_Lines_slave( A, median_size=None, nofroi=120, give_borders=False, give_diff_coeffs=False, tval=-1) : mmax = A.max() A[A0 and <1,\n then all pixel 0 and <1 ,\nthe all pixel =0 and m[1][1]>=0 ): masksDict[ self.labelformat% (m[0]-1) ]=[m[1],m[2]] return masksDict def recomposeGlobalMask(self): offset=0 globalMask = self.mws[0].getSelectionMask().astype("i") for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.mws[1:]): localmask = numpy.less(0,mw.getSelectionMask() ) globalMask[geo] = offset*localmask + mw.getSelectionMask() offset += nofrois self.mws[0].setSelectionMask(globalMask ) return offset, globalMask def decomposeGlobalMask(self): offset=0 globalMask = self.mws[0].getSelectionMask().astype("i") for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.mws[1:]): localmask = numpy.less(0,globalMask[geo] ) Mask = globalMask[geo] - offset *localmask offset += nofrois mw.setSelectionMask(Mask ) def write_mask_on_file(self): filename = Qt.QFileDialog.getSaveFileName() if isinstance(filename, tuple): filename = filename[0] print( filename) if filename is not None: filename=str(filename) self.recomposeGlobalMask() globalMask = self.mws[0].getSelectionMask().astype("i") ef = edf.EdfFile( filename, "w+") ef.WriteImage( {}, globalMask ) def write_masksDict_on_file(self): filename = Qt.QFileDialog.getSaveFileName() if isinstance(filename, tuple): filename = filename[0] print( filename) if filename is not None: filename=str(filename) masksDict = self.getMasksDict() filename=str(filename) f = open(filename, 'wb') pickle.dump(masksDict , f) f.close() def load_masksDict_from_file(self): filename = Qt.QFileDialog.getOpenFileName() if isinstance(filename, tuple): filename = filename[0] print( filename) if filename is not None: filename=str(filename) f = open(filename, 'rb') masksDict = pickle.load( f) f.close() self.load_masksDict( masksDict) def load_masksDict(self, masksDict): self.recomposeGlobalMask() mask = self.mws[0].getSelectionMask().astype("i") mask[:]=0 mask = convert_redmatrix_to_matrix(masksDict,mask, offsetX=0, offsetY=0) self.mws[0].setSelectionMask(mask) self.decomposeGlobalMask() self.annotateAllMasksCallBack() def read_mask_from_file(self): filename = Qt.QFileDialog.getOpenFileName() if isinstance(filename, tuple): filename = filename[0] if filename is not None: filename=str(filename) ef = edf.EdfFile( filename, "r") mask = ef.GetData(0) self.recomposeGlobalMask() self.mws[0].setSelectionMask(mask) self.decomposeGlobalMask() def detectionCallBack(self, thr_s="", Hough=False): print( " in detectionCallBack, Hough " , Hough) itab = self.viewsTab.currentIndex() if itab==0: return globalMask = self.mws[0].getSelectionMask().astype("i") roiroiMask = self.roiroiw.getSelectionMask().astype("i") offset=0 for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:itab-1], self.mws[1:itab]): offset += nofrois (name,geo,nofrois), mw = self.names_geos_nofrois[itab-1], self.mws[itab] self.detectSpotsSubMask( name,geo,nofrois, mw , globalMask , roiroiMask, offset,thr_s=thr_s , Hough=Hough ) self.mws[0].setSelectionMask( globalMask ) def get_geo(self): if self.geo_informations is None: subset_infos = xrs_rois.get_geo_informations( (self.image.shape+(self.layout,) ) ) else: subset_infos = self.geo_informations dl = subset_infos["analyser_nIDs"] dl1k = list(dl[list(dl.keys())[0]].keys()) if len(dl1k)==1: for t in imageview.all_layouts: t.setCurrentIndex(1) return subset_infos def getLabelCorrespondance(self): res = [] itab=1 # print " CORRESPONDANCE " # print self.mws[1:] # print self.names_geos_nofrois[:] for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.mws[1:]): subset_infos = self.get_geo() if "3x4" in str(self.layouts[itab].currentText() ): a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["3x4"] else: a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["Vertical"] # res.extend(list(numpy.array(a_ids) +len(res) )) res.extend(list(numpy.array(range(1,1+12) ) +len(res) )) # print " res " ,res itab+=1 return res def annotateOneMaskCallBack(self): itab = self.viewsTab.currentIndex() if itab>0: subset_infos = self.get_geo() if "3x4" in str(self.layouts[itab].currentText() ): a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["3x4"] else: a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["Vertical"] self.mws[itab].annotateSpots( a_ids, self.get_offset( itab ) ) def annotateAllMasksCallBack(self): itab=1 for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.mws[1:]): subset_infos = self.get_geo() if "3x4" in str(self.layouts[itab].currentText() ): a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["3x4"] else: a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["Vertical"] mw.annotateSpots( a_ids, self.get_offset(itab) ) itab+=1 def get_offset(self,itab): offset=0 for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:itab-1], self.mws[1:itab]): offset += nofrois return offset def relabeliseAllMasksCallBack(self): offset=0 for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.mws[1:]): self.relabeliseSpots( mw, nofrois, name , geo, offset) def relabeliseOneMaskCallBack(self): itab = self.viewsTab.currentIndex() if itab==0: return offset=0 for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:itab-1], self.mws[1:itab]): offset += nofrois (name,geo,nofrois), mw = self.names_geos_nofrois[itab-1], self.mws[itab] self.relabeliseSpots( mw , nofrois, name, geo, offset) def relabeliseSpots(self,mw, nofrois, name, geo, offset): mask = mw.getSelectionMask( ).astype("i") mask = (mask>0).astype("i") newmask = spotdetection.relabelise(mask,mask, nofrois) self.checkNspots(newmask.max(), nofrois , name) mw.setSelectionMask( newmask ) globalMask = self.mws[0].getSelectionMask().astype("i") globalMask[geo] = newmask self.mws[0].setSelectionMask( globalMask ) def resetOneMask(self): itab = self.viewsTab.currentIndex() if itab==0: return mw = self.mws[itab] mw.graph.clearMarkers() mask = mw.getSelectionMask( ).astype("i") mask[:]=0 mw.setSelectionMask( mask) def resetAllMasks(self): ret = self.warnForGloablChange() print( ret) if ret: for (name,geo,nofrois), mw in zip(self.names_geos_nofrois[:], self.mws[1:]): mw.graph.clearMarkers() mask = mw.getSelectionMask( ).astype("i") mask[:]=0 mw.setSelectionMask( mask) def threshold(self,itab,value ): globalMask = self.mws[0].getSelectionMask().astype("i") name,geo,nofrois = self.names_geos_nofrois[itab-1] mw = self.mws[itab] mask = mw.getSelectionMask( ) print( mask.sum()) data = self.image[geo] mask = spotdetection.threshold_mask(mask, data , value ) mw.setSelectionMask(mask ) globalMask[geo] = mask self.mws[0].setSelectionMask( globalMask ) print( mask.sum()) def localThresholdCallBack(self,value): itab = self.viewsTab.currentIndex() if itab==0: return self.threshold(itab,value) def globalThresholdCallBack(self,value): ret = self.warnForGloablChange() print( ret) if ret: for itab in range(1, len(self.mws ) ) : self.threshold(itab,value) def fatten(self, itab, value ) : globalMask = self.mws[0].getSelectionMask().astype("i") name,geo,nofrois = self.names_geos_nofrois[itab-1] mw = self.mws[itab] mask = mw.getSelectionMask( ) if value>0: mask = spotdetection.grow_mask(mask, 1+2*value ) else: mask = spotdetection.shrink_mask(mask, 1-2*value ) mw.setSelectionMask(mask ) globalMask[geo] = mask self.mws[0].setSelectionMask( globalMask ) def fatteningCallBack(self,value): itab = self.viewsTab.currentIndex() if itab==0: return self.fatten(itab,value) def GlobalfatteningCallBack(self,value): print( " in GlobalfatteningCallBack ", value ) ret = self.warnForGloablChange() print( ret) if ret: for itab in range(1, len(self.mws ) ) : self.fatten(itab,value) def checkNspots(self,nspots, nofrois,name ) : if nspots != nofrois: if not SKIP_WARNING : msgBox = Qt.QMessageBox () msgBox.setText("Warning: found %d spots instead of expected %d "%(nspots , nofrois ) ); msgBox.setInformativeText("For detector %s " % name); msgBox.setStandardButtons(Qt.QMessageBox.Ok ); msgBox.setDefaultButton(Qt.QMessageBox.Cancel); ret = msgBox.exec_(); def detectSpotsSubMask( self, name,geo,nofrois, mw , globalMask,roiroiMask, offset ,thr_s="", Hough=False): itab = self.viewsTab.currentIndex() if itab: subset_infos = self.get_geo() if "3x4" in str(self.layouts[itab].currentText() ): a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["3x4"] else: a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["Vertical"] else: a_ids = None submatrix = mw.getImageData() subroiroi = roiroiMask[geo] tval = -1 try: tval = float(thr_s) except: tval=-1 geos = self.get_geo() dl = geos["analyser_nIDs"] dll = dl[list(dl.keys())[0]] ll = dll[list(dll.keys())[0]] nrois = len(ll) mask = spotdetection.get_spots_mask( submatrix,subroiroi, median_size = 5 , tval=tval, nofroi=nrois, Hough=Hough ) self.checkNspots(mask.max(), nofrois , name) mw.setSelectionMask( mask) globalMask[geo]=mask+offset*numpy.less(0,mask) mw.annotateSpots( a_ids , self.get_offset(itab) ) def GlobaldetectionCallBack(self, warn=True, thr_s="", Hough=False): print( " GlobaldetectionCallBack ", warn) if 1 or warn: print( " in Global detectionCallBack" ) ret = self.warnForGloablChange() else: ret = True if ret: offset=0 globalMask = self.mws[0].getSelectionMask().astype("i") roiroiMask = self.roiroiw.getSelectionMask().astype("i") for (name,geo,nofrois), mw in zip(self.names_geos_nofrois, self.mws[1:]): self.detectSpotsSubMask( name,geo,nofrois, mw , globalMask ,roiroiMask, offset ,thr_s=thr_s, Hough=Hough) offset += nofrois self.mws[0].setSelectionMask( globalMask ) def showToggle(self): if self.showIsData : self.showMasks() self.showIsData = not self.showIsData else: self.showDatas() self.showIsData = not self.showIsData def showMasks(self): for (name,geo,nofrois), mw in zip(self.names_geos_nofrois, self.mws[1:]): mask = mw.getSelectionMask().astype("i") mw.setImageData(mask , xScale=(0.0, 1.0), yScale=(0., 1.)) def showDatas(self): Data = self.mws[0].getImageData() for (name,geo,nofrois), mw in zip(self.names_geos_nofrois, self.mws[1:]): d = Data[geo] mask = mw.getSelectionMask().astype("i") if mask.sum(): mm = (d*mask).max() d=numpy.minimum(mm,d ) mw.setImageData(d , xScale=(0.0, 1.0), yScale=(0., 1.)) def warnForGloablChange(self): if not SKIP_WARNING: msgBox = Qt.QMessageBox () msgBox.setText("You are going to recalculate the GLOBAL mask"); msgBox.setInformativeText("This will reset all modifications to local masks. Do you want to proceed?"); msgBox.setStandardButtons(Qt.QMessageBox.Ok | Qt.QMessageBox.Cancel); msgBox.setDefaultButton(Qt.QMessageBox.Cancel); ret = msgBox.exec_(); return ret==Qt.QMessageBox.Ok else: return True def CreateSpotDetectionDockWidget(self): w = spotdetectioncontrol(self, QtCore.Qt.Widget,detectionCallBack=self.detectionCallBack, fatteningCallBack=self.fatteningCallBack, thresholdCallBack = self.localThresholdCallBack, annotateMaskCallBack=self.annotateOneMaskCallBack, relabeliseMaskCallBack=self.relabeliseOneMaskCallBack, resetMask = self.resetOneMask ) self.addDockWidget ( QtCore.Qt.LeftDockWidgetArea, w ) # w.setAllowedAreas (QtCore.Qt.AllDockWidgetAreas) w.show() def globregistration(self): print( " in globregistration ") for itab in range(1,len(self.mws)): self.registerTab(itab) def registration(self): itab = self.viewsTab.currentIndex() print( self.layouts[itab].currentText()) self.registerTab(itab) def registerTab(self, itab): if itab>0: subset_infos = self.get_geo() if "3x4" in str(self.layouts[itab].currentText() ): a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]]["3x4"] else: a_ids = subset_infos["analyser_nIDs"][ subset_infos["subnames"][itab-1]] ["Vertical"] name,geo,nofrois = self.names_geos_nofrois[itab-1] self.registerSpots( self.mws[itab], self.layouts[itab].currentText(), name, nofrois , a_ids) self.mws[itab].annotateSpots(a_ids , self.get_offset(itab)) def registerSpots( self, mw, layoutString , name, nofrois, a_ids) : mask = mw.getSelectionMask().astype("i") newmask = spotdetection.relabelise(mask,mask, nofrois) self.checkNspots(newmask.max(), nofrois , name) mask = newmask nspots = mask.max() spots = [] for i in range(1,nspots+1): zone = (mask==i) mask[zone]=i+100 m = zone.astype("f") msum=m.sum() if msum: ny,nx = m.shape px= (m.sum(axis=0)*numpy.arange(nx)).sum()/msum py= (m.sum(axis=1)*numpy.arange(ny)).sum()/msum spots.append((py,px,i)) self.checkNspots(len(spots), nofrois , name) print( type(str(layoutString))) if "3x4" in str(layoutString): positions = [ [px,py] for (py,px,i) in spots ] print( str( numpy.array(positions))) choices = match.register(numpy.array(positions) ) newspots = [] for (cx,cy),(y,x,i) in zip(choices, spots ): print( x,y, cx, cy) newspots.append((y,x,i, int((cy*4+cx)+1) ) ) else: spots.sort(key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) newspots = [] for k,(y,x,i) in enumerate( spots): newspots.append((y,x,i,k+1) ) print( " NEWSPOTS ", newspots) for (y,x,i,k) in newspots: zone = (mask==(i+100)) mask[zone] = a_ids[k-1] mw.setSelectionMask(mask) def CreateRegistrationWidget(self): w = spotregistrationcontrol(self, QtCore.Qt.Widget,globregistrationCallBack=self.globregistration, registrationCallBack=self.registration ) self.addDockWidget ( QtCore.Qt.LeftDockWidgetArea, w ) w.show() def CreateGlobalSpotDetectionDockWidget(self): w = spotdetectioncontrol(self, QtCore.Qt.Widget,detectionCallBack=self.GlobaldetectionCallBack, fatteningCallBack=self.GlobalfatteningCallBack, thresholdCallBack = self.globalThresholdCallBack, annotateMaskCallBack=self.annotateAllMasksCallBack, relabeliseMaskCallBack=self.relabeliseAllMasksCallBack, resetMask = self.resetAllMasks ) w.detectionButton.setText("GLOBAL detection") w.HoughDetection.setText("GLOBAL Hough") self.addDockWidget ( QtCore.Qt.LeftDockWidgetArea, w ) # w.setAllowedAreas (QtCore.Qt.AllDockWidgetAreas) self.globalSpotDectionWidget = w w.show() def set_roiob(self, roiob): self.roiob=roiob def LoadRemote(self): if self.roiob is None: mb = qt.QMessageBox(); mb.setText("No roi-object has been associated to the roi manager. Cannot Load."); mb.exec_(); return self.showImage(self.roiob) def create_viewWidget(self, image = None, nofrois = None, changeTagOn=False, isglobal=False , layoutsNames = None) : view = imageview(self, isglobal, layoutsNames =layoutsNames) maskW =myMaskImageWidget.MaskImageWidget(self, aspect=True, profileselection=True, maxNRois=nofrois ) maskW.setY1AxisInverted(1) maskW.setAcceptDrops(True) maskW.setDefaultColormap(2, logflag=True) maskW.changeTagOn = changeTagOn maskW.setImageData(image , xScale=(0.0, 1.0), yScale=(0., 1.)) maskW.setSelectionMask(image*0 , plot=True) view.roiContainerWidget.layout().addWidget(maskW) return view, maskW def showImage(self, image, geo_informations=None): self.image=image if geo_informations is None: geo_informations = self.get_geo() self.geo_informations = geo_informations self.image=image subset_infos = geo_informations self.viewsTab.clear() totNofrois = subset_infos["nofrois"] nofrois = totNofrois//len( subset_infos["subgeos"] ) # "analyser_nIDs": {"DETECTOR":{ "Vertical": V1234} } layoutsNames = list(list(subset_infos["analyser_nIDs"].items())[0][1].keys()) layoutsNames.sort(reverse=True) self.mws=[] self.layouts=[] view, mw = self.create_viewWidget(image = image, nofrois = totNofrois , changeTagOn = False, isglobal=True , layoutsNames = layoutsNames) self.viewsTab.addTab(view, "Global") self.mws.append(mw) self.layouts.append(view.registeringLayoutComboBox) self.names_geos_nofrois = list(zip(subset_infos["subnames"], subset_infos["subgeos"], [nofrois]*len(subset_infos["subgeos"]) )) for name,geo,nofr in self.names_geos_nofrois : view, mw = self.create_viewWidget(image = image[ geo ], nofrois = nofrois, changeTagOn = True , layoutsNames = layoutsNames ) self.viewsTab.addTab(view, name) self.mws.append(mw) self.layouts.append(view.registeringLayoutComboBox) view, roiroiw = self.create_viewWidget(image = image, nofrois = 1 , isglobal=True, changeTagOn = False , layoutsNames = layoutsNames) self.viewsTab.addTab(view, "ROI of ROIS") self.roiroiw = roiroiw self.layouts.append(view.registeringLayoutComboBox) def LoadLocal(self, sf=None, fn=None, ns=None): if not FASTDEBUG and (sf is None or not sf) : w = localfilesdialog.localfilesdialog(self.load_user_input) result = w.exec_() if not result: return sf = str(w.SpecFileName_lineEdit.text()) fn = str(w.FileName_lineEdit.text()) ns = w.ScanNumber_spinBox.value() elif sf is not None: pass else: sf = "/data/id20/inhouse/data/run2_18/run5_ihr/hydra" fn = "/data/id20/inhouse/data/run2_18/run5_ihr/edf/hydra_0000.edf" ns = 189 self.load_user_input = { "sf":sf, "fn":fn } self.user_input_signal.emit( self.load_user_input ) template = getTemplateName( fn ) s=specfile.Specfile(sf) Scan = s[ns-1] numbers = Scan.datacol("ccdno") roiob=None print( numbers) imagesum=numpy.array([0.0]) for n in numbers: image = [ edf.EdfFile(tok%n,"r").GetData(0) for tok in template ] image = numpy.concatenate(image, axis=0) # if roiob is None: # roiob = xrs_rois.roi_object() # shape = image.shape # roiob.prepare_rois( len(numbers) , "%dx%d"%shape ) # roiob.process(image) imagesum=imagesum+image self.geo_informations=None self.showImage(imagesum) self.roiob = roiob if False and FASTDEBUG: self.GlobaldetectionCallBack(warn=False) def load_maskDict_from_hdf5(self): filename = Qt.QFileDialog.getOpenFileName(None,'Open hdf5 file with rois',filter="hdf5 (*h5)\nall files ( * )" ) if isinstance(filename, tuple): filename = filename[0] if filename is None: return filename=str(filename) print( filename) if len(filename): import PyMca5.PyMcaGui.HDF5Widget as HDF5Widget storage=[None] def mySlot(ddict): name = ddict["name"] storage[0]=name # browse self.__hdf5Dialog = hdf5dialog() self.__hdf5Dialog.setWindowTitle('Select a Group containing roi_definitions by a double click') self.__hdf5Dialog.mainLayout = self.__hdf5Dialog.verticalLayout_2 fileModel = HDF5Widget.FileModel() fileView = HDF5Widget.HDF5Widget(fileModel) hdf5File = fileModel.openFile(filename) shiftsDataset = None fileView.sigHDF5WidgetSignal.connect(mySlot) self.__hdf5Dialog.mainLayout.addWidget(fileView) self.__hdf5Dialog.resize(400, 200) ret = self.__hdf5Dialog.exec_() print( ret) hdf5File.close() if ret: print( " Obtained " ) name = storage[0] print( name) file= h5py.File(filename,"r") datagroup = file[name] masks={} xrs_rois.load_rois_fromh5(datagroup,masks) file.close() self.load_masksDict(masks) def loadMaskDictFromH5(self, filename ): self.load_maskDict_from_givenhdf5andgroup(filename) def load_maskDict_from_givenhdf5andgroup(self, filename, gname=None): if gname is None: filename, gname = xrs_utilities.split_hdf5_address(filename ) file= h5py.File(filename,"r") datagroup = file[gname] masks={} xrs_rois.load_rois_fromh5(datagroup,masks) file.close() self.load_masksDict(masks) def load_image_from_hdf5(self): print( " load " ) filename = Qt.QFileDialog.getOpenFileName() if isinstance(filename, tuple): filename = filename[0] if filename is None: return print( " OK " ) filename=str(filename) print( filename) if len(filename): import PyMca5.PyMcaGui.HDF5Widget as HDF5Widget storage=[None] def mySlot(ddict): name = ddict["name"] storage[0]=name print( " MY SLOT " ) print( name) # browse self.__hdf5Dialog = hdf5dialog() self.__hdf5Dialog.setWindowTitle('Select your data set by a double click') self.__hdf5Dialog.mainLayout = self.__hdf5Dialog.verticalLayout_2 fileModel = HDF5Widget.FileModel() fileView = HDF5Widget.HDF5Widget(fileModel) hdf5File = fileModel.openFile(filename) shiftsDataset = None fileView.sigHDF5WidgetSignal.connect(mySlot) self.__hdf5Dialog.mainLayout.addWidget(fileView) self.__hdf5Dialog.resize(400, 200) # self.__hdf5Dialog.setModal(True) # self.__hdf5Dialog.show() ret = self.__hdf5Dialog.exec_() hdf5File.close() if ret: print( " Obtained " ) name = storage[0] file= h5py.File(filename,"r") image4roi = file[name][:] file.close() self.showImage(image4roi) # @ui.UILoadable class hdf5dialog(Qt.QDialog): def __init__(self, parent=None): super(hdf5dialog , self).__init__(parent) Qt.loadUi( os.path.join( installation_dir,"resources" , "hdf5dialog.ui" ), self) # Qt.QDialog.__init__(self, parent) # self.loadUi() # load the ui file def convert_redmatrix_to_matrix( masksDict,mask, offsetX=0, offsetY=0): for key, (pos,M) in six.iteritems(masksDict): num=int("".join([c for c in key if c.isdigit()])) S = M.shape inset = (slice(offsetY+pos[0] , offsetY+pos[0]+S[0] ), slice( offsetX+pos[1] , offsetX+pos[1]+S[1] ) ) M=numpy.less(0,M) mask[ inset ][M>0] = (num+1)*M[M>0] return mask def getTemplateName(name): dirname=os.path.dirname(str(name)) name=os.path.basename(str(name)) ls = len(name) fine = None inizio=None for n in list(range(ls))[::-1]: if fine is None and name[n] in "1234567890": fine = n+1 if name[n] in "1234567890": inizio=n if fine is not None and name[n] not in "1234567890": break print( name) print( inizio) print( fine) name = name[:inizio]+"%" + ("0%d"%(fine-inizio)+"d"+name[fine:]) print( name) if name[:2] in ["h_", "v_"]: return 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\xb8\x0d\x3c\x6b\xd1\xbd\x41\x53\xec\x69\x62\x59\x15\x4f\xe2\xcd\ \xa3\xa2\x2c\xbf\x79\x2f\x17\x50\xde\x00\xe7\xd3\x24\x7b\x1d\x49\ \x6e\x44\xfc\x0b\x0d\xc6\x01\xa1\xf5\x81\x45\x51\x1f\xe4\x22\x4a\ \x05\x7c\x01\x8e\x4e\x49\xfe\x18\x2f\x58\xb4\x2e\xcc\x77\xbf\x71\ \xc7\x89\x06\x71\xd6\xbf\x04\xd2\xbf\x80\x01\xce\x02\x7d\x23\xba\ \x80\xee\xce\x84\xee\xb1\xb1\x62\xac\x75\xe1\x55\x59\x15\x06\xb8\ \x0b\x3c\x07\x72\xe0\x3e\x70\xb5\xac\x8a\x93\x22\x3a\xc6\x60\x76\ \xfd\x35\x1c\x26\xfd\xce\x94\xd1\x25\xb4\x2e\x04\x37\xe3\xc3\xe4\ \xb7\x13\x55\x33\xd3\x99\x81\xb6\xce\xb8\x18\xb4\x31\xd0\xfa\xa0\ \x01\xc4\x37\x22\xbe\x11\xe9\xc6\xa6\xea\xfc\x1f\xdb\x37\xde\x59\ \x25\x68\xce\x04\x00\x00\x00\x00\x49\x45\x4e\x44\xae\x42\x60\x82\ \ " def launch4MatPlotLib(layout=None, im4roi=None, manageQApp = True): if manageQApp: dostop=0 app = Qt.QApplication.instance() if app is None: app=Qt.QApplication([]) dostop = 1 w4r = mainwindow(layout=layout) w4r.showImage( im4roi ) w4r.show() if manageQApp: app.exec_() if w4r.isOK: return_obj = w4r.getRoiObj() else: return_obj = None if dostop: app.quit() return return_obj return w4r if __name__=="__main__": app = Qt.QApplication.instance() print( " APP IS " , app) if app is None: app=Qt.QApplication([]) w = mainwindow() w.show() app.exec_() xrstools-0.15.0+git20210910+c147919d/XRStools/roifinder_and_gui.py000066400000000000000000005465251412732462000237510ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import matplotlib.pyplot as plt import shelve from matplotlib.path import Path from . import xrs_utilities, xrs_rois, xrs_scans, roiSelectionWidget, math_functions from matplotlib.widgets import Cursor, Button from scipy.ndimage import measurements from scipy import signal, stats import copy import matplotlib # def findroisColumns(scans,scannumbers,roi_obj, whichroi,logscaling=False): # """ # Constructs a waterfall plot from the columns in a scan, i.e. energy vs. pixel number along the ROI # scannumbers = scannumber or list of scannumbers from which to construct the plot # whichroi = integer (starting from 0) from which ROI to use for constructing the plot # """ # if not isinstance(scannumbers,list): # scannums = [] # scannums.append(scannumbers) # else: # scannums = scannumbers # if not roi_obj.indices: # 'Please define some zoom ROIs first.' # return # if not roi_obj.kind == 'zoom': # 'Currently this feature only works for ROIs of type \'zoom\'.' # return # xinds = np.unique(roi_obj.x_indices[whichroi]) # yinds = np.unique(roi_obj.y_indices[whichroi]) # scanname = 'Scan%03d' % scannums[0] # edfmats = np.zeros_like(scans[scanname].edfmats) # energy = scans[scanname].energy # waterfall = np.zeros((len(yinds),len(energy))) # for scannum in scannums: # scanname = 'Scan%03d' % scannum # scanedf = scans[scanname].edfmats # scanmonitor = scans[scanname].monitor # for ii in range(len(energy)): # edfmats[ii,:,:] += scanedf[ii,:,:]/scanmonitor[ii] # for ii in range(len(energy)): # for jj in range(len(yinds)): # waterfall[jj,ii] = np.sum(edfmats[ii,xinds,yinds[jj]]) # plt.figure() # for ii in range(len(yinds)): # plt.plot(waterfall[ii,:]) # fig = plt.figure() # ax = fig.add_subplot(111) # if logscaling: # ax.imshow(np.log(np.transpose(waterfall)), interpolation='nearest') # else: # ax.imshow(np.transpose(waterfall), interpolation='nearest') # ax.set_aspect('auto') # plt.xlabel('ROI pixel') # plt.ylabel('energy point') # plt.show() # def get_auto_rois_eachdet(scans,DET_PIXEL_NUM ,scannumbers,kernel_size=5,threshold=100.0,logscaling=True,colormap='jet',interpolation='bilinear'): # """ # Define ROIs automatically using median filtering and a variable threshold for each detector # separately. # scannumbers = either single scannumber or list of scannumbers # kernel_size = used kernel size for the median filter (must be an odd integer) # logscaling = set to 'True' if images is to be shown on log-scale (default is True) # colormap = string to define the colormap which is to be used for display (anything # supported by matplotlib, 'Blues' by default) # interpolation = interpolation scheme to be used for displaying the image (anything # supported by matplotlib, 'nearest' by default) # """ # # check that kernel_size is odd # if not kernel_size % 2 == 1: # print( 'The \'kernal_size\' must be an odd number.' ) # return # # create a big image # image = xrs_scans.create_sum_image(scans,scannumbers) # # break down the image into 256x256 pixel images # det_images, offsets = xrs_rois.break_down_det_image(image,DET_PIXEL_NUM) # # create one roi_object per sub-image # temp_objs = [] # for ii in range(det_images.shape[0]): # temp = roi_finder() # temp.get_auto_rois(det_images[ii,:,:],kernel_size=kernel_size,threshold=threshold,logscaling=logscaling,colormap=colormap,interpolation=interpolation) # temp_objs.append(temp) # # merge all roi_objects into one # merged_obj = xrs_rois.merge_roi_objects_by_matrix(temp_objs,image.shape,offsets,DET_PIXEL_NUM) # roi_obj = merged_obj # return roi_obj # def get_polygon_rois_eachdet(scans,DET_PIXEL_NUM, scannumbers,logscaling=True,colormap='Blues',interpolation='nearest'): # """ # Define a polygon shaped ROI from an image constructed from # the sum of all edf-files in 'scannumbers' # image_shape = tuple with shape of the current image (i.e. (256,256)) # scannumbers = either single scannumber or list of scannumbers # logscaling = set to 'True' if images is to be shown on log-scale (default is True) # colormap = string to define the colormap which is to be used for display (anything # supported by matplotlib, 'Blues' by default) # interpolation = interpolation scheme to be used for displaying the image (anything # supported by matplotlib, 'nearest' by default) # """ # # create a big image # image = xrs_scans.create_sum_image(scans,scannumbers) # # break down the image into 256x256 pixel images # det_images, offsets = xrs_rois.break_down_det_image(image,DET_PIXEL_NUM) # # create one roi_object per sub-image # temp_objs = [] # for modind in range(det_images.shape[0]): # temp = roi_finder() # temp.get_polygon_rois( det_images[modind,:,:],modind,logscaling=logscaling,colormap=colormap,interpolation=interpolation) # temp_objs.append(temp) # # merge all roi_objects into one # merged_obj = xrs_rois.merge_roi_objects_by_matrix(temp_objs,image.shape,offsets,DET_PIXEL_NUM) # roi_obj = merged_obj # return roi_obj # def get_zoom_rois(scans,scannumbers,logscaling=True,colormap='Blues',interpolation='nearest'): # # create a big image # image = xrs_scans.create_sum_image(scans,scannumbers) # # create one roi_object per sub-image # roi_obj = roi_finder() # roi_obj.get_zoom_rois(image,logscaling=logscaling,colormap=colormap,interpolation=interpolation) # return roi_obj.roi_obj class roi_finder: def __init__(self): self.roi_obj = xrs_rois.roi_object() # empty roi object def appendROIobject(self,roi_object): self.roi_obj.append(roi_object) def deleterois(self): """ Clear the existing ROIs by creating a fresh roi_object. """ self.roi_obj = xrs_rois.roi_object() def roi_widget(self, input_image, layout="2X3-12", shape = [512,768] ): """ **roi_widget** Use the ROI widget to define ROIs. input_image = 2D array to define the ROIs from layout = detector layout shape = image shape/detector shape """ #%matplotlib qt matplotlib.use('Qt4Agg') w4r = roiSelectionWidget.mainwindow(layout=layout) w4r.showImage( input_image ) w4r.show() self.roi_obj.load_rois_fromMasksDict( w4r.getMasksDict(), newshape = shape ) def get_linear_rois( self, input_image, logscaling=True, height=5, colormap='Blues', interpolation='nearest' ): """ Define ROIs by clicking two points on a 2D image. number_of_rois = integer defining how many ROIs should be determined input_object = 2D array, scan_object, or dictionary of scans to define the ROIs from logscaling = boolean, to determine wether the image is shown on a log-scale (default = True) height = integer defining the height (in pixels) of the ROIs """ # make sure the matplotlib interactive mode is off plt.ioff() # clear all existing rois self.deleterois() # check that the input is a 2d matrix if not len(input_image.shape) == 2: print( 'Please provide a 2D numpy array as input!' ) return # save input image for later use self.roi_obj.input_image = copy.deepcopy(input_image) # calculate the logarithm if 'logscaling' == True if logscaling: # set all zeros to ones: input_image[input_image[:,:] == 0.0] = 1.0 input_image = np.log(np.abs(input_image)) # prepare a figure fig, ax = plt.subplots() plt.subplots_adjust(bottom=0.2) cursor = Cursor(ax, useblit=True, color='red', linewidth=1 ) # Initialize suptitle, which will be updated titlestring = 'Start by clicking the \'Next\'-button.' titleInst=plt.suptitle(titlestring) # generate an image to be displayed figure_obj = plt.imshow(input_image,interpolation=interpolation) # set the colormap for the image figure_obj.set_cmap(colormap) rois = [] class Index: ind = 0 def next(self, event): titlestring = 'Click two points for ROI Nr. %02d, \'Finish\' to end.' %(self.ind+1) titleInst.set_text(titlestring) # Update title # Try needed, as FINISH button closes the figure and ginput() generates _tkinter.TclError try: one_roi = define_lin_roi(height,input_image.shape) for index in one_roi: input_image[index[0],index[1]] += 1.0e6 figure_obj.set_data(input_image) plt.hold(True) plt.draw() rois.append(one_roi) self.ind += 1 # Begin defining the next ROI right after current self.next(self) except KeyboardInterrupt: # to prevent "dead" figures plt.close() pass except: pass def prev(self, event): self.ind -= 1 try: titlestring = 'Click the \'Next\' button again to continue.' titleInst.set_text(titlestring) # Update title # print titlestring for index in rois[-1]: input_image[index[0],index[1]] -= 1.0e6 figure_obj.set_data(input_image) plt.hold(True) rois.pop() except: pass def close(self, event): plt.hold(False) plt.ion() plt.close('all') def dmy(self, event): pass # adding a dummy function for the dummy button callback = Index() axprev = plt.axes([0.5, 0.05, 0.1, 0.075]) axnext = plt.axes([0.61, 0.05, 0.1, 0.075]) axclose = plt.axes([0.72, 0.05, 0.1, 0.075]) axdmy = plt.axes([0.001, 0.001, 0.001, 0.001]) # for some reason the first botton disappears when clicked bdmy = Button(axdmy,'') # which is why I am including a dummy button here bdmy.on_clicked(callback.dmy) # this way, the real buttons work bnext = Button(axnext, 'Next') bnext.on_clicked(callback.next) bprev = Button(axprev, 'Back') bprev.on_clicked(callback.prev) bclose = Button(axclose, 'Finish') bclose.on_clicked(callback.close) plt.show() # assign the defined rois to the roi_object class self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(rois,input_image.shape) self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) self.roi_obj.indices = rois self.roi_obj.kind = 'linear' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(rois) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(rois) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) #plt.draw() def get_zoom_rois( self, input_image, logscaling=True, colormap='Blues', interpolation='nearest'): """ Define ROIs by clicking two points on a 2D image. number_of_rois = integer defining how many ROIs should be determined input_object = 2D array, scan_object, or dictionary of scans to define the ROIs from logscaling = boolean, to determine wether the image is shown on a log-scale (default = True) height = integer defining the height (in pixels) of the ROIs """ # make sure the matplotlib interactive mode is off plt.ioff() # clear all existing rois self.deleterois() # check that the input is a 2d matrix if not len(input_image.shape) == 2: print( 'please provide a 2D numpy array as input!' ) return # save input image for later use self.roi_obj.input_image = copy.deepcopy(input_image) # calculate the logarithm if 'logscaling' == True if logscaling: # set all zeros to ones: input_image[input_image[:,:] == 0.0] = 1.0 input_image = np.log(np.abs(input_image)) # prepare a figure fig, ax = plt.subplots() plt.subplots_adjust(bottom=0.2) cursor = Cursor(ax, useblit=True, color='red', linewidth=1 ) # Initialize suptitle, which will be updated titlestring = 'Start by clicking the \'Next\' button.' titleInst=plt.suptitle(titlestring) # generate an image to be displayed figure_obj = plt.imshow(input_image,interpolation=interpolation) # activate the zoom function already thismanager = plt.get_current_fig_manager() thismanager.toolbar.zoom() # set the colormap for the image figure_obj.set_cmap(colormap) #plt.colorbar() # initialize a matrix for the rois (will be filled with areas of ones, twos, etc rois = [] # print info to start: print( 'Start by clicking the \'Next\' button.' ) class Index: ind = 0 initstage = True next_clicked = False def next( self, event ): # for some reason, the first time this is used, it doesn't work, so here is one dummy round if self.initstage: #self.ind += 1 self.initstage = False plt.sca(ax) one_roi = define_zoom_roi(input_image,verbose=True) #for index in one_roi: # input_image[index[0],index[1]] *= 1.0 # reset the matrix to be displayed figure_obj.set_data(input_image) # reset the zoom plt.xlim(0.0,input_image.shape[1]) plt.ylim(input_image.shape[0],0.0) plt.draw() titlestring = 'Zoom in to define ROI Nr. %02d, hit \'Next\' to continue.' % (self.ind + 1) titleInst.set_text(titlestring) # Update title else: self.ind += 1 plt.sca(ax) one_roi = define_zoom_roi(input_image) for index in one_roi: input_image[index[0],index[1]] += 1.0e10 # reset the matrix to be displayed figure_obj.set_data(input_image) # reset the zoom plt.xlim(0.0,input_image.shape[1]) plt.ylim(input_image.shape[0],0.0) plt.draw() rois.append(one_roi) titlestring = 'Zoom in to define ROI Nr. %02d, hit \'Next\' to continue, \'Finish\' to end.' % (self.ind + 1) titleInst.set_text(titlestring) # Update title def prev(self, event): self.ind -= 1 titlestring = 'Undoing ROI Nr. %02d. Zoom again, click the \'Next\' button to continue.' % (self.ind + 1) titleInst.set_text(titlestring) # Update title #thedata[roimatrix == self.ind+1] -= 1.0e6 #roi_matrix[roimatrix == self.ind+1] = 0.0 for index in rois[-1]: input_image[index[0],index[1]] -= 1.0e10 figure_obj.set_data(input_image) plt.hold(True) rois.pop() def close(self, event): plt.sca(ax) one_roi = define_zoom_roi(input_image) for index in one_roi: input_image[index[0],index[1]] += 1.0e10 # reset the matrix to be displayed figure_obj.set_data(input_image) # reset the zoom plt.xlim(0.0,input_image.shape[1]) plt.ylim(input_image.shape[0],0.0) plt.draw() rois.append(one_roi) titlestring = 'Last ROI is Nr. %02d.' % (self.ind + 1) titleInst.set_text(titlestring) # Update title plt.close() def dmy(self, event): pass # adding a dummy function for the dummy button callback = Index() axprev = plt.axes([0.5, 0.05, 0.1, 0.075]) axnext = plt.axes([0.61, 0.05, 0.1, 0.075]) axclose = plt.axes([0.72, 0.05, 0.1, 0.075]) axdmy = plt.axes([0.001, 0.001, 0.001, 0.001]) # for some reason the first botton disappears when clicked bdmy = Button(axdmy,'') # which is why I am including a dummy button here bdmy.on_clicked(callback.dmy) # this way, the real buttons work bnext = Button(axnext, 'Next') bnext.on_clicked(callback.next) bprev = Button(axprev, 'Back') bprev.on_clicked(callback.prev) bclose = Button(axclose, 'Finish') bclose.on_clicked(callback.close) plt.show() # assign the defined rois to the roi_object class self.roi_obj.roi_matrix = (xrs_rois.convert_inds_to_matrix(rois,input_image.shape)) self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) self.roi_obj.indices = rois self.roi_obj.kind = 'zoom' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(rois) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(rois) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) def get_auto_rois(self,input_image,kernel_size=5,threshold=100.0,logscaling=True,colormap='Blues',interpolation='bilinear'): """ Define ROIs by choosing a threshold using a slider bar under the figure. In this function, the entire detector is shown. input_image = 2D numpy array with the image to be displayed kernal_size = integer defining the median filter window (has to be odd) theshold = initial number defining the upper end value for the slider bar (amax(input_image)/threshold defines this number), can be within GUI logscaling = boolean, if True (default) the logarithm of input_image is displayed colormap = matplotlib color scheme used in the display interpolation = matplotlib interpolation scheme used for the display """ # make sure the matplotlib interactive mode is off plt.ioff() # clear all existing rois self.deleterois() # clear existing figure # plt.clf() # save input image for later use self.roi_obj.input_image = copy.deepcopy(input_image) # calculate the logarithm if 'logscaling' == True if logscaling: # set all zeros to ones: input_image[input_image[:,:] == 0.0] = 1.0 input_image = np.log(np.abs(input_image)) ax = plt.subplot(111) plt.subplots_adjust(left=0.05, bottom=0.2) # print out some instructions plt.suptitle('Use the slider bar to select ROIs, close the plotting window when satisfied.') # initial threshold value thres0 = 0.0 # create a figure object figure_obj = plt.imshow(input_image,interpolation=interpolation) figure_obj.set_cmap(colormap) # prepare the slider bar thresxcolor = 'lightgoldenrodyellow' thresxamp = plt.axes([0.2, 0.10, 0.55, 0.03], axisbg=thresxcolor) maxthreshold=np.floor(np.amax(input_image)) # maximum of slider sthres = plt.Slider(thresxamp, 'Threshold', 0.0, maxthreshold, valinit=thres0) textBox=plt.figtext(0.50, 0.065, 'Multiplier: 1.0',verticalalignment='center') # define what happens when the slider is touched def update(val): # parse a threshold from the slider thres = sthres.val*thresMultiplier.factor # median filter the image newmatrix = signal.medfilt2d(input_image, kernel_size=kernel_size) # set pixels below the threshold to zero belowthres_indices = newmatrix < thres newmatrix[belowthres_indices] = 0 # identify connected regions (this is already the roi_matrix) self.roi_obj.roi_matrix,numfoundrois = measurements.label(newmatrix) print( str(numfoundrois) + ' ROIs found!' ) figure_obj.set_data(newmatrix) plt.draw() # Buttons for changing multiplier for the value of slider class thresMultiplierClass: factor = 1.0; def __new__(cls): return self.factor def increase(self,event): self.factor *=2.0 textBox.set_text('Multiplier: ' + str(self.factor)) return self.factor def decrease(self,event): self.factor /=2.0 textBox.set_text('Multiplier: ' + str(self.factor)) return self.factor # call the update function when the slider is touched sthres.on_changed(update) thresMultiplier = thresMultiplierClass() axincrease = plt.axes([0.8, 0.05, 0.05, 0.03]) axdecrease = plt.axes([0.7, 0.05, 0.05, 0.03]) bnincrease = Button(axincrease, 'x 2') bndecrease = Button(axdecrease, '/ 2') bnincrease.on_clicked(thresMultiplier.increase) # First change threshold bnincrease.on_clicked(update) # Then update image bndecrease.on_clicked(thresMultiplier.decrease) bndecrease.on_clicked(update) # ADDITION ENDS plt.show() # assign the defined rois to the roi_object class self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) self.roi_obj.kind = 'auto' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) def get_auto_rois_eachdet(self, input_image, kernel_size=5, threshold=100.0, logscaling=True, colormap='Blues', interpolation='bilinear'): """ Define ROIs automatically using median filtering and a variable threshold for each detector separately. scannumbers = either single scannumber or list of scannumbers kernel_size = used kernel size for the median filter (must be an odd integer) logscaling = set to 'True' if images is to be shown on log-scale (default is True) colormap = string to define the colormap which is to be used for display (anything supported by matplotlib, 'Blues' by default) interpolation = interpolation scheme to be used for displaying the image (anything supported by matplotlib, 'nearest' by default) """ # check that kernel_size is odd if not kernel_size % 2 == 1: print( 'The \'kernal_size\' must be an odd number.' ) return self.roi_obj.input_image = copy.deepcopy(input_image) # big many pixels in one detector image DET_PIXEL_NUM = input_image.shape[0]/2 # break down the image into 256x256 pixel images det_images, offsets = xrs_rois.break_down_det_image(input_image,DET_PIXEL_NUM) # create one roi_object per sub-image temp_objs = [] for ii in range(det_images.shape[0]): temp = roi_finder() temp.get_auto_rois(det_images[ii,:,:], kernel_size=kernel_size, threshold=threshold, \ logscaling=logscaling, colormap=colormap, interpolation=interpolation) temp_objs.append(temp) # merge all roi_objects into one merged_obj = xrs_rois.merge_roi_objects_by_matrix(temp_objs,input_image.shape,offsets,DET_PIXEL_NUM) # assign the defined rois to the roi_object class self.roi_obj.roi_matrix = merged_obj.roi_matrix self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) self.roi_obj.kind = 'auto' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) def get_polygon_rois(self,input_image,modind=-1,logscaling=True,colormap='Blues',interpolation='nearest'): """ Define ROIs by clicking arbitrary number of points on a 2D image: LEFT CLICK to define the corner points of polygon, MIDDLE CLICK to finish current ROI and move to the next ROI, RIGHT CLICK to cancel the previous point of polygon input_object = 2D array, scan_object, or dictionary of scans to define the ROIs from modind = integer to identify module, if -1 (default), no module info will be in title (the case of one big image) logscaling = boolean, to determine wether the image is shown on a log-scale (default = True) """ # make sure the matplotlib interactive mode is off plt.ioff() # clear all existing rois self.deleterois() # check that the input is a 2d matrix if not len(input_image.shape) == 2: print( 'Please provide a 2D numpy array as input!' ) return # save input image for later use self.roi_obj.input_image = copy.deepcopy(input_image) # calculate the logarithm if 'logscaling' == True if logscaling: # set all zeros to ones: input_image[input_image[:,:] == 0.0] = 1.0 input_image = np.log(np.abs(input_image)) # prepare a figure fig, ax = plt.subplots() plt.subplots_adjust(bottom=0.2) moduleNames='VD:','HR:','VU:','HL:','VB:','HB:','' # for plot title # Initialize suptitle, which will be updated titlestring = '' titleInst=plt.suptitle(titlestring) cursor = Cursor(ax, useblit=True, color='red', linewidth=1 ) # generate an image to be displayed figure_obj = plt.imshow(input_image,interpolation=interpolation) # set the colormap for the image figure_obj.set_cmap(colormap) rois = [] class Index: ind = 1 def next(self, event): titlestring = '%s next ROI is Nr. %02d:\n Left button to new points, middle to finish ROI. Hit \'Finish\' to end with this image.' % (moduleNames[modind], self.ind) titleInst.set_text(titlestring) # Update title # Try needed, as FINISH button closes the figure and ginput() generates _tkinter.TclError try: one_roi = define_polygon_roi(input_image.shape) for index in one_roi: input_image[int(index[0]),int(index[1])] += 1.0e6 figure_obj.set_data(input_image) plt.hold(True) plt.draw() rois.append(one_roi) self.ind += 1 # Begin defining the next ROI right after current self.next(self) except KeyboardInterrupt: # to prevent "dead" figures plt.close() pass except: pass def prev(self, event): self.ind -= 1 for index in rois[-1]: input_image[index[0],index[1]] -= 1.0e6 figure_obj.set_data(input_image) plt.hold(True) plt.draw() rois.pop() self.next(self) def close(self, event): plt.close() def dmy(self, event): pass # adding a dummy function for the dummy button callback = Index() axprev = plt.axes([0.5, 0.05, 0.1, 0.075]) axnext = plt.axes([0.61, 0.05, 0.1, 0.075]) axclose = plt.axes([0.72, 0.05, 0.1, 0.075]) axdmy = plt.axes([0.001, 0.001, 0.001, 0.001]) # for some reason the first botton disappears when clicked bdmy = Button(axdmy,'') # which is why I am including a dummy button here bdmy.on_clicked(callback.dmy) # this way, the real buttons work bnext = Button(axnext, 'Next') bnext.on_clicked(callback.next) bprev = Button(axprev, 'Back') bprev.on_clicked(callback.prev) bclose = Button(axclose, 'Finish') bclose.on_clicked(callback.close) # START: initiate NEXT button press callback.next(self) # assign the defined rois to the roi_object class self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(rois,input_image.shape) self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) self.roi_obj.indices = rois self.roi_obj.kind = 'polygon' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(rois) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(rois) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) def show_rois( self, interpolation='nearest', cmap='Blues' ): """ **show_rois** Creates a figure with the defined ROIs as numbered boxes on it. Args: * interpolation (str) : Interpolation scheme used in the plot. * colormap (str) : Colormap used in the plot. """ self.roi_obj.show( cmap=cmap,interpolation=interpolation ) def import_simo_style_rois( self, roiList, detImageShape=(512,768) ): """ **import_simo_style_rois** Converts Simo-style ROIs to the conventions used here. Arguments: * roiList (list): List of tuples that have [(xmin, xmax, ymin, ymax), (xmin, xmax, ymin, ymax), ...]. * detImageShape (tuple): Shape of the detector image (for convertion to roiMatrix) """ indices = [] for roi in roiList: inds = [] for ii in range(roi[0],roi[1]): for jj in range(roi[2],roi[3]): inds.append((ii,jj)) indices.append(inds) # assign the defined rois to the roi_object class if detImageShape: self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(indices,detImageShape) self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) self.roi_obj.indices = indices self.roi_obj.kind = 'simoStyle' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) def refine_pw_rois(self, roi_obj, pw_data, n_components=2, method='nnma', cov_thresh=-1): """**refine_pw_rois** Use decomposition of pixelwise data for each ROI to find which of the pixels holds data from the sample and which one only has background. Args: * roi_obj (roi_object): ROI object to be refined. * pw_data (list): List containing one 2D numpy array per ROI holding pixel-wise signals. * n_components (int): Number of components in the decomposition. * method (string): Keyword describing which decomposition to be used ('pca', 'ica', 'nnma'). """ # check if available method is used avail_methods = ['pca','ica','nnma'] if not method in avail_methods: print('Please use one of the following methods: ' + str(avail_methods) + '!') return # check if scikit learn is available try: from sklearn.decomposition import FastICA, PCA, ProjectedGradientNMF except ImportError: raise ImportError('Please install the scikit-learn package to use this feature.') return counter = 0 new_rois = {} for data, key in zip(pw_data, sorted(roi_obj.red_rois , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) )): # go through each matrix (one per ROI) # decompose data, choose method if method == 'nnma': # non negative matrix factorisation nnm = ProjectedGradientNMF(n_components=n_components) N = nnm.fit_transform(data) elif method == 'pca': # principal component analysis pca = PCA(n_components=n_components) N = pca.fit_transform(data) elif method == 'ica': # independent component analysis ica = FastICA(n_components=n_components) N = ica.fit_transform(data) else: print('No method: \'' + method + '\' available. Will stop here.' ) # let user decide which component belongs to the data: user_choise = 0 plt.cla() title_txt = 'Click component that resembles the sample spectrum for ROI %02d'%(counter+1) + '.' plt.title(title_txt) legendstr = [] for ii in range(n_components): plt.plot(N[:,ii]) legendstr.append('Component No. %01d' %ii) plt.legend(legendstr) plt.xlabel('points along scan') plt.ylabel('intensity [arb. units]') user_input = np.array(plt.ginput(1,timeout=-1)[0]) # which curve was chosen nearest_points = [(np.abs(N[:,ii]-user_input[1])).argmin() for ii in range(n_components)] user_choice = (np.abs(nearest_points-user_input[0])).argmin() # find covariance for all pixels with user choice covariance = np.array([]) for ii in range(len(data[0,:])): covariance = np.append(covariance, np.cov(data[:,ii],N[:,user_choice])[0,0]) # plot covariance, let user choose the the cutoff in y direction plt.cla() title_txt = 'Click to define a y-threshold for ROI %02d'%(counter+1) + '.' plt.title(title_txt) plt.plot(covariance,'-o') plt.xlabel('pixels in ROI') plt.ylabel('covariance [arb. units]') if cov_thresh < 0: user_cutoff = np.array(plt.ginput(1,timeout=-1)[0]) elif cov_thresh>0 and isinstance(cov_thresh,int): if len(covariance) < cov_thresh: print('ROI has fewer pixels than cov_thresh, will break here.') return else: user_cutoff = np.array([0.0, np.sort(covariance)[-cov_thresh]]) else: print('Please provide cov_thresh as positive integer!') # find the ROI indices above the cutoff, reassign ROI indices inds = covariance >= user_cutoff[1] #print('inds is ', inds) ravel_roi = roi_obj.red_rois[key][1].ravel() ravel_roi[~inds] = 0.0 roi_obj.red_rois[key][1] = np.reshape(ravel_roi, (roi_obj.red_rois[key][1].shape)) # end loop counter += 1 # reassign ROI object self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) def refine_rois_MF(self, hydra_obj, scan_numbers, n_components=2, method='nnma', cov_thresh=-1): """**refine_rois_MF** Use decomposition of pixelwise data for each ROI to find which of the pixels holds data from the sample and which one only has background. Args: * hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used for the refinement of the ROIs. * scan_numbers (int or list): Scan numbers of scans to be used in the refinement. * n_components (int): Number of components in the decomposition. * method (string): Keyword describing which decomposition to be used ('pca', 'ica', 'nnma'). """ # check if available method is used if not method in ['pca','ica','nnma']: print('Please use one of the following methods: ' + str(avail_methods) + '!') return # check if scikit learn is available try: from sklearn.decomposition import FastICA, PCA, ProjectedGradientNMF except ImportError: from sklearn.decomposition import FastICA, PCA from sklearn.decomposition import NMF as ProjectedGradientNMF except: raise ImportError('Please install the scikit-learn package to use this feature.') return # make scan_numbers itarable if isinstance(scan_numbers,list): scannums = scan_numbers elif isinstance(scan_numbers,int): scannums = [scan_numbers] # get EDF-files and pw_data hydra_obj.load_scan(scannums, direct=False) pw_data = hydra_obj.get_pw_matrices( scannums, method='pixel' ) counter = 0 new_rois = {} for data, key in zip(pw_data, sorted(self.roi_obj.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) )): # go through each matrix (one per ROI) # decompose data, choose method if method == 'nnma': # non negative matrix factorisation nnm = ProjectedGradientNMF(n_components=n_components) N = nnm.fit_transform(data) elif method == 'pca': # principal component analysis pca = PCA(n_components=n_components) N = pca.fit_transform(data) elif method == 'ica': # independent component analysis ica = FastICA(n_components=n_components) N = ica.fit_transform(data) else: print('No method: \'' + method + '\' available. Will stop here.' ) # let user decide which component belongs to the data: user_choise = 0 plt.cla() title_txt = 'Click component that resembles the sample spectrum for ROI %02d'%(counter+1) + '.' plt.title(title_txt) legendstr = [] for ii in range(n_components): plt.plot(N[:,ii]) legendstr.append('Component No. %01d' %ii) plt.legend(legendstr) plt.xlabel('points along scan') plt.ylabel('intensity [arb. units]') user_input = np.array(plt.ginput(1,timeout=-1)[0]) # which curve was chosen nearest_points = [(np.abs(N[:,ii]-user_input[1])).argmin() for ii in range(n_components)] user_choice = (np.abs(nearest_points-user_input[0])).argmin() # find covariance for all pixels with user choice covariance = np.array([]) for ii in range(len(data[0,:])): covariance = np.append(covariance, np.cov(data[:,ii],N[:,user_choice])[0,0]) # plot covariance, let user choose the the cutoff in y direction plt.cla() title_txt = 'Click to define a y-threshold for ROI %02d'%(counter+1) + '.' plt.title(title_txt) plt.plot(covariance,'-o') plt.xlabel('pixels in ROI') plt.ylabel('covariance [arb. units]') if cov_thresh < 0: user_cutoff = np.array(plt.ginput(1,timeout=-1)[0]) elif cov_thresh>0 and isinstance(cov_thresh,int): if len(covariance) < cov_thresh: print('ROI has fewer pixels than cov_thresh, will break here.') return else: user_cutoff = np.array([0.0, np.sort(covariance)[-cov_thresh]]) else: print('Please provide cov_thresh as positive integer!') # find the ROI indices above the cutoff, reassign ROI indices inds = covariance >= user_cutoff[1] #print('inds is ', inds) ravel_roi = self.roi_obj.red_rois[key][1].ravel() ravel_roi[~inds] = 0.0 self.roi_obj.red_rois[key][1] = np.reshape(ravel_roi, (self.roi_obj.red_rois[key][1].shape)) # end loop counter += 1 # reassign ROI object self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(self.roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # compact the new red_rois self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix, labelformat= 'ROI%02d') def find_pw_rois(self,roi_obj,pw_data,save_dataset=False): """ **find_pw_rois** Allows for manual refinement of ROIs by plotting the spectra pixel-wise. Loops through the spectra pixel-by-pixel and ROI by ROI, click above the black line to keep the pixel plotted, click below the black line to discard the pixel. Args: * roi_obj (roi_object): ROI object from the XRStools.xrs_rois module with roughly defined ROIs. * pw_data (np.array): List containing one 2D numpy array per ROI holding pixel-wise signals. """ counter = 0 new_indices = [] pixelCounter = 0 for data in pw_data: # go through each ROI refined_indices = [] for ii in range(len(data[0,:])): user_choice = 1 plt.cla() title_txt = 'Click above to keep/below black line to discard pixel for ROI %02d'%(counter+1) + '.' plt.title(title_txt) plt.plot(data[:,ii],'b-') axes = plt.gca() offset = np.mean(axes.get_ylim()) plt.plot(np.zeros(len(data[:,ii])) + offset,'k-') plt.xlabel('points along scan') plt.ylabel('intensity [arb. units]') plt.legend(['Pixel No. %02d'%ii]) # let user click a point on figure user_input = np.array(plt.ginput(1,timeout=-1)[0]) if user_input[1] >= offset: refined_indices.append(roi_obj.indices[counter][ii]) elif user_input[1] < offset: user_choice = 0 else: print('Something fishy happened!') # save spectra and decisions for NN learning if save_dataset: s = shelve.open(save_dataset) try: thekey = 'PixelNo' + str(pixelCounter) s[thekey] = { 'spectrum': data[:,ii], 'decision': user_choice } finally: s.close() pixelCounter += 1 # reorganize pixels inside ROI new_indices.append(refined_indices) counter += 1 # reassign ROI object self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(new_indices,self.roi_obj.input_image.shape) self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) self.roi_obj.indices = new_indices self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(new_indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(new_indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) def refine_rois_PW_old(self, hydra_obj, scan_numbers, save_dataset=False): """ **refine_rois_PW** Allows for manual refinement of ROIs by plotting the spectra pixel-wise. Loops through the spectra pixel-by-pixel and ROI by ROI, click above the black line to keep the pixel plotted, click below the black line to discard the pixel. Args: * hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used for the refinement of the ROIs. * scan_numbers (int or list): Scan numbers of scans to be used in the refinement. """ # make scan_numbers itarable if isinstance(scan_numbers,list): scannums = scan_numbers elif isinstance(scan_numbers,int): scannums = [scan_numbers] # get EDF-files and pw_data hydra_obj.load_scan(scannums, direct=False) pw_data = hydra_obj.get_pw_matrices( scannums, method='pixel' ) counter = 0 new_indices = [] pixelCounter = 0 for data in pw_data: # go through each ROI refined_indices = [] for ii in range(len(data[0,:])): user_choice = 1 plt.cla() title_txt = 'Click above to keep/below black line to discard pixel for ROI %02d'%(counter+1) + '.' plt.title(title_txt) plt.plot(data[:,ii],'b-') axes = plt.gca() offset = np.mean(axes.get_ylim()) plt.plot(np.zeros(len(data[:,ii])) + offset,'k-') plt.xlabel('points along scan') plt.ylabel('intensity [arb. units]') plt.legend(['Pixel No. %02d'%ii]) # let user click a point on figure user_input = np.array(plt.ginput(1,timeout=-1)[0]) if user_input[1] >= offset: refined_indices.append(self.roi_obj.indices[counter][ii]) elif user_input[1] < offset: user_choice = 0 else: print('Something fishy happened!') # save spectra and decisions for NN learning if save_dataset: s = shelve.open(save_dataset) try: thekey = 'PixelNo' + str(pixelCounter) s[thekey] = { 'spectrum': data[:,ii], 'decision': user_choice } finally: s.close() pixelCounter += 1 # reorganize pixels inside ROI new_indices.append(refined_indices) counter += 1 # reassign ROI object self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(new_indices,self.roi_obj.input_image.shape) self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) self.roi_obj.indices = new_indices self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(new_indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(new_indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) def refine_rois_PW(self, hydra_obj, scan_numbers,save_dataset=None): """ **refine_rois_PW** Allows for manual refinement of ROIs by plotting the spectra column-wise. Loops through the spectra column-by-column and ROI by ROI, click above the black line to keep the column plotted, click below the black line to discard the column of pixels. Args: * hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used for the refinement of the ROIs. * scan_numbers (int or list): Scan numbers of scans to be used in the refinement. """ # make scan_numbers itarable if isinstance(scan_numbers,list): scannums = scan_numbers elif isinstance(scan_numbers,int): scannums = [scan_numbers] # get EDF-files and pw_data hydra_obj.load_scan(scannums, direct=False) cw_data = hydra_obj.get_pw_matrices( scannums, method='pixel' ) plt.ioff() counter = 0 for data, key in zip(cw_data, sorted(self.roi_obj.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) )): for ii in range(data.shape[1]): if save_dataset: the_shelve = shelve.open(save_dataset) the_key = str('%s_PixelNo%d'%(key,ii) ) plt.cla() title_txt = 'Click above to keep/below black line to discard column for ROI %02d'%(counter+1) + '.' plt.title(title_txt) plt.plot(data[:,ii],'b-') axes = plt.gca() offset = np.mean(axes.get_ylim()) plt.plot(np.zeros_like(data[:,ii]) + offset,'k-') plt.xlabel('points along scan') plt.ylabel('intensity [arb. units]') plt.legend(['Pixel No. %02d / %02d' %(ii+1,data.shape[1])]) # let user click a point on figure user_input = np.array(plt.ginput(1,timeout=-1)[0]) if user_input[1] >= offset: if save_dataset: the_shelve[the_key] = { 'spectrum': data[:,ii], 'decision': 1 } pass elif user_input[1] < offset: index = np.unravel_index([ii],self.roi_obj.red_rois[key][1].shape ) self.roi_obj.red_rois[key][1][index[0],index[1]] = 0 if save_dataset: the_shelve[the_key] = { 'spectrum': data[:,ii], 'decision': 0 } else: print('Something fishy happened!') if save_dataset: the_shelve.close() counter += 1 # reassign ROI object self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(self.roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # compact red rois self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix, labelformat= 'ROI%02d') def find_cw_rois(self,roi_obj,pw_data): """ **find_cw_rois** Allows for manual refinement of ROIs by plotting the spectra column-wise. Loops through the spectra column-by-column and ROI by ROI, click above the black line to keep the column plotted, click below the black line to discard the column of pixels. Args: * roi_obj (roi_object): ROI object from the XRStools.xrs_rois module with roughly defined ROIs. * pw_data (np.array): List containing one 2D numpy array per ROI holding pixel-wise signals. """ counter = 0 new_indices = [] for data,ind in zip(pw_data,range(len(pw_data))): # go through each ROI refined_indices = [] # find the range/number of columns y_indices = roi_obj.y_indices[ind] y_min = np.amin(y_indices) y_max = np.amax(y_indices) for ii,col in zip(range(y_min,y_max+1),range(len(range(y_min,y_max+1)))): theindices = [] cw_data = np.zeros_like(data[:,0]) for yind,xind in zip(roi_obj.y_indices[ind],roi_obj.x_indices[ind]): if yind == ii: cw_data += data[:,col] theindices.append((xind,yind)) plt.cla() title_txt = 'Click above to keep/below black line to discard column for ROI %02d'%(counter+1) + '.' plt.title(title_txt) plt.plot(cw_data,'b-') axes = plt.gca() offset = np.mean(axes.get_ylim()) plt.plot(np.zeros_like(cw_data) + offset,'k-') plt.xlabel('points along scan') plt.ylabel('intensity [arb. units]') plt.legend(['Column No. %02d'%col]) # let user click a point on figure user_input = np.array(plt.ginput(1,timeout=-1)[0]) if user_input[1] >= offset: for index in theindices: refined_indices.append(index) elif user_input[1] < offset: pass else: print('Something fishy happened!') # reorganize pixels inside ROI new_indices.append(refined_indices) counter += 1 # reassign ROI object self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(new_indices,self.roi_obj.input_image.shape) self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) self.roi_obj.indices = new_indices self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(new_indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(new_indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) def refine_rois_CW_old(self, hydra_obj, scan_numbers): """ **refine_rois_CW** Allows for manual refinement of ROIs by plotting the spectra column-wise. Loops through the spectra column-by-column and ROI by ROI, click above the black line to keep the column plotted, click below the black line to discard the column of pixels. Args: * hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used for the refinement of the ROIs. * scan_numbers (int or list): Scan numbers of scans to be used in the refinement. """ # make scan_numbers itarable if isinstance(scan_numbers,list): scannums = scan_numbers elif isinstance(scan_numbers,int): scannums = [scan_numbers] # get EDF-files and pw_data hydra_obj.load_scan(scannums, direct=False) cw_data = hydra_obj.get_pw_matrices( scannums, method='column' ) plt.ioff() plt.cla() counter = 0 for data, key in zip(cw_data, sorted(self.roi_obj.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) )): print('Processing: ', key) for ii in range(data.shape[1]): print('>>>>>>>>> ', ii) plt.cla() title_txt = 'Click above to keep/below black line to discard column for ROI %02d'%(counter+1) + '.' plt.title(title_txt) plt.plot(data[:,ii],'b-') axes = plt.gca() offset = np.mean(axes.get_ylim()) plt.plot(np.zeros_like(data[:,ii]) + offset,'k-') plt.xlabel('points along scan') plt.ylabel('intensity [arb. units]') plt.legend(['Column No. %02d / %02d' %(ii+1,data.shape[1])]) plt.draw() plt.pause(0.01) # let user click a point on figure user_input = np.array(plt.ginput(1,timeout=-1)[0]) if user_input[1] >= offset: print('Taking pixel ', ii) pass elif user_input[1] < offset: self.roi_obj.red_rois[key][1][:,ii] = 0 else: print('Something fishy happened!') counter += 1 # go through all ROIs and delete empty ones for key in self.roi_obj.red_rois.keys(): if self.roi_obj.red_rois[key][1].shape == (0,0): del self.roi_obj.red_rois[key] # reassign ROI object self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(self.roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # compact red rois self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix, labelformat= 'ROI%02d') def refine_rois_CW(self, hydra_obj, scan_numbers, interpolation='nearest', cmap='Blues'): """ **refine_rois_CW** Allows for manual refinement of ROIs by plotting the spectra column-wise. Loops through the spectra column-by-column and ROI by ROI, click above the black line to keep the column plotted, click below the black line to discard the column of pixels. Args: * hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be usedfor the refinement of the ROIs. * scan_numbers (int or list): Scan numbers of scans to be used in the refinement. * interpolation (str): Interpolation keyword for matplotlib.imshow. * cmap (str): Color-map for plotting. """ plt.ioff() # make scan_numbers itarable if isinstance(scan_numbers,list): scannums = scan_numbers elif isinstance(scan_numbers,int): scannums = [scan_numbers] # make sure there are rois defined if not self.roi_obj.red_rois: print("Please define ROIs first.") return # get EDF-files and cw_data scans = [] for ii in scannums: scan = xrs_scans.Scan() scan.load( hydra_obj.path, hydra_obj.SPECfname, hydra_obj.EDFprefix, hydra_obj.EDFname, \ hydra_obj.EDFpostfix, ii, \ direct=True, roi_obj=self.roi_obj, scaling=None, scan_type='generic', \ en_column=None, moni_column=hydra_obj.moni_column, method='pixel', comp_factor=None,\ rot_angles=None, clean_edf_stack=False, cenom_dict=None, storeInsets=False ) scans.append( scan ) data_dict_norm = {} # sum normalized raw data, if more than one scan if len( scans ) > 1: # undo individual normalization for scan in scans: for key2 in scan.raw_signals: scan.raw_signals[key2] *= scan.monitor[:,None,None] # sum all monitor signals monitor_sum = np.zeros_like( scans[0].monitor ) for scan in scans: monitor_sum += scan.monitor # sum all signals for key2 in scans[0].raw_signals: data_dict_norm[key2] = np.zeros_like( scans[0].raw_signals[key2] ) for scan in scans: data_dict_norm[key2] += scan.raw_signals[key2] # normalize summed signals for key2 in data_dict_norm: data_dict_norm[key2] /= monitor_sum[:,None,None] elif len( scans ) == 1: for key in scans[0].raw_signals: data_dict_norm[key] = scans[0].raw_signals[key] #if 1: # return data_dict_norm cw_data = {} for key in data_dict_norm: cw_data[key] = np.sum( data_dict_norm[key], axis=1 ) # fetch all red_roi keys roi_keys = [key for key in sorted(self.roi_obj.red_rois , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) )] # deep copy all ROIs, set all to zero red_rois_copy = {} for key in sorted( self.roi_obj.red_rois , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): red_rois_copy[key] = copy.deepcopy( self.roi_obj.red_rois[key] ) red_rois_copy[key][1] = np.zeros_like( self.roi_obj.red_rois[key][1] ) # prepare the figure fig, (ax1, ax2) = plt.subplots( 2, 1 ) plt.subplots_adjust(bottom=0.2) title_txt = 'Active ROI is No. %d of %d'%(1, len(cw_data)) + '.' plt.suptitle(title_txt) # plot the very first column spectrum ax1.plot(cw_data[roi_keys[0]][:,0], lw=2) ax1.set_xlabel( 'points along scan' ) ax1.set_ylabel( 'intensity [arb. units]' ) ax1.legend(['Column No. %02d / %02d' %(1, cw_data[roi_keys[0]].shape[1])], frameon=False, loc=1, fontsize='x-small') # plot the very first mask image ax2.plot([0, 0], [-0.5, data_dict_norm['ROI00'].shape[1]+0.5, ], 'k-') ax2.imshow( np.sum(data_dict_norm['ROI00'], axis=0), interpolation=interpolation, cmap=cmap ) ax2.imshow( red_rois_copy[roi_keys[0]][1], interpolation=interpolation, cmap='Greys', alpha=0.5 ) # plotter def plot_all( roi_ind, column_ind, red_mask, col_max ): fig.canvas.flush_events() the_key = roi_keys[roi_ind] # plot the spectra ax1.cla() data = cw_data[roi_keys[roi_ind]] title_txt = 'Active ROI is No. %d of %d.'%(roi_ind+1, len(cw_data)) plt.suptitle(title_txt) ax1.plot(data[:,column_ind], lw=2) #offset = np.mean(ax1.get_ylim()) #ax1.plot(np.zeros_like(data[:,column_ind]) + offset ,'k-') ax1.set_xlabel( 'points along scan' ) ax1.set_ylabel( 'intensity [arb. units]' ) ax1.legend(['Column No. %02d / %02d' %(column_ind+1,data.shape[1])], frameon=False, loc=1, fontsize='x-small') # plot the mask ax2.cla() ax2.plot([column_ind-0.5, column_ind+0.5, column_ind+0.5, column_ind-0.5, column_ind-0.5], [-0.5, -0.5, data_dict_norm[roi_keys[roi_ind]].shape[1]+0.5, data_dict_norm[roi_keys[roi_ind]].shape[1]+0.5, -0.5], 'k-') ax2.imshow( np.sum(data_dict_norm[roi_keys[roi_ind]], axis=0), interpolation=interpolation, cmap=cmap ) ax2.imshow( red_mask, interpolation=interpolation, alpha=0.5 , cmap='Greys') #plt.tight_layout() plt.draw() plt.pause(0.01) class Index(object): def __init__(self, red_rois): self.red_rois = red_rois self.roi_ind = 0 self.column_ind = 0 self.roi_key = roi_keys[self.roi_ind] self.red_mask = np.zeros_like(red_rois_copy[self.roi_key][1]) self.num_ROI_max = len(red_rois) self.num_ROI_min = 0 self.num_col_max = red_rois_copy[self.roi_key][1].shape[1] self.num_col_min = 0 def update_shape(self): self.num_ROI_max = len(self.red_rois) self.num_ROI_min = 0 self.num_col_max = red_rois_copy[self.roi_key][1].shape[1] self.num_col_min = 0 def next_roi(self, event): if self.roi_ind < self.num_ROI_max-1: red_rois_copy[self.roi_key][1] = self.red_mask self.roi_ind += 1 self.roi_key = roi_keys[self.roi_ind] self.red_mask = np.zeros_like(red_rois_copy[self.roi_key][1]) self.column_ind = 0 self.update_shape() if self.roi_ind < self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.column_ind < self.num_col_max and self.column_ind >= self.num_col_min: plot_all( self.roi_ind, self.column_ind, self.red_mask, self.num_col_max ) else: pass def prev_roi(self, event): if self.roi_ind > self.num_ROI_min: self.roi_ind -= 1 self.roi_key = roi_keys[self.roi_ind] self.red_mask = self.red_rois[self.roi_key][1] self.column_ind = 0 self.update_shape() if self.roi_ind < self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.column_ind < self.num_col_max and self.column_ind >= self.num_col_min: plot_all( self.roi_ind, self.column_ind, self.red_mask, self.num_col_max ) else: pass def next_column( self, event ): if self.column_ind < self.num_col_max-1: self.column_ind += 1 self.update_shape() if self.roi_ind < self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.column_ind < self.num_col_max and self.column_ind >= self.num_col_min: plot_all( self.roi_ind, self.column_ind, self.red_mask, self.num_col_max ) else: self.column_ind += 0 def prev_column( self, event ): if self.column_ind > self.num_col_min: self.column_ind -= 1 self.update_shape() if self.roi_ind < self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.column_ind < self.num_col_max and self.column_ind >= self.num_col_min: plot_all( self.roi_ind, self.column_ind, self.red_mask, self.num_col_max ) else: self.column_ind -= 0 def finish( self, event ): for key in sorted(red_rois_copy, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): red_rois_copy[key][1] = self.red_rois[key][1] plt.close() def take( self, event ): if self.column_ind > self.num_col_min and self.column_ind < self.num_col_max: self.red_mask[:,self.column_ind] = self.roi_ind+1 self.red_rois[self.roi_key][1] = self.red_mask if self.roi_ind < self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.column_ind < self.num_col_max and self.column_ind > self.num_col_min: plot_all( self.roi_ind, self.column_ind, self.red_mask, self.num_col_max ) elif self.column_ind <= self.num_col_min: self.red_mask[:,0] = self.roi_ind+1 self.red_rois[self.roi_key][1] = self.red_mask plot_all( self.roi_ind, 0, self.red_mask, self.num_col_max ) elif self.column_ind >= self.num_col_max: self.red_mask[:,-1] = self.roi_ind+1 self.red_rois[self.roi_key][1] = self.red_mask plot_all( self.roi_ind, -1, self.red_mask, self.num_col_max ) else: pass def discard( self, event ): if self.column_ind >= self.num_col_min and self.column_ind <= self.num_col_max: self.red_mask[:,self.column_ind] = 0 self.red_rois[self.roi_key][1] = self.red_mask if self.roi_ind <= self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.column_ind <= self.num_col_max and self.column_ind >= self.num_col_min: plot_all( self.roi_ind, self.column_ind, self.red_mask, self.num_col_max ) else: pass callback = Index(self.roi_obj.red_rois) # buttons for next/prev roi axprev_roi = plt.axes([0.7, 0.05, 0.1, 0.075]) axnext_roi = plt.axes([0.82, 0.05, 0.1, 0.075]) bnext_roi = Button(axnext_roi, 'next ROI') bnext_roi.on_clicked(callback.next_roi) bprev_roi = Button(axprev_roi, 'prev ROI') bprev_roi.on_clicked(callback.prev_roi) # buttons for xext/prev column axprev_column = plt.axes([0.1, 0.05, 0.1, 0.075]) axnext_column = plt.axes([0.22, 0.05, 0.1, 0.075]) bnext_column = Button(axnext_column, 'next Col.') bnext_column.on_clicked(callback.next_column) bprev_prev = Button(axprev_column, 'prev Col.') bprev_prev.on_clicked(callback.prev_column) # take column button axtake = plt.axes([0.34, 0.05, 0.1, 0.075]) btake = Button(axtake, 'take') btake.on_clicked(callback.take) # discard column button axdisc = plt.axes([0.46, 0.05, 0.1, 0.075]) bdisc = Button(axdisc, 'discard') bdisc.on_clicked(callback.discard) # finish button axfin = plt.axes([0.58, 0.05, 0.1, 0.075]) bfin = Button(axfin, 'finish') bfin.on_clicked(callback.finish) # show figure plt.show() #print(len(red_rois_copy)) # reassign ROI object self.roi_obj.red_rois = red_rois_copy self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(self.roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # compact red rois self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix, labelformat= 'ROI%02d') #return red_rois_copy def refine_rois_RW_old(self, hydra_obj, scan_numbers): """ **refine_rois_RW** Allows for manual refinement of ROIs by plotting the spectra row-wise. Loops through the spectra column-by-column and ROI by ROI, click above the black line to keep the column plotted, click below the black line to discard the column of pixels. Args: * hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used for the refinement of the ROIs. * scan_numbers (int or list): Scan numbers of scans to be used in the refinement. """ # make scan_numbers itarable if isinstance(scan_numbers,list): scannums = scan_numbers elif isinstance(scan_numbers,int): scannums = [scan_numbers] # get EDF-files and pw_data hydra_obj.load_scan(scannums, direct=False) cw_data = hydra_obj.get_pw_matrices( scannums, method='row' ) print("SHAPE OF CW DATA: ",cw_data[0].shape) plt.ioff() counter = 0 for data, key in zip(cw_data, sorted(self.roi_obj.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) )): print('Processing: ', key) for ii in range(data.shape[1]): plt.cla() title_txt = 'Click above to keep/below black line to discard row for ROI %02d'%(counter+1) + '.' plt.title(title_txt) plt.plot(data[:,ii],'b-') axes = plt.gca() offset = np.mean(axes.get_ylim()) plt.plot(np.zeros_like(data[:,ii]) + offset,'k-') plt.xlabel('points along scan') plt.ylabel('intensity [arb. units]') plt.legend(['Row No. %02d / %02d' %(ii+1,data.shape[1])]) plt.draw() plt.pause(0.01) # let user click a point on figure user_input = np.array(plt.ginput(1,timeout=-1)[0]) if user_input[1] >= offset: print('Taking pixel ', ii) pass elif user_input[1] < offset: self.roi_obj.red_rois[key][1][ii,:] = 0 else: print('Something fishy happened!') counter += 1 # go through all ROIs and delete empty ones for key in self.roi_obj.red_rois.keys(): if self.roi_obj.red_rois[key][1].shape == (0,0): del self.roi_obj.red_rois[key] # reassign ROI object self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(self.roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # compact red rois self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix, labelformat= 'ROI%02d') def get_zoom_rois_new( self, input_image, logscaling=True, cmap='Blues', interpolation='nearest'): """ Define ROIs by matplotlib's zoom/pan functions. Args: * input_image (np.array): 2D numpy array with the image to be displayed. * logscaling (boolean): Display image using logarithmic scale. * colormap (str): matplotlib color map string. * interpolation (str): matplotlib interpolation for the display. """ # unset interactive mode plt.ioff() # clear all existing rois self.deleterois() # check that the input is a 2d matrix if not len(input_image.shape) == 2: print( 'please provide a 2D numpy array as input!' ) return # save input image for later use self.roi_obj.input_image = copy.deepcopy(input_image) # calculate the logarithm if 'logscaling' == True if logscaling: # set all zeros to ones: input_image[input_image[:,:] == 0.0] = 1.0 input_image = np.log(np.abs(input_image)) # first ROI roi_key = 'ROI00' # prepare a figure fig, ax = plt.subplots() plt.subplots_adjust(bottom=0.2) cursor = Cursor(ax, useblit=True, color='red', linewidth=1 ) ax.imshow( input_image, interpolation=interpolation, cmap=cmap) # no ticks, no labels ax.tick_params( left=False, labelleft=False, right=False, labelright=False, bottom=False, top=False, labelbottom=False ) ax.set_xlim( 0.0, input_image.shape[1] ) ax.set_ylim( input_image.shape[0], 0.0 ) title_txt = 'Active ROI Nr. is %d. of %d'%(1, 0) ax.set_title(title_txt) # activate the zoom function already thismanager = plt.get_current_fig_manager() thismanager.toolbar.zoom() # plotter def plot_all( roi_key, contour_lines ): fig.canvas.flush_events() ax.cla() title_txt = 'Active ROI Nr. is %d. of %d'%( int(roi_key[-2:])+1, len(contour_lines)) ax.imshow( input_image, interpolation=interpolation, cmap=cmap) # no ticks, no labels on axes ax.tick_params( left=False, labelleft=False, right=False, labelright=False, bottom=False, top=False, labelbottom=False ) ax.imshow( input_image, interpolation=interpolation, cmap=cmap) if contour_lines: for key in contour_lines: ax.plot( contour_lines[key][0], contour_lines[key][1], '-k' ) ax.set_xlim( 0.0, input_image.shape[1] ) ax.set_ylim( input_image.shape[0], 0.0 ) ax.set_title(title_txt) plt.draw() plt.pause(0.01) class Index(object): def __init__(self, red_rois): self.red_rois = red_rois self.roi_key = 'ROI00' self.roi_ind = 0 self.num_ROI_min = 0 self.contour_lines = {} def take_and_next( self, event ): # grep axis limits cur_xlim = ax.get_xlim() cur_ylim = ax.get_ylim() # limits = np.array([np.ceil(cur_xlim[0]),\ # np.floor(cur_xlim[1]+0.5), \ # np.floor(cur_ylim[1]+0.5), # np.ceil(cur_ylim[0])]) limits = np.array([np.ceil(cur_xlim[0]), np.floor(cur_xlim[1]), \ np.floor(cur_ylim[1]), np.ceil(cur_ylim[0])]) # make sure they are inside the image inds = limits < 0 limits[inds] = 0 if limits[1] > input_image.shape[1]: limits[1] = input_image.shape[1] if limits[2] > input_image.shape[0]: limits[2] = input_image.shape[0] print('Chosen limits are (x_min, x_max, y_min, y_max): ',limits) self.red_rois[self.roi_key] = [ np.array([int(limits[2]), int(limits[0])]), \ np.zeros((int(limits[3]-limits[2]), int(limits[1]-limits[0])))+self.roi_ind+1 ] self.contour_lines[self.roi_key] = [[limits[0]-0.5, limits[1]+0.5, limits[1]+0.5, \ limits[0]-0.5, limits[0]-0.5], \ [limits[2]-0.5, limits[2]-0.5, limits[3]+0.5, \ limits[3]+0.5, limits[2]-0.5]] plot_all( self.roi_key, self.contour_lines ) # go to next roi self.roi_ind += 1 self.roi_key = 'ROI%02d'%self.roi_ind plot_all( self.roi_key, self.contour_lines ) def middle_mouse_press( self, event ): if event.button == 2: self.take_and_next( event ) def take_and_exit( self, event ): # grep axis limits cur_xlim = ax.get_xlim() cur_ylim = ax.get_ylim() limits = np.array([np.ceil(cur_xlim[0]),\ np.floor(cur_xlim[1]+0.5), \ np.floor(cur_ylim[1]+0.5), np.ceil(cur_ylim[0])]) # make sure they are inside the image inds = limits < 0 limits[inds] = 0 if limits[1] > input_image.shape[1]: limits[1] = input_image.shape[1] if limits[2] > input_image.shape[0]: limits[2] = input_image.shape[0] print('Chosen limits are (x_min, x_max, y_min, y_max): ',limits) self.red_rois[self.roi_key] = [ np.array([int(limits[2]), int(limits[0])]), \ np.zeros((int(limits[3]-limits[2]), int(limits[1]-limits[0])))+self.roi_ind+1 ] self.contour_lines[self.roi_key] = [[limits[0]+0.5, limits[1]+0.5, limits[1]+0.5, \ limits[0]+0.5, limits[0]+0.5], \ [limits[2]+0.5, limits[2]+0.5, limits[3]+0.5, \ limits[3]+0.5, limits[2]+0.5]] plot_all( self.roi_key, self.contour_lines ) plt.close() def discard( self, event ): del self.red_rois[self.roi_key] del self.contour_lines[self.roi_key] plot_all( self.roi_key, self.contour_lines ) def prev_roi(self, event): if self.roi_ind > self.num_ROI_min: self.roi_ind -= 1 self.roi_key = 'ROI%02d'%self.roi_ind plot_all( self.roi_key, self.contour_lines ) else: pass def next_roi(self, event): self.roi_ind += 1 self.roi_key = 'ROI%02d'%self.roi_ind plot_all( self.roi_key, self.contour_lines ) def exit(self, event): plt.close() callback = Index(self.roi_obj.red_rois) # buttons for next/prev roi axprev_roi = plt.axes([0.58, 0.05, 0.1, 0.075]) axnext_roi = plt.axes([0.7, 0.05, 0.1, 0.075]) bnext_roi = Button(axnext_roi, 'next ROI') bnext_roi.on_clicked(callback.next_roi) bprev_roi = Button(axprev_roi, 'prev ROI') bprev_roi.on_clicked(callback.prev_roi) # take and next button axtakeN = plt.axes([0.04, 0.05, 0.17, 0.075]) btakeN = Button(axtakeN, 'take+next\n (mouse wheel)') btakeN.on_clicked(callback.take_and_next) # take and exit axtakeE = plt.axes([0.24, 0.05, 0.17, 0.075]) btakeE = Button(axtakeE, 'take+exit') btakeE.on_clicked(callback.take_and_exit) # discard button axdisc = plt.axes([0.46, 0.05, 0.1, 0.075]) bdisc = Button(axdisc, 'discard') bdisc.on_clicked(callback.discard) # finish button axfin = plt.axes([0.82, 0.05, 0.1, 0.075]) bfin = Button(axfin, 'exit') bfin.on_clicked( callback.exit ) cid = fig.canvas.mpl_connect('button_press_event', callback.middle_mouse_press) # show figure plt.show() # reassign ROI object self.roi_obj.red_rois = callback.red_rois self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(self.roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # compact red rois self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix, labelformat= 'ROI%02d') #return red_rois_copy def refine_rois_RW( self, hydra_obj, scan_numbers, interpolation='nearest', cmap='Blues' ): """ **refine_rois_CW** Allows for manual refinement of ROIs by plotting the spectra row-wise. Loops through the spectra row-by-row and ROI by ROI. Args: * hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be usedfor the refinement of the ROIs. * scan_numbers (int or list): Scan numbers of scans to be used in the refinement. * interpolation (str): Interpolation keyword for matplotlib.imshow. * cmap (str): Color-map for plotting. """ plt.ioff() # make scan_numbers itarable if isinstance(scan_numbers,list): scannums = scan_numbers elif isinstance(scan_numbers,int): scannums = [scan_numbers] # make sure there are rois defined if not self.roi_obj.red_rois: print("Please define ROIs first.") return # get EDF-files and cw_data scans = [] for ii in scannums: scan = xrs_scans.Scan() scan.load( hydra_obj.path, hydra_obj.SPECfname, hydra_obj.EDFprefix, hydra_obj.EDFname, \ hydra_obj.EDFpostfix, ii, \ direct=True, roi_obj=self.roi_obj, scaling=None, scan_type='generic', \ en_column=None, moni_column=hydra_obj.moni_column, method='pixel', comp_factor=None,\ rot_angles=None, clean_edf_stack=False, cenom_dict=None, storeInsets=False ) scans.append( scan ) data_dict_norm = {} # sum normalized raw data, if more than one scan if len( scans ) > 1: # undo individual normalization for scan in scans: for key2 in scan.raw_signals: scan.raw_signals[key2] *= scan.monitor[:,None,None] # sum all monitor signals monitor_sum = np.zeros_like( scans[0].monitor ) for scan in scans: monitor_sum += scan.monitor # sum all signals for key2 in scans[0].raw_signals: data_dict_norm[key2] = np.zeros_like( scans[0].raw_signals[key2] ) for scan in scans: data_dict_norm[key2] += scan.raw_signals[key2] # normalize summed signals for key2 in data_dict_norm: data_dict_norm[key2] /= monitor_sum[:,None,None] elif len( scans ) == 1: for key in scans[0].raw_signals: data_dict_norm[key] = scans[0].raw_signals[key] #if 1: # return data_dict_norm rw_data = {} for key in data_dict_norm: rw_data[key] = np.sum( data_dict_norm[key], axis=2 ) # fetch all red_roi keys roi_keys = [key for key in sorted(self.roi_obj.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) )] # deep copy all ROIs, set all to zero red_rois_copy = {} for key in sorted( self.roi_obj.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): red_rois_copy[key] = copy.deepcopy( self.roi_obj.red_rois[key] ) red_rois_copy[key][1] = np.zeros_like( self.roi_obj.red_rois[key][1] ) # prepare the figure fig, (ax1, ax2) = plt.subplots( 2, 1 ) plt.subplots_adjust(bottom=0.2) title_txt = 'Active ROI is No. %d of %d'%(1, len(rw_data)) + '.' plt.suptitle(title_txt) # plot the very first column spectrum ax1.plot(rw_data[roi_keys[0]][:,0], lw=2) ax1.set_xlabel( 'points along scan' ) ax1.set_ylabel( 'intensity [arb. units]' ) ax1.legend(['Row No. %02d / %02d' %(1, rw_data[roi_keys[0]].shape[1])], frameon=False, loc=1, fontsize='x-small') # plot the very first mask image ax2.plot([-0.5, data_dict_norm['ROI00'].shape[2]+0.5, data_dict_norm['ROI00'].shape[2]+0.5, -0.5], [-0.5, -0.5, 0.5, 0.5 ], 'k-') ax2.imshow( np.sum(data_dict_norm['ROI00'], axis=0), interpolation=interpolation, cmap=cmap ) ax2.imshow( red_rois_copy[roi_keys[0]][1], interpolation=interpolation, cmap='Greys', alpha=0.5 ) # plotter def plot_all( roi_ind, row_ind, red_mask, row_max ): fig.canvas.flush_events() the_key = roi_keys[roi_ind] # plot the spectra ax1.cla() data = rw_data[roi_keys[roi_ind]] title_txt = 'Active ROI is No. %d of %d.'%(roi_ind+1, len(rw_data)) plt.suptitle(title_txt) ax1.plot(data[:,row_ind], lw=2) #offset = np.mean(ax1.get_ylim()) #ax1.plot(np.zeros_like(data[:,column_ind]) + offset ,'k-') ax1.set_xlabel( 'points along scan' ) ax1.set_ylabel( 'intensity [arb. units]' ) ax1.legend(['Row No. %02d / %02d' %(row_ind+1,data.shape[1])], frameon=False, loc=1, fontsize='x-small') # plot the mask ax2.cla() ax2.plot([-0.5, data_dict_norm[roi_keys[roi_ind]].shape[0]+0.5, data_dict_norm[roi_keys[roi_ind]].shape[0]+0.5, -0.5, -0.5 ], [row_ind-0.5, row_ind-0.5, row_ind+0.5, row_ind+0.5, row_ind-0.5 ], 'k-') ax2.imshow( np.sum(data_dict_norm[roi_keys[roi_ind]], axis=0), interpolation=interpolation, cmap=cmap ) ax2.imshow( red_mask, interpolation=interpolation, alpha=0.5 , cmap='Greys') #plt.tight_layout() plt.draw() plt.pause(0.01) class Index(object): def __init__(self, red_rois): self.red_rois = red_rois self.roi_ind = 0 self.row_ind = 0 self.roi_key = roi_keys[self.roi_ind] self.red_mask = np.zeros_like(red_rois_copy[self.roi_key][1]) self.num_ROI_max = len(red_rois) self.num_ROI_min = 0 self.num_row_max = red_rois_copy[self.roi_key][1].shape[0] self.num_row_min = 0 def update_shape(self): self.num_ROI_max = len(self.red_rois) self.num_ROI_min = 0 self.num_row_max = red_rois_copy[self.roi_key][1].shape[0] self.num_row_min = 0 def next_roi(self, event): if self.roi_ind < self.num_ROI_max-1: red_rois_copy[self.roi_key][1] = self.red_mask self.roi_ind += 1 self.roi_key = roi_keys[self.roi_ind] self.red_mask = np.zeros_like(red_rois_copy[self.roi_key][1]) self.row_ind = 0 self.update_shape() if self.roi_ind < self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.row_ind < self.num_row_max and self.row_ind >= self.num_row_min: plot_all( self.roi_ind, self.row_ind, self.red_mask, self.num_row_max ) else: pass def prev_roi(self, event): if self.roi_ind > self.num_ROI_min: self.roi_ind -= 1 self.roi_key = roi_keys[self.roi_ind] self.red_mask = self.red_rois[self.roi_key][1] self.row_ind = 0 self.update_shape() if self.roi_ind < self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.row_ind < self.num_row_max and self.row_ind >= self.num_row_min: plot_all( self.roi_ind, self.row_ind, self.red_mask, self.num_row_max ) else: pass def next_column( self, event ): if self.row_ind < self.num_row_max-1: self.row_ind += 1 self.update_shape() if self.roi_ind < self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.row_ind < self.num_row_max and self.row_ind >= self.num_row_min: plot_all( self.roi_ind, self.row_ind, self.red_mask, self.num_row_max ) else: self.row_ind += 0 def prev_column( self, event ): if self.row_ind > self.num_row_min: self.row_ind -= 1 self.update_shape() if self.roi_ind < self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.row_ind < self.num_row_max and self.row_ind >= self.num_row_min: plot_all( self.roi_ind, self.row_ind, self.red_mask, self.num_row_max ) else: self.row_ind -= 0 def finish( self, event ): for key in sorted(red_rois_copy, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): red_rois_copy[key][1] = self.red_rois[key][1] plt.close() def take( self, event ): if self.row_ind > self.num_row_min and self.row_ind < self.num_row_max: self.red_mask[self.row_ind,:] = self.roi_ind+1 self.red_rois[self.roi_key][1] = self.red_mask if self.roi_ind < self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.row_ind < self.num_row_max and self.row_ind > self.num_row_min: plot_all( self.roi_ind, self.row_ind, self.red_mask, self.num_row_max ) elif self.row_ind <= self.num_row_min: self.red_mask[0,:] = self.roi_ind+1 self.red_rois[self.roi_key][1] = self.red_mask plot_all( self.roi_ind, 0, self.red_mask, self.num_row_max ) elif self.row_ind >= self.num_row_max: self.red_mask[-1,:] = self.roi_ind+1 self.red_rois[self.roi_key][1] = self.red_mask plot_all( self.roi_ind, -1, self.red_mask, self.num_row_max ) else: pass def discard( self, event ): if self.row_ind >= self.num_row_min and self.row_ind <= self.num_row_max: self.red_mask[self.row_ind,:] = 0 self.red_rois[self.roi_key][1] = self.red_mask if self.roi_ind <= self.num_ROI_max and self.roi_ind >= self.num_ROI_min: if self.row_ind <= self.num_row_max and self.row_ind >= self.num_row_min: plot_all( self.roi_ind, self.row_ind, self.red_mask, self.num_row_max ) else: pass callback = Index(self.roi_obj.red_rois) # buttons for next/prev roi axprev_roi = plt.axes([0.7, 0.05, 0.1, 0.075]) axnext_roi = plt.axes([0.82, 0.05, 0.1, 0.075]) bnext_roi = Button(axnext_roi, 'next ROI') bnext_roi.on_clicked(callback.next_roi) bprev_roi = Button(axprev_roi, 'prev ROI') bprev_roi.on_clicked(callback.prev_roi) # buttons for xext/prev column axprev_column = plt.axes([0.1, 0.05, 0.1, 0.075]) axnext_column = plt.axes([0.22, 0.05, 0.1, 0.075]) bnext_column = Button(axnext_column, 'next Row') bnext_column.on_clicked(callback.next_column) bprev_prev = Button(axprev_column, 'prev Row') bprev_prev.on_clicked(callback.prev_column) # take column button axtake = plt.axes([0.34, 0.05, 0.1, 0.075]) btake = Button(axtake, 'take') btake.on_clicked(callback.take) # discard column button axdisc = plt.axes([0.46, 0.05, 0.1, 0.075]) bdisc = Button(axdisc, 'discard') bdisc.on_clicked(callback.discard) # finish button axfin = plt.axes([0.58, 0.05, 0.1, 0.075]) bfin = Button(axfin, 'finish') bfin.on_clicked(callback.finish) # show figure plt.show() #print(len(red_rois_copy)) # reassign ROI object self.roi_obj.red_rois = red_rois_copy self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(self.roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # compact red rois self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix, labelformat= 'ROI%02d') #return red_rois_copy def refine_rois_MF_new( self, hydra_obj, scan_numbers, n_components=2, method='nnma', cov_thresh=-1, cmap='Blues' ): """ **refine_rois_MF** Use decomposition of pixelwise data for each ROI to find which of the pixels holds data from the sample and which one only has background. Args: * hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used for the refinement of the ROIs. * scan_numbers (int or list): Scan numbers of scans to be used in the refinement. * n_components (int): Number of components in the decomposition. * method (string): Keyword describing which decomposition to be used ('pca', 'ica', 'nnma'). """ # check if scikit learn is available try: from sklearn.decomposition import FastICA, PCA, ProjectedGradientNMF except ImportError: from sklearn.decomposition import FastICA, PCA from sklearn.decomposition import NMF as ProjectedGradientNMF except: raise ImportError('Please install the scikit-learn package to use this feature.') return # try importing a cursor try: from matplotlib.widgets import Cursor except: pass # color map for plotting color_m = plt.cm.get_cmap( name=cmap ) # check if available method is used if not method in [ 'pca', 'ica', 'nnma' ]: print('Please use one of the following methods: ' + str(avail_methods) + '!') return # make scan_numbers itarable if isinstance(scan_numbers,list): scannums = scan_numbers elif isinstance(scan_numbers,int): scannums = [scan_numbers] # make sure there are rois defined if not self.roi_obj.red_rois: print("Please define ROIs first.") return # get EDF-files and pw_data scans = [] for ii in scannums: scan = xrs_scans.Scan() scan.load( hydra_obj.path, hydra_obj.SPECfname, hydra_obj.EDFprefix, hydra_obj.EDFname, \ hydra_obj.EDFpostfix, ii, \ direct=True, roi_obj=self.roi_obj, scaling=None, scan_type='generic', \ en_column=None, moni_column=hydra_obj.moni_column, method='pixel', comp_factor=None,\ rot_angles=None, clean_edf_stack=False, cenom_dict=None, storeInsets=False ) scans.append( scan ) data_dict_norm = {} # sum normalized raw data, if more than one scan if len( scans ) > 1: # undo individual normalization for scan in scans: for key2 in scan.raw_signals: scan.raw_signals[key2] *= scan.monitor[:,None,None] # sum all monitor signals monitor_sum = np.zeros_like( scans[0].monitor ) for scan in scans: monitor_sum += scan.monitor # sum all signals for key2 in scans[0].raw_signals: data_dict_norm[key2] = np.zeros_like( scans[0].raw_signals[key2] ) for scan in scans: data_dict_norm[key2] += scan.raw_signals[key2] # normalize summed signals for key2 in data_dict_norm: data_dict_norm[key2] /= monitor_sum[:,None,None] elif len( scans ) == 1: for key in scans[0].raw_signals: data_dict_norm[key] = scans[0].raw_signals[key] # fetch all red_roi keys roi_keys = [key for key in sorted(self.roi_obj.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) )] # deep copy all ROIs, set all to zero red_rois_copy = {} for key in sorted( self.roi_obj.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): red_rois_copy[key] = copy.deepcopy( self.roi_obj.red_rois[key] ) red_rois_copy[key][1] = np.zeros_like( self.roi_obj.red_rois[key][1] ) # get pixel-wise data in matrix form (n_samples, n_features) pw_data = {} for key in data_dict_norm: s = data_dict_norm[key].shape pw_data[key] = data_dict_norm[key].reshape( s[0], s[1]*s[2], order='C' ) # plot the components plt.ioff() fig = plt.figure() ax0 = plt.subplot2grid( (16, 16), (0, 0), colspan=16, rowspan=16 ) new_rois = {} for counter, key in enumerate(sorted(pw_data, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) ): # go through each matrix (one per ROI) # decompose data, choose method if method == 'nnma': # non negative matrix factorisation nnm = ProjectedGradientNMF( n_components=n_components ) N = nnm.fit_transform( pw_data[key] ) elif method == 'pca': # principal component analysis pca = PCA( n_components=n_components ) N = pca.fit_transform( pw_data[key] ) elif method == 'ica': # independent component analysis ica = FastICA( n_components=n_components ) N = ica.fit_transform( pw_data[key] ) else: print('No method: \'' + method + '\' available. Will stop here.' ) return # let user decide which component belongs to the data: user_choise = 0 # prepare figure plt.cla() title_txt = 'Click component that resembles the sample spectrum for ROI %02d'%(counter+1) + '.' plt.title(title_txt) # try: # cursor1 = Cursor(ax0, useblit=True, color='black', linewidth=1) # except: # pass legendstr = [] for ii in range(n_components): ax0.plot(N[:,ii], label='Component No. %01d'%ii, color=color_m( (ii+1)/(n_components+1)-0.01 ) ) ax0.legend( frameon=False ) ax0.set_xlabel('points along scan') ax0.set_ylabel('intensity [arb. units]') plt.draw() # which curve was chosen user_input = np.array( plt.ginput(1,timeout=-1)[0] ) nearest_points = [(np.abs(N[:,ii]-user_input[1])).argmin() for ii in range(n_components)] user_choice = (np.abs(nearest_points-user_input[0])).argmin() # find covariance for all pixels with user choice covariance = np.array([]) for ii in range(len(pw_data[key][0,:])): covariance = np.append( covariance, np.cov(pw_data[key][:,ii], N[:,user_choice])[0,0] ) # try with qui-squared test # covariance = np.array([]) # for ii in range(len(pw_data[key][0,:])): # chi_s = stats.chisquare( pw_data[key][:,ii], N[:,user_choice] ) # print( chi_s ) # covariance = np.append( covariance, chi_s[0] ) # plot covariance, let user choose the the cutoff in y direction plt.cla() title_txt = 'Click to define a y-threshold for ROI %02d'%(counter+1) + '.' plt.title( title_txt ) ax0.plot( covariance, '-o', color=color_m(0.9) ) ax0.set_xlabel( 'pixels in ROI' ) ax0.set_ylabel( 'covariance [arb. units]' ) plt.draw() try: cursor1 = Cursor(ax0, useblit=True, color='black', linewidth=1, vertOn=False ) except: pass if cov_thresh < 0: user_cutoff = np.array( plt.ginput(1,timeout=-1)[0] ) elif cov_thresh>0 and isinstance(cov_thresh,int): if len( covariance ) < cov_thresh: print('ROI has fewer pixels than cov_thresh, will break here.') return else: user_cutoff = np.array([0.0, np.sort(covariance)[-cov_thresh]]) else: print('Please provide cov_thresh as positive integer!') # find the ROI indices above the cutoff, reassign ROI indices inds = covariance >= user_cutoff[1] s = data_dict_norm[key].shape red_rois_copy[key][1][np.reshape(inds, (s[1],s[2]))] = counter+1 # reassign ROI object self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix( red_rois_copy, np.zeros(self.roi_obj.input_image.shape)) self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) self.roi_obj.kind = 'refined' self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # compact the new red_rois # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix, labelformat= 'ROI%02d') self.roi_obj.red_rois = red_rois_copy # ======= # """ # Class to define ROIs from a 2D image. # """ # def __init__(self): # self.roi_obj = xrs_rois.roi_object() # empty roi object # def appendROIobject(self,roi_object): # self.roi_obj.append(roi_object) # def deleterois(self): # """ # Clear the existing ROIs by creating a fresh roi_object. # """ # self.roi_obj = xrs_rois.roi_object() # def roi_widget(self, input_image, layout="2X3-12", shape = [512,768], qapp=None ): # """ **roi_widget** # Use the ROI widget to define ROIs. # input_image = 2D array to define the ROIs from # layout = detector layout # shape = image shape/detector shape # """ # #%matplotlib qt # # plt.switch_backend('Qt4Agg') # w4r = roiSelectionWidget.mainwindow(layout=layout) # w4r.showImage( input_image ) # w4r.show() # if qapp is not None: # qapp.exec_() # assert( w4r.isOK ) # self.roi_obj.load_rois_fromMasksDict( w4r.getMasksDict(), newshape = shape ) # self.roi_obj.input_image = input_image # return w4r # def get_linear_rois(self,input_image,logscaling=True,height=5,colormap='Blues',interpolation='nearest'): # """ # Define ROIs by clicking two points on a 2D image. # number_of_rois = integer defining how many ROIs should be determined # input_object = 2D array, scan_object, or dictionary of scans to define the ROIs from # logscaling = boolean, to determine wether the image is shown on a log-scale (default = True) # height = integer defining the height (in pixels) of the ROIs # """ # # make sure the matplotlib interactive mode is off # plt.ioff() # # clear all existing rois # self.deleterois() # # check that the input is a 2d matrix # if not len(input_image.shape) == 2: # print( 'Please provide a 2D numpy array as input!' ) # return # # save input image for later use # self.roi_obj.input_image = copy.deepcopy(input_image) # # calculate the logarithm if 'logscaling' == True # if logscaling: # # set all zeros to ones: # input_image[input_image[:,:] == 0.0] = 1.0 # input_image = np.log(np.abs(input_image)) # # prepare a figure # fig, ax = plt.subplots() # plt.subplots_adjust(bottom=0.2) # cursor = Cursor(ax, useblit=True, color='red', linewidth=1 ) # # Initialize suptitle, which will be updated # titlestring = 'Start by clicking the \'Next\'-button.' # titleInst=plt.suptitle(titlestring) # # generate an image to be displayed # figure_obj = plt.imshow(input_image,interpolation=interpolation) # # set the colormap for the image # figure_obj.set_cmap(colormap) # rois = [] # class Index: # ind = 0 # def next(self, event): # titlestring = 'Click two points for ROI Nr. %02d, \'Finish\' to end.' %(self.ind+1) # titleInst.set_text(titlestring) # Update title # # Try needed, as FINISH button closes the figure and ginput() generates _tkinter.TclError # try: # one_roi = define_lin_roi(height,input_image.shape) # for index in one_roi: # input_image[index[0],index[1]] += 1.0e6 # figure_obj.set_data(input_image) # plt.hold(True) # plt.draw() # rois.append(one_roi) # self.ind += 1 # # Begin defining the next ROI right after current # self.next(self) # except KeyboardInterrupt: # to prevent "dead" figures # plt.close() # pass # except: # pass # def prev(self, event): # self.ind -= 1 # try: # titlestring = 'Click the \'Next\' button again to continue.' # titleInst.set_text(titlestring) # Update title # # print titlestring # for index in rois[-1]: # input_image[index[0],index[1]] -= 1.0e6 # figure_obj.set_data(input_image) # plt.hold(True) # rois.pop() # except: # pass # def close(self, event): # plt.close() # def dmy(self, event): # pass # adding a dummy function for the dummy button # callback = Index() # axprev = plt.axes([0.5, 0.05, 0.1, 0.075]) # axnext = plt.axes([0.61, 0.05, 0.1, 0.075]) # axclose = plt.axes([0.72, 0.05, 0.1, 0.075]) # axdmy = plt.axes([0.001, 0.001, 0.001, 0.001]) # for some reason the first botton disappears when clicked # bdmy = Button(axdmy,'') # which is why I am including a dummy button here # bdmy.on_clicked(callback.dmy) # this way, the real buttons work # bnext = Button(axnext, 'Next') # bnext.on_clicked(callback.next) # bprev = Button(axprev, 'Back') # bprev.on_clicked(callback.prev) # bclose = Button(axclose, 'Finish') # bclose.on_clicked(callback.close) # plt.show() # # assign the defined rois to the roi_object class # self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(rois,input_image.shape) # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) # self.roi_obj.indices = rois # self.roi_obj.kind = 'linear' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(rois) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(rois) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # def get_zoom_rois(self,input_image,logscaling=True,colormap='Blues',interpolation='nearest'): # """ # Define ROIs by clicking two points on a 2D image. # number_of_rois = integer defining how many ROIs should be determined # input_object = 2D array, scan_object, or dictionary of scans to define the ROIs from # logscaling = boolean, to determine wether the image is shown on a log-scale (default = True) # height = integer defining the height (in pixels) of the ROIs # """ # # make sure the matplotlib interactive mode is off # plt.ioff() # # clear all existing rois # self.deleterois() # # check that the input is a 2d matrix # if not len(input_image.shape) == 2: # print( 'please provide a 2D numpy array as input!' ) # return # # save input image for later use # self.roi_obj.input_image = copy.deepcopy(input_image) # # calculate the logarithm if 'logscaling' == True # if logscaling: # # set all zeros to ones: # input_image[input_image[:,:] == 0.0] = 1.0 # input_image = np.log(np.abs(input_image)) # # prepare a figure # fig, ax = plt.subplots() # plt.subplots_adjust(bottom=0.2) # cursor = Cursor(ax, useblit=True, color='red', linewidth=1 ) # # Initialize suptitle, which will be updated # titlestring = 'Start by clicking the \'Next\' button.' # titleInst=plt.suptitle(titlestring) # # generate an image to be displayed # figure_obj = plt.imshow(input_image,interpolation=interpolation) # # activate the zoom function already # thismanager = plt.get_current_fig_manager() # thismanager.toolbar.zoom() # # set the colormap for the image # figure_obj.set_cmap(colormap) # #plt.colorbar() # # initialize a matrix for the rois (will be filled with areas of ones, twos, etc # rois = [] # # print info to start: # print( 'Start by clicking the \'Next\' button.' ) # class Index: # ind = 0 # initstage = True # next_clicked = False # def next(self, event): # # for some reason, the first time this is used, it doesn't work, so here is one dummy round # if self.initstage: # #self.ind += 1 # self.initstage = False # plt.sca(ax) # one_roi = define_zoom_roi(input_image,verbose=True) # #for index in one_roi: # # input_image[index[0],index[1]] *= 1.0 # # reset the matrix to be displayed # figure_obj.set_data(input_image) # # reset the zoom # plt.xlim(0.0,input_image.shape[1]) # plt.ylim(input_image.shape[0],0.0) # plt.draw() # titlestring = 'Zoom in to define ROI Nr. %02d, hit \'Next\' to continue.' % (self.ind + 1) # titleInst.set_text(titlestring) # Update title # else: # self.ind += 1 # plt.sca(ax) # one_roi = define_zoom_roi(input_image) # for index in one_roi: # input_image[index[0],index[1]] += 1.0e10 # # reset the matrix to be displayed # figure_obj.set_data(input_image) # # reset the zoom # plt.xlim(0.0,input_image.shape[1]) # plt.ylim(input_image.shape[0],0.0) # plt.draw() # rois.append(one_roi) # titlestring = 'Zoom in to define ROI Nr. %02d, hit \'Next\' to continue, \'Finish\' to end.' % (self.ind + 1) # titleInst.set_text(titlestring) # Update title # def prev(self, event): # self.ind -= 1 # titlestring = 'Undoing ROI Nr. %02d. Zoom again, click the \'Next\' button to continue.' % (self.ind + 1) # titleInst.set_text(titlestring) # Update title # #thedata[roimatrix == self.ind+1] -= 1.0e6 # #roi_matrix[roimatrix == self.ind+1] = 0.0 # for index in rois[-1]: # input_image[index[0],index[1]] -= 1.0e10 # figure_obj.set_data(input_image) # plt.hold(True) # rois.pop() # def close(self, event): # plt.sca(ax) # one_roi = define_zoom_roi(input_image) # for index in one_roi: # input_image[index[0],index[1]] += 1.0e10 # # reset the matrix to be displayed # figure_obj.set_data(input_image) # # reset the zoom # plt.xlim(0.0,input_image.shape[1]) # plt.ylim(input_image.shape[0],0.0) # plt.draw() # rois.append(one_roi) # titlestring = 'Last ROI is Nr. %02d.' % (self.ind + 1) # titleInst.set_text(titlestring) # Update title # plt.close() # def dmy(self, event): # pass # adding a dummy function for the dummy button # callback = Index() # axprev = plt.axes([0.5, 0.05, 0.1, 0.075]) # axnext = plt.axes([0.61, 0.05, 0.1, 0.075]) # axclose = plt.axes([0.72, 0.05, 0.1, 0.075]) # axdmy = plt.axes([0.001, 0.001, 0.001, 0.001]) # for some reason the first botton disappears when clicked # bdmy = Button(axdmy,'') # which is why I am including a dummy button here # bdmy.on_clicked(callback.dmy) # this way, the real buttons work # bnext = Button(axnext, 'Next') # bnext.on_clicked(callback.next) # bprev = Button(axprev, 'Back') # bprev.on_clicked(callback.prev) # bclose = Button(axclose, 'Finish') # bclose.on_clicked(callback.close) # plt.show() # # assign the defined rois to the roi_object class # self.roi_obj.roi_matrix = (xrs_rois.convert_inds_to_matrix(rois,input_image.shape)) # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) # self.roi_obj.indices = rois # self.roi_obj.kind = 'zoom' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(rois) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(rois) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # def get_auto_rois(self,input_image,kernel_size=5,threshold=100.0,logscaling=True,colormap='Blues',interpolation='bilinear'): # """ # Define ROIs by choosing a threshold using a slider bar under the figure. In this function, the entire # detector is shown. # input_image = 2D numpy array with the image to be displayed # kernal_size = integer defining the median filter window (has to be odd) # theshold = initial number defining the upper end value for the slider bar (amax(input_image)/threshold defines this number), can be within GUI # logscaling = boolean, if True (default) the logarithm of input_image is displayed # colormap = matplotlib color scheme used in the display # interpolation = matplotlib interpolation scheme used for the display # """ # # make sure the matplotlib interactive mode is off # plt.ioff() # # clear all existing rois # self.deleterois() # # clear existing figure # # plt.clf() # # save input image for later use # self.roi_obj.input_image = copy.deepcopy(input_image) # # calculate the logarithm if 'logscaling' == True # if logscaling: # # set all zeros to ones: # input_image[input_image[:,:] == 0.0] = 1.0 # input_image = np.log(np.abs(input_image)) # ax = plt.subplot(111) # plt.subplots_adjust(left=0.05, bottom=0.2) # # print out some instructions # plt.suptitle('Use the slider bar to select ROIs, close the plotting window when satisfied.') # # initial threshold value # thres0 = 0.0 # # create a figure object # figure_obj = plt.imshow(input_image,interpolation=interpolation) # figure_obj.set_cmap(colormap) # # prepare the slider bar # thresxcolor = 'lightgoldenrodyellow' # thresxamp = plt.axes([0.2, 0.10, 0.55, 0.03], axisbg=thresxcolor) # maxthreshold=np.floor(np.amax(input_image)) # maximum of slider # sthres = plt.Slider(thresxamp, 'Threshold', 0.0, maxthreshold, valinit=thres0) # textBox=plt.figtext(0.50, 0.065, 'Multiplier: 1.0',verticalalignment='center') # # define what happens when the slider is touched # def update(val): # # parse a threshold from the slider # thres = sthres.val*thresMultiplier.factor # # median filter the image # newmatrix = signal.medfilt2d(input_image, kernel_size=kernel_size) # # set pixels below the threshold to zero # belowthres_indices = newmatrix < thres # newmatrix[belowthres_indices] = 0 # # identify connected regions (this is already the roi_matrix) # self.roi_obj.roi_matrix,numfoundrois = measurements.label(newmatrix) # print( str(numfoundrois) + ' ROIs found!' ) # figure_obj.set_data(newmatrix) # plt.draw() # # Buttons for changing multiplier for the value of slider # class thresMultiplierClass: # factor = 1.0; # def __new__(cls): # return self.factor # def increase(self,event): # self.factor *=2.0 # textBox.set_text('Multiplier: ' + str(self.factor)) # return self.factor # def decrease(self,event): # self.factor /=2.0 # textBox.set_text('Multiplier: ' + str(self.factor)) # return self.factor # # call the update function when the slider is touched # sthres.on_changed(update) # thresMultiplier = thresMultiplierClass() # axincrease = plt.axes([0.8, 0.05, 0.05, 0.03]) # axdecrease = plt.axes([0.7, 0.05, 0.05, 0.03]) # bnincrease = Button(axincrease, 'x 2') # bndecrease = Button(axdecrease, '/ 2') # bnincrease.on_clicked(thresMultiplier.increase) # First change threshold # bnincrease.on_clicked(update) # Then update image # bndecrease.on_clicked(thresMultiplier.decrease) # bndecrease.on_clicked(update) # # ADDITION ENDS # plt.show() # # assign the defined rois to the roi_object class # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) # self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) # self.roi_obj.kind = 'auto' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # def get_auto_rois_eachdet(self, input_image, kernel_size=5, threshold=100.0, logscaling=True, colormap='Blues', interpolation='bilinear'): # """ # Define ROIs automatically using median filtering and a variable threshold for each detector # separately. # scannumbers = either single scannumber or list of scannumbers # kernel_size = used kernel size for the median filter (must be an odd integer) # logscaling = set to 'True' if images is to be shown on log-scale (default is True) # colormap = string to define the colormap which is to be used for display (anything # supported by matplotlib, 'Blues' by default) # interpolation = interpolation scheme to be used for displaying the image (anything # supported by matplotlib, 'nearest' by default) # """ # # check that kernel_size is odd # if not kernel_size % 2 == 1: # print( 'The \'kernal_size\' must be an odd number.' ) # return # self.roi_obj.input_image = copy.deepcopy(input_image) # # big many pixels in one detector image # DET_PIXEL_NUM = input_image.shape[0]//2 # # break down the image into 256x256 pixel images # det_images, offsets = xrs_rois.break_down_det_image(input_image,DET_PIXEL_NUM) # # create one roi_object per sub-image # temp_objs = [] # for ii in range(det_images.shape[0]): # temp = roi_finder() # temp.get_auto_rois(det_images[ii,:,:], kernel_size=kernel_size, threshold=threshold, \ # logscaling=logscaling, colormap=colormap, interpolation=interpolation) # temp_objs.append(temp) # # merge all roi_objects into one # merged_obj = xrs_rois.merge_roi_objects_by_matrix(temp_objs,input_image.shape,offsets,DET_PIXEL_NUM) # # assign the defined rois to the roi_object class # self.roi_obj.roi_matrix = merged_obj.roi_matrix # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) # self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) # self.roi_obj.kind = 'auto' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # def get_polygon_rois(self,input_image,modind=-1,logscaling=True,colormap='Blues',interpolation='nearest'): # """ # Define ROIs by clicking arbitrary number of points on a 2D image: # LEFT CLICK to define the corner points of polygon, # MIDDLE CLICK to finish current ROI and move to the next ROI, # RIGHT CLICK to cancel the previous point of polygon # input_object = 2D array, scan_object, or dictionary of scans to define the ROIs from # modind = integer to identify module, if -1 (default), no module info will be in title (the case of one big image) # logscaling = boolean, to determine wether the image is shown on a log-scale (default = True) # """ # # make sure the matplotlib interactive mode is off # plt.ioff() # # clear all existing rois # self.deleterois() # # check that the input is a 2d matrix # if not len(input_image.shape) == 2: # print( 'Please provide a 2D numpy array as input!' ) # return # # save input image for later use # self.roi_obj.input_image = copy.deepcopy(input_image) # # calculate the logarithm if 'logscaling' == True # if logscaling: # # set all zeros to ones: # input_image[input_image[:,:] == 0.0] = 1.0 # input_image = np.log(np.abs(input_image)) # # prepare a figure # fig, ax = plt.subplots() # plt.subplots_adjust(bottom=0.2) # moduleNames='VD:','HR:','VU:','HL:','VB:','HB:','' # for plot title # # Initialize suptitle, which will be updated # titlestring = '' # titleInst=plt.suptitle(titlestring) # cursor = Cursor(ax, useblit=True, color='red', linewidth=1 ) # # generate an image to be displayed # figure_obj = plt.imshow(input_image,interpolation=interpolation) # # set the colormap for the image # figure_obj.set_cmap(colormap) # rois = [] # class Index: # ind = 1 # def next(self, event): # titlestring = '%s next ROI is Nr. %02d:\n Left button to new points, middle to finish ROI. Hit \'Finish\' to end with this image.' % (moduleNames[modind], self.ind) # titleInst.set_text(titlestring) # Update title # # Try needed, as FINISH button closes the figure and ginput() generates _tkinter.TclError # try: # one_roi = define_polygon_roi(input_image.shape) # for index in one_roi: # input_image[int(index[0]),int(index[1])] += 1.0e6 # figure_obj.set_data(input_image) # plt.hold(True) # plt.draw() # rois.append(one_roi) # self.ind += 1 # # Begin defining the next ROI right after current # self.next(self) # except KeyboardInterrupt: # to prevent "dead" figures # plt.close() # pass # except: # pass # def prev(self, event): # self.ind -= 1 # for index in rois[-1]: # input_image[index[0],index[1]] -= 1.0e6 # figure_obj.set_data(input_image) # plt.hold(True) # plt.draw() # rois.pop() # self.next(self) # def close(self, event): # plt.close() # def dmy(self, event): # pass # adding a dummy function for the dummy button # callback = Index() # axprev = plt.axes([0.5, 0.05, 0.1, 0.075]) # axnext = plt.axes([0.61, 0.05, 0.1, 0.075]) # axclose = plt.axes([0.72, 0.05, 0.1, 0.075]) # axdmy = plt.axes([0.001, 0.001, 0.001, 0.001]) # for some reason the first botton disappears when clicked # bdmy = Button(axdmy,'') # which is why I am including a dummy button here # bdmy.on_clicked(callback.dmy) # this way, the real buttons work # bnext = Button(axnext, 'Next') # bnext.on_clicked(callback.next) # bprev = Button(axprev, 'Back') # bprev.on_clicked(callback.prev) # bclose = Button(axclose, 'Finish') # bclose.on_clicked(callback.close) # # START: initiate NEXT button press # callback.next(self) # # assign the defined rois to the roi_object class # self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(rois,input_image.shape) # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) # self.roi_obj.indices = rois # self.roi_obj.kind = 'polygon' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(rois) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(rois) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # def show_rois(self,interpolation='nearest',colormap='Blues'): # """ **show_rois** # Creates a figure with the defined ROIs as numbered boxes on it. # Args: # ----- # interpolation (str) : Interpolation scheme used in the plot. # colormap (str) : Colormap used in the plot. # """ # self.roi_obj.show_rois(colormap=colormap,interpolation=interpolation) # def import_simo_style_rois(self,roiList,detImageShape=(512,768)): # """ **import_simo_style_rois** # Converts Simo-style ROIs to the conventions used here. # Arguments: # ---------- # roiList (list): List of tuples that have [(xmin, xmax, ymin, ymax), (xmin, xmax, ymin, ymax), ...]. # detImageShape (tuple): Shape of the detector image (for convertion to roiMatrix) # """ # indices = [] # for roi in roiList: # inds = [] # for ii in range(roi[0],roi[1]): # for jj in range(roi[2],roi[3]): # inds.append((ii,jj)) # indices.append(inds) # # assign the defined rois to the roi_object class # if detImageShape: # self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(indices,detImageShape) # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) # self.roi_obj.indices = indices # self.roi_obj.kind = 'simoStyle' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(indices) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(indices) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # def refine_pw_rois(self, roi_obj, pw_data, n_components=2, method='nnma', cov_thresh=-1): # """**refine_pw_rois** # Use decomposition of pixelwise data for each ROI to find which of the pixels holds # data from the sample and which one only has background. # Args: # ----- # roi_obj (roi_object): ROI object to be refined. # pw_data (list): List containing one 2D numpy array per ROI holding pixel-wise signals. # n_components (int): Number of components in the decomposition. # method (string): Keyword describing which decomposition to be used ('pca', 'ica', 'nnma'). # """ # # check if available method is used # avail_methods = ['pca','ica','nnma'] # if not method in avail_methods: # print('Please use one of the following methods: ' + str(avail_methods) + '!') # return # # check if scikit learn is available # try: # from sklearn.decomposition import FastICA, PCA, ProjectedGradientNMF # except ImportError: # raise ImportError('Please install the scikit-learn package to use this feature.') # return # counter = 0 # new_rois = {} # for data, key in zip(pw_data, sorted(roi_obj.red_rois)): # go through each matrix (one per ROI) # # decompose data, choose method # if method == 'nnma': # non negative matrix factorisation # nnm = ProjectedGradientNMF(n_components=n_components) # N = nnm.fit_transform(data) # elif method == 'pca': # principal component analysis # pca = PCA(n_components=n_components) # N = pca.fit_transform(data) # elif method == 'ica': # independent component analysis # ica = FastICA(n_components=n_components) # N = ica.fit_transform(data) # else: # print('No method: \'' + method + '\' available. Will stop here.' ) # # let user decide which component belongs to the data: # user_choise = 0 # plt.cla() # title_txt = 'Click component that resembles the sample spectrum for ROI %02d'%(counter+1) + '.' # plt.title(title_txt) # legendstr = [] # for ii in range(n_components): # plt.plot(N[:,ii]) # legendstr.append('Component No. %01d' %ii) # plt.legend(legendstr) # plt.xlabel('points along scan') # plt.ylabel('intensity [arb. units]') # user_input = np.array(plt.ginput(1,timeout=-1)[0]) # # which curve was chosen # nearest_points = [(np.abs(N[:,ii]-user_input[1])).argmin() for ii in range(n_components)] # user_choice = (np.abs(nearest_points-user_input[0])).argmin() # # find covariance for all pixels with user choice # covariance = np.array([]) # for ii in range(len(data[0,:])): # covariance = np.append(covariance, np.cov(data[:,ii],N[:,user_choice])[0,0]) # # plot covariance, let user choose the the cutoff in y direction # plt.cla() # title_txt = 'Click to define a y-threshold for ROI %02d'%(counter+1) + '.' # plt.title(title_txt) # plt.plot(covariance,'-o') # plt.xlabel('pixels in ROI') # plt.ylabel('covariance [arb. units]') # if cov_thresh < 0: # user_cutoff = np.array(plt.ginput(1,timeout=-1)[0]) # elif cov_thresh>0 and isinstance(cov_thresh,int): # if len(covariance) < cov_thresh: # print('ROI has fewer pixels than cov_thresh, will break here.') # return # else: # user_cutoff = np.array([0.0, np.sort(covariance)[-cov_thresh]]) # else: # print('Please provide cov_thresh as positive integer!') # # find the ROI indices above the cutoff, reassign ROI indices # inds = covariance >= user_cutoff[1] # #print('inds is ', inds) # ravel_roi = roi_obj.red_rois[key][1].ravel() # ravel_roi[~inds] = 0.0 # roi_obj.red_rois[key][1] = np.reshape(ravel_roi, (roi_obj.red_rois[key][1].shape)) # # end loop # counter += 1 # # reassign ROI object # self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) # self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) # self.roi_obj.kind = 'refined' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # def refine_rois_MF(self, hydra_obj, scan_numbers, n_components=2, method='nnma', cov_thresh=-1): # """**refine_rois_MF** # Use decomposition of pixelwise data for each ROI to find which of the pixels holds # data from the sample and which one only has background. # Args: # ----- # hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used # for the refinement of the ROIs. # scan_numbers (int or list): Scan numbers of scans to be used in the refinement. # n_components (int): Number of components in the decomposition. # method (string): Keyword describing which decomposition to be used ('pca', 'ica', 'nnma'). # """ # # check if available method is used # if not method in ['pca','ica','nnma']: # print('Please use one of the following methods: ' + str(avail_methods) + '!') # return # # check if scikit learn is available # try: # from sklearn.decomposition import FastICA, PCA, ProjectedGradientNMF # except ImportError: # raise ImportError('Please install the scikit-learn package to use this feature.') # return # # make scan_numbers itarable # if isinstance(scan_numbers,list): # scannums = scan_numbers # elif isinstance(scan_numbers,int): # scannums = [scan_numbers] # # get EDF-files and pw_data # hydra_obj.load_scan(scannums, direct=False) # pw_data = hydra_obj.get_pw_matrices( scannums, method='pixel' ) # counter = 0 # new_rois = {} # for data, key in zip(pw_data, sorted(self.roi_obj.red_rois)): # go through each matrix (one per ROI) # # decompose data, choose method # if method == 'nnma': # non negative matrix factorisation # nnm = ProjectedGradientNMF(n_components=n_components) # N = nnm.fit_transform(data) # elif method == 'pca': # principal component analysis # pca = PCA(n_components=n_components) # N = pca.fit_transform(data) # elif method == 'ica': # independent component analysis # ica = FastICA(n_components=n_components) # N = ica.fit_transform(data) # else: # print('No method: \'' + method + '\' available. Will stop here.' ) # # let user decide which component belongs to the data: # user_choise = 0 # plt.cla() # title_txt = 'Click component that resembles the sample spectrum for ROI %02d , key %s'%(counter+1, key) + '.' # plt.title(title_txt) # legendstr = [] # for ii in range(n_components): # plt.plot(N[:,ii]) # legendstr.append('Component No. %01d' %ii) # plt.legend(legendstr) # plt.xlabel('points along scan') # plt.ylabel('intensity [arb. units]') # user_input = np.array(plt.ginput(1,timeout=-1)[0]) # # which curve was chosen # nearest_points = [(np.abs(N[:,ii]-user_input[1])).argmin() for ii in range(n_components)] # user_choice = (np.abs(nearest_points-user_input[0])).argmin() # # find covariance for all pixels with user choice # covariance = np.array([]) # for ii in range(len(data[0,:])): # covariance = np.append(covariance, np.cov(data[:,ii],N[:,user_choice])[0,0]) # # plot covariance, let user choose the the cutoff in y direction # plt.cla() # title_txt = 'Click to define a y-threshold for ROI %02d'%(counter+1) + '.' # plt.title(title_txt) # plt.plot(covariance,'-o') # plt.xlabel('pixels in ROI') # plt.ylabel('covariance [arb. units]') # if cov_thresh < 0: # user_cutoff = np.array(plt.ginput(1,timeout=-1)[0]) # elif cov_thresh>0 and isinstance(cov_thresh,int): # if len(covariance) < cov_thresh: # print('ROI has fewer pixels than cov_thresh, will break here.') # return # else: # user_cutoff = np.array([0.0, np.sort(covariance)[-cov_thresh]]) # else: # print('Please provide cov_thresh as positive integer!') # # find the ROI indices above the cutoff, reassign ROI indices # inds = covariance >= user_cutoff[1] # #print('inds is ', inds) # ravel_roi = self.roi_obj.red_rois[key][1].ravel() # ravel_roi[~inds] = 0.0 # self.roi_obj.red_rois[key][1] = np.reshape(ravel_roi, (self.roi_obj.red_rois[key][1].shape)) # # end loop # counter += 1 # # reassign ROI object # self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(self.roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) # self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) # self.roi_obj.kind = 'refined' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # # compact the new red_rois # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix, labelformat= 'ROI%02d') # def find_pw_rois(self,roi_obj,pw_data,save_dataset=False): # """ # **find_pw_rois** # Allows for manual refinement of ROIs by plotting the spectra pixel-wise. # Loops through the spectra pixel-by-pixel and ROI by ROI, click above the # black line to keep the pixel plotted, click below the black line to discard # the pixel. # Args: # ----- # roi_obj (roi_object): ROI object from the XRStools.xrs_rois module with roughly defined ROIs. # pw_data (np.array): List containing one 2D numpy array per ROI holding pixel-wise signals. # """ # counter = 0 # new_indices = [] # pixelCounter = 0 # for data in pw_data: # go through each ROI # refined_indices = [] # for ii in range(len(data[0,:])): # user_choice = 1 # plt.cla() # title_txt = 'Click above to keep/below black line to discard pixel for ROI %02d'%(counter+1) + '.' # plt.title(title_txt) # plt.plot(data[:,ii],'b-') # axes = plt.gca() # offset = np.mean(axes.get_ylim()) # plt.plot(np.zeros(len(data[:,ii])) + offset,'k-') # plt.xlabel('points along scan') # plt.ylabel('intensity [arb. units]') # plt.legend(['Pixel No. %02d'%ii]) # # let user click a point on figure # user_input = np.array(plt.ginput(1,timeout=-1)[0]) # if user_input[1] >= offset: # refined_indices.append(roi_obj.indices[counter][ii]) # elif user_input[1] < offset: # user_choice = 0 # else: # print('Something fishy happened!') # # save spectra and decisions for NN learning # if save_dataset: # s = shelve.open(save_dataset) # try: # thekey = 'PixelNo' + str(pixelCounter) # s[thekey] = { 'spectrum': data[:,ii], 'decision': user_choice } # finally: # s.close() # pixelCounter += 1 # # reorganize pixels inside ROI # new_indices.append(refined_indices) # counter += 1 # # reassign ROI object # self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(new_indices,self.roi_obj.input_image.shape) # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) # self.roi_obj.indices = new_indices # self.roi_obj.kind = 'refined' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(new_indices) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(new_indices) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # def refine_rois_PW_old(self, hydra_obj, scan_numbers, save_dataset=False): # """ **refine_rois_PW** # Allows for manual refinement of ROIs by plotting the spectra pixel-wise. # Loops through the spectra pixel-by-pixel and ROI by ROI, click above the # black line to keep the pixel plotted, click below the black line to discard # the pixel. # Args: # ----- # hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used # for the refinement of the ROIs. # scan_numbers (int or list): Scan numbers of scans to be used in the refinement. # """ # # make scan_numbers itarable # if isinstance(scan_numbers,list): # scannums = scan_numbers # elif isinstance(scan_numbers,int): # scannums = [scan_numbers] # # get EDF-files and pw_data # hydra_obj.load_scan(scannums, direct=False) # pw_data = hydra_obj.get_pw_matrices( scannums, method='pixel' ) # counter = 0 # new_indices = [] # pixelCounter = 0 # for data in pw_data: # go through each ROI # refined_indices = [] # for ii in range(len(data[0,:])): # user_choice = 1 # plt.cla() # title_txt = 'Click above to keep/below black line to discard pixel for ROI %02d'%(counter+1) + '.' # plt.title(title_txt) # plt.plot(data[:,ii],'b-') # axes = plt.gca() # offset = np.mean(axes.get_ylim()) # plt.plot(np.zeros(len(data[:,ii])) + offset,'k-') # plt.xlabel('points along scan') # plt.ylabel('intensity [arb. units]') # plt.legend(['Pixel No. %02d'%ii]) # # let user click a point on figure # user_input = np.array(plt.ginput(1,timeout=-1)[0]) # if user_input[1] >= offset: # refined_indices.append(self.roi_obj.indices[counter][ii]) # elif user_input[1] < offset: # user_choice = 0 # else: # print('Something fishy happened!') # # save spectra and decisions for NN learning # if save_dataset: # s = shelve.open(save_dataset) # try: # thekey = 'PixelNo' + str(pixelCounter) # s[thekey] = { 'spectrum': data[:,ii], 'decision': user_choice } # finally: # s.close() # pixelCounter += 1 # # reorganize pixels inside ROI # new_indices.append(refined_indices) # counter += 1 # # reassign ROI object # self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(new_indices,self.roi_obj.input_image.shape) # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) # self.roi_obj.indices = new_indices # self.roi_obj.kind = 'refined' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(new_indices) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(new_indices) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # def refine_rois_PW(self, hydra_obj, scan_numbers): # """ **refine_rois_PW** # Allows for manual refinement of ROIs by plotting the spectra column-wise. # Loops through the spectra column-by-column and ROI by ROI, click above the # black line to keep the column plotted, click below the black line to discard # the column of pixels. # Args: # ----- # hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used # for the refinement of the ROIs. # scan_numbers (int or list): Scan numbers of scans to be used in the refinement. # """ # # make scan_numbers itarable # if isinstance(scan_numbers,list): # scannums = scan_numbers # elif isinstance(scan_numbers,int): # scannums = [scan_numbers] # # get EDF-files and pw_data # hydra_obj.load_scan(scannums, direct=False) # cw_data = hydra_obj.get_pw_matrices( scannums, method='pixel' ) # plt.ioff() # counter = 0 # for data, key in zip(cw_data, sorted(self.roi_obj.red_rois)): # for ii in range(data.shape[1]): # plt.cla() # title_txt = 'Click above to keep/below black line to discard column for ROI %02d'%(counter+1) + '.' # plt.title(title_txt) # plt.plot(data[:,ii],'b-') # axes = plt.gca() # offset = np.mean(axes.get_ylim()) # plt.plot(np.zeros_like(data[:,ii]) + offset,'k-') # plt.xlabel('points along scan') # plt.ylabel('intensity [arb. units]') # plt.legend(['Pixel No. %02d / %02d' %(ii+1,data.shape[1])]) # # let user click a point on figure # user_input = np.array(plt.ginput(1,timeout=-1)[0]) # if user_input[1] >= offset: # pass # elif user_input[1] < offset: # self.roi_obj.red_rois[key][1].ravel()[ii] = 0 # else: # print('Something fishy happened!') # counter += 1 # # reassign ROI object # self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(self.roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) # self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) # self.roi_obj.kind = 'refined' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # # compact red rois # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix, labelformat= 'ROI%02d') # def find_cw_rois(self,roi_obj,pw_data): # """ # **find_cw_rois** # Allows for manual refinement of ROIs by plotting the spectra column-wise. # Loops through the spectra column-by-column and ROI by ROI, click above the # black line to keep the column plotted, click below the black line to discard # the column of pixels. # Args: # ----- # roi_obj (roi_object): ROI object from the XRStools.xrs_rois module with roughly defined ROIs. # pw_data (np.array): List containing one 2D numpy array per ROI holding pixel-wise signals. # """ # counter = 0 # new_indices = [] # for data,ind in zip(pw_data,range(len(pw_data))): # go through each ROI # refined_indices = [] # # find the range/number of columns # y_indices = roi_obj.y_indices[ind] # y_min = np.amin(y_indices) # y_max = np.amax(y_indices) # for ii,col in zip(range(y_min,y_max+1),range(len(range(y_min,y_max+1)))): # theindices = [] # cw_data = np.zeros_like(data[:,0]) # for yind,xind in zip(roi_obj.y_indices[ind],roi_obj.x_indices[ind]): # if yind == ii: # cw_data += data[:,col] # theindices.append((xind,yind)) # plt.cla() # title_txt = 'Click above to keep/below black line to discard column for ROI %02d'%(counter+1) + '.' # plt.title(title_txt) # plt.plot(cw_data,'b-') # axes = plt.gca() # offset = np.mean(axes.get_ylim()) # plt.plot(np.zeros_like(cw_data) + offset,'k-') # plt.xlabel('points along scan') # plt.ylabel('intensity [arb. units]') # plt.legend(['Column No. %02d'%col]) # # let user click a point on figure # user_input = np.array(plt.ginput(1,timeout=-1)[0]) # if user_input[1] >= offset: # for index in theindices: # refined_indices.append(index) # elif user_input[1] < offset: # pass # else: # print('Something fishy happened!') # # reorganize pixels inside ROI # new_indices.append(refined_indices) # counter += 1 # # reassign ROI object # self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(new_indices,self.roi_obj.input_image.shape) # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) # self.roi_obj.indices = new_indices # self.roi_obj.kind = 'refined' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(new_indices) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(new_indices) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # def refine_rois_CW(self, hydra_obj, scan_numbers): # """ **refine_rois_CW** # Allows for manual refinement of ROIs by plotting the spectra column-wise. # Loops through the spectra column-by-column and ROI by ROI, click above the # black line to keep the column plotted, click below the black line to discard # the column of pixels. # Args: # ----- # hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used # for the refinement of the ROIs. # scan_numbers (int or list): Scan numbers of scans to be used in the refinement. # """ # # make scan_numbers itarable # if isinstance(scan_numbers,list): # scannums = scan_numbers # elif isinstance(scan_numbers,int): # scannums = [scan_numbers] # # get EDF-files and pw_data # hydra_obj.load_scan(scannums, direct=False) # cw_data = hydra_obj.get_pw_matrices( scannums, method='column' ) # plt.ioff() # counter = 0 # for data, key in zip(cw_data, sorted(self.roi_obj.red_rois)): # for ii in range(data.shape[1]): # plt.cla() # title_txt = 'Click above to keep/below black line to discard column for ROI %02d'%(counter+1) + '.' # plt.title(title_txt) # plt.plot(data[:,ii],'b-') # axes = plt.gca() # offset = np.mean(axes.get_ylim()) # plt.plot(np.zeros_like(data[:,ii]) + offset,'k-') # plt.xlabel('points along scan') # plt.ylabel('intensity [arb. units]') # plt.legend(['Column No. %02d / %02d' %(ii+1,data.shape[1])]) # # let user click a point on figure # user_input = np.array(plt.ginput(1,timeout=-1)[0]) # if user_input[1] >= offset: # pass # elif user_input[1] < offset: # self.roi_obj.red_rois[key][1][:,ii] = 0 # else: # print('Something fishy happened!') # counter += 1 # # go through all ROIs and delete empty ones # for key in self.roi_obj.red_rois.keys(): # if self.roi_obj.red_rois[key][1].shape == (0,0): # del self.roi_obj.red_rois[key] # # reassign ROI object # self.roi_obj.roi_matrix = xrs_rois.convert_redmatrix_to_matrix(self.roi_obj.red_rois, np.zeros(self.roi_obj.input_image.shape)) # self.roi_obj.indices = xrs_rois.convert_matrix_rois_to_inds(self.roi_obj.roi_matrix) # self.roi_obj.kind = 'refined' # self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) # self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) # self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) # self.roi_obj.number_of_rois = int(np.amax(self.roi_obj.roi_matrix)) # # compact red rois # self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix, labelformat= 'ROI%02d') def define_lin_roi(height,image_shape,verbose=False): """ Lets you pick 2 points on a current image and returns a linear ROI of height (2*height+1). height = number of pixels that define the height of the ROI image_shape = tuple with shape of the current image (i.e. (256,256)) """ endpoints = list(np.round(plt.ginput(2,timeout=-1))) # check that selected points are in the image for point in endpoints: if point[0] < 0.0: point = 0 if point[0] > image_shape[1]: point = image_shape[1] if point[1] < 0.0: point[1] = 0 if point[1] > image_shape[0]: point[1] = image_shape[0] # check that point 2 is bigger than point 1 in the x direction if endpoints[1][0]< endpoints[0][0]: endpoints[0],endpoints[1] = endpoints[1],endpoints[0] # print the limits of the rectangle in the shell if not verbose: print( 'The selected points are: ', [[endpoints[0][1],endpoints[0][0]],[endpoints[1][1],endpoints[1][0]]] ) roix = np.arange(endpoints[0][0],endpoints[1][0]) roiy = [round(num) for num in np.polyval(np.polyfit([endpoints[0][0],endpoints[1][0]],[endpoints[0][1],endpoints[1][1]],1),roix)] roi = [] height = np.arange(-height,height) for n in range(len(roix)): for ind in height: roi.append((int(roiy[n]+ind),int(roix[n]))) return roi def define_zoom_roi(input_image,verbose=False): """ Parses the current figure limits and uses them to define a rectangle of " roi_number"s in the matrix given by roi_matrix. input_image = unzoomed figure roi_matrix = current roi matrix which will be altered """ # input_image.shape prints (y-length, x-length) # parse the figure limits from the current zoom # limits = np.round(plt.axis()) # x-min, x-max, y-max, y-min frac_limits = plt.axis() # x-min, x-max, y-max, y-min as floats print( frac_limits ) limits = np.array([np.ceil(frac_limits[0]), np.floor(frac_limits[1]+0.5), np.floor(frac_limits[2]+0.5), np.ceil(frac_limits[3])]) # x-min, x-max, y-max, y-min as ints # check that selected zoom area is not outside the image inds = limits < 0 limits[inds] = 0 if limits[1] > input_image.shape[1]: limits[1] = input_image.shape[1] if limits[2] > input_image.shape[0]: limits[2] = input_image.shape[0] # sort the limits in ascenging order limitsy = limits[2:4] # vertical limitsy.sort() limitsx = limits[0:2] # horizontal limitsx.sort() # print the limits of the rectangle in the shell if not verbose: print( 'The selected limits are: ', limitsx, limitsy ) # prepare a n*m matrix with one roi T = np.zeros(input_image.shape) T[int(limitsy[0]):int(limitsy[1]),int(limitsx[0]):int(limitsx[1])] = 1 indsy,indsx = np.where(T == 1) roi = [] for n in range(len(indsx)): roi.append((int(indsy[n]),int(indsx[n]))) return roi def show_rois(roi_matrix): """ Creates a figure with the defined ROIs as numbered boxes on it. """ roi_matrix # check if there are ROIs defined if not np.any(roi_matrix): print( 'Please select some rois first.' ) # make a figure else: plt.ioff() plt.imshow(roi_matrix) plt.xlabel('x-axis [pixel]') plt.ylabel('y-axis [pixel]') # add a label with the number to the center of each ROI for ii in range(int(np.amax(roi_matrix))): # find center of the ROI and write the label inds = np.where(roi_matrix[:,:] == ii+1) xcenter = np.mean(inds[1]) ycenter = np.mean(inds[0]) string = '%02d' % (ii+1) plt.text(xcenter,ycenter,string) plt.show() plt.ion() def test_roifinder(roi_type_str, imagesize = [512,768], scan = None ): """ Runs the roi_finder class on a random image of given type for testing purposes. scan[0] = absolute path to a spec file scan[1] = energycolumn='energy' scan[2] = monitorcolumn='kap4dio' scan[3] = scan number from which to take images """ strings = ['zoom','linear','auto'] if not roi_type_str in strings: print( 'Only ' + str(strings) + ' testable, choose one of them!' ) return # make a random image if not scan: rand_image = np.random.rand(imagesize[0],imagesize[1]) else: import xrs_read, xrs_utilities read_obj = xrs_read.read_id20(scan[0],energycolumn=scan[1],monitorcolumn=scan[2]) read_obj.loadelastic(scan[3]) key = 'Scan%03d' % scan[3] rand_image = xrs_utilities.sumx(read_obj.scans[key].edfmats) # create a roi_finder object roi_finder_obj = roi_finder() if roi_type_str == 'zoom': roi_finder_obj.get_zoom_rois(rand_image,logscaling=True,colormap='Blues',interpolation='nearest') roi_finder_obj.show_rois() elif roi_type_str == 'linear': roi_finder_obj.get_linear_rois(rand_image,logscaling=True,height=5,colormap='Blues',interpolation='nearest') roi_finder_obj.show_rois() elif roi_type_str == 'auto': roi_finder_obj.get_auto_rois(rand_image,kernel_size=5,threshold=1.0,logscaling=True,colormap='Blues',interpolation='nearest') roi_finder_obj.show_rois() def create_diff_image(scans,scannumbers,energy_keV): """ Returns a summed image from all scans with numbers 'scannumbers'. scans = dictionary of objects from the scan-class scannumbers = single scannumber, or list of scannumbers from which an image should be constructed """ # make 'scannumbers' iterable (even if it is just an integer) numbers = [] if not isinstance(scannumbers,list): numbers.append(scannumbers) else: numbers = scannumbers key = 'Scan%03d' % numbers[0] below_image = np.zeros_like(scans[key].edfmats[0,:,:]) above_image = np.zeros_like(scans[key].edfmats[0,:,:]) # find indices below and above 'energy' below_inds = scans[key].energy < energy_keV above_inds = scans[key].energy > energy_keV for number in numbers: key = 'Scan%03d' % number for ii in below_inds: below_image += scans[key].edfmats[ii,:,:] for ii in above_inds: above_image += scans[key].edfmats[ii,:,:] return (above_image - below_image) def define_polygon_roi(image_shape,verbose=False): """ Define a polygon shaped ROI from a current image by selecting points. """ tmptuple=plt.ginput(0,timeout=-1,show_clicks=True) pnts=np.array(tmptuple) if verbose: print( 'The selected points are:' ) print( pnts ) # Create the polygon path from points clicked path=Path(pnts) # bounding box: everything outside this is necessarily zero bbx = np.arange( np.floor(min( pnts[:,0])), np.ceil(max( pnts[:,0]))) bby = np.arange( np.floor(min( pnts[:,1])), np.ceil(max( pnts[:,1]))) roi=[] for ii in bbx: for jj in bby: # Test point if path.contains_point([ii, jj]): roi.append( (jj, ii)) return roi xrstools-0.15.0+git20210910+c147919d/XRStools/runcowan.py000066400000000000000000000263331412732462000221240ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: runcowan.py import os import numpy as np from scipy import interpolate, signal, integrate, constants, optimize, ndimage import pylab from six.moves import range # the scheme: # take rcn-file # run RCN2.sh # edit rcf to rcg # run RCG2.sh # create rac-file # run RAC2.sh # create/take plo-file # run PLO2.sh # read .dat with readracah # square difference, minimize that def spline2(x,y,x2): """ Extrapolates the smaller and larger valuea as a constant """ xmin = np.min(x) xmax = np.max(x) imin = x == xmin imax = x == xmax f = interpolate.interp1d(x,y, bounds_error=False, fill_value=0.0) y2 = f(x2) i = np.where(x2xmax) y2[i] = y[imax] return y2 def gauss_kern(size, sizey=None): """ Returns a normalized 2D gauss kernel array for convolutions """ size = int(size) if not sizey: sizey = size else: sizey = int(sizey) #print size, sizey x, y = mgrid[-size:size+1, -sizey:sizey+1] g = exp(-(x**2/float(size)+y**2/float(sizey))) return g / g.sum() def blur_image(im, n, ny=None) : """ blurs the image by convolving with a gaussian kernel of typical size n. The optional keyword argument ny allows for a different size in the y direction. """ g = gauss_kern(n, sizey=ny) improc = signal.convolve(im,g, mode='valid') return(improc) def gauss(x,a): """ returns a gaussian with peak value normalized to unity a[0] = peak position a[1] = Full Width at Half Maximum """ y = np.exp(-np.log(2.0)*((x-a[0])/a[1]*2.0)**2.0) return y def lorentz(x,x0,fwhm): """ % OUTPUT = LORENTZ(X,X0,FWHM) % X = x-scale (row or column vector) % X0 = peak position % FWHM = Full Width at Half Maximum of the Lorentzian """ y = (((x-x0)/(fwhm/2.0))**2.0+1.0)**(-1.0) return y def lorentz2(x,x0,w): y = 0.5*w/((x-x0)**2.0+(0.5*w)**2.0)/np.pi return y def convg(x,y,fwhm): """ Convolution with Gaussian """ dx = np.min(np.absolute(np.diff(x))) x2 = np.arange(np.min(x)-1.5*fwhm, np.max(x)+1.5*fwhm, dx) xg = np.arange(-np.floor(2.0*fwhm/dx)*dx, np.floor(2.0*fwhm/dx)*dx, dx) yg = gauss(xg,[0,fwhm]) yg = yg/np.sum(yg) y2 = spline2(x,y,x2) c = np.convolve(y2,yg, mode='full') n = int( np.floor(np.max(np.shape(xg))//2)) c = c[n:len(c)-n+1] # not sure about the +- 1 here f = interpolate.interp1d(x2,c) return f(x) def readracah(fname,degauss=0.5,delorentz=0.4): lines = open(fname,'r').readlines() foundsticks = [] A = [] B = [] counter = 0 for line in lines: counter += 1 if 'Sticks' in line: break for line in lines[:counter-2]: A.append([float(line.strip().split()[0]), float(line.strip().split()[1])]) for line in lines[counter+1:]: B.append([float(line.strip().split()[0]), float(line.strip().split()[1])]) espectr = np.array([col[0] for col in A]) yspectr = np.array([col[1] for col in A]) estick = np.array([col[0] for col in B]) ystick = np.array([col[1] for col in B]) # e = espectr e = np.arange( np.min(estick)-10.0, np.max(estick)+10,np.mean(np.diff(espectr))) y = np.zeros_like(e) for ii in range(len(estick)): y += lorentz(e,estick[ii],delorentz)*ystick[ii] y = convg(e,y,degauss) return e,y,estick,ystick,espectr,yspectr def dqtox400(tendq,dt=0,ds=0): dq = tendq/10 x400 = np.sqrt(30.0)*(6.0*dq-7.0/2.0*dt) x420 = -5.0/2.0*np.sqrt(42.0)*dt x220 = -np.sqrt(70.0)*ds return x400,x420,x220 def x400todq(x400,x420=0,x220=0): dq = x400/np.sqrt(30.0)/6.0-7.0/30.0*x420/np.sqrt(42.0) tendq = 10.0*dq ds = -x220/np.sqrt(70.0) dt = -2.0/5.0*x420/np.sqrt(42.0) return tendq,dt,ds def createracinput(fname1,fname2,tendq): x400 = dqtox400(tendq)[0] fid1 = open(fname1,'r').readlines() fid2 = open(fname2,'w+') for line in fid1: if not 'BRANCH 4+' in line: fid2.write(line) else: string = ' BRANCH 4+ > 0 0+ %.2f\n' % x400 fid2.write(string) fid2.close() def writercginput_r1(fnamef,fnameg,scscale): fidf = open(fnamef,'r').readlines() fidg = open(fnameg,'w+') string = ' 10 1 0 14 2 4 1 1 SHELL03000000 SPIN03000000 INTER8 \n' fidg.write(string) string = ' 0 %2d99%2d%2d 8065.47800 0000000 \n' % (scscale,scscale,scscale) fidg.write(string) for line in fidf[2:]: fidg.write(line) fidg.close() def writercginput_r2(fnamef,fnameg,scscale): fidf = open(fnamef,'r').readlines() fidg = open(fnameg,'w+') string = ' 10 1 0 14 2 6 2 2 SHELL03000000 SPIN03000000 INTER8 \n' fidg.write(string) string = ' 0 %2d99%2d%2d 8065.47800 0000000 \n' % (scscale,scscale,scscale) fidg.write(string) string = ' 1 2 1 12 1 10 00 9 00000000 3 8065.4790 .00 1\n' fidg.write(string) for line in fidf[3:]: fidg.write(line) fidg.close() def writercginput_r3(fnamef,fnameg,scscale): fidf = open(fnamef,'r').readlines() fidg = open(fnameg,'w+') string = ' 10 1 0 14 2 4 3 3 SHELL03000000 SPIN03000000 INTER8 \n' fidg.write(string) string = ' 0 %2d99%2d%2d 8065.47800 0000000 \n' % (scscale,scscale,scscale) fidg.write(string) for line in fidf[2:]: if '//R1//' in line: s = 'Fe2+ 3P06 3D06 Fe2+ 3p05 3D07 1.21660( 3P//R3// 3D)-0.991HR -91 -96\n' fidg.write(s) else: fidg.write(line) fidg.close() def for_fitfe2_r1(a,e,y): scscale = a[1]#int(np.around(80)) # a[1] tendq = a[0] eshift = 1.5 if scscale<0.0 or scscale>99.0 or tendq<-10.0 or tendq>7.0: d = 1000000 os.system('../batch/RCN2.sh fe3_r1') writercginput_r1('fe3_r1.rcf','fe3_r1.rcg',scscale) os.system('../batch/RCG2.sh fe3_r1') createracinput('rac_Oh_template.rac','fe3_r1.rac',tendq) os.system('../batch/RAC2.sh fe3_r1') os.system('../batch/PLO2.sh fe3_r1') et,yt,estick,ystick,espectr,yspectr = readracah('fe3_r1.dat',degauss=1.5,delorentz=0.4) espectr = espectr-eshift f = interpolate.interp1d(espectr, yspectr,bounds_error=False,fill_value=0.0) yspectr = f(e) yspectr = yspectr/np.trapz(yspectr,e)*np.trapz(y,e) pylab.plot(e,yspectr,e,y) pylab.show(block=False) #return np.sum((yt-y)**2.0) return yspectr def make_10dq_series(tendqs,scscale,degauss=1.0,delorentz=0.4): """ calculates spectra for a series of 10dq values writes a file with columns: energy, 10dq1, 10dq2, ... """ sticks = {} spectra = {} spectra2 = {} # run the first tendq to get the output shape etc. for tendq in tendqs: os.system('../batch/RCN2.sh fe3_r2') writercginput_r2('fe3_r2.rcf','fe3_r2.rcg',scscale) os.system('../batch/RCG2.sh fe3_r2') createracinput('rac_Oh_template_r2.rac','fe3_r2.rac',tendq) os.system('../batch/RAC2.sh fe3_r2') os.system('../batch/PLO2.sh fe3_r2') et,yt,estick,ystick,espectr,yspectr = readracah('fe3_r2.dat',degauss=degauss,delorentz=delorentz) sticks[str(tendq)] = estick,ystick spectra[str(tendq)] = et,yt spectra2[str(tendq)] = espectr,yspectr return sticks, spectra, spectra2 def write_dict_to_txt(dictionary,prefix,postfix): for key in dictionary: fname = prefix + key + postfix data = np.zeros((len(dictionary[key][0]),len(dictionary[key]))) for ii in range(len(dictionary[key])): data[:,ii] = dictionary[key][ii] np.savetxt(fname,data) # make calculations for Christopher Weiss tendqs = np.arange(1.0,5.1,0.1) scscale = 80.0 sticks, spectra, spectra2 = make_10dq_series(tendqs,scscale,degauss=1.0,delorentz=0.4) from pylab import * ion() thelegend = [] for key in spectra: thelegend.append(key) plot(spectra[key][0],spectra[key][1]) legend(thelegend) #exp = np.loadtxt('/home/christoph/data/fe_data_alex/2/fe3oct_lq.dat') #e = exp[:,0] #y = exp[:,1] #fitfunc = lambda p, x, y: for_fitfe2_r1(p,x,y) # Target function #errfunc = lambda p, x, y: fitfunc(p, x, y) - y # Distance to the target function #p0 = [2.1,70] # Initial guess for the parameters #p1, success = optimize.leastsq(errfunc, p0[:], args=(e, y)) #a = for_fitfe2_r1(p1,e,y) #p1 = [2.5,70] #pylab.plot(e,y,e,for_fitfe2_r1(p1,e,y)) #pylab.show() #print p1, success #optimize.leastsq(for_fitfe2_r1, [1.3, 2.1], args=(e,y)) # work in xrstools/cowans/WORK directory # take rcn-file # run RCN2.sh #os.system('../batch/RCN2.sh fe3_r1') # edit rcf to rcg #writercginput_r1('fe3_r1.rcf','fe3_r1.rcg',80) # run RCG2.sh #os.system('../batch/RCG2.sh fe3_r1') # create rac-file #createracinput('rac_Oh_template.rac','fe3_r1.rac',1.3) # run RAC2.sh #os.system('../batch/RAC2.sh fe3_r1') # create/take plo-file # run PLO2.sh #os.system('../batch/PLO2.sh fe3_r1') # read .dat with readracah #e,y,estick,ystick,espectr,yspectr = readracah('fe3_r1.dat',degauss=1.5,delorentz=0.4) # square difference, minimize that #pylab.plot(e,y,espectr,yspectr) #pylab.show() #function [e,y,estick,ystick,espectr,yspectr]=readracah(fname,degauss,delorentz,delorentz_split); #%function [e,y,estick,ystick,espectr,yspectr]=readracah(fname,degauss,delorentz,delonrentz_split); #% degauss = fwhm of gaussian broadening (eV); delorentz=fwhm of lorentzian broadening # #try, degauss; catch, degauss=0.5; end #try, delorentz; catch, delorentz=0.4; end #try, delorentz_split; catch; delorentz_split=[]; end #if length(delorentz_split)~=length(delorentz)-1, error('Need also lorentz conv split'); end # #fid=fopen(fname,'r'); #foundsticks=[];A=[];B=[]; #while ~feof(fid) & length(foundsticks)==0, # s=fgetl(fid); # foundsticks=strfind(s,'Sticks'); # if length(foundsticks)==0, # A=[A;str2num(s)]; # end #end #while ~feof(fid); # s=fgetl(fid); # B=[B;str2num(s)]; #end #fclose(fid); #espectr=A(:,1);yspectr=A(:,2); #estick=B(:,1); ystick=B(:,2); #%e=espectr; #e=[min(estick)-10:mean(diff(espectr)):max(estick)+10]'; #delor=ones(size(estick))*delorentz(1); #y=zeros(size(e)); #for ii=1:length(delorentz_split) # delor(find(estick>delorentz_split(ii)))=delorentz(ii+1); #% ystick(find(estick>delorentz_split(ii)))=ystick(find(estick>delorentz_split(ii)))*1.3; #end # #for ii=1:length(estick); # y=y+lorentz2(e,estick(ii),delor(ii))*ystick(ii); #end #y=convg(e,y,degauss); #function d=fitti4(a,e,y); #%scscale=a(1); #scscale=70; #tendq=a(1); #eshift=a(2); #scscale=round(scscale); #if (scscale<1 | scscale>99 | tendq<1 | tendq>3), d=1000000; #else #!c:\cowan\batch\rcn2 ti4 #writercginp; #!c:\cowan\batch\rcg2 ti4 #createracinput('ti4orig.rac','ti4.rac',tendq); #!c:\cowan\batch\rac2 ti4 #!c:\cowan\batch\plo2 ti4 #[et,yt]=readracah('ti4_r1.dat',1.0,[0.2 0.9]*2,[466.7]); #et=et-eshift; #yt=cnan(interp1(et,yt,e)); #y=y/isum(e,y)*isum(e,yt); #plot(e,y,e,yt);drawnow #d=sum((yt-y).^2); #end xrstools-0.15.0+git20210910+c147919d/XRStools/scripts/000077500000000000000000000000001412732462000213765ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/scripts/XRS_raman_extraction000077500000000000000000000002061412732462000254140ustar00rootroot00000000000000#!python if __name__ == '__main__': import XRStools as XRStools from XRStools.ramanWidget.MainWindow import main main() xrstools-0.15.0+git20210910+c147919d/XRStools/scripts/XRS_swissknife000077500000000000000000000002121412732462000242400ustar00rootroot00000000000000#!python if __name__ == '__main__': import XRStools as XRStools import XRStools.XRS_swissknife XRStools.XRS_swissknife.main() xrstools-0.15.0+git20210910+c147919d/XRStools/scripts/XRS_wizard000077500000000000000000000003711412732462000233610ustar00rootroot00000000000000#!python if __name__ == '__main__': import XRStools as XRStools import XRStools.WIZARD.XRS_wizard as XRS_wizard XRS_wizard.version = XRStools.version XRS_wizard.Wizard.version = XRStools.version XRStools.WIZARD.XRS_wizard.main() xrstools-0.15.0+git20210910+c147919d/XRStools/setup.py000066400000000000000000000013341412732462000214220ustar00rootroot00000000000000 import os from numpy.distutils.misc_util import Configuration def configuration(parent_package='', top_path=None): config = Configuration('XRStools', parent_package, top_path) config.add_subpackage('roiNmaSelectionGui') config.add_subpackage('WIZARD') config.add_subpackage('ramanWidget') config.add_subpackage('XRStools_c') config.add_subpackage('resources') # includes third_party only if it is available local_path = os.path.join(top_path, parent_package, "XRStools", "third_party") if os.path.exists(local_path): config.add_subpackage('third_party') return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration=configuration) xrstools-0.15.0+git20210910+c147919d/XRStools/simple_roi_finder.py000066400000000000000000000452121412732462000237560ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import logging import numpy as np import math import matplotlib.pyplot as plt import shelve from matplotlib.path import Path from matplotlib.widgets import Cursor, Button from scipy.ndimage import measurements from scipy import signal, stats, sparse import copy from . import xrs_rois, xrs_read try: from skimage import draw __have_skimage__ = True except: logging.getLogger(__name__).warning( "WARNING: Skimage not available. Lasso selection disabled. You can count on rectangular selection only. . You can install skimage." ) __have_skimage__ = False import matplotlib from matplotlib.widgets import RectangleSelector from matplotlib.widgets import LassoSelector from matplotlib.widgets import RadioButtons from matplotlib.widgets import Slider from matplotlib.widgets import TextBox from matplotlib import gridspec # from ixsscan.roi import Roi class ZoomRoiFinder: """Simple ROI finder for rectangular ROIs. Parameters: ----------- input_image : np.array (2D) Image to use for the selection of the ROI. roi : roi.Roi object log_scaling : boolean Switch if image is to be displayed with logarithmic scaling. Default is 'True'. cmap : str Matplotlib colormap keyword. Default is 'Blues'. interpolation : str Keyword for matplotlib.imshow. Default is 'nearest'. ToDo: - use matplotlib RectangleSelector widget - implement alteration of input ROI obj. - use roi class rectangle definition function """ def reset_roi_obj(self): self.roi_obj._reset(self) def __init__(self, input_image, roi=None, log_scaling=True, cmap="Blues", interpolation="nearest"): if roi is None: self.roi_obj = Roi() else: self.roi_obj = roi # check that the input is a 2d matrix assert len(input_image.shape) == 2, f"Provided input image is of shape {input_image.shape}, need 2D image to proceed." # save input image for later use self.input_image = copy.deepcopy(input_image) # calculate the logarithm if 'logscaling' == True if log_scaling: # set all zeros to ones: input_image[input_image[:, :] == 0.0] = 1.0 input_image = np.log(np.abs(input_image)) # first ROI index roi_ind = 0 # prepare a figure fig, ax = plt.subplots() plt.subplots_adjust(bottom=0.2) cursor = Cursor(ax, useblit=True, color="black", linewidth=1) ax.imshow(input_image, interpolation=interpolation, cmap=cmap) # no ticks, no labels ax.tick_params(left=False, labelleft=False, right=False, labelright=False, bottom=False, top=False, labelbottom=False) ax.set_xlim(0.0, input_image.shape[1]) ax.set_ylim(input_image.shape[0], 0.0) title_txt = f"Active ROI is ROI No. {1} of {0}" ax.set_title(title_txt) # activate the zoom function already this_manager = plt.get_current_fig_manager() this_manager.toolbar.zoom() # plotter def plot_all(roi_ind, contour_lines): """Function to update plot window. Parameters ---------- roi_ind: ROI index (int). contour_lines: Dictionary of corners to draw contour lines around ROIs. """ fig.canvas.flush_events() ax.cla() title_txt = f"Active ROI is ROI No. {roi_ind} of {len(contour_lines)}" ax.imshow(input_image, interpolation=interpolation, cmap=cmap) # no ticks, no labels on axes ax.tick_params( left=False, labelleft=False, right=False, labelright=False, bottom=False, top=False, labelbottom=False ) ax.imshow(input_image, interpolation=interpolation, cmap=cmap) if contour_lines: for key in contour_lines: ax.plot(contour_lines[key][0], contour_lines[key][1], "-k") ax.set_xlim(0.0, input_image.shape[1]) ax.set_ylim(input_image.shape[0], 0.0) ax.set_title(title_txt) plt.draw() plt.pause(0.01) class Index(object): def __init__(self, red_rois): self.red_rois = red_rois self.roi_key = "ROI00" self.roi_ind = 0 self.num_ROI_min = 0 self.contour_lines = {} def take_and_next(self, event): # grep axis limits cur_xlim = ax.get_xlim() cur_ylim = ax.get_ylim() limits = np.array([np.ceil(cur_xlim[0]), np.floor(cur_xlim[1]), np.floor(cur_ylim[1]), np.ceil(cur_ylim[0])]) # make sure they are inside the image inds = limits < 0 limits[inds] = 0 if limits[1] > input_image.shape[1]: limits[1] = input_image.shape[1] if limits[2] > input_image.shape[0]: limits[2] = input_image.shape[0] print(f"Chosen limits are (x_min, x_max, y_min, y_max): ", limits) self.red_rois[self.roi_ind] = (np.array([int(limits[2]), int(limits[0])])), sparse.coo_matrix( np.ones((int(limits[3] - limits[2]), int(limits[1] - limits[0]))) ) self.contour_lines[self.roi_ind] = [ [limits[0] - 0.5, limits[1] + 0.5, limits[1] + 0.5, limits[0] - 0.5, limits[0] - 0.5], [limits[2] - 0.5, limits[2] - 0.5, limits[3] + 0.5, limits[3] + 0.5, limits[2] - 0.5], ] plot_all(self.roi_ind, self.contour_lines) # go to next roi self.roi_ind += 1 self.roi_key = f"ROI{self.roi_ind:02d}" plot_all(self.roi_key, self.contour_lines) def middle_mouse_press(self, event): if event.button == 2: self.take_and_next(event) def take_and_exit(self, event): # grep axis limits cur_xlim = ax.get_xlim() cur_ylim = ax.get_ylim() limits = np.array( [np.ceil(cur_xlim[0]), np.floor(cur_xlim[1] + 0.5), np.floor(cur_ylim[1] + 0.5), np.ceil(cur_ylim[0])] ) # make sure they are inside the image inds = limits < 0 limits[inds] = 0 if limits[1] > input_image.shape[1]: limits[1] = input_image.shape[1] if limits[2] > input_image.shape[0]: limits[2] = input_image.shape[0] print(f"Chosen limits are (x_min, x_max, y_min, y_max): ", limits) self.red_rois[self.roi_ind] = (np.array([int(limits[2]), int(limits[0])])), sparse.coo_matrix( np.ones((int(limits[3] - limits[2]), int(limits[1] - limits[0]))) ) self.contour_lines[self.roi_ind] = [ [limits[0] + 0.5, limits[1] + 0.5, limits[1] + 0.5, limits[0] + 0.5, limits[0] + 0.5], [limits[2] + 0.5, limits[2] + 0.5, limits[3] + 0.5, limits[3] + 0.5, limits[2] + 0.5], ] plot_all(self.roi_ind, self.contour_lines) plt.close() def discard(self, event): del self.red_rois[self.roi_key] del self.contour_lines[self.roi_key] plot_all(self.roi_key, self.contour_lines) def prev_roi(self, event): if self.roi_ind > self.num_ROI_min: self.roi_ind -= 1 self.roi_key = f"ROI{self.roi_ind:02d}" plot_all(self.roi_key, self.contour_lines) else: pass def next_roi(self, event): self.roi_ind += 1 self.roi_key = f"ROI{self.roi_ind:02d}" plot_all(self.roi_key, self.contour_lines) def exit(self, event): plt.close() callback = Index(self.roi_obj._rois) # buttons for next/prev roi axprev_roi = plt.axes([0.58, 0.05, 0.1, 0.075]) axnext_roi = plt.axes([0.7, 0.05, 0.1, 0.075]) bnext_roi = Button(axnext_roi, "next ROI") bnext_roi.on_clicked(callback.next_roi) bprev_roi = Button(axprev_roi, "prev ROI") bprev_roi.on_clicked(callback.prev_roi) # take and next button axtakeN = plt.axes([0.04, 0.05, 0.17, 0.075]) btakeN = Button(axtakeN, "take+next\n (mouse wheel)") btakeN.on_clicked(callback.take_and_next) # take and exit axtakeE = plt.axes([0.24, 0.05, 0.17, 0.075]) btakeE = Button(axtakeE, "take+exit") btakeE.on_clicked(callback.take_and_exit) # discard button axdisc = plt.axes([0.46, 0.05, 0.1, 0.075]) bdisc = Button(axdisc, "discard") bdisc.on_clicked(callback.discard) # finish button axfin = plt.axes([0.82, 0.05, 0.1, 0.075]) bfin = Button(axfin, "exit") bfin.on_clicked(callback.exit) cid = fig.canvas.mpl_connect("button_press_event", callback.middle_mouse_press) # show figure plt.show() # assign rois self.roi_obj._rois = callback.red_rois self.roi_obj.configure_extraction(restrain_to=None) class SimpleRoiFinder: def __init__(self, roi=None): if roi is None: # self.roi_obj = Roi() self.roi_obj = xrs_rois.roi_object() else: self.roi_obj = roi def to_roi(self, event): self.roi_obj.mask_into_rois(self.mask) def cancel_roi(self, event): ID = self.roi_id_slider.val self.mask[self.mask == ID] = np.nan self.mask_figure_obj.remove() self.mask_figure_obj = self.ax_imshow.imshow( (np.minimum(0, self.mask) + 1) * self.input_image, "jet", interpolation=self.interpolation, alpha=0.5 ) self.annotate_spots() self.fig_imshow.canvas.draw() self.fig_imshow.canvas.flush_events() def prepare_image(self, exp_dir = "/data/run7_ihr_gz/hydra", scans = [592, 593, 594, 595, 596, 597, 598, 599] , monitorcolumn = 'kapraman' ): experiment = xrs_read.read_id20( exp_dir ,monitorcolumn=monitorcolumn) edfmat = experiment.read_just_first_scanimage(scans[0]) image4roi = experiment.SumDirect( scans ) return image4roi def gui_session(self, input_image, modind=-1, logscaling=True, colormap="Blues", interpolation="nearest", forbidden_mask = None): self.forbidden_mask = forbidden_mask # check that the input is a 2d matrix if not len(input_image.shape) == 2: print("Please provide a 2D numpy array as input!") return # calculate the logarithm if 'logscaling' == True if logscaling: # set all zeros to ones: input_image[input_image[:, :] == 0.0] = 1.0 input_image = np.log(np.abs(input_image)) self.roi_obj.input_image = input_image self.mask = self.roi_obj.get_rois_as_mask(input_image.shape) self.input_image = input_image self.interpolation = interpolation # FIG 1 ( fig_imageshow with axis ax_imshow) fig_imshow, ax_imshow = plt.subplots() plt.subplots_adjust(bottom=0.05, top=1, left=0.05, right=1) # FIG2 fig2 = plt.figure() axcolor = "lightgoldenrodyellow" ax_which_action = plt.axes([0.05, 0.7, 0.15, 0.15], facecolor=axcolor) ax_which_selection = plt.axes([0.3, 0.7, 0.15, 0.15], facecolor=axcolor) ax_suppr = plt.axes([0.05, 0.4, 0.15, 0.15], facecolor=axcolor) ax_save = plt.axes([0.3, 0.4, 0.15, 0.15], facecolor=axcolor) ax_slider = plt.axes([0.1, 0.05, 0.8, 0.05], facecolor=axcolor) titlestring = "" titleInst = plt.suptitle(titlestring) cursor = Cursor(ax_imshow, useblit=True, color="red", linewidth=1) # generate an image to be displayed figure_obj = ax_imshow.imshow(input_image, interpolation=interpolation) # set the colormap for the image figure_obj.set_cmap(colormap) self.mask_figure_obj = ax_imshow.imshow( (np.minimum(0, self.mask) + 1) * input_image, "jet", interpolation=interpolation, alpha=0.5 ) ## ~~~~~~~~~~~~~~~~~~ Button Cancel Roi ~~~~~~~~~~~~~~~~~~~~~~~~~ self.b_cancel = Button(ax_suppr, "Suppr.", color=axcolor, hovercolor="red") self.b_cancel.on_clicked(self.cancel_roi) # ---------------------------------------------------------- ## ~~~~~~~~~~~~~~~~~~ Button To Roi ~~~~~~~~~~~~~~~~~~~~~~~~~ self.b_save = Button(ax_save, "To Roi Obj", color=axcolor, hovercolor="red") self.b_save.on_clicked(self.to_roi) # ---------------------------------------------------------- ## ~~~~~~~~~~~~~~~~~~~~~~ which Action ~~~~~~~~~~~~~~~~~~~~ axcolor = "lightgoldenrodyellow" rax = plt.axes(ax_which_action, facecolor=axcolor) radio_which_action = RadioButtons(rax, ("add", "subtract")) def select_action(label): self.action = label plt.draw() radio_which_action.on_clicked(select_action) self.radio_which_action = radio_which_action # --------------------------------------------------------------------- ## ~~~~~~~~~~~~~~~~~~~~~~ which selection type ~~~~~~~~~~~~~~~~~~~~ axcolor = "lightgoldenrodyellow" rax = plt.axes(ax_which_selection, facecolor=axcolor) radio_which_selection_type = RadioButtons(rax, ("None", "Lasso", "Rectangle")) def select_selection(label): if label == "Lasso" and not __have_skimage__: logging.getLogger(__name__).warning( "WARNING: Skimage.draw not available. Lasso selector cannot work. You can install skimage" ) radio_which_selection_type.set_active(0) return self.lasso_selection.set_connection(label == "Lasso") self.rectangle_selection.set_connection(label == "Rectangle") return radio_which_selection_type.on_clicked(select_selection) # --------------------------------------------------------------------- roi_id_slider = Slider(ax_slider, "ID", 0, 72, valinit=0, valstep=1) self.roi_id_slider = roi_id_slider self.fig_imshow = fig_imshow self.ax_imshow = ax_imshow self.annotations = [] self.lasso_selection = HandSelection(axes=ax_imshow, selection_type="Lasso", gui_session=self) self.rectangle_selection = HandSelection(axes=ax_imshow, selection_type="Rectangle", gui_session=self) self.annotate_spots() plt.show() def on_selection_event(self, selection_type=None, args=None): """this method is meant to be called by HandSelection objects""" ID = self.roi_id_slider.val if selection_type == "Lasso": (points,) = args rr, cc = draw.polygon(np.array(points)[:, 1].astype(np.int), np.array(points)[:, 0].astype(np.int)) if len(rr) == 0 or len(cc) == 0: return else: if self.radio_which_action.value_selected == "add": self.mask[rr, cc] = ID else: self.mask[rr, cc] = math.nan else: start, end = args x1, y1, x2, y2 = map(int, (start.xdata, start.ydata, end.xdata, end.ydata)) x1, x2 = sorted((x1, x2)) y1, y2 = sorted((y1, y2)) if self.radio_which_action.value_selected == "add": self.mask[y1:y2, x1:x2] = ID else: self.mask[y1:y2, x1:x2] = math.nan if self.forbidden_mask is not None: self.mask[ self.forbidden_mask==1 ] = math.nan self.mask_figure_obj.remove() self.mask_figure_obj = None self.fig_imshow.canvas.draw() self.fig_imshow.canvas.flush_events() # self.gs.mask_figure_obj = self.current_ax.imshow( (1-np.isnan(self.gs.mask))*self.gs.input_image , 'jet', interpolation=self.gs.interpolation, alpha=0.5) self.mask_figure_obj = self.ax_imshow.imshow( (np.minimum(0, self.mask) + 1) * self.input_image, "jet", interpolation=self.interpolation, alpha=0.5 ) self.annotate_spots() self.fig_imshow.canvas.draw() self.fig_imshow.canvas.flush_events() def annotate_spots(self): while self.annotations: ann = self.annotations.pop() ann.remove() tmp_mask = np.copy(self.mask) tmp_mask[np.isnan(tmp_mask)] = -1 nspots = int(tmp_mask.max()) + 1 for i in range(0, nspots): m = (self.mask == i).astype("f") msum = m.sum() if msum: ny, nx = m.shape px = (m.sum(axis=0) * np.arange(nx)).sum() / msum py = (m.sum(axis=1) * np.arange(ny)).sum() / msum info = "(#%d)" % i self.annotations.append( self.ax_imshow.annotate( info, xy=(px, py), xycoords="data", xytext=(-20, 20), textcoords="offset pixels", horizontalalignment="right", verticalalignment="bottom", bbox=dict(boxstyle="round,pad=0.5", fc="yellow", alpha=0.4), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=0"), ) ) class HandSelection(object): def __init__(self, axes=None, selection_type=None, gui_session=None): self.current_ax = axes self.selection_type = selection_type self.gs = gui_session self.selector = None def onselect(self, *args): self.gs.on_selection_event(selection_type=self.selection_type, args=args) def connect(self): if self.selection_type == "Lasso": self.selector = LassoSelector(self.current_ax, onselect=self.onselect) elif self.selection_type == "Rectangle": self.selector = RectangleSelector(self.current_ax, onselect=self.onselect) def set_connection(self, value): if not value: if self.selector is not None: self.selector.disconnect_events() self.selector = None else: self.connect() xrstools-0.15.0+git20210910+c147919d/XRStools/spotdetection.py000066400000000000000000000460471412732462000231600ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import scipy.misc from scipy import signal import scipy from scipy.ndimage import maximum_filter import scipy.ndimage.morphology as morph import scipy.ndimage.measurements as meas ## import skimage ## import skimage.transform from numpy import gradient from six.moves import range from six.moves import zip LENMIN=10 def gradX(x): res=np.zeros_like(x) res[:, :-1]=x[:,:-1]-x[:,1:] return res def gradXm(x): res=np.zeros_like(x) res[:,1: ]=-x[:,:-1]+x[:,1:] return res def gradY(x): res=np.zeros_like(x) res[ :-1]=x[:-1]-x[1:] return res def gradYm(x): res=np.zeros_like(x) res[1: ]=-x[:-1]+x[1:] return res def lap(x): res = -( gradX(gradXm(x))+gradXm(gradX(x))+ gradY(gradYm(x)) + gradYm(gradY(x)) )/2.0 return res def Diff(x ,coeffs): print( coeffs.shape) print( x.shape) DY,DX = gradient(x) yy , xy = gradient(DY) dum , xx = gradient(DX) # xx = gradient(gradient(x,axis=1),axis=1) # yy = gradient(gradient(x,axis=0),axis=1) # xy = gradient(gradient(x,axis=0),axis=0) res = xx*coeffs[2] + yy*coeffs[0]+ +2*coeffs[1]*xy return res def shrink(tmp, binning): n_cols = (tmp.shape[0] // binning) n_rows = (tmp.shape[1] // binning) tmp = tmp[:n_rows * binning,:n_cols * binning] tmp.shape = [ n_rows, binning, n_cols, binning ] shrinked = tmp.max(axis=3).max(axis=1) return shrinked def deshrink( large , shrinked, binning ): n_cols = (large.shape[0] // binning) n_rows = (large.shape[1] // binning) tmp = large[:n_rows * binning,:n_cols * binning] tmp.shape = [ n_rows, binning, n_cols, binning ] shrinked=shrinked[:] shrinked.shape=[ n_rows,1 , n_cols, 1 ] tmp[:] = shrinked def ReadFile(name): data=scipy.misc.imread(name)*1.0 data=np.max(data, axis=-1) return data def CercaAnelli(A, lines=False): A=A.astype("d") # Nmarge=5 # A[:Nmarge ,:]=1 # A[:,:Nmarge ]=1 # A[-Nmarge:,:]=1 # A[:,-Nmarge:]=1 Ax = np.concatenate( [A[1:], A[:1] ] )- np.concatenate( [A[-1:], A[:-1] ] ) Ax = 2*Ax + np.concatenate( [Ax[:, 1:], Ax[:, :1] ], axis=1 ) + np.concatenate( [Ax[:,-1:], Ax[:,:-1] ] , axis=1 ) Ay = np.concatenate( [A[:, 1:], A[:, :1] ] , axis=1 ) - np.concatenate( [A[:, -1:], A[:, :-1] ] , axis=1 ) Ay = 2*Ay + np.concatenate( [Ay[ 1:], Ay[ :1] ], axis=0 ) + np.concatenate( [Ay[-1:], Ay[:-1] ] , axis=0 ) if lines: res=Canny_lines(Ax,Ay) else: res=Canny(Ax,Ay) # print len(res) return res def IsMaximum( i,j,slope): totry = [[i-1,j-1],[i,j-1],[i+1,j-1],[i-1,j],[i+1,j],[i-1,j+1],[i,j+1],[i+1,j+1], ] for punto in totry: if( slope[punto[0], punto[1] ]> slope[i,j]): return 0 return 1 def Canny(Ax,Ay): Angles = np.arctan2( Ax, -Ay ) Angles=Angles*180/np.pi Angles = np.floor( Angles/45 + 0.5) slope = Ax*Ax + Ay*Ay Nmarge=6 MaximumRatio=100.0 # maxfits = maximum_filter(slope, size=3) # indici = np.where(slope==maxfits) # ListLocalMaxima=[ [i,j] for i,j in zip(indici[0], indici[1]) ] Ni=Ax.shape[0] Nj=Ax.shape[1] ListLocalMaxima=[] for i in range( Nmarge, Ni-Nmarge): for j in range( Nmarge, Nj-Nmarge): if( IsMaximum( i,j,slope) ): ListLocalMaxima.append( [i,j] ) # print len(ListLocalMaxima) # raise steps={-5:[-1,1], -4:[-1,0],-3:[-1,-1], -2:[0,-1],-1:[1,-1],0:[1,0],1:[1,1],2:[0,1],3:[-1,1],4:[-1,0], 5:[-1,-1]} EdgeList=[] endpoints=[] EdgePoints=np.zeros(Ax.shape ) EdgePoints[:Nmarge ,:]=-1 EdgePoints[:,:Nmarge ]=-1 EdgePoints[-Nmarge:,:]=-1 EdgePoints[:,-Nmarge:]=-1 StartPoints = ListLocalMaxima for starting in StartPoints: EdgePointsTmp = np.zeros(Ax.shape ) s0=starting[0] s1=starting[1] pentevalue= slope[s0, s1 ] if(EdgePoints[ s0, s1 ]==0): Edge=[] Edge.append( [s0,s1] ) prendi=0 traccia=0 while(1): if( slope[s0, s1 ]/pentevalue < 1.0/MaximumRatio): prendi=0 traccia=1 break EdgePointsTmp[ s0, s1 ]=1 direction = Angles[s0,s1] ss = [ steps[direction-1], steps[direction], steps[direction+1]] pttrs = [ [s0+ss[0][0],s1+ss[0][1]], [s0+ss[1][0],s1+ss[1][1]], [s0+ss[2][0],s1+ss[2][1]] ] values = [ [slope[ pttrs[i][0], pttrs[i][1] ],i] for i in range(3) ] imax= max(values)[1] s0=pttrs[imax][0] s1=pttrs[imax][1] if(EdgePoints[ s0, s1 ]): prendi=0 traccia=1 break if( EdgePointsTmp[ s0, s1 ] ): prendi=1 traccia=1 Edge.reverse() newedge = [ ] for p in Edge : if tuple(p)== (s0,s1): break newedge.append(p) Edge = [[s0,s1]]+newedge break else: Edge.append( [s0,s1] ) if(prendi): endpoints.append([s0,s1]) EdgeList.append(Edge) if traccia: for p in Edge: EdgePoints[p[0],p[1]]=1 # values = [ [len(edge),edge] for edge in EdgeList] # edge = max(values)[1] values = [ edge for edge in EdgeList if len(edge)>10 ] # edge = max(values)[1] # return [edge] return values def Canny_lines(Ax,Ay): Angles = np.arctan2( Ax, -Ay ) Angles=Angles*180/np.pi Angles = np.floor( Angles/45 + 0.5) slope = Ax*Ax + Ay*Ay Nmarge=1 MaximumRatio=10000.0 # maxfits = maximum_filter(slope, size=3) # indici = np.where(slope==maxfits) # ListLocalMaxima=[ [i,j] for i,j in zip(indici[0], indici[1]) ] Ni=Ax.shape[0] Nj=Ax.shape[1] ListLocalMaxima=[] for i in range( Nmarge, Ni-Nmarge): for j in range( Nmarge, Nj-Nmarge): if( IsMaximum( i,j,slope) ): ListLocalMaxima.append( [i,j] ) # print len(ListLocalMaxima) # raise steps={-5:np.array([-1,1]), -4:np.array([-1,0]),-3:np.array([-1,-1]), -2:np.array([0,-1]),-1:np.array([1,-1]), 0:np.array([1,0]),1:np.array([1,1]),2:np.array([0,1]),3:np.array([-1,1]),4:np.array([-1,0]), 5:np.array([-1,-1])} EdgeList=[] endpoints=[] EdgePoints=np.zeros(Ax.shape ) EdgePoints[:Nmarge ,:]=-1 EdgePoints[:,:Nmarge ]=-1 EdgePoints[-Nmarge:,:]=-1 EdgePoints[:,-Nmarge:]=-1 StartPoints = ListLocalMaxima stack = [] for starting in StartPoints: s0=starting[0] s1=starting[1] pentevalue= slope[s0, s1 ] stack.append( [ (s0, s1), pentevalue, 1 ] ) while len(stack): starting, pentevalue, direction_fact = stack[-1] stack=stack[:-1] print( " inizio da ", starting) EdgePointsTmp = np.zeros(Ax.shape ) s0=starting[0] s1=starting[1] pentevalue= slope[s0, s1 ] if(EdgePoints[ s0, s1 ]==0): Edge=[] Edge.append( [s0,s1] ) prendi=0 traccia=0 while(1): print( s0,s1) if( slope[s0, s1 ]/pentevalue < 1.0/MaximumRatio): if direction_fact==1 : prendi=0 traccia=0 if len(Edge)>4: stack.append( [ Edge[-4], pentevalue, -1 ]) print( " troppo debole Inverto") else: prendi= len(Edge)>5 traccia=1 print( " troppo debole finisco") break EdgePointsTmp[ s0, s1 ]=1 direction = Angles[s0,s1] ss = np.array([ steps[direction-1], steps[direction], steps[direction+1]])*direction_fact pttrs = [ [s0+ss[0][0],s1+ss[0][1]], [s0+ss[1][0],s1+ss[1][1]], [s0+ss[2][0],s1+ss[2][1]] ] values = [ [slope[ pttrs[i][0], pttrs[i][1] ],i] for i in range(3) ] imax= max(values)[1] s0=pttrs[imax][0] s1=pttrs[imax][1] if(EdgePoints[ s0, s1 ]): print( " scontro vecchio in ", s0, s1) if direction_fact==-1: Edge=Edge[:-10] prendi=1 traccia=1 break else: EdgePointsTmp[:]=0 prendi=0 traccia=0 if len(Edge)>10: stack.append( [ Edge[-4], pentevalue, -1 ]) print( " troppo debole Inverto") break if( EdgePointsTmp[ s0, s1 ] ): print( " scontro nuovo ") prendi=1 traccia=1 Edge.reverse() newedge = [ ] for p in Edge : if tuple(p)== (s0,s1): break newedge.append(p) Edge = [[s0,s1]]+newedge break else: print( " continup ") Edge.append( [s0,s1] ) if(prendi): endpoints.append([s0,s1]) EdgeList.append(Edge) if traccia: for p in Edge: EdgePoints[p[0],p[1]]=1 # values = [ [len(edge),edge] for edge in EdgeList] # edge = max(values)[1] values = [ edge for edge in EdgeList if len(edge)>10 ] # edge = max(values)[1] # return [edge] return values def divergenza( Ax,Ay, edge): if( edge[-1] != edge[0]): edge.append(edge[0]) res=0 for i in range(len(edge)-1): p1=edge[i] p2=edge[i+1] Ax1=Ax[p1[0],p1[1] ] Ax2=Ax[p2[0],p2[1] ] Ay1=Ay[p1[0],p1[1] ] Ay2=Ay[p2[0],p2[1] ] Dx = p2[0]-p1[0] Dy = p2[1]-p1[1] res=res+ ( Dx*(Ay1+Ay2) -Dy*(Ax1+Ax2) )*0.5 return res def threshold_mask(mask, image, value ) : n=mask.max() npix=3 image = signal.medfilt2d(image, kernel_size=3) for i in range(1,n+1): icount=0 tmp_mask_old = 0 while(icount<100): tmp_mask = np.equal( i, mask ) if not np.any(tmp_mask): continue mask[:]=mask*(1-tmp_mask) massimo = (image*tmp_mask).max() print( " MASSIMO " , massimo) tmp_mask_2 = morph.grey_dilation(tmp_mask, footprint=np.ones([npix,npix]), structure=np.zeros([npix,npix])) print( " Npunti , value " , tmp_mask.sum(), tmp_mask_2.sum() , value) tmp_mask = np.less( value*massimo, image )*tmp_mask_2 print( " Npunti " , tmp_mask.sum()) mask[:]+=tmp_mask*i if ( tmp_mask^tmp_mask_old).sum()==0: break tmp_mask_old = tmp_mask print( " ================ ") print( mask.sum()) icount+=1 return mask def grow_mask(input, npix ) : output = morph.grey_dilation(input, footprint=np.ones([npix,npix]), structure=np.zeros([npix,npix])) return output def shrink_mask(input, npix ) : output = morph.grey_erosion(input, footprint=np.ones([npix,npix]), structure=np.zeros([npix,npix])) return output def get_spots_mask( A, rrA, median_size=5, nofroi=12, give_borders=False, tval=-1, Hough=False ) : if not Hough: res=get_spots_mask_Normal( A, rrA, median_size=median_size, nofroi=nofroi, give_borders=False, tval=-1 ) return res else: res=get_spots_mask_Lines( A, rrA, median_size=median_size, nofroi=nofroi, give_borders=False, tval=-1 ) return res def get_spots_mask_Normal( A, rrA, median_size=None, nofroi=12, give_borders=False, tval=-1, Hough=False ) : print( " qui Hough " , Hough) A = A*(1-rrA) A = signal.medfilt2d(A, kernel_size=median_size) mmax = A.max() A[Aamed/100] newmask = np.zeros(filled.shape,"i") i=1 for l in labs: newmask[np.equal(labels,l)]=i i+=1 return newmask def get_spots_mask_Lines( A,rrA, median_size=None, nofroi=12, give_borders=False, tval=-1 ): A=A*(1-rrA) thematrix = np.zeros( [A.shape[0]+4, A.shape[1]+4 ] , A.dtype) thematrix[2:-2 ,2:-2 ] = A thematrix=scipy.ndimage.filters.gaussian_filter(thematrix, 3.0) Nrows , Ncols = thematrix.shape submatrix = np.array(thematrix) mask, diff_coeffs = get_spots_mask_Lines_slave( submatrix , nofroi=nofroi, give_diff_coeffs=True , tval=tval) # ###################### # nspots = mask.max() # if nspots!=12: # print "WARNING: LESS SPOTS WERE FOUND : " , nspots # else: # print "GOOD!: FOUND : " , nspots, " SPOTS " # for l in range(1,nspots+1) : # maskzone = np.equal(mask,l) # submatrix[maskzone] = submatrix.max() # import pylab # f1=pylab.figure() # from matplotlib.colors import LogNorm # pylab.imshow(submatrix, norm=LogNorm(vmin=0.01)) # pylab.show() # ################################# ############################################################## submatrix = np.array(thematrix) for iter in range( (median_size*2)**2): submatrix = submatrix + Diff(submatrix,diff_coeffs )*0.5*0.2 newsub=np.zeros_like(submatrix) newsub[2:-2 , 2:-2 ] = submatrix [2:-2 , 2:-2 ] submatrix=newsub mask = get_spots_mask_Lines_slave( submatrix , nofroi=nofroi, give_diff_coeffs=False , tval=tval ) # ###################### # nspots = mask.max() # if nspots!=12: # print "WARNING: LESS SPOTS WERE FOUND : " , nspots # else: # print "GOOD!: FOUND : " , nspots, " SPOTS " # for l in range(1,nspots+1) : # maskzone = np.equal(mask,l) # submatrix[maskzone] = submatrix.max() # import pylab # f1=pylab.figure() # from matplotlib.colors import LogNorm # pylab.imshow(submatrix, norm=LogNorm(vmin=0.01)) # pylab.show() # ################################# nspots = mask.max() if nspots!= nofroi: print( "WARNING: LESS SPOTS WERE FOUND : " , nspots) else: print( "GOOD!: FOUND : " , nspots, " SPOTS " ) return mask[2:-2,2:-2] def get_spots_mask_Lines_slave( A, median_size=None, nofroi=120, give_borders=False, give_diff_coeffs=False, tval=-1) : mmax = A.max() A[A= slope[i,j]): return 0 return 1 def Canny(Ax,Ay): Angles = np.arctan2( Ax, -Ay ) Angles=Angles*180/np.pi Angles = np.floor( Angles/45 + 0.5) slope = Ax*Ax + Ay*Ay Nmarge=1 MaximumRatio=10000.0 # maxfits = maximum_filter(slope, size=3) # indici = np.where(slope==maxfits) # ListLocalMaxima=[ [i,j] for i,j in zip(indici[0], indici[1]) ] Ni=Ax.shape[0] Nj=Ax.shape[1] ListLocalMaxima=[] for i in range( Nmarge, Ni-Nmarge): for j in range( Nmarge, Nj-Nmarge): if( IsMaximum( i,j,slope) ): ListLocalMaxima.append( [i,j] ) # print len(ListLocalMaxima) # raise steps={-5:np.array([-1,1]), -4:np.array([-1,0]),-3:np.array([-1,-1]), -2:np.array([0,-1]),-1:np.array([1,-1]), 0:np.array([1,0]),1:np.array([1,1]),2:np.array([0,1]),3:np.array([-1,1]),4:np.array([-1,0]), 5:np.array([-1,-1])} EdgeList=[] endpoints=[] EdgePoints=np.zeros(Ax.shape ) EdgePoints[:Nmarge ,:]=-1 EdgePoints[:,:Nmarge ]=-1 EdgePoints[-Nmarge:,:]=-1 EdgePoints[:,-Nmarge:]=-1 StartPoints = ListLocalMaxima stack = [] for starting in StartPoints: s0=starting[0] s1=starting[1] pentevalue= slope[s0, s1 ] stack.append( [ (s0, s1), pentevalue, 1 ] ) while len(stack): starting, pentevalue, direction_fact = stack[-1] stack=stack[:-1] print( " inizio da ", starting) EdgePointsTmp = np.zeros(Ax.shape ) s0=starting[0] s1=starting[1] pentevalue= slope[s0, s1 ] if(EdgePoints[ s0, s1 ]==0): Edge=[] Edge.append( [s0,s1] ) prendi=0 traccia=0 while(1): print( s0,s1) if( slope[s0, s1 ]/pentevalue < 1.0/MaximumRatio): if direction_fact==1 : prendi=0 traccia=0 if len(Edge)>4: stack.append( [ Edge[-4], pentevalue, -1 ]) print( " troppo debole Inverto") else: prendi= len(Edge)>5 traccia=1 print( " troppo debole finisco") break EdgePointsTmp[ s0, s1 ]=1 direction = Angles[s0,s1] ss = np.array([ steps[direction-1], steps[direction], steps[direction+1]])*direction_fact pttrs = [ [s0+ss[0][0],s1+ss[0][1]], [s0+ss[1][0],s1+ss[1][1]], [s0+ss[2][0],s1+ss[2][1]] ] values = [ [slope[ pttrs[i][0], pttrs[i][1] ],i] for i in range(3) ] imax= max(values)[1] s0=pttrs[imax][0] s1=pttrs[imax][1] if(EdgePoints[ s0, s1 ]): print( " scontro vecchio in ", s0, s1) if direction_fact==-1: Edge=Edge[:-10] prendi=1 traccia=1 break else: EdgePointsTmp[:]=0 prendi=0 traccia=0 if len(Edge)>10: stack.append( [ Edge[-4], pentevalue, -1 ]) print( " troppo debole Inverto") break if( EdgePointsTmp[ s0, s1 ] ): print( " scontro nuovo ") prendi=1 traccia=1 Edge.reverse() newedge = [ ] for p in Edge : if tuple(p)== (s0,s1): break newedge.append(p) Edge = [[s0,s1]]+newedge break else: print( " continup ") Edge.append( [s0,s1] ) if(prendi): endpoints.append([s0,s1]) EdgeList.append(Edge) if traccia: for p in Edge: EdgePoints[p[0],p[1]]=1 # values = [ [len(edge),edge] for edge in EdgeList] # edge = max(values)[1] values = [ edge for edge in EdgeList if len(edge)>10 ] # edge = max(values)[1] # return [edge] return values def divergenza( Ax,Ay, edge): if( edge[-1] != edge[0]): edge.append(edge[0]) res=0 for i in range(len(edge)-1): p1=edge[i] p2=edge[i+1] Ax1=Ax[p1[0],p1[1] ] Ax2=Ax[p2[0],p2[1] ] Ay1=Ay[p1[0],p1[1] ] Ay2=Ay[p2[0],p2[1] ] Dx = p2[0]-p1[0] Dy = p2[1]-p1[1] res=res+ ( Dx*(Ay1+Ay2) -Dy*(Ax1+Ax2) )*0.5 return res def threshold_mask(mask, image, value ) : n=mask.max() npix=3 image = signal.medfilt2d(image, kernel_size=3) for i in range(1,n+1): icount=0 tmp_mask_old = 0 while(icount<100): tmp_mask = np.equal( i, mask ) if not np.any(tmp_mask): continue mask[:]=mask*(1-tmp_mask) massimo = (image*tmp_mask).max() print( " MASSIMO " , massimo) tmp_mask_2 = morph.grey_dilation(tmp_mask, footprint=np.ones([npix,npix]), structure=np.zeros([npix,npix])) print( " Npunti , value " , tmp_mask.sum(), tmp_mask_2.sum() , value) tmp_mask = np.less( value*massimo, image )*tmp_mask_2 print( " Npunti " , tmp_mask.sum()) mask[:]+=tmp_mask*i if ( tmp_mask-tmp_mask_old).sum()==0: break tmp_mask_old = tmp_mask print( " ================ ") print( mask.sum()) icount+=1 return mask def grow_mask(input, npix ) : output = morph.grey_dilation(input, footprint=np.ones([npix,npix]), structure=np.zeros([npix,npix])) return output def shrink_mask(input, npix ) : output = morph.grey_erosion(input, footprint=np.ones([npix,npix]), structure=np.zeros([npix,npix])) return output def get_spots_mask( A, median_size=None, nofroi=120, give_borders=False, give_diff_coeffs=False, tval=-1) : # A[:216]=0 # A[229:]=0 EdfFile.EdfFile("res.edf","w+").WriteImage({},A) # A = signal.medfilt2d(A, kernel_size=median_size) mmax = A.max() A[Aamed/100] newmask = np.zeros(filled.shape,"i") i=1 for l in labs: print( i) print( np.equal(labels,l).sum(),l) newmask[np.equal(labels,l)]=i i+=1 return newmask from PyMca5.PyMcaIO import EdfFile if __name__=="__main__": #thematrix=EdfFile.EdfFile("test.edf").GetData(0) tmp = np.load("A.npy") thematrix_=np.zeros_like(tmp) thematrix_[:]+=tmp for i in range(1,0): thematrix_[:,i:] +=tmp[:,:-i] thematrix_[:,:-i] +=tmp[:,i:] # thematrix_[:,1:] +=tmp[:,:-1]#*0.9 # thematrix_[:,2:] +=tmp[:,:-2]#*0.8 # thematrix_[:,3:] +=tmp[:,:-3]#*0.4 # thematrix_[:,4:] +=tmp[:,:-4]#*0.3 # thematrix_[:,5:] +=tmp[:,:-5]#*0.1 # thematrix_[:,:-1]+=tmp[:,1:]#*0.9 # thematrix_[:,:-2]+=tmp[:,2:]#*0.8 # thematrix_[:,:-3]+=tmp[:,3:]#*0.6 # thematrix_[:,:-4]+=tmp[:,4:]#*0.3 # thematrix_[:,:-5]+=tmp[:,5:]#*0.1 # thematrix_[:]=tmp thematrix = np.zeros( [thematrix_.shape[0]+4, thematrix_.shape[1]+4 ] , thematrix_.dtype) thematrix[2:-2 ,2:-2 ] = thematrix_ thematrix=scipy.ndimage.filters.gaussian_filter(thematrix, 3.0) # order=0, output=None, mode='reflect', cval=0.0, truncate=4.0)[source] Nrows , Ncols = thematrix.shape if (1): submatrix = np.array(thematrix) mask, diff_coeffs = get_spots_mask( submatrix , give_diff_coeffs=True ) nspots = mask.max() if nspots!=12: print( "WARNING: LESS SPOTS WERE FOUND : " , nspots) else: print( "GOOD!: FOUND : " , nspots, " SPOTS " ) for l in range(1,nspots+1) : maskzone = np.equal(mask,l) submatrix[maskzone] = submatrix.max() import pylab f1=pylab.figure() pylab.imshow(submatrix, norm=LogNorm(vmin=0.01)) ############################################################## submatrix = np.array(thematrix) for iter in range(100): submatrix = submatrix + Diff(submatrix,diff_coeffs )*0.5*0.2 newsub=np.zeros_like(submatrix) newsub[2:-2 , 2:-2 ] = submatrix [2:-2 , 2:-2 ] submatrix=newsub mask = get_spots_mask( submatrix ) nspots = mask.max() if nspots!=12: print( "WARNING: LESS SPOTS WERE FOUND : " , nspots) else: print( "GOOD!: FOUND : " , nspots, " SPOTS " ) for l in range(1,nspots+1) : maskzone = np.equal(mask,l) submatrix[maskzone] = submatrix.max() import pylab f2=pylab.figure() pylab.imshow(submatrix, norm=LogNorm(vmin=0.01)) pylab.show() xrstools-0.15.0+git20210910+c147919d/XRStools/superr.py000066400000000000000000000220041412732462000215770ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import h5py import math from six.moves import range myrank=0 try: import skimage.restoration except: print( " ATTENTION : SKIMAGE RESORATION NOT LOADED ") def mdiv(grad): res = np.zeros(grad.shape[1:]) res[ :-1 , :, : ] += this_grad[0, :-1 , :,:] res[ 1:-1 , :, : ] -= this_grad[0, :-2 , :,:] res[ -1 , :, : ] -= this_grad[0, -2 , :,:] res[ :, :-1 , : ] += this_grad[1, :, :-1 ,:] res[ :, 1:-1 , : ] -= this_grad[1, :, :-2 ,:] res[ :, -1 , : ] -= this_grad[1, :, -2 ,:] res[ :, : , :-1 ] += this_grad[1, : ,:, :-1 ] res[ :, : , 1:-1 ] -= this_grad[1, : ,:, :-2 ] res[ :, : , -1 ] -= this_grad[1, : ,:, -2 ] return res def mygradient(img): shape = [3 ] + list(img.shape) gradient = np.zeros(shape, dtype=img.dtype) gradient[0,:,:,: ] = np.diff(img, axis=0) gradient[1,:,:,: ] = np.diff(img, axis=1) gradient[2,:,:,: ] = np.diff(img, axis=2) return gradient def v_project(v,weight ): norms = np.minimum( weight, np.sqrt( v[0]*v[0] + v[1]*v[1] )) return v/ norms def my_denoise_tv_chambolle_positive(image, weight=0.1, n_iter_max=200): ndim = image.ndim g = np.zeros_like(p) x = np.zeros_like(image) tmpxa = np.zeros_like(image) v = np.zeros((image.ndim, ) + image.shape, dtype=image.dtype) i = 0 sigma = 1.0/math.sqrt(8.0) tau = 1.0/math.sqrt(8.0) while i < n_iter_max: tmpxa[:] = x + sigma * ( ( image-x) + mydiv( v ) ) tmpxa[:] = np.maximum (tmpxa) tmpxa[:] = tmpxa-x x[:] = x + tmpxa tmpxa[:] = x + tmpxa v[:] = v + tau * mygrad(tmpxa) v = v_project(v,weight ) return x def _denoise_tv_chambolle_nd(image, weight=0.1, eps=2.e-4, n_iter_max=200, positivity=False, cdim = None): """Perform total-variation denoising on n-dimensional images. Parameters ---------- image : ndarray n-D input data to be denoised. weight : float, optional Denoising weight. The greater `weight`, the more denoising (at the expense of fidelity to `input`). eps : float, optional Relative difference of the value of the cost function that determines the stop criterion. The algorithm stops when: (E_(n-1) - E_n) < eps * E_0 n_iter_max : int, optional Maximal number of iterations used for the optimization. positivity : bool, optional Adds positivity constraint Returns ------- out : ndarray Denoised array of floats. Notes ----- Rudin, Osher and Fatemi algorithm. LICENCE ------- Copyright (C) 2011, the scikit-image team All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of skimage nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. this software is provided by the author ``as is'' and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. in no event shall the author be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage. """ ndim = image.ndim p = np.zeros((image.ndim, ) + image.shape, dtype=image.dtype) g = np.zeros_like(p) d = np.zeros_like(image) i = 0 while i < n_iter_max: if i > 0: # d will be the (negative) divergence of p d = -p.sum(0) slices_d = [slice(None), ] * ndim slices_p = [slice(None), ] * (ndim + 1) for ax in range(ndim): slices_d[ax] = slice(1, None) slices_p[ax+1] = slice(0, -1) slices_p[0] = ax if cdim is None: d[tuple(slices_d)] += p[tuple(slices_p)] else: d[tuple(slices_d)] += p[tuple(slices_p)] *cdim[ax] slices_d[ax] = slice(None) slices_p[ax+1] = slice(None) out_nopos = image + d else: out_nopos = image if not positivity: out = out_nopos else: out = np.maximum(0, out_nopos) removed = np.minimum(out_nopos, 0) d = d-removed E = (d ** 2).sum() # g stores the gradients of out along each axis # e.g. g[0] is the first order finite difference along axis 0 slices_g = [slice(None), ] * (ndim + 1) for ax in range(ndim): slices_g[ax+1] = slice(0, -1) slices_g[0] = ax if cdim is None: g[tuple(slices_g)] = np.diff(out, axis=ax) else: g[tuple(slices_g)] = np.diff(out, axis=ax)*cdim[ax] slices_g[ax+1] = slice(None) norm = np.sqrt((g ** 2).sum(axis=0))[np.newaxis, ...] E += weight * norm.sum() tau = 1. / (2.*ndim) norm *= tau / weight norm += 1. p -= tau * g p /= norm E /= float(image.size) if i == 0: E_init = E E_previous = E else: if np.abs(E_previous - E) < eps * E_init: break else: E_previous = E i += 1 return out def superr( scalDD, scalDS, scalSS, niter=15, beta=1.0e-8): """ - scalDS which is an array [ZDIM,YDIM,XDIM] , type "d" . - scalDD which is the total sum of the squared datas. - scalSS which is an array [XDIM,XDIM] , type "d" . """ ZDIM,YDIM,XDIM = scalDS.shape Volume = np.zeros( [ZDIM,YDIM,XDIM] ,"f" ) assert( scalSS.shape == (XDIM,XDIM)) scalSS = scalSS.astype("f") scalDS = scalDS.astype("f") print( " SHAPES ", scalDS.shape, scalSS.shape ) Fista ( scalDD, scalDS, scalSS, Volume,niter=niter, beta=beta) return Volume def calculate_grad( scalDD, scalDS , scalSS, solution, grad) : grad [:] = np.tensordot( solution, scalSS, axes=[[-1],[-1]]) err = (grad*solution).sum() if scalDS is not None: err -= (scalDS*solution).sum()*2 err += scalDD grad [:] -= scalDS return err/2 def Fista( scalDD, scalDS , scalSS, solution , niter=500, beta=0.1 ): grad = np.zeros_like(solution) grad2 = np.zeros_like(solution) x_old = np.zeros_like(solution) y = np.zeros_like(solution) err = 0.0 err=calculate_grad( scalDD, scalDS , scalSS, solution, grad) for i in range(20): calculate_grad(None, None , scalSS, grad, grad2) Lip = math.sqrt( np.linalg.norm(grad2/100000.0) )*100000 grad[:] = grad2/ Lip if myrank==0: print( "LIP ", Lip) Lip = Lip*1.2 t=1.0 y[:] = solution x_old[:] = solution for iter in range(abs(niter)): err = calculate_grad(scalDD, scalDS , scalSS, y, grad) solution[:] = y - grad/Lip # solution[:]=skimage.restoration.denoise_tv_chambolle(solution, weight=beta, eps=0.000002) solution[:]=_denoise_tv_chambolle_nd(solution, weight=beta, eps=0.000002, positivity=True, cdim = [0,0,1]) ## solution[:] = np.maximum(solution, 0) tnew = ( 1+math.sqrt(1.0+4*t*t) )/2 y[:] = solution +(t-1)/tnew *( solution - x_old ) t = tnew if niter<0: t=1 x_old[:] = solution if myrank==0: print( " errore est %e mod_grad est %e\n" % ( err, grad.std()) ) xrstools-0.15.0+git20210910+c147919d/XRStools/superresolution.py000066400000000000000000000224141412732462000235460ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: superresolution.py from .helpers import * import os import numpy as np import pylab from scipy import io from itertools import groupby from scipy.interpolate import Rbf, RectBivariateSpline from scipy.optimize import leastsq, fmin from .xrs_imaging import * from six.moves import range class imageset: """ class to make SR-images from list of LR-images """ def __init__(self): self.list_of_images = [] self.xshifts = [] self.yshifts = [] self.shifts = [] self.srimage = [] self.srxscale = [] self.sryscale = [] self.refimagenum = [] def estimate_xshifts(self,whichimage=None): if not whichimage: ind = 0 # first image in list_of_images is the reference image else: ind = whichimage origx = self.list_of_images[ind].xscale origy = self.list_of_images[ind].yscale origim = self.list_of_images[ind].matrix xshifts = [] for image in self.list_of_images: newx = image.xscale newy = image.yscale newim = image.matrix xshifts.append(estimate_xshift(origx,origy,origim,newx,newy,newim)) self.refimagenum = ind self.xshifts = xshifts def estimate_yshifts(self,whichimage=None): if not whichimage: ind = 0 # first image in list_of_images is the reference image else: ind = whichimage origx = self.list_of_images[ind].xscale origy = self.list_of_images[ind].yscale origim = self.list_of_images[ind].matrix yshifts = [] for image in self.list_of_images: newx = image.xscale newy = image.yscale newim = image.matrix yshifts.append(estimate_yshift(origx,origy,origim,newx,newy,newim)) self.refimagenum = ind self.yshifts = yshifts def estimate_shifts(self,whichimage=None): if not whichimage: ind = 0 # first image in list_of_images is the reference image else: ind = whichimage origx = self.list_of_images[ind].xscale origy = self.list_of_images[ind].yscale origim = self.list_of_images[ind].matrix shifts = [] for image in self.list_of_images: newx = image.xscale newy = image.yscale newim = image.matrix shifts.append(estimate_shift(origx,origy,origim,newx,newy,newim)) self.refimagenum = ind self.shifts = shifts def interpolate_xshift_images(self,scaling,whichimages=None): if not whichimages: inds = list(range(len(self.list_of_images))) elif not isinstance(whichimages,list): inds = [] inds.append(whichimages) else: inds = whichimages newim = np.zeros((len(inds),np.shape(self.list_of_images[inds[0]].matrix)[0]*scaling,np.shape(self.list_of_images[inds[0]].matrix)[1])) newx = np.linspace(self.list_of_images[inds[0]].xscale[0]-self.xshifts[inds[0]],self.list_of_images[inds[0]].xscale[-1]-self.xshifts[inds[0]],len(self.list_of_images[inds[0]].xscale)*scaling) newy = self.list_of_images[inds[0]].yscale for n in range(len(inds)): print( self.xshifts[inds[n]]) oldim = self.list_of_images[inds[n]].matrix oldx = self.list_of_images[inds[n]].xscale-self.xshifts[inds[n]] oldy = self.list_of_images[inds[n]].yscale newim[n,:,:] = interpolate_image(oldx,oldy,oldim,newx,newy) self.srimage = np.sum(newim,axis=0) self.srxscale = newx self.sryscale = newy def interpolate_yshift_images(self,scaling,whichimages=None): if not whichimages: inds = list(range(len(self.list_of_images))) elif not isinstance(whichimages,list): inds = [] inds.append(whichimages) else: inds = whichimages newim = np.zeros((len(inds),np.shape(self.list_of_images[inds[0]].matrix)[0]*scaling,np.shape(self.list_of_images[inds[0]].matrix)[1])) newx = self.list_of_images[0].xscale newy = np.linspace(self.list_of_images[inds[0]].yscale[0]-self.yshifts[inds[0]],self.list_of_images[inds[0]].yscale[-1]-self.yshifts[inds[0]],len(self.list_of_images[inds[0]].yscale)*scaling) for n in range(len(inds)): oldim = self.list_of_images[inds[n]].matrix oldx = self.list_of_images[inds[n]].xscale oldy = self.list_of_images[inds[n]].yscale-self.yshifts[inds[n]] newim[n,:,:] = interpolate_image(oldx,oldy,oldim,newx,newy) self.srimage = np.sum(newim,axis=0) self.srxscale = newx self.sryscale = newy def interpolate_shift_images(self,scaling,whichimages=None): if not whichimages: inds = list(range(len(self.list_of_images))) elif not isinstance(whichimages,list): inds = [] inds.append(whichimages) else: inds = whichimages if len(scaling)<2: scaling = [scaling, scaling] print( inds, self.list_of_images[inds[0]].xscale[0], self.shifts[inds[0]], self.list_of_images[inds[0]].xscale[-1]) newim = np.zeros((len(self.list_of_images),np.shape(self.list_of_images[inds[0]].matrix)[0]*scaling[0],np.shape(self.list_of_images[inds[0]].matrix)[1]*scaling[1])) newx = np.linspace(self.list_of_images[inds[0]].xscale[0]-self.shifts[inds[0]][0],self.list_of_images[inds[0]].xscale[-1]-self.shifts[inds[0]][0],len(self.list_of_images[inds[0]].xscale)*scaling[0]) newy = np.linspace(self.list_of_images[inds[0]].yscale[0]-self.shifts[inds[0]][1],self.list_of_images[inds[0]].yscale[-1]-self.shifts[inds[0]][1],len(self.list_of_images[inds[0]].yscale)*scaling[1]) for n in range(len(inds)): oldim = self.list_of_images[inds[n]].matrix oldx = self.list_of_images[inds[n]].xscale-self.shifts[inds[n]][0] oldy = self.list_of_images[inds[n]].yscale-self.shifts[inds[n]][1] newim[n,:,:] = interpolate_image(oldx,oldy,oldim,newx,newy) self.srimage = np.sum(newim,axis=0) self.srxscale = newx self.sryscale = newy def plotSR(self): X,Y = pylab.meshgrid(self.srxscale,self.sryscale) pylab.pcolor(X,Y,np.transpose(self.srimage)) pylab.show(block=False) def plotLR(self,whichimage): if not isinstance(whichimage,list): inds = [] inds.append(whichimage) else: inds = list(whichimage) for ind in inds: X,Y = pylab.meshgrid(self.list_of_images[ind].xscale,self.list_of_images[ind].yscale) pylab.figure(ind) pylab.pcolor(X,Y,np.transpose(self.list_of_images[ind].matrix)) pylab.show(block=False) def save(self): pass def load(self): pass def loadkimberlite(self,matfilename): data_dict = io.loadmat(matfilename) sorted_keys = sorted(data_dict.keys(), key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) sy = data_dict['sy'][0] allsx = [] for key in sorted_keys[3:12]: allsx.append(data_dict[key][0]) allmats = [] for key in sorted_keys[13:]: allmats.append(data_dict[key]) alllengths = [] for sx in allsx: alllengths.append(len(sx)) ind = np.where(alllengths == np.max(alllengths))[0][0] # spline everything onto longest sx-scale for n in range(len(allmats)): print( np.shape(allsx[n]), np.shape(sy), np.shape(allmats[n])) ip = RectBivariateSpline(allsx[n],sy,allmats[n]) allmats[n] = ip(allsx[ind],sy) allsx[n] = allsx[ind] allimages = [] for n in range(len(allmats)): allimages.append(LRimage(allmats[n],allsx[n],sy)) self.list_of_images = allimages def loadhe3070(self,matfilename): data_dict = io.loadmat(matfilename) sy = data_dict['det'][0][0]['sy'][0][0][0] allsx = [] allmats = [] for n in range(9): allsx.append(np.reshape(data_dict['det'][0][n]['sx'][0][0]-data_dict['det'][0][n]['sx'][0][0][0],len(data_dict['det'][0][n]['sx'][0][0],))) allmats.append(data_dict['det'][0][n]['img'][0][0]) alllengths = [] for sx in allsx: alllengths.append(len(sx)) ind = np.where(alllengths == np.max(alllengths))[0][0] for n in range(len(allmats)): print( np.shape(allsx[n]), np.shape(sy), np.shape(np.transpose(allmats[n]))) ip = RectBivariateSpline(allsx[n],sy,np.transpose(allmats[n])) allmats[n] = ip(allsx[ind],sy) allsx[n] = allsx[ind] allimages = [] for n in range(len(allmats)): allimages.append(LRimage(allmats[n],allsx[n],sy)) self.list_of_images = allimages def loadimage(self,filename): f = open(filename,'rb') self.list_of_images.append(pickle.load(f)) f.close() def correct_scattering_angle(self,whichimage,tth): pass xrstools-0.15.0+git20210910+c147919d/XRStools/theory.py000066400000000000000000000441611412732462000216010ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: theory.py from .xrs_utilities import * from .math_functions import * import string import numpy as np from numpy import array import pylab import math from scipy import interpolate, signal, integrate, constants, optimize from re import findall from six.moves import range __metaclass__ = type # new style classes class HFspecpredict: def __init__(self,formulas,concentrations,rho_formu,correctasym=None,E0=9.68,eloss=np.arange(0,1,0.0001),alpha=None,beta=None,samthick=None): if not isinstance(formulas,list): theformulas = [] theformulas.append(formulas) else: theformulas = formulas self.formulas = theformulas self.concentrations = concentrations self.E0 = E0 self.eloss = eloss self.rho_formu = rho_formu self.rho = 0.0 if len(rho_formu)>1: for n in range(len(rho_formu)): self.rho += rho_formu[n]*concentrations[n] else: self.rho = rho_formu if not correctasym: correctasym = [] for formula in formulas: elements,stoichiometries = parseformula(formula) correctasym.append(np.zeros(len(elements))) self.correctasym = correctasym self.alpha = alpha self.beta = beta if self.beta<0: # transmission geometry self.tth = alpha-beta else: # reflection geometry self.tth = 180.0 - (alpha+beta) self.thickness = samthick # in [cm] now self.eloss,self.J,self.C,self.V,self.q = makeprofile_compds(self.formulas,self.concentrations,E0=self.E0,tth=self.tth,correctasym=self.correctasym) # sample self absorption self.alpha_r = alpha self.beta_r = beta self.mu_in,self.mu_out = mpr_compds(self.eloss/1e3+self.E0,self.formulas,self.concentrations,self.E0,self.rho_formu) self.ac = abscorr2(self.mu_in,self.mu_out,self.alpha_r,self.beta_r,self.thickness) self.J = self.J/self.ac*self.rho self.C = self.C/self.ac*self.rho self.V = self.V/self.ac*self.rho def plotHFspec(self): pylab.plot(self.eloss,self.J,self.eloss,self.C,self.eloss,self.V) pylab.legend(('sum','core contribution','valence contribution')) pylab.xlabel('energy loss [eV]') pylab.ylabel('S(q,w) [1/eV]') #pylab.title('About as simple as it gets, folks') pylab.grid(False) pylab.show(block=False) def plotmurho(self): pass def plotresult(self): pass class HFspecpredict_series: def __init__(self,formulas,concentrations,rho_formu,correctasym=None,E0=9.68,eloss=np.arange(0,1,0.0001),alpha=0.0,beta=-30.0,samthick=0.1): self.concentrations = concentrations self.eloss = eloss self.rho_formu = rho_formu self.rho = 0 if len(rho_formu)>1: for n in range(len(rho_formu)): self.rho += rho_formu[n]*concentrations[n] else: self.rho = rho_formu if not correctasym: correctasym = [] for formula in formulas: elements,stoichiometries = parseformula(formula) correctasym.append(np.zeros(len(elements))) self.correctasym = correctasym # make everything iterable # formulas if not isinstance(formulas,list): theformulas = [] theformulas.append(formulas) else: theformulas = formulas self.formulas = theformulas # E0 if not isinstance(E0,list): theE0s = [] theE0s.append(E0) else: theE0s = E0 self.E0 = theE0s # alpha if not isinstance(alpha,list): thealphas = [] thealphas.append(alpha) else: thealphas = alpha self.alpha = thealphas # beta if not isinstance(beta,list): thebetas = [] thebetas.append(beta) else: thebetas = beta self.beta = thebetas # tth self.tth = [] for anglea in self.alpha: for angleb in self.beta: if angleb<0: # transmission geometry tth = anglea-angleb self.tth.append(tth) else: # reflection geometry tth = 180.0 - (anglea+angleb) self.tth.append(tth) # sample thickness if not isinstance(samthick,list): thethickness = [] thethickness.append(samthick) else: thethickness = samthick self.thickness = thethickness # in [cm] now # now calculate spectra for all possible configurations (several E0, several tth, several samthick) # one E0, one tth, one thickness if len(self.E0)==1 and len(self.alpha)==1 and len(self.beta)==1 and len(self.thickness)==1: self.eloss,self.J,self.C,self.V,self.q = makeprofile_compds(self.formulas,self.concentrations,E0=self.E0[0],tth=self.tth[0],correctasym=self.correctasym) # sample self absorption self.mu_in,self.mu_out = mpr_compds(self.eloss/1e3+self.E0,self.formulas,self.concentrations,self.E0,self.rho_formu) self.ac = abscorr2(self.mu_in,self.mu_out,self.alpha[0],self.beta[0],self.thickness[0]) self.J = self.J/self.ac*self.rho self.C = self.C/self.ac*self.rho self.V = self.V/self.ac*self.rho # several E0, one tth, one thickness if len(self.E0) > 1: eloss,j,c,v,q = makeprofile_compds(self.formulas,self.concentrations,E0=self.E0[0],tth=self.tth[0],correctasym=self.correctasym) self.eloss = eloss self.J = np.zeros((len(eloss),len(self.E0))) self.C = np.zeros((len(eloss),len(self.E0))) self.V = np.zeros((len(eloss),len(self.E0))) self.q = np.zeros((len(eloss),len(self.E0))) self.ac = np.zeros((len(eloss),len(self.E0))) for n in range(len(self.E0)): eloss,j,c,v,q = makeprofile_compds(self.formulas,self.concentrations,E0=self.E0[n],tth=self.tth[0],correctasym=self.correctasym) mu_in, mu_out = mpr_compds(eloss/1e3+self.E0[n],self.formulas,self.concentrations,self.E0[n],self.rho_formu) ac = abscorr2(mu_in,mu_out,self.alpha[0],self.beta[0],self.thickness[0]) j = j/ac*self.rho c = c/ac*self.rho v = v/ac*self.rho self.J[:,n] = np.interp(self.eloss,eloss,j) self.C[:,n] = np.interp(self.eloss,eloss,c) self.V[:,n] = np.interp(self.eloss,eloss,v) self.q[:,n] = np.interp(self.eloss,eloss,q) self.ac[:,n] = np.interp(self.eloss,eloss,ac) # several incidence angles: if len(self.alpha) > 1: eloss,j,c,v,q = makeprofile_compds(self.formulas,self.concentrations,E0=self.E0[0],tth=self.tth[0],correctasym=self.correctasym) self.eloss = eloss self.J = np.zeros((len(eloss),len(self.alpha))) self.C = np.zeros((len(eloss),len(self.alpha))) self.V = np.zeros((len(eloss),len(self.alpha))) self.q = np.zeros((len(eloss),len(self.alpha))) self.ac = np.zeros((len(eloss),len(self.alpha))) for n in range(len(self.alpha)): eloss,j,c,v,q = makeprofile_compds(self.formulas,self.concentrations,E0=self.E0[0],tth=self.tth[n],correctasym=self.correctasym) mu_in, mu_out = mpr_compds(eloss/1e3+self.E0[0],self.formulas,self.concentrations,self.E0[0],self.rho_formu) ac = abscorr2(mu_in,mu_out,self.alpha[n],self.beta[0],self.thickness[0]) j = j/ac*self.rho c = c/ac*self.rho v = v/ac*self.rho self.J[:,n] = np.interp(self.eloss,eloss,j) self.C[:,n] = np.interp(self.eloss,eloss,c) self.V[:,n] = np.interp(self.eloss,eloss,v) self.q[:,n] = np.interp(self.eloss,eloss,q) self.ac[:,n] = np.interp(self.eloss,eloss,ac) # several exit angles: if len(self.beta) > 1: eloss,j,c,v,q = makeprofile_compds(self.formulas,self.concentrations,E0=self.E0[0],tth=self.tth[0],correctasym=self.correctasym) self.eloss = eloss self.J = np.zeros((len(eloss),len(self.beta))) self.C = np.zeros((len(eloss),len(self.beta))) self.V = np.zeros((len(eloss),len(self.beta))) self.q = np.zeros((len(eloss),len(self.beta))) self.ac = np.zeros((len(eloss),len(self.beta))) for n in range(len(self.beta)): eloss,j,c,v,q = makeprofile_compds(self.formulas,self.concentrations,E0=self.E0[0],tth=self.tth[n],correctasym=self.correctasym) mu_in, mu_out = mpr_compds(eloss/1e3+self.E0[0],self.formulas,self.concentrations,self.E0[0],self.rho_formu) ac = abscorr2(mu_in,mu_out,self.alpha[0],self.beta[n],self.thickness[0]) j = j/ac*self.rho c = c/ac*self.rho v = v/ac*self.rho self.J[:,n] = np.interp(self.eloss,eloss,j) self.C[:,n] = np.interp(self.eloss,eloss,c) self.V[:,n] = np.interp(self.eloss,eloss,v) self.q[:,n] = np.interp(self.eloss,eloss,q) self.ac[:,n] = np.interp(self.eloss,eloss,ac) # several sample thicknesses: if len(self.thickness) > 1: eloss,j,c,v,q = makeprofile_compds(self.formulas,self.concentrations,E0=self.E0[0],tth=self.tth[0],correctasym=self.correctasym) self.eloss = eloss self.J = np.zeros((len(eloss),len(self.thickness))) self.C = np.zeros((len(eloss),len(self.thickness))) self.V = np.zeros((len(eloss),len(self.thickness))) self.q = np.zeros((len(eloss),len(self.thickness))) self.ac = np.zeros((len(eloss),len(self.thickness))) for n in range(len(self.thickness)): eloss,j,c,v,q = makeprofile_compds(self.formulas,self.concentrations,E0=self.E0[0],tth=self.tth[0],correctasym=self.correctasym) mu_in, mu_out = mpr_compds(eloss/1e3+self.E0[0],self.formulas,self.concentrations,self.E0[0],self.rho_formu) ac = abscorr2(mu_in,mu_out,self.alpha[0],self.beta[0],self.thickness[n]) j = j/ac*self.rho c = c/ac*self.rho v = v/ac*self.rho self.J[:,n] = np.interp(self.eloss,eloss,j) self.C[:,n] = np.interp(self.eloss,eloss,c) self.V[:,n] = np.interp(self.eloss,eloss,v) self.q[:,n] = np.interp(self.eloss,eloss,q) self.ac[:,n] = np.interp(self.eloss,eloss,ac) def plotHFspec(self): pylab.plot(self.eloss,self.J,self.eloss,self.C,self.eloss,self.V) pylab.legend(('sum','core contribution','valence contribution')) pylab.xlabel('energy loss [eV]') pylab.ylabel('S(q,w) [1/eV]') #pylab.title('About as simple as it gets, folks') pylab.grid(False) pylab.show(block=False) class HFspectrum: def __init__(self,data,formulas,concentrations,correctasym=None,correctasym_pertth=None, initialise=True): """ class for building S(q,w) from tabulated Hartree-Fock Compton profiles to use in the extraction algorithm. data = instance of one of the classes from the xrs_read module (like read_id20) formulas = single string or list of strings of chemical sum formulas of which the sample is made up concentrations = single value or list of concentrations of how the different chemical formulas are mixed (sum should be 1) correctasym = single value or list of scaling values for the HR-correction to the 1s, 2s, and 2p shells. one value per element in the list of formulas correctasym_pertth = list of additional scaling values for each scattering angle, so that a momentum transfer dependent asymmetry correction is possible (i.e. no correction for low q, finite correction for high q data) """ if not initialise: return # from raw data self.eloss = data.eloss self.tth = data.tth self.E0 = data.E0 self.cenom = data.cenom # new info self.formulas = formulas self.concentrations = concentrations if not correctasym: correctasym = [] for formula in formulas: elements,stoichiometries = parseformula(formula) correctasym.append(np.zeros(len(elements))) self.correctasym = correctasym # make one profile per scattering angle and spline it onto the exp. energy loss scale self.J = np.zeros(np.shape(data.signals)) self.C = np.zeros(np.shape(data.signals)) self.V = np.zeros(np.shape(data.signals)) self.q = np.zeros(np.shape(data.signals)) if not correctasym_pertth: for n in [ nn for nn in range(len(data.signals[0,:])) if ((data.signals[:,nn]).sum()>0) ]: el,j,c,v,q = makeprofile_compds(formulas,concentrations,E0=self.cenom[n],tth=data.tth[n],correctasym=self.correctasym) f = interpolate.interp1d(el,j, bounds_error=False, fill_value=0.0) self.J[:,n] = f(data.eloss) f = interpolate.interp1d(el,c, bounds_error=False, fill_value=0.0) self.C[:,n] = f(data.eloss) f = interpolate.interp1d(el,v, bounds_error=False, fill_value=0.0) self.V[:,n] = f(data.eloss) f = interpolate.interp1d(el,q, bounds_error=False, fill_value=0.0) self.q[:,n] = f(data.eloss) else: if len(correctasym_pertth) != len(self.tth): print( 'Please provide a Python list of one scaling factor [0,1] per scattering angle!') print( 'Currently %d scattering angles defined, but %d scaling factors provided!' % (len(self.tth), len(correctasym_pertth))) return else: for n in [ nn for nn in range(len(data.signals[0,:])) if ((data.signals[:,nn]).sum()>0) ]: print( self.correctasym, correctasym_pertth[n]) el,j,c,v,q = makeprofile_compds(formulas,concentrations,E0=self.cenom[n],tth=data.tth[n],correctasym=np.array(self.correctasym)*correctasym_pertth[n]) f = interpolate.interp1d(el,j, bounds_error=False, fill_value=0.0) self.J[:,n] = f(data.eloss) f = interpolate.interp1d(el,c, bounds_error=False, fill_value=0.0) self.C[:,n] = f(data.eloss) f = interpolate.interp1d(el,v, bounds_error=False, fill_value=0.0) self.V[:,n] = f(data.eloss) f = interpolate.interp1d(el,q, bounds_error=False, fill_value=0.0) self.q[:,n] = f(data.eloss) # correct interpolation errors in q (first couple of values are 0.0, replace by smallest value) THIS NEEDS TO BE FIXED for n in [ nn for nn in range(len(data.signals[0,:])) if ((data.signals[:,nn]).sum()>0) ]: inds = np.where(self.q[:,n] == 0) self.q[inds,n] = self.q[np.where(self.q[:,n]>0)[0][0],n] def save_state_hdf5(self, filename, groupname, comment=""): import h5py h5 = h5py.File(filename,"a") h5.require_group(groupname) h5group = h5[groupname] if( "J" in list(h5group.keys()) ): raise Exception(" Read data already present in " + filename+":"+groupname) for key in ["eloss" ,"tth" ,"E0" ,"cenom","formulas" ,"concentrations" ,"correctasym" ,"J" ,"C" ,"V", "q"]: data = getattr(self,key) if key == "concentrations": s=data[0] for t in data[1:]: s=s+" "+t data=s h5group[key] = data h5group["comment"] = comment h5.flush() h5.close() def load_state_hdf5(self, filename, groupname): import h5py h5 = h5py.File(filename,"r") h5group = h5[groupname] chiavi = { "eloss":array , "tth":array , "E0":float , "cenom":array, "formulas":array , "concentrations":array , "correctasym":array , "J":array , "C":array , "V":array, "q":array } for key in chiavi: if key== "concentrations": data=str(h5group[key]) data=string.split(data," ") setattr(self,key, data) else: setattr(self,key, chiavi[key](array(h5group[key])) ) h5.flush() h5.close() def plotHFC(self): pylab.clf() for n in range(len(self.C[0,:])): pylab.plot(self.eloss,self.C[:,n]) pylab.title('core HF profile') pylab.xlabel('energy loss [eV]') pylab.ylabel('S(q,w) [1/eV]') pylab.grid(False) pylab.show(block=False) def plotHFJ(self): pylab.clf() for n in range(len(self.J[0,:])): pylab.plot(self.eloss,self.J[:,n]) pylab.title('total HF profile') pylab.xlabel('energy loss [eV]') pylab.ylabel('S(q,w) [1/eV]') pylab.grid(False) pylab.show(block=False) def plotHFV(self): pylab.clf() for n in range(len(self.V[0,:])): pylab.plot(self.eloss,self.V[:,n]) pylab.title('valence HF profile') pylab.xlabel('energy loss [eV]') pylab.ylabel('S(q,w) [1/eV]') pylab.grid(False) pylab.show(block=False) xrstools-0.15.0+git20210910+c147919d/XRStools/things/000077500000000000000000000000001412732462000212035ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/XRStools/things/Ba24Si100_rawdata_APS11.dat000066400000000000000000011507301412732462000254610ustar00rootroot00000000000000 3.0858303e+00 9.9266189e-04 1.5782976e-02 1.1427125e-02 1.5635588e-02 -1.2245361e-04 1.0647405e-02 2.7535155e-03 1.1157269e-02 1.4246379e-02 2.1839518e-02 4.2354042e-03 2.0429475e-03 3.2305503e-03 8.2939891e-03 1.0387934e-02 1.2375552e-02 5.1938525e-03 1.2059821e-02 1.5919192e-02 4.1030201e+00 6.4292775e-04 1.0130736e-02 6.9955510e-03 9.9660799e-03 9.9960300e-04 7.8157259e-03 1.7458543e-03 6.3905836e-03 9.0662986e-03 1.3985101e-02 2.9614182e-03 1.4185697e-03 2.1462736e-03 5.5302461e-03 6.7341094e-03 7.5286850e-03 3.2825709e-03 7.5203714e-03 9.9820658e-03 5.0937013e+00 5.0536897e-04 6.8293213e-03 4.8630238e-03 6.7624574e-03 1.1355390e-03 6.2272295e-03 1.5214173e-03 4.0659108e-03 6.1544638e-03 9.0028068e-03 2.2201583e-03 1.0774612e-03 1.5279675e-03 3.9602925e-03 4.7452924e-03 5.3984025e-03 2.0637120e-03 4.9631160e-03 6.7994594e-03 6.0846388e+00 4.2576252e-04 5.1136190e-03 3.6248229e-03 5.1955842e-03 1.0685069e-03 5.2945728e-03 1.4079231e-03 2.6708883e-03 4.1244414e-03 6.2930094e-03 1.7685205e-03 8.7025184e-04 1.3431906e-03 2.8468137e-03 3.5624120e-03 4.0035594e-03 1.5513927e-03 3.3093878e-03 4.7993197e-03 7.1026253e+00 2.9412561e-04 3.9410930e-03 2.7948759e-03 3.8615099e-03 1.1988906e-03 4.6376729e-03 1.4966933e-03 2.0979824e-03 3.3666822e-03 5.1656190e-03 1.5655615e-03 6.9128689e-04 1.0732521e-03 2.2160408e-03 2.5598341e-03 3.1542284e-03 1.1295526e-03 2.5591876e-03 3.6188283e-03 8.0940826e+00 2.7517515e-04 3.3295381e-03 2.2626825e-03 3.3443850e-03 1.2393036e-03 4.2936094e-03 1.6269102e-03 1.7279375e-03 2.6918445e-03 3.9681672e-03 1.3753868e-03 6.2136730e-04 1.0975580e-03 1.7022143e-03 1.9908889e-03 2.3468482e-03 9.6039682e-04 2.0098858e-03 2.8397067e-03 9.0857964e+00 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3.0959258e-04 5.1736220e-04 4.5248648e-04 1.7520564e-04 2.6782741e-04 2.7669100e-04 2.1704423e-04 4.0679101e-04 4.6850770e-05 4.1187987e-04 3.4804310e-04 9.2708673e+02 8.5553752e-05 1.2274061e-04 2.2515069e-04 3.4475592e-04 3.7343077e-04 5.1245010e-04 4.5249872e-04 2.0970755e-04 2.9718066e-04 5.7726721e-04 5.6059749e-04 1.7887163e-04 3.2722777e-04 3.1274654e-04 1.9468222e-04 3.8457450e-04 8.8068261e-05 4.4608276e-04 3.6202415e-04 9.3007800e+02 8.5558611e-05 1.3717019e-04 2.2748371e-04 4.1023527e-04 3.9677513e-04 4.6957703e-04 4.5773326e-04 2.4533922e-04 3.1903415e-04 4.5186711e-04 5.5622886e-04 1.4150331e-04 3.4856950e-04 2.3380378e-04 1.9996242e-04 4.6167262e-04 6.0678160e-05 3.8589187e-04 3.5370750e-04 9.3310500e+02 8.9546059e-05 1.3419791e-04 2.4324272e-04 3.1070700e-04 3.6948352e-04 5.2735564e-04 5.2757476e-04 2.0352058e-04 3.2037482e-04 5.4633257e-04 5.2899898e-04 1.9157095e-04 2.9554865e-04 3.5037681e-04 2.1353778e-04 4.1117740e-04 8.2824246e-05 4.5206900e-04 3.6381605e-04 9.3610046e+02 8.9708245e-05 1.2688966e-04 1.9450897e-04 2.9936407e-04 4.0430190e-04 5.5002618e-04 4.5783117e-04 2.7559152e-04 2.8284997e-04 5.4308676e-04 5.1975403e-04 1.2359888e-04 2.5579744e-04 3.3395914e-04 2.5586823e-04 4.2223379e-04 4.9895444e-05 3.0605231e-04 2.7710418e-04 9.3909800e+02 1.0135522e-04 1.4610745e-04 2.2713082e-04 3.6297505e-04 4.1644429e-04 4.8761572e-04 4.8211459e-04 2.4636456e-04 2.8330012e-04 6.0068823e-04 5.3393043e-04 1.9205148e-04 2.8880294e-04 2.7952260e-04 2.4501615e-04 4.1283353e-04 8.3083723e-05 4.0942541e-04 3.6431168e-04 9.4209763e+02 1.0159431e-04 1.5199308e-04 2.1007742e-04 3.9189923e-04 3.7194492e-04 4.7537725e-04 4.5581977e-04 2.6334872e-04 2.7436825e-04 5.9071093e-04 4.8403695e-04 1.9596174e-04 2.8139092e-04 2.6917930e-04 2.9352022e-04 3.9636138e-04 6.9568238e-05 4.5278733e-04 2.6596056e-04 9.4509934e+02 1.1703231e-04 1.2408535e-04 2.7098941e-04 3.0082036e-04 4.1459396e-04 5.1413648e-04 4.2148895e-04 2.5681743e-04 2.8441105e-04 6.3729139e-04 5.7429037e-04 1.7444700e-04 3.2507406e-04 2.4208218e-04 2.2748890e-04 4.5540194e-04 6.4066245e-05 3.5358101e-04 3.2627865e-04 9.4810314e+02 1.0923045e-04 1.4841952e-04 2.8864748e-04 3.9061964e-04 3.8705520e-04 5.3306066e-04 4.7838617e-04 2.3372336e-04 2.8025556e-04 6.0585550e-04 5.6039582e-04 1.3355270e-04 2.7592716e-04 2.7253769e-04 2.6283484e-04 4.0318659e-04 6.1171989e-05 4.6877945e-04 3.7618255e-04 9.5110904e+02 8.6463848e-05 1.5318726e-04 2.5001263e-04 3.9978868e-04 3.7982641e-04 5.2140928e-04 4.4399526e-04 2.5055719e-04 3.3863680e-04 6.1283222e-04 4.8853929e-04 1.4927887e-04 3.1980357e-04 3.0747222e-04 2.2120508e-04 3.8291029e-04 7.2397280e-05 4.5573690e-04 3.3133186e-04 9.5408322e+02 1.0978707e-04 1.2328758e-04 1.9402621e-04 3.6399721e-04 3.4419113e-04 5.1986480e-04 4.3160500e-04 2.6748006e-04 2.9183567e-04 5.5339783e-04 5.0139892e-04 1.6225564e-04 2.9087900e-04 2.6000700e-04 3.0495671e-04 4.5987789e-04 6.1527125e-05 4.3537197e-04 3.0997673e-04 9.5709328e+02 8.6790037e-05 1.7158382e-04 2.5265864e-04 3.5384266e-04 3.4458460e-04 5.2272460e-04 4.0976948e-04 2.6098330e-04 3.4587718e-04 5.7459895e-04 5.0807003e-04 1.8458905e-04 2.7475734e-04 2.6443067e-04 2.5760119e-04 4.1037539e-04 5.8814115e-05 4.0122475e-04 3.1811657e-04 9.6010544e+02 1.1385181e-04 1.3095600e-04 2.6774891e-04 3.5438729e-04 3.8847698e-04 5.2632822e-04 4.8290792e-04 2.6442275e-04 3.1866065e-04 5.1133467e-04 5.2104254e-04 1.2489850e-04 3.0243345e-04 2.6716813e-04 2.1644110e-04 4.4121160e-04 3.3747592e-05 4.2656933e-04 3.2672242e-04 9.6308582e+02 8.6907989e-05 1.6139730e-04 2.7302402e-04 3.0705900e-04 3.5773087e-04 5.6457377e-04 4.7268047e-04 2.1481843e-04 2.9820570e-04 6.8216160e-04 5.2762028e-04 2.0976783e-04 3.0783464e-04 3.0782354e-04 2.9177793e-04 4.3006815e-04 4.7712058e-05 3.3859630e-04 3.4825850e-04 9.6610216e+02 1.4495622e-04 1.7249921e-04 2.9504533e-04 4.1700730e-04 3.7212374e-04 5.2475590e-04 4.5191497e-04 2.3226299e-04 3.5105951e-04 5.4221400e-04 5.0376167e-04 1.2546075e-04 3.3309945e-04 2.9453550e-04 2.6571329e-04 4.5658697e-04 3.0906335e-05 4.1476044e-04 3.6853075e-04 9.6908667e+02 7.9543873e-05 1.6332599e-04 2.4723773e-04 3.2402023e-04 3.3114693e-04 5.7322603e-04 4.6975575e-04 3.0165256e-04 2.7785987e-04 4.9793254e-04 4.8473363e-04 1.6955443e-04 2.6804411e-04 2.5932754e-04 2.7135420e-04 4.1785763e-04 9.2646856e-05 4.1081020e-04 3.3783482e-04 9.7210720e+02 1.2228926e-04 1.4528251e-04 2.9718519e-04 3.6688691e-04 3.7466775e-04 5.5850672e-04 4.3376768e-04 2.3577364e-04 3.0358055e-04 6.2703393e-04 5.1374972e-04 1.4162371e-04 3.3995474e-04 3.4088820e-04 2.5742400e-04 4.9630307e-04 3.6873351e-05 4.3876492e-04 2.5974159e-04 9.7509586e+02 1.1115423e-04 1.3217641e-04 2.8484297e-04 3.6829406e-04 3.3810439e-04 5.3915737e-04 4.3757387e-04 2.9996273e-04 3.0150157e-04 6.0050537e-04 5.1566120e-04 1.6443533e-04 3.0551241e-04 2.5254813e-04 2.9574458e-04 5.3120254e-04 5.0978320e-05 4.0605138e-04 3.7659390e-04 9.7808659e+02 1.2285369e-04 1.8124354e-04 2.4570267e-04 3.8177262e-04 3.7847997e-04 5.4801821e-04 4.9683776e-04 2.4955654e-04 3.5065383e-04 5.7013151e-04 5.2198202e-04 1.3270306e-04 2.8913573e-04 2.9207176e-04 2.5065517e-04 4.4387074e-04 3.6908160e-05 4.5794354e-04 3.1686725e-04 9.8107938e+02 1.0347384e-04 1.6975702e-04 2.6351627e-04 4.0162094e-04 4.0220837e-04 5.0622987e-04 4.4276346e-04 2.2320245e-04 2.8026966e-04 5.6722472e-04 5.1643503e-04 1.8342301e-04 2.8660047e-04 2.6037665e-04 2.9279286e-04 4.7405702e-04 6.5107152e-05 3.9942667e-04 3.9318477e-04 9.8410828e+02 1.0331841e-04 1.7504123e-04 2.4485127e-04 3.8849389e-04 3.6422347e-04 4.9673278e-04 4.5785986e-04 2.2308463e-04 2.7833324e-04 5.7989050e-04 4.8676112e-04 1.8978412e-04 3.3016478e-04 2.8611263e-04 2.5103570e-04 4.6378208e-04 5.1012692e-05 3.8619768e-04 3.6933363e-04 9.8710523e+02 1.5376101e-04 1.7712810e-04 3.2464057e-04 3.7262542e-04 3.9244201e-04 5.2325163e-04 4.2132953e-04 2.4643355e-04 3.5273497e-04 6.1156748e-04 5.2740888e-04 1.6745278e-04 2.8316286e-04 2.8234664e-04 2.4230666e-04 3.6132443e-04 5.9338962e-05 4.0053757e-04 3.5999520e-04 9.9010426e+02 1.0339268e-04 1.7338354e-04 2.5924916e-04 3.1803226e-04 3.5533130e-04 5.8898856e-04 4.8432575e-04 2.5978187e-04 3.6049311e-04 5.2039712e-04 5.3786563e-04 1.6769787e-04 2.2580672e-04 2.4611919e-04 2.0805617e-04 4.4775365e-04 7.3527587e-05 4.4819056e-04 3.7208668e-04 9.9310536e+02 1.2329183e-04 1.5804658e-04 2.6579365e-04 3.8942035e-04 3.7408178e-04 5.3654335e-04 4.5236708e-04 2.4377080e-04 3.3884895e-04 6.0505007e-04 5.2434883e-04 1.3637142e-04 3.0403435e-04 2.7314480e-04 2.5660493e-04 4.7093061e-04 6.8246544e-05 3.7566453e-04 3.2895923e-04 9.9610854e+02 1.1500673e-04 1.6123130e-04 2.3571329e-04 3.5892889e-04 4.0712280e-04 4.7611777e-04 4.5913361e-04 2.7329153e-04 3.5968071e-04 5.7622069e-04 4.6759797e-04 1.6434000e-04 2.6141232e-04 3.2385638e-04 2.5278640e-04 4.0429167e-04 4.8166811e-05 4.2564920e-04 3.7347928e-04 9.9907963e+02 1.3468056e-04 1.5613393e-04 2.7917347e-04 3.9315823e-04 3.6010422e-04 5.6808106e-04 4.8011830e-04 2.7984284e-04 3.4894626e-04 6.0659625e-04 5.4731605e-04 1.5186753e-04 2.8679146e-04 3.3495023e-04 2.7215210e-04 4.0643413e-04 6.5167307e-05 4.1122368e-04 3.6545361e-04 1.0001046e+03 9.1925239e-05 1.4665219e-04 2.9168056e-04 3.6726694e-04 4.1037072e-04 5.5538114e-04 4.7486999e-04 2.4616420e-04 2.9851724e-04 6.2758327e-04 5.4322752e-04 1.7073756e-04 2.9244744e-04 2.7074496e-04 2.2186827e-04 4.3716714e-04 6.2502144e-05 4.6960991e-04 3.1741819e-04 xrstools-0.15.0+git20210910+c147919d/XRStools/things/ComptonProfiles.dat000066400000000000000000022056251412732462000250340ustar00rootroot00000000000000#F ComptonProfiles.dat #C This file has been created using ComptonProfiles.pro #D Wed Jan 29 16:17:10 2003 #C This file belongs to the DABAX library. More information on #C DABAX can be found at: #C http://www.esrf.fr/computing/scientific/dabax #UT Compton Profiles of the elements and their sub-shells #UD Compton Profiles of the elements and their sub-shells #UD This file contains the Compton Profiles of #UD the elements (Z=1-102) #UD #UD REFERENCE: #UD F. Biggs, L. B. Mendelsohn and J B Mann #UD Hartree-Fock Compton profiles for the elements #UD Atomic Data and Nuclear Data Tables 16, 201-309 (1975) #UD #UD Nota: The number of electrons that occupy the levels #UD are under the UOCCUP keyword. #UD The binding energies [in eV] are in the UBIND keyword. #UD #UD These data has been obtained from the LSCAT extension #UD to the EGS4 program from Namito et. al. #UD #UD Columns: #UD col1: pz in atomic units #UD col2: Total compton profile (sum over the atomic electrons #UD col3,...coln: Compton profile for the individual sub-shells #UD #S 1 H #N 3 #UOCCUP 1 #UBIND 14.00 #L pz total Shell_1 0.000 0.8490 0.8490 0.05000 0.8420 0.8420 0.1000 0.8240 0.8240 0.1500 0.7940 0.7940 0.2000 0.7550 0.7550 0.3000 0.6550 0.6550 0.4000 0.5440 0.5440 0.5000 0.4350 0.4350 0.6000 0.3370 0.3370 0.7000 0.2570 0.2570 0.8000 0.1920 0.1920 1.000 0.1060 0.1060 1.200 0.05840 0.05840 1.400 0.03270 0.03270 1.600 0.01880 0.01880 1.800 0.01110 0.01110 2.000 0.006790 0.006790 2.400 0.002750 0.002750 3.000 0.0008490 0.0008490 4.000 0.0001730 0.0001730 5.000 4.830E-05 4.830E-05 6.000 1.680E-05 1.680E-05 7.000 6.790E-06 6.790E-06 8.000 3.090E-06 3.090E-06 10.00 8.200E-07 8.200E-07 15.00 7.400E-08 7.400E-08 20.00 1.300E-08 1.300E-08 30.00 1.200E-09 1.200E-09 40.00 2.300E-10 2.300E-10 60.00 4.300E-11 4.300E-11 100.0 2.600E-11 2.600E-11 #S 2 He #N 3 #UOCCUP 2 #UBIND 24.60 #L pz total Shell_1 0.000 1.070 0.5350 0.05000 1.070 0.5340 0.1000 1.060 0.5280 0.1500 1.040 0.5200 0.2000 1.020 0.5090 0.3000 0.9560 0.4780 0.4000 0.8780 0.4390 0.5000 0.7910 0.3960 0.6000 0.7000 0.3500 0.7000 0.6110 0.3060 0.8000 0.5270 0.2640 1.000 0.3820 0.1910 1.200 0.2710 0.1360 1.400 0.1910 0.09550 1.600 0.1340 0.06720 1.800 0.09520 0.04760 2.000 0.06800 0.03400 2.400 0.03580 0.01790 3.000 0.01480 0.007400 4.000 0.004130 0.002060 5.000 0.001400 0.0006980 6.000 0.0005470 0.0002740 7.000 0.0002400 0.0001200 8.000 0.0001160 5.780E-05 10.00 3.300E-05 1.600E-05 15.00 3.200E-06 1.600E-06 20.00 5.800E-07 2.900E-07 30.00 5.200E-08 2.600E-08 40.00 9.300E-09 4.700E-09 60.00 8.600E-10 4.300E-10 100.0 7.800E-11 3.900E-11 #S 3 Li #N 4 #UOCCUP 2 1 #UBIND 54.80 1.0000 #L pz total Shell_1 Shell_2 0.000 2.590 0.3290 1.940 0.05000 2.530 0.3280 1.870 0.1000 2.340 0.3270 1.690 0.1500 2.080 0.3250 1.430 0.2000 1.780 0.3220 1.140 0.3000 1.240 0.3150 0.6120 0.4000 0.8840 0.3050 0.2740 0.5000 0.6930 0.2930 0.1080 0.6000 0.5980 0.2790 0.04110 0.7000 0.5460 0.2630 0.01940 0.8000 0.5080 0.2470 0.01410 1.000 0.4390 0.2130 0.01340 1.200 0.3720 0.1800 0.01200 1.400 0.3080 0.1490 0.009580 1.600 0.2510 0.1220 0.007240 1.800 0.2030 0.09900 0.005360 2.000 0.1640 0.07980 0.003950 2.400 0.1050 0.05130 0.002180 3.000 0.05410 0.02660 0.0009620 4.000 0.01920 0.009430 0.0002950 5.000 0.007530 0.003710 0.0001080 6.000 0.003270 0.001610 4.480E-05 7.000 0.001550 0.0007640 2.060E-05 8.000 0.0007880 0.0003890 1.030E-05 10.00 0.0002400 0.0001200 3.100E-06 15.00 2.500E-05 1.200E-05 3.100E-07 20.00 4.800E-06 2.400E-06 5.900E-08 30.00 4.400E-07 2.200E-07 5.400E-09 40.00 7.900E-08 3.900E-08 9.600E-10 60.00 7.100E-09 3.500E-09 8.600E-11 100.0 3.600E-10 1.800E-10 4.400E-12 #S 4 Be #N 4 #UOCCUP 2 2 #UBIND 111.7 3.000 #L pz total Shell_1 Shell_2 0.000 3.160 0.2370 1.340 0.05000 3.110 0.2370 1.320 0.1000 2.980 0.2360 1.250 0.1500 2.770 0.2360 1.150 0.2000 2.520 0.2350 1.020 0.3000 1.950 0.2320 0.7430 0.4000 1.430 0.2280 0.4860 0.5000 1.030 0.2230 0.2930 0.6000 0.7660 0.2180 0.1650 0.7000 0.6000 0.2110 0.08920 0.8000 0.5030 0.2040 0.04750 1.000 0.4090 0.1880 0.01640 1.200 0.3630 0.1710 0.01080 1.400 0.3270 0.1530 0.01040 1.600 0.2920 0.1360 0.01020 1.800 0.2570 0.1190 0.009390 2.000 0.2240 0.1040 0.008230 2.400 0.1660 0.07710 0.005800 3.000 0.1020 0.04810 0.003150 4.000 0.04520 0.02150 0.001150 5.000 0.02060 0.009860 0.0004610 6.000 0.009940 0.004770 0.0002040 7.000 0.005070 0.002440 9.870E-05 8.000 0.002730 0.001310 5.110E-05 10.00 0.0009100 0.0004400 1.600E-05 15.00 1.000E-04 5.000E-05 1.800E-06 20.00 2.100E-05 9.900E-06 3.400E-07 30.00 1.900E-06 9.400E-07 3.200E-08 40.00 3.600E-07 1.700E-07 5.800E-09 60.00 3.200E-08 1.500E-08 5.200E-10 100.0 1.500E-09 7.400E-10 2.500E-11 #S 5 B #N 5 #UOCCUP 2 2 1 #UBIND 191.0 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.990 0.1860 1.000 0.6150 0.05000 2.970 0.1860 0.9920 0.6150 0.1000 2.910 0.1860 0.9630 0.6140 0.1500 2.820 0.1850 0.9170 0.6120 0.2000 2.690 0.1850 0.8570 0.6060 0.3000 2.370 0.1840 0.7110 0.5810 0.4000 2.000 0.1820 0.5520 0.5330 0.5000 1.640 0.1790 0.4050 0.4670 0.6000 1.310 0.1760 0.2840 0.3930 0.7000 1.050 0.1730 0.1910 0.3210 0.8000 0.8430 0.1690 0.1240 0.2560 1.000 0.5780 0.1610 0.05000 0.1570 1.200 0.4390 0.1510 0.02090 0.09430 1.400 0.3620 0.1410 0.01150 0.05680 1.600 0.3140 0.1300 0.009240 0.03460 1.800 0.2780 0.1190 0.008970 0.02150 2.000 0.2490 0.1090 0.008930 0.01350 2.400 0.1990 0.08840 0.008080 0.005710 3.000 0.1390 0.06280 0.005730 0.001780 4.000 0.07330 0.03380 0.002680 0.0003450 5.000 0.03830 0.01790 0.001220 8.880E-05 6.000 0.02040 0.009610 0.0005860 2.820E-05 7.000 0.01120 0.005320 0.0002980 1.040E-05 8.000 0.006410 0.003040 0.0001610 4.290E-06 10.00 0.002300 0.001100 5.400E-05 9.300E-07 15.00 0.0002900 0.0001400 6.300E-06 5.000E-08 20.00 6.100E-05 2.900E-05 1.300E-06 5.700E-09 30.00 6.000E-06 2.900E-06 1.200E-07 2.500E-10 40.00 1.100E-06 5.300E-07 2.200E-08 2.500E-11 60.00 1.000E-07 4.800E-08 2.000E-09 1.000E-12 100.0 4.800E-09 2.300E-09 9.400E-11 1.700E-14 #S 6 C #N 5 #UOCCUP 2 2 2 #UBIND 284.7 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.890 0.1530 0.8040 0.4880 0.05000 2.880 0.1530 0.7980 0.4880 0.1000 2.850 0.1530 0.7830 0.4880 0.1500 2.800 0.1530 0.7590 0.4870 0.2000 2.730 0.1530 0.7260 0.4850 0.3000 2.540 0.1520 0.6410 0.4750 0.4000 2.290 0.1510 0.5410 0.4530 0.5000 2.020 0.1490 0.4390 0.4200 0.6000 1.740 0.1480 0.3430 0.3790 0.7000 1.480 0.1460 0.2600 0.3330 0.8000 1.240 0.1440 0.1920 0.2870 1.000 0.8810 0.1390 0.09820 0.2040 1.200 0.6410 0.1330 0.04770 0.1400 1.400 0.4900 0.1260 0.02340 0.09500 1.600 0.3940 0.1200 0.01300 0.06440 1.800 0.3310 0.1120 0.009090 0.04380 2.000 0.2860 0.1050 0.007950 0.03010 2.400 0.2260 0.09050 0.007750 0.01460 3.000 0.1650 0.07010 0.006950 0.005300 4.000 0.09760 0.04330 0.004250 0.001200 5.000 0.05690 0.02580 0.002270 0.0003340 6.000 0.03320 0.01530 0.001200 0.0001100 7.000 0.01960 0.009130 0.0006490 4.160E-05 8.000 0.01190 0.005550 0.0003650 1.750E-05 10.00 0.004600 0.002200 0.0001300 3.900E-06 15.00 0.0006600 0.0003100 1.600E-05 2.200E-07 20.00 0.0001400 6.800E-05 3.400E-06 2.600E-08 30.00 1.500E-05 7.000E-06 3.300E-07 1.200E-09 40.00 2.800E-06 1.300E-06 6.200E-08 1.200E-10 60.00 2.500E-07 1.200E-07 5.600E-09 4.800E-12 100.0 1.200E-08 5.800E-09 2.700E-10 8.000E-14 #S 7 N #N 5 #UOCCUP 2 2 3 #UBIND 409.9 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.830 0.1300 0.6720 0.4070 0.05000 2.820 0.1300 0.6690 0.4070 0.1000 2.800 0.1300 0.6600 0.4070 0.1500 2.770 0.1300 0.6460 0.4070 0.2000 2.730 0.1300 0.6250 0.4060 0.3000 2.610 0.1290 0.5720 0.4010 0.4000 2.440 0.1290 0.5070 0.3900 0.5000 2.240 0.1280 0.4350 0.3720 0.6000 2.020 0.1270 0.3640 0.3480 0.7000 1.800 0.1260 0.2960 0.3190 0.8000 1.580 0.1240 0.2360 0.2870 1.000 1.200 0.1210 0.1420 0.2240 1.200 0.9020 0.1170 0.08070 0.1680 1.400 0.6880 0.1130 0.04430 0.1240 1.600 0.5380 0.1090 0.02440 0.09080 1.800 0.4350 0.1040 0.01420 0.06620 2.000 0.3610 0.09870 0.009530 0.04840 2.400 0.2690 0.08810 0.006940 0.02610 3.000 0.1910 0.07240 0.006760 0.01090 4.000 0.1180 0.04950 0.005290 0.002870 5.000 0.07420 0.03250 0.003330 0.0008820 6.000 0.04650 0.02090 0.001950 0.0003090 7.000 0.02930 0.01330 0.001140 0.0001210 8.000 0.01870 0.008580 0.0006740 5.230E-05 10.00 0.007900 0.003700 0.0002600 1.200E-05 15.00 0.001300 0.0005900 3.500E-05 7.300E-07 20.00 0.0002900 0.0001400 7.500E-06 8.900E-08 30.00 3.100E-05 1.500E-05 7.600E-07 4.100E-09 40.00 6.000E-06 2.800E-06 1.400E-07 4.200E-10 60.00 5.500E-07 2.600E-07 1.300E-08 1.700E-11 100.0 2.700E-08 1.300E-08 6.200E-10 3.000E-13 #S 8 O #N 5 #UOCCUP 2 2 4 #UBIND 543.1 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.780 0.1130 0.5790 0.3500 0.05000 2.780 0.1130 0.5770 0.3500 0.1000 2.770 0.1130 0.5710 0.3500 0.1500 2.750 0.1130 0.5610 0.3490 0.2000 2.720 0.1130 0.5480 0.3490 0.3000 2.640 0.1130 0.5130 0.3460 0.4000 2.520 0.1120 0.4680 0.3400 0.5000 2.380 0.1120 0.4170 0.3300 0.6000 2.210 0.1110 0.3630 0.3150 0.7000 2.030 0.1100 0.3110 0.2970 0.8000 1.840 0.1090 0.2610 0.2750 1.000 1.480 0.1070 0.1750 0.2280 1.200 1.160 0.1050 0.1110 0.1830 1.400 0.9140 0.1020 0.06840 0.1430 1.600 0.7230 0.09860 0.04090 0.1110 1.800 0.5800 0.09520 0.02450 0.08520 2.000 0.4750 0.09150 0.01510 0.06530 2.400 0.3370 0.08380 0.007650 0.03850 3.000 0.2270 0.07180 0.006050 0.01790 4.000 0.1390 0.05290 0.005610 0.005420 5.000 0.09030 0.03730 0.004140 0.001840 6.000 0.05950 0.02570 0.002710 0.0006900 7.000 0.03940 0.01740 0.001710 0.0002840 8.000 0.02630 0.01180 0.001070 0.0001260 10.00 0.01200 0.005500 0.0004400 3.000E-05 15.00 0.002100 0.001000 6.500E-05 1.900E-06 20.00 0.0005200 0.0002400 1.500E-05 2.400E-07 30.00 5.800E-05 2.800E-05 1.500E-06 1.100E-08 40.00 1.100E-05 5.400E-06 2.900E-07 1.200E-09 60.00 1.100E-06 5.100E-07 2.700E-08 5.000E-11 100.0 5.200E-08 2.500E-08 1.300E-09 8.500E-13 #S 9 F #N 5 #UOCCUP 2 2 5 #UBIND 696.7 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.750 0.1000 0.5080 0.3070 0.05000 2.750 0.1000 0.5070 0.3070 0.1000 2.740 0.1000 0.5030 0.3070 0.1500 2.730 0.1000 0.4960 0.3070 0.2000 2.710 0.1000 0.4870 0.3060 0.3000 2.650 0.09990 0.4620 0.3050 0.4000 2.570 0.09960 0.4300 0.3010 0.5000 2.460 0.09920 0.3930 0.2950 0.6000 2.330 0.09870 0.3530 0.2860 0.7000 2.190 0.09820 0.3120 0.2740 0.8000 2.030 0.09750 0.2710 0.2590 1.000 1.710 0.09600 0.1970 0.2240 1.200 1.400 0.09420 0.1360 0.1880 1.400 1.140 0.09220 0.09110 0.1540 1.600 0.9220 0.08990 0.05920 0.1250 1.800 0.7500 0.08740 0.03780 0.09980 2.000 0.6150 0.08470 0.02410 0.07950 2.400 0.4310 0.07890 0.01070 0.05030 3.000 0.2790 0.06970 0.005790 0.02550 4.000 0.1630 0.05420 0.005390 0.008720 5.000 0.1060 0.04050 0.004580 0.003240 6.000 0.07220 0.02950 0.003350 0.001300 7.000 0.04960 0.02110 0.002280 0.0005630 8.000 0.03430 0.01500 0.001520 0.0002600 10.00 0.01700 0.007500 0.0006700 6.600E-05 15.00 0.003300 0.001500 0.0001100 4.400E-06 20.00 0.0008500 0.0004000 2.600E-05 5.700E-07 30.00 1.000E-04 4.800E-05 2.800E-06 2.800E-08 40.00 2.000E-05 9.600E-06 5.400E-07 3.000E-09 60.00 1.900E-06 9.200E-07 5.000E-08 1.300E-10 100.0 9.400E-08 4.500E-08 2.400E-09 2.100E-12 #S 10 Ne #N 5 #UOCCUP 2 2 6 #UBIND 870.4 48.50 21.60 #L pz total Shell_1 Shell_2 Shell_3 0.000 2.730 0.09000 0.4530 0.2740 0.05000 2.720 0.09000 0.4520 0.2740 0.1000 2.720 0.08990 0.4490 0.2740 0.1500 2.710 0.08990 0.4440 0.2730 0.2000 2.700 0.08980 0.4380 0.2730 0.3000 2.650 0.08970 0.4200 0.2720 0.4000 2.590 0.08950 0.3970 0.2700 0.5000 2.510 0.08920 0.3690 0.2660 0.6000 2.410 0.08880 0.3380 0.2600 0.7000 2.300 0.08840 0.3050 0.2520 0.8000 2.170 0.08800 0.2720 0.2410 1.000 1.890 0.08690 0.2090 0.2160 1.200 1.610 0.08550 0.1550 0.1880 1.400 1.350 0.08400 0.1100 0.1590 1.600 1.120 0.08230 0.07670 0.1330 1.800 0.9270 0.08050 0.05220 0.1100 2.000 0.7710 0.07840 0.03510 0.09060 2.400 0.5440 0.07400 0.01590 0.06070 3.000 0.3460 0.06680 0.006560 0.03320 4.000 0.1940 0.05430 0.004930 0.01260 5.000 0.1240 0.04240 0.004660 0.005050 6.000 0.08510 0.03230 0.003770 0.002160 7.000 0.05970 0.02410 0.002780 0.0009840 8.000 0.04240 0.01780 0.001960 0.0004730 10.00 0.02200 0.009600 0.0009400 0.0001300 15.00 0.004800 0.002200 0.0001700 9.000E-06 20.00 0.001300 0.0006000 4.200E-05 1.200E-06 30.00 0.0001600 7.600E-05 4.700E-06 6.000E-08 40.00 3.300E-05 1.600E-05 9.300E-07 6.700E-09 60.00 3.300E-06 1.500E-06 8.700E-08 2.800E-10 100.0 1.600E-07 7.600E-08 4.200E-09 4.800E-12 #S 11 Na #N 6 #UOCCUP 2 2 6 1 #UBIND 1072. 63.40 30.40 1.0000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 0.000 4.360 0.08150 0.3900 0.2250 2.070 0.05000 4.280 0.08150 0.3900 0.2250 1.990 0.1000 4.060 0.08150 0.3880 0.2250 1.770 0.1500 3.740 0.08150 0.3850 0.2250 1.460 0.2000 3.400 0.08140 0.3810 0.2240 1.130 0.3000 2.800 0.08130 0.3690 0.2240 0.5560 0.4000 2.430 0.08110 0.3540 0.2230 0.2230 0.5000 2.240 0.08090 0.3350 0.2220 0.07960 0.6000 2.140 0.08070 0.3140 0.2200 0.03190 0.7000 2.060 0.08040 0.2910 0.2160 0.02050 0.8000 1.980 0.08000 0.2670 0.2120 0.01910 1.000 1.810 0.07920 0.2180 0.1990 0.01800 1.200 1.610 0.07820 0.1720 0.1820 0.01420 1.400 1.410 0.07710 0.1320 0.1640 0.010000 1.600 1.220 0.07580 0.09800 0.1440 0.006720 1.800 1.050 0.07430 0.07130 0.1250 0.004400 2.000 0.8950 0.07280 0.05100 0.1080 0.002860 2.400 0.6550 0.06940 0.02520 0.07740 0.001190 3.000 0.4230 0.06370 0.009430 0.04600 0.0003390 4.000 0.2320 0.05340 0.004840 0.01920 0.0001150 5.000 0.1460 0.04330 0.004720 0.008320 0.0001090 6.000 0.09960 0.03420 0.004200 0.003770 9.890E-05 7.000 0.07060 0.02650 0.003350 0.001800 7.940E-05 8.000 0.05100 0.02030 0.002510 0.0008960 5.960E-05 10.00 0.02700 0.01200 0.001300 0.0002500 3.100E-05 15.00 0.006600 0.003000 0.0002600 1.900E-05 6.100E-06 20.00 0.001900 0.0008700 6.700E-05 2.600E-06 1.500E-06 30.00 0.0002500 0.0001200 7.900E-06 1.400E-07 1.800E-07 40.00 5.200E-05 2.400E-05 1.600E-06 1.500E-08 3.600E-08 60.00 5.200E-06 2.400E-06 1.500E-07 6.500E-10 3.400E-09 100.0 2.600E-07 1.200E-07 7.400E-09 1.100E-11 1.700E-10 #S 12 Mg #N 6 #UOCCUP 2 2 6 2 #UBIND 1303. 88.60 49.30 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 0.000 5.160 0.07450 0.3420 0.1920 1.590 0.05000 5.080 0.07450 0.3420 0.1920 1.550 0.1000 4.860 0.07450 0.3400 0.1920 1.440 0.1500 4.540 0.07450 0.3380 0.1920 1.280 0.2000 4.150 0.07440 0.3360 0.1920 1.090 0.3000 3.350 0.07430 0.3280 0.1920 0.6960 0.4000 2.710 0.07420 0.3170 0.1910 0.3870 0.5000 2.290 0.07400 0.3050 0.1910 0.1930 0.6000 2.050 0.07380 0.2900 0.1900 0.09110 0.7000 1.910 0.07360 0.2730 0.1880 0.04490 0.8000 1.830 0.07330 0.2550 0.1860 0.02720 1.000 1.700 0.07270 0.2180 0.1790 0.02120 1.200 1.560 0.07200 0.1810 0.1690 0.02070 1.400 1.420 0.07110 0.1460 0.1580 0.01810 1.600 1.260 0.07010 0.1150 0.1440 0.01440 1.800 1.120 0.06900 0.08830 0.1300 0.01070 2.000 0.9800 0.06780 0.06670 0.1160 0.007630 2.400 0.7470 0.06510 0.03640 0.08940 0.003640 3.000 0.4990 0.06050 0.01430 0.05780 0.001120 4.000 0.2750 0.05210 0.005170 0.02670 0.0002370 5.000 0.1710 0.04350 0.004590 0.01240 0.0001740 6.000 0.1160 0.03540 0.004410 0.005930 0.0001700 7.000 0.08220 0.02830 0.003790 0.002950 0.0001480 8.000 0.06010 0.02230 0.003020 0.001520 0.0001180 10.00 0.03300 0.01400 0.001700 0.0004500 6.700E-05 15.00 0.008600 0.003800 0.0003900 3.700E-05 1.500E-05 20.00 0.002600 0.001200 1.000E-04 5.200E-06 3.900E-06 30.00 0.0003600 0.0001700 1.300E-05 2.800E-07 4.700E-07 40.00 7.800E-05 3.600E-05 2.600E-06 3.200E-08 9.600E-08 60.00 8.000E-06 3.700E-06 2.500E-07 1.400E-09 9.300E-09 100.0 4.000E-07 1.900E-07 1.200E-08 2.400E-11 4.500E-10 #S 13 Al #N 7 #UOCCUP 2 2 6 2 1 #UBIND 1558. 118.0 72.80 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.150 0.06860 0.3050 0.1680 1.240 0.9190 0.05000 5.110 0.06860 0.3040 0.1680 1.220 0.9180 0.1000 5.000 0.06860 0.3040 0.1680 1.170 0.9160 0.1500 4.820 0.06850 0.3020 0.1680 1.080 0.9040 0.2000 4.570 0.06850 0.3000 0.1680 0.9750 0.8790 0.3000 3.970 0.06840 0.2950 0.1670 0.7290 0.7760 0.4000 3.320 0.06830 0.2870 0.1670 0.4940 0.6190 0.5000 2.760 0.06820 0.2780 0.1670 0.3080 0.4520 0.6000 2.340 0.06810 0.2670 0.1660 0.1800 0.3070 0.7000 2.040 0.06790 0.2550 0.1660 0.1010 0.1990 0.8000 1.840 0.06770 0.2410 0.1640 0.05750 0.1240 1.000 1.620 0.06720 0.2130 0.1610 0.02710 0.04520 1.200 1.490 0.06660 0.1830 0.1550 0.02330 0.01620 1.400 1.380 0.06590 0.1530 0.1480 0.02310 0.006660 1.600 1.260 0.06510 0.1260 0.1390 0.02120 0.004020 1.800 1.140 0.06420 0.1010 0.1290 0.01790 0.003500 2.000 1.030 0.06330 0.08010 0.1180 0.01430 0.003460 2.400 0.8160 0.06110 0.04790 0.09640 0.008060 0.003280 3.000 0.5680 0.05750 0.02070 0.06730 0.002930 0.002490 4.000 0.3220 0.05050 0.006250 0.03430 0.0005410 0.001270 5.000 0.1990 0.04320 0.004450 0.01710 0.0002520 0.0006180 6.000 0.1340 0.03610 0.004390 0.008630 0.0002410 0.0003070 7.000 0.09480 0.02960 0.004040 0.004470 0.0002270 0.0001570 8.000 0.06960 0.02390 0.003420 0.002390 0.0001940 8.330E-05 10.00 0.03900 0.01500 0.002100 0.0007500 0.0001200 2.600E-05 15.00 0.01100 0.004700 0.0005300 6.600E-05 3.000E-05 2.200E-06 20.00 0.003500 0.001600 0.0001500 9.700E-06 8.200E-06 3.200E-07 30.00 0.0005100 0.0002400 1.900E-05 5.300E-07 1.000E-06 1.700E-08 40.00 0.0001100 5.200E-05 4.000E-06 6.100E-08 2.100E-07 2.000E-09 60.00 1.200E-05 5.500E-06 3.900E-07 2.700E-09 2.100E-08 8.600E-11 100.0 6.000E-07 2.800E-07 1.900E-08 4.800E-11 1.000E-09 1.600E-12 #S 14 Si #N 7 #UOCCUP 2 2 6 2 2 #UBIND 1839. 149.0 99.30 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.130 0.06350 0.2750 0.1490 1.040 0.7440 0.05000 5.110 0.06350 0.2750 0.1490 1.030 0.7440 0.1000 5.040 0.06350 0.2740 0.1490 0.9920 0.7430 0.1500 4.920 0.06350 0.2730 0.1490 0.9400 0.7390 0.2000 4.760 0.06350 0.2720 0.1490 0.8720 0.7280 0.3000 4.330 0.06340 0.2680 0.1490 0.7070 0.6810 0.4000 3.800 0.06330 0.2620 0.1490 0.5310 0.5970 0.5000 3.260 0.06320 0.2550 0.1490 0.3740 0.4900 0.6000 2.770 0.06310 0.2470 0.1480 0.2480 0.3800 0.7000 2.370 0.06300 0.2370 0.1480 0.1580 0.2810 0.8000 2.060 0.06280 0.2270 0.1470 0.09790 0.2010 1.000 1.670 0.06240 0.2050 0.1450 0.04070 0.09460 1.200 1.470 0.06190 0.1810 0.1420 0.02560 0.04190 1.400 1.340 0.06140 0.1560 0.1370 0.02370 0.01830 1.600 1.240 0.06080 0.1320 0.1310 0.02350 0.008720 1.800 1.140 0.06010 0.1100 0.1240 0.02210 0.005240 2.000 1.040 0.05930 0.09060 0.1160 0.01940 0.004210 2.400 0.8610 0.05760 0.05840 0.09920 0.01290 0.004010 3.000 0.6280 0.05450 0.02790 0.07400 0.005660 0.003600 4.000 0.3690 0.04880 0.008220 0.04140 0.001160 0.002170 5.000 0.2300 0.04250 0.004500 0.02210 0.0003610 0.001150 6.000 0.1530 0.03630 0.004250 0.01180 0.0002870 0.0005990 7.000 0.1080 0.03040 0.004120 0.006360 0.0002830 0.0003190 8.000 0.07970 0.02520 0.003690 0.003520 0.0002580 0.0001740 10.00 0.04600 0.01700 0.002500 0.001200 0.0001800 5.600E-05 15.00 0.01300 0.005600 0.0007000 0.0001100 4.900E-05 5.200E-06 20.00 0.004500 0.002000 0.0002100 1.700E-05 1.400E-05 7.700E-07 30.00 0.0007000 0.0003200 2.800E-05 9.400E-07 1.900E-06 4.300E-08 40.00 0.0001600 7.300E-05 5.900E-06 1.100E-07 3.900E-07 5.000E-09 60.00 1.700E-05 7.800E-06 5.900E-07 4.900E-09 3.900E-08 2.200E-10 100.0 8.700E-07 4.000E-07 2.900E-08 8.900E-11 1.900E-09 4.100E-12 #S 15 P #N 7 #UOCCUP 2 2 6 2 3 #UBIND 2149. 189.0 135.0 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.110 0.05920 0.2510 0.1340 0.8970 0.6310 0.05000 5.100 0.05920 0.2500 0.1340 0.8900 0.6310 0.1000 5.050 0.05910 0.2500 0.1340 0.8680 0.6310 0.1500 4.970 0.05910 0.2490 0.1340 0.8330 0.6290 0.2000 4.860 0.05910 0.2480 0.1340 0.7860 0.6240 0.3000 4.550 0.05910 0.2450 0.1340 0.6690 0.5990 0.4000 4.130 0.05900 0.2410 0.1340 0.5360 0.5510 0.5000 3.660 0.05890 0.2350 0.1340 0.4070 0.4830 0.6000 3.180 0.05880 0.2290 0.1340 0.2950 0.4040 0.7000 2.750 0.05870 0.2220 0.1340 0.2050 0.3250 0.8000 2.380 0.05860 0.2140 0.1330 0.1380 0.2530 1.000 1.850 0.05830 0.1960 0.1320 0.06110 0.1420 1.200 1.530 0.05790 0.1760 0.1300 0.03200 0.07410 1.400 1.350 0.05740 0.1560 0.1270 0.02430 0.03710 1.600 1.220 0.05690 0.1360 0.1230 0.02330 0.01850 1.800 1.130 0.05640 0.1160 0.1180 0.02320 0.009800 2.000 1.040 0.05570 0.09820 0.1120 0.02200 0.006090 2.400 0.8850 0.05430 0.06740 0.09910 0.01700 0.004330 3.000 0.6730 0.05180 0.03530 0.07810 0.008930 0.004200 4.000 0.4150 0.04700 0.01110 0.04750 0.002210 0.003040 5.000 0.2530 0.04160 0.004920 0.02720 0.0005790 0.001780 6.000 0.1750 0.03620 0.004090 0.01520 0.0003300 0.0009820 7.000 0.1240 0.03090 0.004060 0.008560 0.0003170 0.0005440 8.000 0.09070 0.02610 0.003820 0.004890 0.0003060 0.0003060 10.00 0.05300 0.01800 0.002800 0.001700 0.0002300 1.000E-04 15.00 0.01600 0.006600 0.0008900 0.0001700 7.200E-05 1.000E-05 20.00 0.005600 0.002400 0.0002800 2.700E-05 2.200E-05 1.600E-06 30.00 0.0009200 0.0004200 3.900E-05 1.600E-06 3.000E-06 9.100E-08 40.00 0.0002200 9.800E-05 8.400E-06 1.900E-07 6.500E-07 1.100E-08 60.00 2.300E-05 1.100E-05 8.500E-07 8.500E-09 6.500E-08 4.800E-10 100.0 1.200E-06 5.700E-07 4.300E-08 1.500E-10 3.300E-09 8.700E-12 #S 16 S #N 7 #UOCCUP 2 2 6 2 4 #UBIND 2472. 229.0 164.0 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.100 0.05530 0.2300 0.1220 0.7940 0.5510 0.05000 5.080 0.05530 0.2300 0.1220 0.7880 0.5510 0.1000 5.050 0.05530 0.2300 0.1220 0.7730 0.5500 0.1500 5.000 0.05530 0.2290 0.1220 0.7480 0.5490 0.2000 4.920 0.05530 0.2280 0.1220 0.7150 0.5470 0.3000 4.680 0.05530 0.2260 0.1220 0.6280 0.5330 0.4000 4.360 0.05520 0.2230 0.1220 0.5260 0.5040 0.5000 3.960 0.05520 0.2180 0.1220 0.4210 0.4590 0.6000 3.530 0.05510 0.2130 0.1220 0.3240 0.4040 0.7000 3.110 0.05500 0.2080 0.1220 0.2400 0.3430 0.8000 2.720 0.05490 0.2010 0.1220 0.1730 0.2830 1.000 2.090 0.05460 0.1870 0.1210 0.08470 0.1790 1.200 1.680 0.05430 0.1710 0.1190 0.04290 0.1060 1.400 1.410 0.05390 0.1540 0.1170 0.02720 0.05960 1.600 1.240 0.05350 0.1370 0.1150 0.02310 0.03260 1.800 1.130 0.05300 0.1200 0.1110 0.02270 0.01790 2.000 1.040 0.05250 0.1030 0.1070 0.02250 0.01030 2.400 0.8940 0.05130 0.07460 0.09710 0.01960 0.005080 3.000 0.7050 0.04930 0.04230 0.08000 0.01220 0.004360 4.000 0.4560 0.04520 0.01460 0.05240 0.003710 0.003720 5.000 0.2960 0.04060 0.005820 0.03190 0.0009830 0.002430 6.000 0.1990 0.03580 0.004040 0.01880 0.0004070 0.001440 7.000 0.1400 0.03110 0.003920 0.01100 0.0003390 0.0008290 8.000 0.1030 0.02670 0.003830 0.006500 0.0003360 0.0004820 10.00 0.06000 0.01900 0.003100 0.002400 0.0002800 0.0001700 15.00 0.01900 0.007500 0.001100 0.0002600 9.900E-05 1.800E-05 20.00 0.006900 0.002900 0.0003600 4.200E-05 3.200E-05 2.900E-06 30.00 0.001200 0.0005300 5.300E-05 2.600E-06 4.600E-06 1.700E-07 40.00 0.0002900 0.0001300 1.200E-05 3.100E-07 1.000E-06 2.000E-08 60.00 3.200E-05 1.500E-05 1.200E-06 1.400E-08 1.000E-07 9.300E-10 100.0 1.700E-06 7.800E-07 6.100E-08 2.600E-10 5.200E-09 1.700E-11 #S 17 Cl #N 7 #UOCCUP 2 2 6 2 5 #UBIND 2823. 270.0 201.0 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.080 0.05200 0.2130 0.1120 0.7130 0.4900 0.05000 5.070 0.05200 0.2130 0.1120 0.7090 0.4900 0.1000 5.050 0.05200 0.2130 0.1120 0.6980 0.4890 0.1500 5.010 0.05200 0.2120 0.1120 0.6800 0.4890 0.2000 4.950 0.05200 0.2120 0.1120 0.6550 0.4870 0.3000 4.770 0.05190 0.2100 0.1120 0.5890 0.4790 0.4000 4.510 0.05190 0.2070 0.1120 0.5090 0.4600 0.5000 4.190 0.05180 0.2040 0.1120 0.4230 0.4310 0.6000 3.810 0.05180 0.2000 0.1120 0.3400 0.3920 0.7000 3.420 0.05170 0.1950 0.1120 0.2650 0.3460 0.8000 3.040 0.05160 0.1900 0.1120 0.2010 0.2980 1.000 2.370 0.05140 0.1780 0.1110 0.1080 0.2060 1.200 1.880 0.05110 0.1650 0.1100 0.05690 0.1340 1.400 1.540 0.05080 0.1500 0.1090 0.03310 0.08260 1.600 1.310 0.05050 0.1360 0.1070 0.02430 0.04930 1.800 1.160 0.05010 0.1210 0.1050 0.02210 0.02880 2.000 1.050 0.04970 0.1070 0.1010 0.02190 0.01690 2.400 0.8980 0.04870 0.08020 0.09400 0.02080 0.006950 3.000 0.7250 0.04690 0.04870 0.08030 0.01500 0.004400 4.000 0.4920 0.04350 0.01850 0.05600 0.005560 0.004120 5.000 0.3290 0.03950 0.007200 0.03610 0.001630 0.003020 6.000 0.2240 0.03530 0.004200 0.02240 0.0005600 0.001930 7.000 0.1580 0.03110 0.003770 0.01360 0.0003660 0.001170 8.000 0.1160 0.02710 0.003740 0.008310 0.0003520 0.0007020 10.00 0.06700 0.02000 0.003200 0.003200 0.0003200 0.0002600 15.00 0.02200 0.008400 0.001300 0.0003800 0.0001300 3.000E-05 20.00 0.008200 0.003400 0.0004500 6.400E-05 4.400E-05 4.900E-06 30.00 0.001500 0.0006600 7.000E-05 4.000E-06 6.600E-06 3.000E-07 40.00 0.0003700 0.0001700 1.600E-05 4.900E-07 1.500E-06 3.600E-08 60.00 4.200E-05 1.900E-05 1.700E-06 2.300E-08 1.500E-07 1.600E-09 100.0 2.300E-06 1.000E-06 8.500E-08 4.200E-10 7.800E-09 3.000E-11 #S 18 Ar #N 7 #UOCCUP 2 2 6 2 6 #UBIND 3206. 326.3 249.3 29.20 15.80 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 0.000 5.060 0.04900 0.1980 0.1040 0.6490 0.4420 0.05000 5.060 0.04900 0.1980 0.1040 0.6460 0.4420 0.1000 5.040 0.04900 0.1980 0.1040 0.6370 0.4410 0.1500 5.010 0.04900 0.1980 0.1040 0.6230 0.4410 0.2000 4.960 0.04900 0.1970 0.1040 0.6040 0.4400 0.3000 4.820 0.04900 0.1960 0.1040 0.5520 0.4350 0.4000 4.620 0.04890 0.1930 0.1040 0.4890 0.4230 0.5000 4.350 0.04890 0.1910 0.1040 0.4190 0.4020 0.6000 4.040 0.04880 0.1870 0.1040 0.3480 0.3740 0.7000 3.690 0.04880 0.1840 0.1040 0.2810 0.3400 0.8000 3.330 0.04870 0.1790 0.1030 0.2220 0.3020 1.000 2.660 0.04850 0.1700 0.1030 0.1300 0.2240 1.200 2.110 0.04830 0.1590 0.1020 0.07230 0.1560 1.400 1.700 0.04800 0.1460 0.1010 0.04150 0.1040 1.600 1.420 0.04780 0.1340 0.1000 0.02750 0.06650 1.800 1.220 0.04740 0.1210 0.09830 0.02240 0.04160 2.000 1.080 0.04710 0.1080 0.09610 0.02110 0.02570 2.400 0.9070 0.04620 0.08430 0.09040 0.02080 0.01020 3.000 0.7360 0.04470 0.05420 0.07940 0.01700 0.004650 4.000 0.5200 0.04180 0.02260 0.05860 0.007580 0.004200 5.000 0.3590 0.03830 0.009020 0.03980 0.002530 0.003450 6.000 0.2490 0.03470 0.004620 0.02580 0.0008310 0.002380 7.000 0.1770 0.03090 0.003680 0.01630 0.0004200 0.001520 8.000 0.1300 0.02720 0.003610 0.01030 0.0003620 0.0009490 10.00 0.07500 0.02100 0.003300 0.004100 0.0003500 0.0003700 15.00 0.02500 0.009200 0.001500 0.0005300 0.0001600 4.500E-05 20.00 0.009700 0.003900 0.0005600 9.300E-05 5.800E-05 7.700E-06 30.00 0.001900 0.0008100 9.100E-05 6.000E-06 9.200E-06 4.800E-07 40.00 0.0004700 0.0002100 2.100E-05 7.500E-07 2.100E-06 6.000E-08 60.00 5.500E-05 2.500E-05 2.200E-06 3.500E-08 2.200E-07 2.800E-09 100.0 3.000E-06 1.400E-06 1.200E-07 6.500E-10 1.100E-08 5.100E-11 #S 19 K #N 8 #UOCCUP 2 2 6 2 6 1 #UBIND 3608. 378.6 295.5 34.80 18.30 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 0.000 6.900 0.04640 0.1850 0.09650 0.5700 0.3760 2.460 0.05000 6.760 0.04640 0.1850 0.09650 0.5680 0.3760 2.320 0.1000 6.390 0.04640 0.1850 0.09650 0.5620 0.3760 1.960 0.1500 5.890 0.04640 0.1850 0.09650 0.5520 0.3760 1.490 0.2000 5.400 0.04640 0.1840 0.09650 0.5390 0.3750 1.020 0.3000 4.650 0.04630 0.1830 0.09650 0.5030 0.3730 0.3710 0.4000 4.260 0.04630 0.1810 0.09650 0.4570 0.3670 0.1110 0.5000 4.030 0.04630 0.1790 0.09650 0.4050 0.3570 0.04910 0.6000 3.820 0.04620 0.1760 0.09640 0.3490 0.3420 0.04270 0.7000 3.580 0.04620 0.1730 0.09640 0.2950 0.3220 0.04190 0.8000 3.320 0.04610 0.1700 0.09630 0.2430 0.2980 0.03730 1.000 2.780 0.04600 0.1620 0.09600 0.1560 0.2420 0.02330 1.200 2.280 0.04580 0.1520 0.09560 0.09460 0.1850 0.01230 1.400 1.870 0.04550 0.1420 0.09490 0.05630 0.1340 0.006040 1.600 1.550 0.04530 0.1310 0.09390 0.03550 0.09330 0.002950 1.800 1.320 0.04500 0.1200 0.09260 0.02570 0.06280 0.001570 2.000 1.150 0.04470 0.1090 0.09090 0.02210 0.04130 0.001030 2.400 0.9290 0.04400 0.08720 0.08660 0.02120 0.01740 0.0008230 3.000 0.7470 0.04270 0.05880 0.07780 0.01920 0.006250 0.0007780 4.000 0.5420 0.04020 0.02670 0.06000 0.01020 0.004590 0.0004350 5.000 0.3860 0.03720 0.01120 0.04280 0.003900 0.004170 0.0001670 6.000 0.2730 0.03390 0.005350 0.02890 0.001330 0.003120 5.590E-05 7.000 0.1960 0.03060 0.003720 0.01900 0.0005620 0.002120 2.230E-05 8.000 0.1440 0.02720 0.003480 0.01230 0.0004010 0.001370 1.510E-05 10.00 0.08400 0.02100 0.003300 0.005200 0.0003800 0.0005700 1.400E-05 15.00 0.02800 0.010000 0.001700 0.0007100 0.0002000 7.300E-05 7.600E-06 20.00 0.01100 0.004500 0.0006700 0.0001300 7.800E-05 1.300E-05 2.900E-06 30.00 0.002300 0.0009700 0.0001200 8.800E-06 1.300E-05 8.500E-07 4.900E-07 40.00 0.0005900 0.0002600 2.700E-05 1.100E-06 3.000E-06 1.100E-07 1.100E-07 60.00 7.000E-05 3.200E-05 2.900E-06 5.300E-08 3.200E-07 5.000E-09 1.200E-08 100.0 3.900E-06 1.800E-06 1.500E-07 9.900E-10 1.700E-08 9.000E-11 6.300E-10 #S 20 Ca #N 8 #UOCCUP 2 2 6 2 6 2 #UBIND 4039. 438.0 348.0 44.00 24.80 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 0.000 7.870 0.04400 0.1740 0.09020 0.5080 0.3300 1.950 0.05000 7.720 0.04400 0.1740 0.09020 0.5070 0.3300 1.870 0.1000 7.320 0.04400 0.1740 0.09020 0.5030 0.3300 1.680 0.1500 6.740 0.04400 0.1740 0.09020 0.4960 0.3300 1.400 0.2000 6.100 0.04400 0.1730 0.09020 0.4860 0.3300 1.090 0.3000 4.960 0.04400 0.1720 0.09020 0.4600 0.3290 0.5470 0.4000 4.230 0.04390 0.1710 0.09020 0.4260 0.3260 0.2280 0.5000 3.850 0.04390 0.1690 0.09020 0.3860 0.3200 0.09350 0.6000 3.620 0.04390 0.1670 0.09010 0.3420 0.3110 0.05320 0.7000 3.440 0.04380 0.1640 0.09010 0.2980 0.2990 0.04660 0.8000 3.250 0.04380 0.1610 0.09000 0.2540 0.2830 0.04630 1.000 2.830 0.04360 0.1540 0.08990 0.1760 0.2440 0.03920 1.200 2.400 0.04350 0.1460 0.08950 0.1150 0.1990 0.02640 1.400 2.000 0.04330 0.1380 0.08900 0.07240 0.1550 0.01540 1.600 1.680 0.04310 0.1280 0.08830 0.04600 0.1160 0.008310 1.800 1.420 0.04280 0.1180 0.08730 0.03140 0.08350 0.004390 2.000 1.220 0.04260 0.1090 0.08610 0.02450 0.05840 0.002460 2.400 0.9660 0.04200 0.08910 0.08270 0.02120 0.02710 0.001340 3.000 0.7590 0.04090 0.06260 0.07580 0.02040 0.009050 0.001260 4.000 0.5590 0.03870 0.03070 0.06070 0.01280 0.004830 0.0008480 5.000 0.4100 0.03600 0.01370 0.04510 0.005590 0.004650 0.0003760 6.000 0.2960 0.03320 0.006370 0.03170 0.002060 0.003790 0.0001370 7.000 0.2160 0.03020 0.003930 0.02160 0.0008060 0.002730 5.120E-05 8.000 0.1600 0.02710 0.003390 0.01440 0.0004660 0.001850 2.760E-05 10.00 0.09300 0.02100 0.003300 0.006400 0.0004100 0.0008100 2.300E-05 15.00 0.03200 0.01100 0.001900 0.0009400 0.0002500 0.0001100 1.400E-05 20.00 0.01300 0.005000 0.0007900 0.0001800 1.000E-04 2.100E-05 5.800E-06 30.00 0.002700 0.001200 0.0001400 1.300E-05 1.800E-05 1.400E-06 1.000E-06 40.00 0.0007200 0.0003200 3.400E-05 1.600E-06 4.200E-06 1.800E-07 2.400E-07 60.00 8.900E-05 4.000E-05 3.800E-06 7.800E-08 4.600E-07 8.400E-09 2.600E-08 100.0 5.000E-06 2.300E-06 2.000E-07 1.500E-09 2.400E-08 1.700E-10 1.400E-09 #S 21 Sc #N 9 #UOCCUP 2 2 6 2 6 1 2 #UBIND 4490. 498.0 400.3 51.10 28.30 0.8000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 7.670 0.04180 0.1640 0.08470 0.4710 0.3040 0.3100 1.840 0.05000 7.550 0.04180 0.1640 0.08470 0.4700 0.3040 0.3100 1.780 0.1000 7.200 0.04180 0.1640 0.08470 0.4670 0.3040 0.3100 1.610 0.1500 6.700 0.04180 0.1640 0.08470 0.4610 0.3040 0.3100 1.370 0.2000 6.140 0.04180 0.1630 0.08470 0.4530 0.3040 0.3100 1.090 0.3000 5.080 0.04180 0.1620 0.08470 0.4320 0.3030 0.3090 0.5890 0.4000 4.370 0.04180 0.1610 0.08470 0.4040 0.3010 0.3070 0.2660 0.5000 3.960 0.04180 0.1600 0.08470 0.3710 0.2970 0.3030 0.1120 0.6000 3.730 0.04170 0.1580 0.08460 0.3340 0.2900 0.2940 0.05690 0.7000 3.550 0.04170 0.1550 0.08460 0.2960 0.2810 0.2820 0.04350 0.8000 3.380 0.04160 0.1530 0.08460 0.2580 0.2700 0.2660 0.04220 1.000 3.000 0.04150 0.1470 0.08440 0.1870 0.2400 0.2290 0.03890 1.200 2.590 0.04140 0.1400 0.08420 0.1280 0.2030 0.1900 0.02890 1.400 2.200 0.04120 0.1330 0.08380 0.08440 0.1650 0.1540 0.01850 1.600 1.860 0.04110 0.1250 0.08330 0.05490 0.1290 0.1230 0.01080 1.800 1.580 0.04080 0.1160 0.08250 0.03690 0.09710 0.09740 0.006030 2.000 1.350 0.04060 0.1080 0.08160 0.02710 0.07120 0.07680 0.003380 2.400 1.040 0.04010 0.09010 0.07900 0.02080 0.03590 0.04760 0.001470 3.000 0.7910 0.03910 0.06570 0.07340 0.02020 0.01240 0.02350 0.001200 4.000 0.5760 0.03720 0.03440 0.06070 0.01450 0.004900 0.007660 0.0009430 5.000 0.4290 0.03490 0.01630 0.04670 0.007190 0.004690 0.002700 0.0004790 6.000 0.3160 0.03240 0.007650 0.03410 0.002920 0.004130 0.001030 0.0001940 7.000 0.2340 0.02970 0.004330 0.02390 0.001140 0.003160 0.0004240 7.360E-05 8.000 0.1740 0.02690 0.003380 0.01650 0.0005620 0.002250 0.0001870 3.400E-05 10.00 0.1000 0.02200 0.003200 0.007600 0.0004100 0.001000 4.300E-05 2.300E-05 15.00 0.03500 0.01100 0.002100 0.001200 0.0002800 0.0001600 2.300E-06 1.600E-05 20.00 0.01500 0.005500 0.0009200 0.0002400 0.0001200 3.000E-05 2.300E-07 7.000E-06 30.00 0.003200 0.001300 0.0001800 1.700E-05 2.300E-05 2.100E-06 6.600E-09 1.300E-06 40.00 0.0008800 0.0003800 4.300E-05 2.300E-06 5.500E-06 2.700E-07 4.600E-10 3.100E-07 60.00 0.0001100 5.000E-05 4.900E-06 1.100E-07 6.200E-07 1.300E-08 9.800E-12 3.500E-08 100.0 6.400E-06 2.900E-06 2.600E-07 2.100E-09 3.300E-08 2.400E-10 1.500E-13 1.800E-09 #S 22 Ti #N 9 #UOCCUP 2 2 6 2 6 2 2 #UBIND 4966. 561.4 456.9 58.40 32.60 0.8000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 7.510 0.03990 0.1550 0.07980 0.4410 0.2820 0.2750 1.760 0.05000 7.400 0.03990 0.1550 0.07980 0.4400 0.2820 0.2750 1.710 0.1000 7.100 0.03990 0.1550 0.07980 0.4370 0.2820 0.2750 1.560 0.1500 6.660 0.03990 0.1550 0.07980 0.4320 0.2820 0.2750 1.340 0.2000 6.140 0.03990 0.1540 0.07980 0.4260 0.2820 0.2750 1.090 0.3000 5.160 0.03990 0.1540 0.07980 0.4080 0.2820 0.2750 0.6190 0.4000 4.460 0.03980 0.1530 0.07980 0.3850 0.2800 0.2740 0.2970 0.5000 4.040 0.03980 0.1510 0.07980 0.3570 0.2770 0.2710 0.1300 0.6000 3.800 0.03980 0.1500 0.07980 0.3260 0.2730 0.2660 0.06230 0.7000 3.640 0.03980 0.1480 0.07980 0.2930 0.2660 0.2590 0.04190 0.8000 3.480 0.03970 0.1460 0.07970 0.2590 0.2570 0.2490 0.03840 1.000 3.150 0.03960 0.1410 0.07960 0.1940 0.2330 0.2230 0.03700 1.200 2.770 0.03950 0.1350 0.07940 0.1390 0.2030 0.1920 0.02970 1.400 2.390 0.03940 0.1280 0.07920 0.09500 0.1700 0.1620 0.02050 1.600 2.040 0.03920 0.1210 0.07870 0.06370 0.1380 0.1340 0.01280 1.800 1.740 0.03900 0.1140 0.07820 0.04310 0.1080 0.1100 0.007570 2.000 1.500 0.03880 0.1060 0.07740 0.03060 0.08220 0.08940 0.004370 2.400 1.140 0.03840 0.09050 0.07540 0.02090 0.04470 0.05860 0.001700 3.000 0.8390 0.03750 0.06800 0.07090 0.01950 0.01650 0.03090 0.001120 4.000 0.5960 0.03590 0.03780 0.06030 0.01570 0.005220 0.01100 0.0009740 5.000 0.4470 0.03380 0.01890 0.04790 0.008720 0.004600 0.004140 0.0005610 6.000 0.3350 0.03160 0.009140 0.03600 0.003890 0.004300 0.001660 0.0002520 7.000 0.2510 0.02910 0.004920 0.02610 0.001590 0.003500 0.0007050 0.0001010 8.000 0.1890 0.02670 0.003470 0.01850 0.0007170 0.002610 0.0003180 4.340E-05 10.00 0.1100 0.02200 0.003100 0.009000 0.0004100 0.001300 7.600E-05 2.200E-05 15.00 0.03900 0.01200 0.002200 0.001500 0.0003100 0.0002100 4.200E-06 1.700E-05 20.00 0.01700 0.006000 0.001000 0.0003100 0.0001500 4.100E-05 4.300E-07 8.000E-06 30.00 0.003700 0.001500 0.0002100 2.400E-05 2.900E-05 3.000E-06 1.300E-08 1.600E-06 40.00 0.001100 0.0004500 5.300E-05 3.200E-06 7.100E-06 3.900E-07 9.000E-10 3.900E-07 60.00 0.0001400 6.100E-05 6.200E-06 1.600E-07 8.000E-07 1.900E-08 1.900E-11 4.400E-08 100.0 8.000E-06 3.600E-06 3.300E-07 3.000E-09 4.300E-08 3.500E-10 6.500E-14 2.400E-09 #S 23 V #N 9 #UOCCUP 2 2 6 2 6 3 2 #UBIND 5466. 627.2 516.0 66.30 37.20 0.7000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 7.380 0.03810 0.1470 0.07550 0.4150 0.2640 0.2500 1.690 0.05000 7.280 0.03810 0.1470 0.07550 0.4140 0.2640 0.2500 1.650 0.1000 7.010 0.03810 0.1470 0.07550 0.4120 0.2640 0.2500 1.510 0.1500 6.610 0.03810 0.1470 0.07550 0.4080 0.2640 0.2500 1.320 0.2000 6.140 0.03810 0.1460 0.07550 0.4030 0.2640 0.2500 1.090 0.3000 5.210 0.03810 0.1460 0.07550 0.3880 0.2640 0.2500 0.6430 0.4000 4.530 0.03810 0.1450 0.07550 0.3680 0.2630 0.2490 0.3240 0.5000 4.110 0.03810 0.1440 0.07550 0.3440 0.2610 0.2480 0.1470 0.6000 3.860 0.03800 0.1420 0.07550 0.3170 0.2570 0.2450 0.06910 0.7000 3.700 0.03800 0.1410 0.07550 0.2880 0.2520 0.2400 0.04200 0.8000 3.560 0.03800 0.1390 0.07540 0.2580 0.2450 0.2330 0.03560 1.000 3.260 0.03790 0.1350 0.07540 0.1990 0.2260 0.2140 0.03460 1.200 2.920 0.03780 0.1300 0.07520 0.1470 0.2010 0.1900 0.02960 1.400 2.560 0.03760 0.1240 0.07500 0.1040 0.1730 0.1650 0.02170 1.600 2.220 0.03750 0.1180 0.07470 0.07210 0.1440 0.1400 0.01440 1.800 1.910 0.03740 0.1110 0.07420 0.04950 0.1160 0.1180 0.008970 2.000 1.650 0.03720 0.1040 0.07370 0.03490 0.09150 0.09840 0.005380 2.400 1.260 0.03680 0.09040 0.07200 0.02160 0.05320 0.06740 0.002030 3.000 0.9020 0.03610 0.06970 0.06840 0.01870 0.02120 0.03770 0.001050 4.000 0.6210 0.03460 0.04080 0.05950 0.01640 0.005870 0.01450 0.0009610 5.000 0.4650 0.03280 0.02150 0.04850 0.01010 0.004460 0.005790 0.0006220 6.000 0.3530 0.03080 0.01080 0.03760 0.004940 0.004340 0.002430 0.0003080 7.000 0.2670 0.02860 0.005700 0.02800 0.002150 0.003750 0.001070 0.0001330 8.000 0.2030 0.02630 0.003690 0.02040 0.0009460 0.002920 0.0004960 5.650E-05 10.00 0.1200 0.02200 0.003000 0.010000 0.0004200 0.001500 0.0001200 2.200E-05 15.00 0.04300 0.01200 0.002300 0.001900 0.0003300 0.0002700 7.100E-06 1.800E-05 20.00 0.01800 0.006500 0.001200 0.0004000 0.0001700 5.500E-05 7.500E-07 8.900E-06 30.00 0.004300 0.001800 0.0002500 3.200E-05 3.500E-05 4.100E-06 2.300E-08 1.900E-06 40.00 0.001200 0.0005300 6.500E-05 4.300E-06 8.900E-06 5.500E-07 1.600E-09 4.700E-07 60.00 0.0001700 7.300E-05 7.700E-06 2.200E-07 1.000E-06 2.700E-08 3.500E-11 5.400E-08 100.0 9.900E-06 4.500E-06 4.200E-07 4.300E-09 5.600E-08 5.400E-10 1.200E-13 2.900E-09 #S 24 Cr #N 9 #UOCCUP 2 2 6 2 6 5 1 #UBIND 5991. 697.8 579.1 75.20 43.10 2.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 6.250 0.03650 0.1400 0.07160 0.3980 0.2530 0.2610 1.850 0.05000 6.190 0.03650 0.1400 0.07160 0.3980 0.2530 0.2610 1.790 0.1000 6.020 0.03650 0.1400 0.07160 0.3950 0.2530 0.2610 1.620 0.1500 5.770 0.03650 0.1400 0.07160 0.3920 0.2520 0.2610 1.380 0.2000 5.490 0.03650 0.1390 0.07160 0.3870 0.2520 0.2610 1.110 0.3000 4.960 0.03650 0.1390 0.07160 0.3740 0.2520 0.2610 0.6110 0.4000 4.580 0.03640 0.1380 0.07160 0.3560 0.2510 0.2600 0.2820 0.5000 4.340 0.03640 0.1370 0.07160 0.3340 0.2490 0.2570 0.1170 0.6000 4.180 0.03640 0.1360 0.07160 0.3100 0.2460 0.2510 0.05120 0.7000 4.040 0.03640 0.1340 0.07160 0.2840 0.2420 0.2440 0.03130 0.8000 3.890 0.03630 0.1330 0.07160 0.2560 0.2360 0.2340 0.02750 1.000 3.560 0.03630 0.1290 0.07150 0.2020 0.2200 0.2090 0.02670 1.200 3.180 0.03620 0.1250 0.07140 0.1520 0.1980 0.1830 0.02220 1.400 2.800 0.03610 0.1200 0.07120 0.1110 0.1730 0.1560 0.01610 1.600 2.440 0.03600 0.1140 0.07100 0.07820 0.1470 0.1330 0.01080 1.800 2.110 0.03580 0.1090 0.07060 0.05460 0.1210 0.1110 0.006840 2.000 1.830 0.03570 0.1030 0.07020 0.03850 0.09740 0.09330 0.004210 2.400 1.390 0.03530 0.08990 0.06890 0.02250 0.05940 0.06500 0.001630 3.000 0.9840 0.03470 0.07090 0.06590 0.01770 0.02550 0.03760 0.0007400 4.000 0.6550 0.03340 0.04340 0.05840 0.01640 0.006730 0.01530 0.0006730 5.000 0.4850 0.03180 0.02410 0.04880 0.01110 0.004270 0.006460 0.0004820 6.000 0.3690 0.03000 0.01260 0.03880 0.005910 0.004200 0.002830 0.0002610 7.000 0.2820 0.02800 0.006630 0.02970 0.002740 0.003810 0.001290 0.0001210 8.000 0.2170 0.02600 0.004050 0.02210 0.001230 0.003120 0.0006140 5.330E-05 10.00 0.1300 0.02200 0.002900 0.01200 0.0004300 0.001800 0.0001600 1.700E-05 15.00 0.04700 0.01300 0.002400 0.002300 0.0003500 0.0003300 9.600E-06 1.300E-05 20.00 0.02000 0.007000 0.001300 0.0005100 0.0001900 7.100E-05 1.000E-06 7.100E-06 30.00 0.004900 0.002000 0.0003000 4.200E-05 4.200E-05 5.500E-06 3.200E-08 1.600E-06 40.00 0.001500 0.0006200 7.800E-05 5.800E-06 1.100E-05 7.400E-07 2.400E-09 4.100E-07 60.00 0.0002000 8.800E-05 9.400E-06 3.000E-07 1.300E-06 3.700E-08 5.300E-11 4.800E-08 100.0 1.200E-05 5.400E-06 5.300E-07 5.900E-09 7.000E-08 7.400E-10 3.100E-13 2.600E-09 #S 25 Mn #N 9 #UOCCUP 2 2 6 2 6 5 2 #UBIND 6538. 769.0 643.1 82.40 47.30 1.100 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 7.160 0.03500 0.1330 0.06810 0.3730 0.2350 0.2150 1.590 0.05000 7.080 0.03500 0.1330 0.06810 0.3720 0.2350 0.2150 1.550 0.1000 6.850 0.03500 0.1330 0.06810 0.3700 0.2350 0.2150 1.440 0.1500 6.510 0.03500 0.1330 0.06810 0.3670 0.2350 0.2150 1.270 0.2000 6.110 0.03500 0.1330 0.06810 0.3640 0.2350 0.2150 1.080 0.3000 5.280 0.03500 0.1320 0.06810 0.3520 0.2350 0.2150 0.6770 0.4000 4.630 0.03500 0.1320 0.06810 0.3380 0.2340 0.2140 0.3680 0.5000 4.210 0.03490 0.1310 0.06810 0.3200 0.2330 0.2140 0.1800 0.6000 3.950 0.03490 0.1300 0.06810 0.2990 0.2310 0.2120 0.08560 0.7000 3.790 0.03490 0.1290 0.06810 0.2760 0.2280 0.2100 0.04580 0.8000 3.670 0.03490 0.1270 0.06810 0.2530 0.2230 0.2060 0.03260 1.000 3.430 0.03480 0.1240 0.06810 0.2050 0.2110 0.1950 0.02960 1.200 3.150 0.03470 0.1200 0.06800 0.1590 0.1940 0.1800 0.02770 1.400 2.840 0.03460 0.1160 0.06780 0.1190 0.1730 0.1620 0.02250 1.600 2.520 0.03450 0.1110 0.06760 0.08700 0.1500 0.1440 0.01650 1.800 2.220 0.03440 0.1060 0.06740 0.06230 0.1270 0.1260 0.01120 2.000 1.950 0.03430 0.1000 0.06700 0.04450 0.1050 0.1090 0.007310 2.400 1.510 0.03400 0.08910 0.06600 0.02500 0.06800 0.08010 0.002920 3.000 1.060 0.03340 0.07170 0.06350 0.01750 0.03150 0.04930 0.001050 4.000 0.6920 0.03220 0.04570 0.05720 0.01660 0.008370 0.02160 0.0008630 5.000 0.5070 0.03080 0.02650 0.04880 0.01230 0.004380 0.009590 0.0006830 6.000 0.3870 0.02920 0.01440 0.03970 0.007070 0.004170 0.004380 0.0004050 7.000 0.2980 0.02740 0.007710 0.03110 0.003520 0.003950 0.002070 0.0002030 8.000 0.2310 0.02550 0.004530 0.02360 0.001640 0.003380 0.001010 9.300E-05 10.00 0.1400 0.02200 0.002900 0.01300 0.0004900 0.002000 0.0002700 2.500E-05 15.00 0.05100 0.01300 0.002500 0.002700 0.0003600 0.0004200 1.700E-05 1.800E-05 20.00 0.02200 0.007400 0.001400 0.0006300 0.0002100 9.300E-05 1.900E-06 1.000E-05 30.00 0.005600 0.002200 0.0003400 5.400E-05 5.000E-05 7.500E-06 6.200E-08 2.400E-06 40.00 0.001700 0.0007100 9.300E-05 7.600E-06 1.300E-05 1.000E-06 4.600E-09 6.500E-07 60.00 0.0002400 1.000E-04 1.100E-05 4.000E-07 1.600E-06 5.200E-08 1.000E-10 7.700E-08 100.0 1.500E-05 6.600E-06 6.500E-07 8.000E-09 8.900E-08 1.000E-09 8.200E-13 4.300E-09 #S 26 Fe #N 9 #UOCCUP 2 2 6 2 6 6 2 #UBIND 7111. 848.0 711.9 91.60 53.00 0.8000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 7.060 0.03360 0.1270 0.06500 0.3550 0.2230 0.2010 1.550 0.05000 6.990 0.03360 0.1270 0.06500 0.3540 0.2230 0.2010 1.510 0.1000 6.780 0.03360 0.1270 0.06500 0.3530 0.2230 0.2010 1.410 0.1500 6.470 0.03360 0.1270 0.06500 0.3500 0.2230 0.2010 1.250 0.2000 6.090 0.03360 0.1270 0.06500 0.3470 0.2230 0.2010 1.070 0.3000 5.310 0.03360 0.1270 0.06500 0.3370 0.2230 0.2010 0.6900 0.4000 4.670 0.03360 0.1260 0.06500 0.3240 0.2220 0.2010 0.3870 0.5000 4.250 0.03360 0.1250 0.06500 0.3090 0.2210 0.2000 0.1960 0.6000 3.990 0.03350 0.1240 0.06500 0.2900 0.2200 0.1990 0.09460 0.7000 3.830 0.03350 0.1230 0.06500 0.2700 0.2170 0.1970 0.04920 0.8000 3.710 0.03350 0.1220 0.06500 0.2490 0.2140 0.1950 0.03240 1.000 3.500 0.03340 0.1190 0.06490 0.2050 0.2030 0.1860 0.02730 1.200 3.240 0.03340 0.1160 0.06490 0.1630 0.1890 0.1740 0.02640 1.400 2.960 0.03330 0.1120 0.06480 0.1250 0.1710 0.1590 0.02240 1.600 2.660 0.03320 0.1080 0.06460 0.09350 0.1510 0.1430 0.01710 1.800 2.370 0.03310 0.1030 0.06440 0.06840 0.1310 0.1270 0.01210 2.000 2.090 0.03300 0.09830 0.06410 0.04960 0.1100 0.1120 0.008150 2.400 1.640 0.03270 0.08800 0.06320 0.02750 0.07430 0.08450 0.003430 3.000 1.160 0.03220 0.07210 0.06120 0.01730 0.03670 0.05410 0.001130 4.000 0.7380 0.03120 0.04760 0.05590 0.01630 0.01020 0.02500 0.0008000 5.000 0.5330 0.02990 0.02870 0.04850 0.01300 0.004540 0.01170 0.0006860 6.000 0.4050 0.02840 0.01620 0.04030 0.008060 0.004030 0.005530 0.0004420 7.000 0.3140 0.02680 0.008880 0.03220 0.004290 0.003940 0.002700 0.0002370 8.000 0.2440 0.02510 0.005130 0.02500 0.002090 0.003510 0.001360 0.0001150 10.00 0.1500 0.02200 0.002900 0.01400 0.0005700 0.002300 0.0003800 2.900E-05 15.00 0.05500 0.01400 0.002500 0.003200 0.0003700 0.0005100 2.500E-05 1.700E-05 20.00 0.02500 0.007800 0.001500 0.0007700 0.0002300 0.0001200 2.900E-06 1.100E-05 30.00 0.006300 0.002400 0.0003900 6.900E-05 5.900E-05 9.900E-06 9.600E-08 2.700E-06 40.00 0.001900 0.0008100 0.0001100 9.900E-06 1.600E-05 1.400E-06 7.200E-09 7.500E-07 60.00 0.0002800 0.0001200 1.400E-05 5.300E-07 2.000E-06 7.100E-08 1.600E-10 9.100E-08 100.0 1.800E-05 7.900E-06 7.900E-07 1.100E-08 1.100E-07 1.400E-09 1.300E-12 5.100E-09 #S 27 Co #N 9 #UOCCUP 2 2 6 2 6 7 2 #UBIND 7711. 926.6 786.0 101.4 59.40 0.4000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 6.980 0.03230 0.1220 0.06210 0.3390 0.2120 0.1900 1.510 0.05000 6.910 0.03230 0.1220 0.06210 0.3390 0.2120 0.1900 1.480 0.1000 6.720 0.03230 0.1220 0.06210 0.3370 0.2120 0.1900 1.380 0.1500 6.420 0.03230 0.1220 0.06210 0.3350 0.2120 0.1900 1.240 0.2000 6.070 0.03230 0.1220 0.06210 0.3320 0.2120 0.1900 1.060 0.3000 5.330 0.03230 0.1210 0.06210 0.3240 0.2120 0.1890 0.7000 0.4000 4.710 0.03230 0.1210 0.06210 0.3120 0.2120 0.1890 0.4040 0.5000 4.280 0.03230 0.1200 0.06210 0.2980 0.2110 0.1890 0.2110 0.6000 4.020 0.03230 0.1190 0.06210 0.2820 0.2090 0.1880 0.1040 0.7000 3.860 0.03230 0.1180 0.06210 0.2640 0.2070 0.1870 0.05330 0.8000 3.750 0.03220 0.1170 0.06210 0.2450 0.2050 0.1850 0.03300 1.000 3.550 0.03220 0.1150 0.06210 0.2050 0.1960 0.1780 0.02540 1.200 3.320 0.03210 0.1120 0.06200 0.1660 0.1840 0.1680 0.02490 1.400 3.060 0.03210 0.1080 0.06190 0.1300 0.1690 0.1550 0.02200 1.600 2.780 0.03200 0.1050 0.06180 0.09920 0.1510 0.1410 0.01740 1.800 2.500 0.03190 0.1010 0.06160 0.07410 0.1330 0.1270 0.01280 2.000 2.230 0.03180 0.09610 0.06140 0.05460 0.1140 0.1130 0.008900 2.400 1.760 0.03150 0.08680 0.06070 0.03030 0.07970 0.08790 0.003960 3.000 1.260 0.03110 0.07220 0.05900 0.01750 0.04170 0.05820 0.001250 4.000 0.7910 0.03010 0.04920 0.05450 0.01580 0.01230 0.02830 0.0007400 5.000 0.5620 0.02900 0.03080 0.04810 0.01340 0.004890 0.01380 0.0006750 6.000 0.4250 0.02770 0.01800 0.04060 0.008950 0.003910 0.006780 0.0004690 7.000 0.3290 0.02620 0.01010 0.03310 0.005070 0.003870 0.003410 0.0002690 8.000 0.2580 0.02470 0.005850 0.02620 0.002600 0.003580 0.001760 0.0001380 10.00 0.1600 0.02100 0.002900 0.01600 0.0006900 0.002500 0.0005100 3.500E-05 15.00 0.06000 0.01400 0.002500 0.003700 0.0003700 0.0006100 3.600E-05 1.600E-05 20.00 0.02700 0.008200 0.001600 0.0009400 0.0002500 0.0001500 4.200E-06 1.100E-05 30.00 0.007000 0.002700 0.0004500 8.700E-05 6.800E-05 1.300E-05 1.400E-07 3.100E-06 40.00 0.002200 0.0009100 0.0001300 1.300E-05 1.900E-05 1.800E-06 1.100E-08 8.500E-07 60.00 0.0003300 0.0001400 1.700E-05 6.900E-07 2.400E-06 9.400E-08 2.500E-10 1.100E-07 100.0 2.100E-05 9.400E-06 9.600E-07 1.400E-08 1.300E-07 1.900E-09 1.700E-12 6.000E-09 #S 28 Ni #N 9 #UOCCUP 2 2 6 2 6 8 2 #UBIND 8332. 1010. 859.9 110.9 66.60 0.6000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 6.900 0.03110 0.1170 0.05950 0.3250 0.2020 0.1790 1.470 0.05000 6.840 0.03110 0.1170 0.05950 0.3240 0.2020 0.1790 1.440 0.1000 6.660 0.03110 0.1170 0.05950 0.3230 0.2020 0.1790 1.350 0.1500 6.380 0.03110 0.1170 0.05950 0.3210 0.2020 0.1790 1.220 0.2000 6.050 0.03110 0.1170 0.05950 0.3180 0.2020 0.1790 1.050 0.3000 5.340 0.03110 0.1160 0.05950 0.3110 0.2020 0.1790 0.7090 0.4000 4.740 0.03110 0.1160 0.05950 0.3010 0.2020 0.1790 0.4200 0.5000 4.320 0.03110 0.1150 0.05950 0.2880 0.2010 0.1790 0.2250 0.6000 4.050 0.03110 0.1150 0.05950 0.2740 0.2000 0.1780 0.1130 0.7000 3.890 0.03110 0.1140 0.05950 0.2580 0.1990 0.1770 0.05800 0.8000 3.780 0.03110 0.1130 0.05950 0.2410 0.1960 0.1760 0.03420 1.000 3.590 0.03100 0.1110 0.05950 0.2040 0.1890 0.1700 0.02380 1.200 3.390 0.03100 0.1080 0.05940 0.1680 0.1790 0.1620 0.02340 1.400 3.150 0.03090 0.1050 0.05930 0.1340 0.1660 0.1510 0.02130 1.600 2.880 0.03080 0.1020 0.05920 0.1040 0.1510 0.1390 0.01750 1.800 2.610 0.03070 0.09790 0.05910 0.07930 0.1340 0.1270 0.01330 2.000 2.350 0.03060 0.09390 0.05890 0.05950 0.1170 0.1140 0.009540 2.400 1.890 0.03040 0.08550 0.05830 0.03340 0.08440 0.09040 0.004480 3.000 1.360 0.03000 0.07210 0.05690 0.01800 0.04660 0.06170 0.001420 4.000 0.8490 0.02920 0.05050 0.05310 0.01520 0.01470 0.03140 0.0006880 5.000 0.5950 0.02820 0.03260 0.04750 0.01370 0.005440 0.01590 0.0006520 6.000 0.4460 0.02700 0.01970 0.04080 0.009720 0.003840 0.008090 0.0004870 7.000 0.3460 0.02560 0.01140 0.03380 0.005840 0.003760 0.004190 0.0002980 8.000 0.2710 0.02420 0.006650 0.02730 0.003160 0.003600 0.002220 0.0001620 10.00 0.1700 0.02100 0.003100 0.01700 0.0008500 0.002600 0.0006700 4.200E-05 15.00 0.06400 0.01400 0.002500 0.004300 0.0003600 0.0007100 4.900E-05 1.600E-05 20.00 0.02900 0.008500 0.001700 0.001100 0.0002700 0.0001800 5.900E-06 1.200E-05 30.00 0.007800 0.002900 0.0005000 0.0001100 7.700E-05 1.600E-05 2.100E-07 3.400E-06 40.00 0.002500 0.001000 0.0001500 1.600E-05 2.200E-05 2.300E-06 1.600E-08 9.600E-07 60.00 0.0003800 0.0001600 2.000E-05 8.900E-07 2.800E-06 1.200E-07 3.600E-10 1.200E-07 100.0 2.500E-05 1.100E-05 1.200E-06 1.800E-08 1.600E-07 2.500E-09 2.400E-12 7.100E-09 #S 29 Cu #N 9 #UOCCUP 2 2 6 2 6 10 1 #UBIND 8980. 1099. 939.4 122.5 75.80 2.800 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 5.930 0.03010 0.1120 0.05710 0.3150 0.1960 0.1850 1.640 0.05000 5.880 0.03010 0.1120 0.05710 0.3150 0.1960 0.1850 1.600 0.1000 5.760 0.03000 0.1120 0.05710 0.3140 0.1960 0.1850 1.480 0.1500 5.580 0.03000 0.1120 0.05710 0.3120 0.1960 0.1850 1.310 0.2000 5.370 0.03000 0.1120 0.05710 0.3090 0.1960 0.1850 1.100 0.3000 4.940 0.03000 0.1120 0.05710 0.3020 0.1960 0.1850 0.6860 0.4000 4.600 0.03000 0.1110 0.05710 0.2930 0.1950 0.1850 0.3700 0.5000 4.380 0.03000 0.1110 0.05710 0.2820 0.1950 0.1840 0.1790 0.6000 4.240 0.03000 0.1100 0.05710 0.2680 0.1940 0.1830 0.08190 0.7000 4.140 0.03000 0.1100 0.05710 0.2530 0.1930 0.1810 0.03920 0.8000 4.050 0.03000 0.1090 0.05710 0.2370 0.1900 0.1790 0.02340 1.000 3.860 0.02990 0.1070 0.05710 0.2030 0.1840 0.1710 0.01800 1.200 3.620 0.02990 0.1040 0.05700 0.1690 0.1750 0.1610 0.01770 1.400 3.360 0.02980 0.1020 0.05700 0.1370 0.1630 0.1480 0.01560 1.600 3.070 0.02980 0.09860 0.05690 0.1080 0.1490 0.1350 0.01250 1.800 2.790 0.02970 0.09530 0.05680 0.08310 0.1340 0.1220 0.009360 2.000 2.520 0.02960 0.09170 0.05660 0.06310 0.1180 0.1100 0.006750 2.400 2.030 0.02940 0.08410 0.05610 0.03600 0.08720 0.08690 0.003250 3.000 1.470 0.02900 0.07180 0.05500 0.01850 0.05020 0.05990 0.001060 4.000 0.9150 0.02830 0.05160 0.05170 0.01450 0.01690 0.03140 0.0004610 5.000 0.6320 0.02730 0.03430 0.04670 0.01360 0.006070 0.01640 0.0004430 6.000 0.4700 0.02630 0.02140 0.04070 0.01020 0.003780 0.008620 0.0003510 7.000 0.3620 0.02510 0.01280 0.03430 0.006500 0.003590 0.004590 0.0002290 8.000 0.2850 0.02380 0.007530 0.02820 0.003690 0.003520 0.002490 0.0001310 10.00 0.1800 0.02100 0.003300 0.01800 0.001000 0.002700 0.0007800 3.600E-05 15.00 0.06800 0.01400 0.002400 0.004800 0.0003500 0.0008100 6.100E-05 1.100E-05 20.00 0.03100 0.008900 0.001800 0.001300 0.0002800 0.0002100 7.500E-06 8.400E-06 30.00 0.008500 0.003200 0.0005600 0.0001300 8.600E-05 2.000E-05 2.800E-07 2.600E-06 40.00 0.002800 0.001100 0.0001700 2.000E-05 2.500E-05 3.000E-06 2.200E-08 7.700E-07 60.00 0.0004400 0.0001900 2.300E-05 1.100E-06 3.300E-06 1.600E-07 5.100E-10 1.000E-07 100.0 2.900E-05 1.300E-05 1.400E-06 2.400E-08 1.900E-07 3.300E-09 3.500E-12 5.900E-09 #S 30 Zn #N 9 #UOCCUP 2 2 6 2 6 10 2 #UBIND 9661. 1196. 1030. 139.9 89.60 10.20 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 0.000 6.760 0.02900 0.1080 0.05490 0.2990 0.1860 0.1620 1.410 0.05000 6.700 0.02900 0.1080 0.05490 0.2990 0.1860 0.1620 1.380 0.1000 6.550 0.02900 0.1080 0.05490 0.2980 0.1860 0.1620 1.300 0.1500 6.300 0.02900 0.1080 0.05490 0.2970 0.1860 0.1620 1.180 0.2000 6.010 0.02900 0.1080 0.05490 0.2940 0.1860 0.1620 1.040 0.3000 5.360 0.02900 0.1080 0.05490 0.2890 0.1850 0.1620 0.7210 0.4000 4.790 0.02900 0.1070 0.05490 0.2810 0.1850 0.1620 0.4460 0.5000 4.380 0.02900 0.1070 0.05490 0.2710 0.1850 0.1620 0.2510 0.6000 4.110 0.02900 0.1060 0.05490 0.2590 0.1840 0.1620 0.1320 0.7000 3.940 0.02900 0.1060 0.05490 0.2460 0.1830 0.1610 0.06870 0.8000 3.830 0.02900 0.1050 0.05490 0.2320 0.1810 0.1600 0.03830 1.000 3.660 0.02890 0.1030 0.05490 0.2010 0.1760 0.1570 0.02160 1.200 3.490 0.02890 0.1010 0.05480 0.1700 0.1690 0.1510 0.02060 1.400 3.290 0.02880 0.09850 0.05480 0.1400 0.1590 0.1430 0.01980 1.600 3.060 0.02880 0.09580 0.05470 0.1130 0.1470 0.1340 0.01720 1.800 2.820 0.02870 0.09280 0.05460 0.08850 0.1340 0.1240 0.01380 2.000 2.580 0.02860 0.08960 0.05450 0.06850 0.1200 0.1140 0.01050 2.400 2.120 0.02840 0.08260 0.05410 0.04000 0.09150 0.09330 0.005450 3.000 1.560 0.02810 0.07130 0.05310 0.02000 0.05530 0.06710 0.001830 4.000 0.9810 0.02740 0.05240 0.05030 0.01420 0.01990 0.03700 0.0006240 5.000 0.6720 0.02660 0.03580 0.04600 0.01360 0.007140 0.02010 0.0005880 6.000 0.4960 0.02560 0.02300 0.04060 0.01090 0.003940 0.01090 0.0004970 7.000 0.3810 0.02450 0.01410 0.03470 0.007270 0.003530 0.005940 0.0003430 8.000 0.3000 0.02330 0.008470 0.02890 0.004340 0.003500 0.003300 0.0002070 10.00 0.1900 0.02100 0.003500 0.01900 0.001300 0.002900 0.001100 6.100E-05 15.00 0.07300 0.01400 0.002400 0.005400 0.0003500 0.0009300 8.900E-05 1.400E-05 20.00 0.03300 0.009200 0.001900 0.001500 0.0002900 0.0002600 1.100E-05 1.200E-05 30.00 0.009400 0.003400 0.0006200 0.0001600 9.700E-05 2.500E-05 4.300E-07 3.900E-06 40.00 0.003100 0.001300 0.0001900 2.500E-05 3.000E-05 3.800E-06 3.400E-08 1.200E-06 60.00 0.0005000 0.0002200 2.700E-05 1.400E-06 4.000E-06 2.100E-07 8.100E-10 1.600E-07 100.0 3.400E-05 1.500E-05 1.600E-06 3.000E-08 2.300E-07 4.300E-09 6.200E-12 9.400E-09 #S 31 Ga #N 10 #UOCCUP 2 2 6 2 6 10 2 1 #UBIND 1.037E+04 1298. 1126. 158.0 104.0 18.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 0.000 6.930 0.02810 0.1040 0.05280 0.2850 0.1760 0.1460 1.180 0.9150 0.05000 6.900 0.02810 0.1040 0.05280 0.2840 0.1760 0.1460 1.160 0.9140 0.1000 6.800 0.02810 0.1040 0.05280 0.2840 0.1760 0.1460 1.110 0.9120 0.1500 6.630 0.02810 0.1040 0.05280 0.2820 0.1760 0.1460 1.040 0.9010 0.2000 6.420 0.02810 0.1040 0.05280 0.2810 0.1760 0.1460 0.9440 0.8760 0.3000 5.860 0.02810 0.1040 0.05280 0.2750 0.1760 0.1460 0.7230 0.7730 0.4000 5.260 0.02810 0.1030 0.05280 0.2690 0.1750 0.1460 0.5060 0.6180 0.5000 4.710 0.02800 0.1030 0.05280 0.2600 0.1750 0.1460 0.3270 0.4520 0.6000 4.290 0.02800 0.1030 0.05280 0.2500 0.1750 0.1450 0.1980 0.3090 0.7000 3.980 0.02800 0.1020 0.05280 0.2380 0.1740 0.1450 0.1160 0.2010 0.8000 3.770 0.02800 0.1010 0.05280 0.2260 0.1730 0.1450 0.06730 0.1260 1.000 3.520 0.02800 0.09970 0.05280 0.1990 0.1690 0.1430 0.03020 0.04640 1.200 3.350 0.02790 0.09780 0.05280 0.1700 0.1630 0.1400 0.02440 0.01680 1.400 3.180 0.02790 0.09560 0.05270 0.1430 0.1550 0.1350 0.02410 0.007120 1.600 3.000 0.02780 0.09310 0.05270 0.1170 0.1450 0.1290 0.02270 0.004470 1.800 2.800 0.02780 0.09040 0.05260 0.09340 0.1330 0.1220 0.01960 0.003980 2.000 2.590 0.02770 0.08750 0.05250 0.07360 0.1210 0.1140 0.01580 0.003950 2.400 2.180 0.02750 0.08110 0.05210 0.04430 0.09520 0.09710 0.008990 0.003670 3.000 1.650 0.02720 0.07070 0.05130 0.02190 0.06020 0.07290 0.003220 0.002550 4.000 1.050 0.02660 0.05300 0.04890 0.01390 0.02330 0.04240 0.0008910 0.0009810 5.000 0.7150 0.02590 0.03710 0.04510 0.01360 0.008500 0.02380 0.0007840 0.0003260 6.000 0.5240 0.02500 0.02450 0.04030 0.01140 0.004240 0.01330 0.0007010 0.0001310 7.000 0.4010 0.02390 0.01540 0.03490 0.008020 0.003510 0.007470 0.0005120 9.210E-05 8.000 0.3150 0.02280 0.009450 0.02950 0.005010 0.003480 0.004240 0.0003250 8.920E-05 10.00 0.2000 0.02000 0.003900 0.02000 0.001600 0.003000 0.001400 1.000E-04 8.000E-05 15.00 0.07800 0.01400 0.002300 0.006000 0.0003500 0.001100 0.0001200 1.900E-05 2.900E-05 20.00 0.03600 0.009400 0.001900 0.001800 0.0003000 0.0003100 1.600E-05 1.700E-05 8.300E-06 30.00 0.010000 0.003600 0.0006800 0.0001900 0.0001100 3.200E-05 6.400E-07 6.000E-06 8.500E-07 40.00 0.003500 0.001400 0.0002200 3.100E-05 3.400E-05 4.800E-06 5.200E-08 1.900E-06 1.300E-07 60.00 0.0005700 0.0002400 3.100E-05 1.800E-06 4.700E-06 2.700E-07 1.300E-09 2.600E-07 7.100E-09 100.0 4.000E-05 1.800E-05 1.900E-06 3.900E-08 2.800E-07 5.600E-09 1.000E-11 1.500E-08 1.500E-10 #S 32 Ge #N 10 #UOCCUP 2 2 6 2 6 10 2 2 #UBIND 1.110E+04 1413. 1228. 181.0 124.0 29.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 0.000 7.030 0.02720 0.1010 0.05090 0.2710 0.1670 0.1330 1.030 0.7690 0.05000 7.010 0.02720 0.1010 0.05090 0.2710 0.1670 0.1330 1.020 0.7690 0.1000 6.940 0.02720 0.1010 0.05090 0.2700 0.1670 0.1330 0.9850 0.7680 0.1500 6.820 0.02720 0.1000 0.05090 0.2690 0.1670 0.1330 0.9340 0.7630 0.2000 6.660 0.02720 0.1000 0.05090 0.2670 0.1670 0.1330 0.8660 0.7510 0.3000 6.210 0.02720 0.1000 0.05090 0.2630 0.1670 0.1330 0.7020 0.6980 0.4000 5.660 0.02720 0.09990 0.05090 0.2570 0.1660 0.1330 0.5270 0.6050 0.5000 5.100 0.02720 0.09950 0.05090 0.2500 0.1660 0.1330 0.3710 0.4890 0.6000 4.600 0.02710 0.09910 0.05090 0.2410 0.1660 0.1330 0.2460 0.3710 0.7000 4.190 0.02710 0.09850 0.05090 0.2310 0.1650 0.1330 0.1560 0.2690 0.8000 3.870 0.02710 0.09790 0.05090 0.2200 0.1640 0.1320 0.09710 0.1870 1.000 3.480 0.02710 0.09650 0.05090 0.1950 0.1610 0.1310 0.04150 0.08370 1.200 3.250 0.02710 0.09480 0.05090 0.1700 0.1560 0.1300 0.02770 0.03510 1.400 3.080 0.02700 0.09280 0.05080 0.1440 0.1500 0.1270 0.02620 0.01500 1.600 2.930 0.02700 0.09050 0.05080 0.1200 0.1420 0.1230 0.02580 0.007750 1.800 2.760 0.02690 0.08810 0.05070 0.09780 0.1320 0.1180 0.02380 0.005610 2.000 2.580 0.02680 0.08540 0.05060 0.07840 0.1210 0.1120 0.02040 0.005200 2.400 2.220 0.02670 0.07960 0.05030 0.04860 0.09800 0.09840 0.01280 0.005090 3.000 1.720 0.02640 0.07000 0.04960 0.02410 0.06480 0.07710 0.005010 0.003950 4.000 1.110 0.02590 0.05350 0.04750 0.01380 0.02700 0.04720 0.001210 0.001700 5.000 0.7590 0.02520 0.03830 0.04420 0.01350 0.01010 0.02760 0.0009380 0.0005950 6.000 0.5530 0.02430 0.02590 0.03990 0.01180 0.004700 0.01590 0.0008800 0.0002260 7.000 0.4220 0.02340 0.01670 0.03500 0.008730 0.003530 0.009130 0.0006800 0.0001370 8.000 0.3310 0.02240 0.01050 0.02990 0.005700 0.003440 0.005290 0.0004530 0.0001260 10.00 0.2100 0.02000 0.004300 0.02100 0.001900 0.003100 0.001800 0.0001500 0.0001200 15.00 0.08300 0.01500 0.002300 0.006600 0.0003500 0.001200 0.0001700 2.300E-05 4.800E-05 20.00 0.03800 0.009700 0.001900 0.002000 0.0003100 0.0003600 2.300E-05 2.100E-05 1.400E-05 30.00 0.01100 0.003900 0.0007400 0.0002300 0.0001200 3.900E-05 9.300E-07 8.100E-06 1.500E-06 40.00 0.003900 0.001500 0.0002500 3.800E-05 3.900E-05 6.100E-06 7.700E-08 2.600E-06 2.300E-07 60.00 0.0006500 0.0002800 3.600E-05 2.200E-06 5.600E-06 3.400E-07 1.900E-09 3.700E-07 1.300E-08 100.0 4.700E-05 2.000E-05 2.200E-06 4.800E-08 3.400E-07 7.200E-09 1.500E-11 2.200E-08 2.800E-10 #S 33 As #N 10 #UOCCUP 2 2 6 2 6 10 2 3 #UBIND 1.187E+04 1527. 1335. 204.0 143.0 42.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 0.000 7.100 0.02630 0.09710 0.04910 0.2580 0.1580 0.1230 0.9220 0.6740 0.05000 7.080 0.02630 0.09710 0.04910 0.2580 0.1580 0.1230 0.9140 0.6740 0.1000 7.030 0.02630 0.09710 0.04910 0.2580 0.1580 0.1230 0.8900 0.6730 0.1500 6.940 0.02630 0.09700 0.04910 0.2570 0.1580 0.1230 0.8520 0.6700 0.2000 6.820 0.02630 0.09700 0.04910 0.2550 0.1580 0.1230 0.8010 0.6640 0.3000 6.460 0.02630 0.09680 0.04910 0.2520 0.1580 0.1230 0.6730 0.6320 0.4000 5.990 0.02630 0.09650 0.04910 0.2460 0.1580 0.1230 0.5310 0.5730 0.5000 5.460 0.02630 0.09620 0.04910 0.2400 0.1580 0.1230 0.3960 0.4910 0.6000 4.930 0.02630 0.09580 0.04910 0.2320 0.1580 0.1230 0.2800 0.3990 0.7000 4.460 0.02630 0.09530 0.04910 0.2230 0.1570 0.1220 0.1900 0.3100 0.8000 4.070 0.02630 0.09480 0.04910 0.2130 0.1560 0.1220 0.1250 0.2320 1.000 3.530 0.02630 0.09350 0.04910 0.1920 0.1540 0.1220 0.05520 0.1200 1.200 3.210 0.02620 0.09190 0.04910 0.1690 0.1500 0.1210 0.03210 0.05690 1.400 3.010 0.02620 0.09010 0.04910 0.1450 0.1450 0.1190 0.02750 0.02630 1.600 2.850 0.02610 0.08810 0.04900 0.1220 0.1380 0.1160 0.02730 0.01290 1.800 2.710 0.02610 0.08580 0.04900 0.1010 0.1300 0.1130 0.02640 0.007890 2.000 2.550 0.02600 0.08340 0.04890 0.08260 0.1210 0.1090 0.02390 0.006360 2.400 2.240 0.02590 0.07810 0.04870 0.05280 0.09990 0.09790 0.01650 0.006070 3.000 1.770 0.02570 0.06930 0.04810 0.02660 0.06890 0.07970 0.007190 0.005240 4.000 1.170 0.02510 0.05380 0.04630 0.01390 0.03070 0.05140 0.001650 0.002550 5.000 0.8040 0.02450 0.03930 0.04330 0.01330 0.01200 0.03120 0.001070 0.0009540 6.000 0.5830 0.02370 0.02720 0.03950 0.01210 0.005320 0.01850 0.001030 0.0003540 7.000 0.4440 0.02290 0.01790 0.03500 0.009370 0.003620 0.01090 0.0008450 0.0001880 8.000 0.3480 0.02200 0.01150 0.03020 0.006390 0.003410 0.006460 0.0005900 0.0001600 10.00 0.2300 0.02000 0.004700 0.02100 0.002300 0.003200 0.002300 0.0002200 0.0001500 15.00 0.08800 0.01500 0.002200 0.007200 0.0003500 0.001400 0.0002300 2.700E-05 6.800E-05 20.00 0.04100 0.009900 0.002000 0.002300 0.0003200 0.0004300 3.200E-05 2.500E-05 2.100E-05 30.00 0.01200 0.004100 0.0008000 0.0002700 0.0001300 4.800E-05 1.300E-06 1.000E-05 2.400E-06 40.00 0.004200 0.001600 0.0002700 4.600E-05 4.500E-05 7.600E-06 1.100E-07 3.500E-06 3.700E-07 60.00 0.0007300 0.0003100 4.100E-05 2.700E-06 6.500E-06 4.300E-07 2.800E-09 5.000E-07 2.100E-08 100.0 5.300E-05 2.300E-05 2.600E-06 6.000E-08 4.000E-07 9.300E-09 2.100E-11 3.100E-08 4.600E-10 #S 34 Se #N 10 #UOCCUP 2 2 6 2 6 10 2 4 #UBIND 1.266E+04 1654. 1449. 231.0 164.0 56.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 0.000 7.160 0.02550 0.09390 0.04750 0.2470 0.1510 0.1140 0.8390 0.6040 0.05000 7.140 0.02550 0.09390 0.04750 0.2470 0.1510 0.1140 0.8330 0.6040 0.1000 7.100 0.02550 0.09390 0.04750 0.2460 0.1510 0.1140 0.8140 0.6030 0.1500 7.040 0.02550 0.09390 0.04750 0.2450 0.1510 0.1140 0.7850 0.6020 0.2000 6.940 0.02550 0.09380 0.04750 0.2440 0.1510 0.1140 0.7450 0.5980 0.3000 6.650 0.02550 0.09360 0.04750 0.2410 0.1510 0.1140 0.6430 0.5780 0.4000 6.240 0.02550 0.09340 0.04750 0.2360 0.1510 0.1140 0.5260 0.5380 0.5000 5.760 0.02550 0.09310 0.04750 0.2300 0.1500 0.1140 0.4090 0.4790 0.6000 5.250 0.02550 0.09270 0.04750 0.2230 0.1500 0.1140 0.3040 0.4080 0.7000 4.760 0.02550 0.09230 0.04750 0.2160 0.1500 0.1140 0.2170 0.3330 0.8000 4.320 0.02550 0.09180 0.04750 0.2070 0.1490 0.1140 0.1500 0.2630 1.000 3.660 0.02550 0.09060 0.04750 0.1880 0.1470 0.1140 0.07020 0.1510 1.200 3.240 0.02540 0.08920 0.04750 0.1670 0.1440 0.1130 0.03810 0.07970 1.400 2.980 0.02540 0.08760 0.04740 0.1450 0.1400 0.1120 0.02910 0.04000 1.600 2.800 0.02540 0.08570 0.04740 0.1240 0.1340 0.1100 0.02800 0.02020 1.800 2.650 0.02530 0.08370 0.04730 0.1040 0.1270 0.1080 0.02770 0.01130 2.000 2.510 0.02530 0.08150 0.04730 0.08640 0.1190 0.1040 0.02620 0.007910 2.400 2.230 0.02510 0.07660 0.04710 0.05680 0.1010 0.09630 0.01980 0.006730 3.000 1.810 0.02490 0.06840 0.04660 0.02930 0.07230 0.08090 0.009640 0.006280 4.000 1.230 0.02440 0.05400 0.04500 0.01420 0.03440 0.05490 0.002240 0.003490 5.000 0.8480 0.02390 0.04010 0.04240 0.01300 0.01420 0.03470 0.001190 0.001410 6.000 0.6150 0.02320 0.02830 0.03900 0.01230 0.006130 0.02120 0.001160 0.0005280 7.000 0.4660 0.02240 0.01910 0.03490 0.009940 0.003790 0.01280 0.001000 0.0002520 8.000 0.3650 0.02150 0.01250 0.03050 0.007060 0.003380 0.007730 0.0007340 0.0001920 10.00 0.2400 0.02000 0.005200 0.02200 0.002800 0.003300 0.002900 0.0002900 0.0001800 15.00 0.09400 0.01500 0.002200 0.007900 0.0003600 0.001500 0.0003000 3.200E-05 9.000E-05 20.00 0.04400 0.010000 0.002000 0.002600 0.0003300 0.0005000 4.200E-05 2.800E-05 3.000E-05 30.00 0.01300 0.004300 0.0008600 0.0003200 0.0001500 5.800E-05 1.800E-06 1.300E-05 3.500E-06 40.00 0.004600 0.001800 0.0003000 5.500E-05 5.100E-05 9.400E-06 1.600E-07 4.400E-06 5.600E-07 60.00 0.0008200 0.0003400 4.700E-05 3.300E-06 7.600E-06 5.500E-07 4.000E-09 6.500E-07 3.200E-08 100.0 6.100E-05 2.700E-05 3.000E-06 7.500E-08 4.800E-07 1.200E-08 2.900E-11 4.100E-08 7.000E-10 #S 35 Br #N 10 #UOCCUP 2 2 6 2 6 10 2 5 #UBIND 1.347E+04 1782. 1565. 257.0 184.0 69.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 0.000 7.200 0.02480 0.09090 0.04590 0.2360 0.1440 0.1070 0.7720 0.5490 0.05000 7.190 0.02480 0.09090 0.04590 0.2360 0.1440 0.1070 0.7670 0.5490 0.1000 7.160 0.02480 0.09090 0.04590 0.2360 0.1440 0.1070 0.7520 0.5490 0.1500 7.110 0.02480 0.09090 0.04590 0.2350 0.1440 0.1070 0.7290 0.5480 0.2000 7.030 0.02480 0.09080 0.04590 0.2340 0.1440 0.1070 0.6970 0.5450 0.3000 6.790 0.02480 0.09070 0.04590 0.2310 0.1440 0.1070 0.6140 0.5320 0.4000 6.440 0.02480 0.09040 0.04590 0.2270 0.1440 0.1070 0.5160 0.5040 0.5000 6.010 0.02480 0.09020 0.04590 0.2220 0.1430 0.1070 0.4150 0.4610 0.6000 5.530 0.02480 0.08980 0.04590 0.2160 0.1430 0.1070 0.3200 0.4060 0.7000 5.050 0.02480 0.08940 0.04590 0.2090 0.1430 0.1070 0.2380 0.3450 0.8000 4.590 0.02480 0.08900 0.04590 0.2010 0.1420 0.1070 0.1720 0.2830 1.000 3.840 0.02470 0.08790 0.04590 0.1840 0.1410 0.1070 0.08550 0.1770 1.200 3.320 0.02470 0.08660 0.04590 0.1650 0.1380 0.1060 0.04540 0.1020 1.400 2.990 0.02470 0.08510 0.04590 0.1450 0.1350 0.1050 0.03140 0.05510 1.600 2.770 0.02460 0.08340 0.04590 0.1250 0.1300 0.1040 0.02840 0.02920 1.800 2.610 0.02460 0.08160 0.04580 0.1070 0.1240 0.1020 0.02820 0.01610 2.000 2.470 0.02450 0.07960 0.04580 0.08950 0.1170 0.1000 0.02760 0.01020 2.400 2.220 0.02440 0.07510 0.04560 0.06050 0.1010 0.09380 0.02260 0.007280 3.000 1.840 0.02420 0.06750 0.04510 0.03210 0.07510 0.08120 0.01220 0.007030 4.000 1.280 0.02380 0.05400 0.04380 0.01470 0.03800 0.05770 0.003030 0.004440 5.000 0.8920 0.02330 0.04090 0.04150 0.01280 0.01650 0.03780 0.001340 0.001950 6.000 0.6470 0.02260 0.02940 0.03840 0.01240 0.007120 0.02380 0.001260 0.0007560 7.000 0.4890 0.02190 0.02030 0.03470 0.01040 0.004050 0.01470 0.001140 0.0003350 8.000 0.3820 0.02110 0.01350 0.03060 0.007690 0.003390 0.009090 0.0008780 0.0002270 10.00 0.2500 0.01900 0.005800 0.02300 0.003200 0.003300 0.003500 0.0003800 0.0002100 15.00 0.09900 0.01500 0.002100 0.008500 0.0003900 0.001700 0.0003800 3.900E-05 0.0001100 20.00 0.04700 0.010000 0.002000 0.002900 0.0003300 0.0005700 5.600E-05 3.100E-05 3.900E-05 30.00 0.01400 0.004500 0.0009200 0.0003700 0.0001600 7.000E-05 2.500E-06 1.500E-05 4.800E-06 40.00 0.005100 0.001900 0.0003400 6.500E-05 5.800E-05 1.200E-05 2.200E-07 5.500E-06 7.800E-07 60.00 0.0009100 0.0003800 5.300E-05 4.000E-06 8.800E-06 6.800E-07 5.600E-09 8.300E-07 4.600E-08 100.0 7.000E-05 3.000E-05 3.500E-06 9.200E-08 5.600E-07 1.500E-08 3.800E-11 6.300E-08 1.000E-09 #S 36 Kr #N 14 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 #UBIND 1.433E+04 1925. 1731. 1678. 293.0 222.0 214.0 95.00 94.00 27.00 14.00 14.00 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 0.000 7.190 0.02340 0.08620 0.04330 0.04430 0.2220 0.1340 0.1370 0.1010 0.1010 0.7050 0.4960 0.5080 0.05000 7.180 0.02340 0.08620 0.04330 0.04430 0.2220 0.1340 0.1370 0.1010 0.1010 0.7010 0.4960 0.5080 0.1000 7.150 0.02340 0.08620 0.04330 0.04430 0.2220 0.1340 0.1370 0.1010 0.1010 0.6890 0.4960 0.5080 0.1500 7.110 0.02340 0.08610 0.04330 0.04430 0.2210 0.1340 0.1370 0.1010 0.1010 0.6710 0.4950 0.5070 0.2000 7.050 0.02340 0.08610 0.04330 0.04430 0.2200 0.1340 0.1370 0.1010 0.1010 0.6460 0.4940 0.5050 0.3000 6.860 0.02340 0.08590 0.04330 0.04430 0.2180 0.1340 0.1370 0.1010 0.1010 0.5810 0.4850 0.4960 0.4000 6.570 0.02340 0.08580 0.04330 0.04430 0.2140 0.1340 0.1370 0.1010 0.1010 0.5010 0.4670 0.4750 0.5000 6.200 0.02340 0.08550 0.04330 0.04430 0.2100 0.1340 0.1370 0.1010 0.1010 0.4160 0.4360 0.4420 0.6000 5.770 0.02340 0.08520 0.04330 0.04430 0.2050 0.1340 0.1370 0.1010 0.1010 0.3330 0.3960 0.3980 0.7000 5.300 0.02340 0.08490 0.04330 0.04430 0.1990 0.1340 0.1360 0.1010 0.1010 0.2590 0.3480 0.3480 0.8000 4.850 0.02340 0.08450 0.04330 0.04430 0.1920 0.1340 0.1360 0.1010 0.1010 0.1950 0.2980 0.2950 1.000 4.040 0.02340 0.08360 0.04330 0.04430 0.1780 0.1320 0.1350 0.1000 0.1010 0.1040 0.2020 0.1960 1.200 3.440 0.02330 0.08250 0.04330 0.04430 0.1610 0.1310 0.1330 0.1000 0.1010 0.05610 0.1260 0.1210 1.400 3.030 0.02330 0.08120 0.04330 0.04420 0.1440 0.1280 0.1300 0.09960 0.1000 0.03530 0.07450 0.06980 1.600 2.760 0.02330 0.07980 0.04330 0.04420 0.1260 0.1240 0.1260 0.09880 0.09940 0.02880 0.04230 0.03900 1.800 2.580 0.02320 0.07820 0.04320 0.04420 0.1090 0.1200 0.1210 0.09750 0.09810 0.02770 0.02390 0.02190 2.000 2.440 0.02320 0.07640 0.04320 0.04410 0.09340 0.1140 0.1150 0.09580 0.09630 0.02760 0.01430 0.01320 2.400 2.200 0.02310 0.07250 0.04310 0.04400 0.06550 0.1010 0.1010 0.09090 0.09130 0.02470 0.007970 0.007820 3.000 1.860 0.02290 0.06590 0.04270 0.04360 0.03630 0.07810 0.07740 0.08050 0.08070 0.01540 0.007310 0.007340 4.000 1.330 0.02260 0.05380 0.04160 0.04240 0.01560 0.04290 0.04150 0.05970 0.05950 0.004380 0.005450 0.005270 5.000 0.9330 0.02210 0.04180 0.03980 0.04050 0.01230 0.02010 0.01900 0.04060 0.04030 0.001560 0.002730 0.002540 6.000 0.6770 0.02150 0.03100 0.03730 0.03770 0.01210 0.008920 0.008370 0.02640 0.02610 0.001310 0.001140 0.001040 7.000 0.5120 0.02090 0.02200 0.03410 0.03440 0.01080 0.004620 0.004450 0.01680 0.01650 0.001250 0.0004830 0.0004420 8.000 0.4000 0.02020 0.01520 0.03050 0.03060 0.008500 0.003390 0.003420 0.01060 0.01040 0.001040 0.0002740 0.0002640 10.00 0.2600 0.01900 0.006800 0.02300 0.02300 0.004000 0.003200 0.003300 0.004200 0.004100 0.0005100 0.0002200 0.0002300 15.00 0.1100 0.01400 0.002000 0.009500 0.009100 0.0004500 0.001900 0.001800 0.0005000 0.0004800 5.000E-05 0.0001400 0.0001400 20.00 0.05100 0.010000 0.001900 0.003500 0.003200 0.0003200 0.0007200 0.0006700 7.700E-05 7.300E-05 3.300E-05 5.500E-05 5.100E-05 30.00 0.01600 0.004900 0.001000 0.0005200 0.0004500 0.0001900 1.000E-04 8.800E-05 3.700E-06 3.400E-06 1.900E-05 7.600E-06 6.600E-06 40.00 0.006000 0.002200 0.0004100 1.000E-04 8.200E-05 7.300E-05 1.900E-05 1.500E-05 3.500E-07 3.100E-07 7.600E-06 1.400E-06 1.100E-06 60.00 0.001200 0.0004900 7.500E-05 7.800E-06 5.600E-06 1.300E-05 1.400E-06 9.900E-07 1.100E-08 8.600E-09 1.300E-06 1.000E-07 7.300E-08 100.0 0.0001100 4.900E-05 6.100E-06 2.800E-07 1.500E-07 1.000E-06 4.800E-08 2.600E-08 1.300E-10 9.300E-11 1.000E-07 3.500E-09 1.900E-09 #S 37 Rb #N 15 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 1 #UBIND 1.520E+04 2065. 1865. 1805. 321.0 248.0 239.0 1120. 111.0 29.00 14.00 14.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 0.000 9.080 0.02270 0.08350 0.04190 0.04290 0.2130 0.1290 0.1310 0.09500 0.09570 0.6310 0.4330 0.4430 2.560 0.05000 8.930 0.02270 0.08350 0.04190 0.04290 0.2130 0.1290 0.1310 0.09500 0.09570 0.6290 0.4330 0.4430 2.400 0.1000 8.500 0.02270 0.08350 0.04190 0.04290 0.2130 0.1290 0.1310 0.09500 0.09570 0.6200 0.4330 0.4430 2.000 0.1500 7.950 0.02270 0.08340 0.04190 0.04290 0.2120 0.1290 0.1310 0.09500 0.09570 0.6070 0.4330 0.4420 1.480 0.2000 7.410 0.02270 0.08340 0.04190 0.04290 0.2110 0.1290 0.1310 0.09500 0.09570 0.5890 0.4320 0.4410 0.9770 0.3000 6.630 0.02270 0.08330 0.04190 0.04290 0.2090 0.1290 0.1310 0.09500 0.09570 0.5390 0.4280 0.4370 0.3240 0.4000 6.200 0.02270 0.08310 0.04190 0.04290 0.2060 0.1290 0.1310 0.09500 0.09570 0.4780 0.4180 0.4250 0.09720 0.5000 5.910 0.02270 0.08290 0.04190 0.04290 0.2020 0.1280 0.1310 0.09500 0.09570 0.4100 0.4000 0.4060 0.05560 0.6000 5.600 0.02270 0.08260 0.04190 0.04290 0.1980 0.1280 0.1310 0.09500 0.09570 0.3410 0.3750 0.3790 0.05370 0.7000 5.250 0.02270 0.08230 0.04190 0.04290 0.1920 0.1280 0.1300 0.09500 0.09570 0.2760 0.3430 0.3450 0.05000 0.8000 4.880 0.02270 0.08200 0.04190 0.04290 0.1870 0.1280 0.1300 0.09500 0.09560 0.2170 0.3070 0.3060 0.04140 1.000 4.160 0.02270 0.08110 0.04190 0.04290 0.1730 0.1270 0.1290 0.09490 0.09560 0.1260 0.2280 0.2250 0.02240 1.200 3.560 0.02270 0.08010 0.04190 0.04290 0.1580 0.1260 0.1280 0.09470 0.09540 0.07110 0.1570 0.1520 0.01040 1.400 3.110 0.02260 0.07900 0.04190 0.04290 0.1430 0.1230 0.1250 0.09440 0.09500 0.04320 0.1010 0.09580 0.004610 1.600 2.800 0.02260 0.07760 0.04190 0.04290 0.1270 0.1200 0.1220 0.09380 0.09440 0.03200 0.06130 0.05750 0.002300 1.800 2.580 0.02260 0.07620 0.04190 0.04280 0.1110 0.1160 0.1180 0.09290 0.09340 0.02900 0.03640 0.03370 0.001540 2.000 2.420 0.02250 0.07450 0.04180 0.04280 0.09550 0.1120 0.1130 0.09160 0.09210 0.02870 0.02180 0.02010 0.001370 2.400 2.180 0.02250 0.07100 0.04170 0.04270 0.06860 0.1000 0.1010 0.08790 0.08830 0.02720 0.01030 0.009930 0.001340 3.000 1.860 0.02230 0.06490 0.04140 0.04230 0.03920 0.07960 0.07900 0.07950 0.07970 0.01880 0.008260 0.008380 0.0009880 4.000 1.370 0.02200 0.05360 0.04050 0.04130 0.01670 0.04600 0.04470 0.06110 0.06100 0.006020 0.006860 0.006730 0.0003230 5.000 0.9750 0.02150 0.04220 0.03890 0.03960 0.01210 0.02270 0.02150 0.04310 0.04290 0.001960 0.003780 0.003570 9.670E-05 6.000 0.7100 0.02100 0.03180 0.03660 0.03710 0.01190 0.01040 0.009710 0.02880 0.02860 0.001440 0.001680 0.001540 6.560E-05 7.000 0.5360 0.02040 0.02310 0.03370 0.03410 0.01100 0.005200 0.004960 0.01880 0.01850 0.001410 0.0007140 0.0006500 6.430E-05 8.000 0.4190 0.01980 0.01610 0.03050 0.03060 0.008990 0.003520 0.003520 0.01210 0.01190 0.001230 0.0003690 0.0003510 5.630E-05 10.00 0.2700 0.01800 0.007400 0.02400 0.02300 0.004500 0.003200 0.003300 0.005000 0.004900 0.0006500 0.0002700 0.0002700 3.000E-05 15.00 0.1100 0.01400 0.002000 0.010000 0.009700 0.0005100 0.002000 0.002000 0.0006200 0.0006000 6.600E-05 0.0001800 0.0001800 3.000E-06 20.00 0.05400 0.01100 0.001900 0.003900 0.003600 0.0003200 0.0008100 0.0007600 9.800E-05 9.300E-05 3.600E-05 7.500E-05 6.900E-05 1.600E-06 30.00 0.01700 0.005100 0.001100 0.0006000 0.0005200 0.0002000 0.0001200 1.000E-04 4.900E-06 4.500E-06 2.300E-05 1.100E-05 9.500E-06 1.000E-06 40.00 0.006500 0.002300 0.0004500 0.0001200 9.700E-05 8.100E-05 2.300E-05 1.900E-05 4.700E-07 4.200E-07 9.400E-06 2.100E-06 1.700E-06 4.300E-07 60.00 0.001300 0.0005400 8.400E-05 9.400E-06 6.700E-06 1.500E-05 1.700E-06 1.200E-06 1.400E-08 1.200E-08 1.700E-06 1.500E-07 1.100E-07 7.600E-08 100.0 0.0001300 5.600E-05 7.100E-06 3.400E-07 1.800E-07 1.200E-06 6.000E-08 3.200E-08 1.800E-10 1.300E-10 1.400E-07 5.400E-09 2.800E-09 6.200E-09 #S 38 Sr #N 15 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 2 #UBIND 1.610E+04 2216. 2007. 1940. 358.0 280.0 269.0 135.0 133.0 38.00 22.00 22.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 0.000 10.10 0.02210 0.08090 0.04060 0.04160 0.2040 0.1230 0.1250 0.09010 0.09070 0.5730 0.3890 0.3970 2.060 0.05000 9.970 0.02210 0.08090 0.04060 0.04160 0.2040 0.1230 0.1250 0.09010 0.09070 0.5710 0.3890 0.3970 1.970 0.1000 9.480 0.02210 0.08090 0.04060 0.04160 0.2040 0.1230 0.1250 0.09010 0.09070 0.5640 0.3890 0.3970 1.740 0.1500 8.810 0.02210 0.08090 0.04060 0.04160 0.2040 0.1230 0.1250 0.09010 0.09070 0.5540 0.3890 0.3970 1.410 0.2000 8.070 0.02210 0.08080 0.04060 0.04160 0.2030 0.1230 0.1250 0.09010 0.09070 0.5400 0.3880 0.3960 1.060 0.3000 6.830 0.02210 0.08070 0.04060 0.04160 0.2010 0.1230 0.1250 0.09010 0.09070 0.5020 0.3860 0.3930 0.4860 0.4000 6.090 0.02210 0.08060 0.04060 0.04160 0.1980 0.1230 0.1250 0.09010 0.09070 0.4540 0.3790 0.3870 0.1870 0.5000 5.690 0.02210 0.08040 0.04060 0.04160 0.1950 0.1230 0.1250 0.09010 0.09070 0.3990 0.3680 0.3740 0.08210 0.6000 5.420 0.02210 0.08010 0.04060 0.04160 0.1910 0.1230 0.1250 0.09010 0.09070 0.3410 0.3520 0.3560 0.06040 0.7000 5.150 0.02210 0.07990 0.04060 0.04160 0.1860 0.1230 0.1250 0.09010 0.09070 0.2850 0.3300 0.3330 0.05910 0.8000 4.860 0.02210 0.07950 0.04060 0.04160 0.1810 0.1230 0.1250 0.09010 0.09070 0.2320 0.3030 0.3040 0.05680 1.000 4.240 0.02210 0.07880 0.04060 0.04160 0.1690 0.1220 0.1240 0.09000 0.09060 0.1450 0.2410 0.2390 0.04100 1.200 3.660 0.02200 0.07790 0.04060 0.04160 0.1550 0.1210 0.1230 0.08990 0.09050 0.08620 0.1780 0.1740 0.02330 1.400 3.200 0.02200 0.07680 0.04060 0.04160 0.1410 0.1190 0.1210 0.08960 0.09020 0.05250 0.1230 0.1180 0.01170 1.600 2.850 0.02200 0.07560 0.04060 0.04160 0.1260 0.1160 0.1180 0.08920 0.08980 0.03650 0.08030 0.07610 0.005650 1.800 2.590 0.02190 0.07420 0.04060 0.04150 0.1110 0.1130 0.1150 0.08860 0.08910 0.03070 0.05050 0.04720 0.003120 2.000 2.410 0.02190 0.07280 0.04050 0.04150 0.09720 0.1090 0.1100 0.08760 0.08820 0.02950 0.03120 0.02900 0.002290 2.400 2.160 0.02180 0.06950 0.04040 0.04140 0.07140 0.09910 0.09960 0.08480 0.08520 0.02890 0.01360 0.01290 0.002110 3.000 1.870 0.02170 0.06380 0.04020 0.04110 0.04210 0.08060 0.08020 0.07800 0.07830 0.02200 0.009020 0.009150 0.001750 4.000 1.400 0.02140 0.05330 0.03940 0.04020 0.01790 0.04890 0.04760 0.06210 0.06200 0.008010 0.008080 0.008010 0.0006660 5.000 1.010 0.02100 0.04260 0.03790 0.03870 0.01210 0.02530 0.02410 0.04520 0.04500 0.002520 0.004900 0.004670 0.0001950 6.000 0.7430 0.02050 0.03260 0.03590 0.03650 0.01170 0.01200 0.01120 0.03120 0.03090 0.001590 0.002330 0.002150 0.0001090 7.000 0.5610 0.02000 0.02400 0.03330 0.03370 0.01110 0.005920 0.005590 0.02080 0.02050 0.001550 0.001010 0.0009220 0.0001050 8.000 0.4380 0.01940 0.01710 0.03030 0.03050 0.009410 0.003730 0.003680 0.01360 0.01340 0.001410 0.0004940 0.0004610 9.640E-05 10.00 0.2900 0.01800 0.008100 0.02400 0.02400 0.005100 0.003100 0.003200 0.005800 0.005700 0.0008100 0.0003000 0.0003100 5.600E-05 15.00 0.1200 0.01400 0.002100 0.01100 0.010000 0.0005900 0.002200 0.002100 0.0007600 0.0007300 8.800E-05 0.0002300 0.0002200 6.000E-06 20.00 0.05700 0.01100 0.001900 0.004200 0.003900 0.0003100 0.0009100 0.0008500 0.0001200 0.0001200 4.000E-05 9.700E-05 9.000E-05 2.700E-06 30.00 0.01800 0.005300 0.001100 0.0006800 0.0005900 0.0002100 0.0001400 0.0001200 6.400E-06 5.900E-06 2.700E-05 1.500E-05 1.300E-05 1.800E-06 40.00 0.007000 0.002500 0.0004900 0.0001400 0.0001100 9.000E-05 2.700E-05 2.200E-05 6.300E-07 5.500E-07 1.200E-05 2.900E-06 2.300E-06 7.800E-07 60.00 0.001500 0.0005900 9.400E-05 1.100E-05 8.000E-06 1.700E-05 2.100E-06 1.500E-06 1.900E-08 1.600E-08 2.100E-06 2.200E-07 1.600E-07 1.400E-07 100.0 0.0001500 6.300E-05 8.100E-06 4.200E-07 2.200E-07 1.400E-06 7.500E-08 4.000E-08 2.500E-10 1.700E-10 1.800E-07 7.800E-09 4.100E-09 1.200E-08 #S 39 Y #N 16 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 1 2 #UBIND 1.704E+04 2373. 2155. 2080. 395.0 313.0 301.0 160.0 158.0 46.00 26.00 26.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 0.000 9.920 0.02150 0.07850 0.03940 0.04040 0.1960 0.1180 0.1200 0.08560 0.08630 0.5330 0.3610 0.3660 0.4540 1.900 0.05000 9.780 0.02150 0.07850 0.03940 0.04040 0.1960 0.1180 0.1200 0.08560 0.08630 0.5310 0.3610 0.3660 0.4540 1.830 0.1000 9.390 0.02150 0.07850 0.03940 0.04040 0.1960 0.1180 0.1200 0.08560 0.08630 0.5260 0.3610 0.3660 0.4540 1.640 0.1500 8.830 0.02150 0.07850 0.03940 0.04040 0.1960 0.1180 0.1200 0.08560 0.08630 0.5180 0.3610 0.3660 0.4540 1.370 0.2000 8.200 0.02150 0.07840 0.03940 0.04040 0.1950 0.1180 0.1200 0.08560 0.08630 0.5060 0.3610 0.3660 0.4540 1.070 0.3000 7.070 0.02150 0.07830 0.03940 0.04040 0.1930 0.1180 0.1200 0.08560 0.08630 0.4750 0.3590 0.3640 0.4500 0.5440 0.4000 6.320 0.02150 0.07820 0.03940 0.04040 0.1910 0.1180 0.1200 0.08560 0.08630 0.4340 0.3540 0.3590 0.4390 0.2310 0.5000 5.880 0.02150 0.07800 0.03940 0.04040 0.1880 0.1180 0.1200 0.08560 0.08630 0.3880 0.3460 0.3510 0.4170 0.09980 0.6000 5.580 0.02150 0.07780 0.03940 0.04040 0.1840 0.1180 0.1200 0.08560 0.08630 0.3380 0.3330 0.3370 0.3840 0.06200 0.7000 5.320 0.02150 0.07750 0.03940 0.04040 0.1800 0.1180 0.1200 0.08560 0.08620 0.2880 0.3160 0.3190 0.3440 0.05660 0.8000 5.030 0.02150 0.07730 0.03940 0.04040 0.1750 0.1180 0.1200 0.08560 0.08620 0.2400 0.2950 0.2970 0.3010 0.05620 1.000 4.420 0.02140 0.07660 0.03940 0.04040 0.1650 0.1170 0.1190 0.08560 0.08620 0.1570 0.2440 0.2430 0.2180 0.04600 1.200 3.830 0.02140 0.07570 0.03940 0.04040 0.1520 0.1160 0.1180 0.08550 0.08610 0.09790 0.1880 0.1860 0.1510 0.02950 1.400 3.330 0.02140 0.07470 0.03940 0.04040 0.1390 0.1150 0.1170 0.08530 0.08590 0.06080 0.1370 0.1340 0.1010 0.01630 1.600 2.940 0.02140 0.07360 0.03930 0.04040 0.1260 0.1130 0.1140 0.08500 0.08560 0.04100 0.09430 0.09100 0.06660 0.008360 1.800 2.640 0.02130 0.07240 0.03930 0.04030 0.1120 0.1100 0.1110 0.08450 0.08510 0.03240 0.06230 0.05940 0.04320 0.004440 2.000 2.430 0.02130 0.07100 0.03930 0.04030 0.09840 0.1060 0.1080 0.08390 0.08440 0.02980 0.04010 0.03780 0.02770 0.002840 2.400 2.150 0.02120 0.06800 0.03920 0.04020 0.07380 0.09760 0.09840 0.08170 0.08220 0.02930 0.01730 0.01640 0.01120 0.002260 3.000 1.860 0.02110 0.06280 0.03900 0.04000 0.04480 0.08110 0.08090 0.07620 0.07660 0.02420 0.009510 0.009660 0.003260 0.002050 4.000 1.420 0.02080 0.05300 0.03830 0.03920 0.01920 0.05140 0.05020 0.06250 0.06260 0.010000 0.008770 0.008860 0.001600 0.0009100 5.000 1.050 0.02040 0.04280 0.03700 0.03780 0.01220 0.02790 0.02660 0.04700 0.04680 0.003190 0.005850 0.005680 0.001540 0.0002760 6.000 0.7750 0.02000 0.03320 0.03520 0.03580 0.01150 0.01370 0.01280 0.03330 0.03300 0.001720 0.002980 0.002800 0.001280 0.0001290 7.000 0.5870 0.01950 0.02490 0.03290 0.03330 0.01120 0.006780 0.006340 0.02270 0.02240 0.001640 0.001350 0.001240 0.0009450 0.0001190 8.000 0.4570 0.01900 0.01800 0.03010 0.03040 0.009740 0.004030 0.003920 0.01520 0.01500 0.001540 0.0006360 0.0005910 0.0006580 0.0001130 10.00 0.3000 0.01800 0.008800 0.02400 0.02400 0.005600 0.003100 0.003200 0.006700 0.006500 0.0009600 0.0003300 0.0003400 0.0003000 7.100E-05 15.00 0.1200 0.01400 0.002100 0.01100 0.01100 0.0006900 0.002300 0.002200 0.0009200 0.0008900 0.0001100 0.0002600 0.0002600 4.200E-05 8.300E-06 20.00 0.06000 0.01100 0.001800 0.004600 0.004300 0.0003100 0.001000 0.0009500 0.0001600 0.0001500 4.200E-05 0.0001200 0.0001100 7.100E-06 3.100E-06 30.00 0.01900 0.005400 0.001200 0.0007700 0.0006700 0.0002200 0.0001600 0.0001400 8.300E-06 7.600E-06 3.100E-05 1.900E-05 1.700E-05 3.800E-07 2.200E-06 40.00 0.007600 0.002600 0.0005300 0.0001600 0.0001300 9.900E-05 3.300E-05 2.700E-05 8.200E-07 7.300E-07 1.400E-05 3.700E-06 3.100E-06 3.700E-08 9.800E-07 60.00 0.001600 0.0006500 0.0001100 1.300E-05 9.500E-06 1.900E-05 2.600E-06 1.800E-06 2.600E-08 2.100E-08 2.600E-06 2.900E-07 2.100E-07 1.200E-09 1.900E-07 100.0 0.0001600 7.000E-05 9.200E-06 5.100E-07 2.700E-07 1.600E-06 9.300E-08 4.900E-08 3.300E-10 2.200E-10 2.200E-07 1.100E-08 5.600E-09 1.500E-11 1.600E-08 #S 40 Zr #N 16 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 2 2 #UBIND 1.800E+04 2534. 2309. 2225. 433.0 347.0 333.0 185.0 182.0 54.00 31.00 31.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 0.000 9.760 0.02090 0.07620 0.03820 0.03920 0.1890 0.1140 0.1160 0.08160 0.08230 0.5010 0.3390 0.3420 0.3930 1.800 0.05000 9.640 0.02090 0.07620 0.03820 0.03920 0.1890 0.1140 0.1160 0.08160 0.08230 0.5000 0.3390 0.3420 0.3930 1.740 0.1000 9.300 0.02090 0.07620 0.03820 0.03920 0.1890 0.1140 0.1160 0.08160 0.08230 0.4960 0.3390 0.3420 0.3930 1.580 0.1500 8.810 0.02090 0.07620 0.03820 0.03920 0.1880 0.1140 0.1160 0.08160 0.08230 0.4880 0.3390 0.3420 0.3930 1.340 0.2000 8.240 0.02090 0.07610 0.03820 0.03920 0.1880 0.1140 0.1160 0.08160 0.08230 0.4790 0.3380 0.3420 0.3930 1.070 0.3000 7.190 0.02090 0.07610 0.03820 0.03920 0.1860 0.1140 0.1160 0.08160 0.08230 0.4520 0.3370 0.3410 0.3920 0.5790 0.4000 6.460 0.02090 0.07590 0.03820 0.03920 0.1840 0.1140 0.1160 0.08160 0.08230 0.4170 0.3330 0.3370 0.3870 0.2640 0.5000 6.010 0.02090 0.07580 0.03820 0.03920 0.1810 0.1130 0.1160 0.08160 0.08230 0.3770 0.3270 0.3310 0.3750 0.1150 0.6000 5.720 0.02090 0.07560 0.03820 0.03920 0.1780 0.1130 0.1160 0.08160 0.08230 0.3330 0.3170 0.3210 0.3570 0.06450 0.7000 5.470 0.02090 0.07530 0.03820 0.03920 0.1740 0.1130 0.1160 0.08160 0.08230 0.2880 0.3040 0.3060 0.3320 0.05370 0.8000 5.200 0.02090 0.07510 0.03820 0.03920 0.1700 0.1130 0.1150 0.08160 0.08230 0.2450 0.2870 0.2890 0.3030 0.05310 1.000 4.620 0.02090 0.07440 0.03820 0.03920 0.1600 0.1130 0.1150 0.08160 0.08220 0.1670 0.2440 0.2440 0.2380 0.04710 1.200 4.020 0.02080 0.07370 0.03820 0.03920 0.1490 0.1120 0.1140 0.08150 0.08220 0.1080 0.1950 0.1940 0.1780 0.03310 1.400 3.490 0.02080 0.07280 0.03820 0.03920 0.1370 0.1110 0.1130 0.08140 0.08200 0.06860 0.1470 0.1450 0.1280 0.01980 1.600 3.060 0.02080 0.07170 0.03820 0.03920 0.1250 0.1090 0.1110 0.08120 0.08180 0.04580 0.1060 0.1030 0.08940 0.01080 1.800 2.730 0.02080 0.07060 0.03820 0.03920 0.1120 0.1060 0.1080 0.08080 0.08150 0.03450 0.07310 0.07050 0.06130 0.005810 2.000 2.480 0.02070 0.06930 0.03810 0.03920 0.09930 0.1040 0.1050 0.08030 0.08090 0.03020 0.04880 0.04660 0.04140 0.003450 2.400 2.150 0.02070 0.06660 0.03810 0.03910 0.07580 0.09600 0.09690 0.07860 0.07920 0.02910 0.02150 0.02040 0.01830 0.002290 3.000 1.850 0.02050 0.06170 0.03790 0.03890 0.04740 0.08130 0.08120 0.07430 0.07470 0.02580 0.01020 0.01030 0.005480 0.002170 4.000 1.440 0.02030 0.05260 0.03720 0.03820 0.02070 0.05360 0.05240 0.06260 0.06270 0.01210 0.009170 0.009380 0.001970 0.001120 5.000 1.080 0.01990 0.04300 0.03610 0.03700 0.01240 0.03040 0.02900 0.04840 0.04820 0.004010 0.006690 0.006590 0.001880 0.0003630 6.000 0.8070 0.01950 0.03380 0.03450 0.03520 0.01130 0.01550 0.01450 0.03520 0.03490 0.001900 0.003670 0.003490 0.001660 0.0001490 7.000 0.6130 0.01910 0.02570 0.03240 0.03290 0.01110 0.007750 0.007210 0.02460 0.02430 0.001690 0.001730 0.001600 0.001280 0.0001240 8.000 0.4780 0.01860 0.01890 0.02990 0.03020 0.009980 0.004420 0.004230 0.01680 0.01650 0.001640 0.0008140 0.0007530 0.0009200 0.0001220 10.00 0.3100 0.01700 0.009500 0.02400 0.02400 0.006100 0.003000 0.003100 0.007600 0.007400 0.001100 0.0003600 0.0003700 0.0004400 8.300E-05 15.00 0.1300 0.01400 0.002200 0.01200 0.01100 0.0008100 0.002400 0.002400 0.001100 0.001100 0.0001400 0.0002900 0.0002900 6.500E-05 1.100E-05 20.00 0.06300 0.01100 0.001800 0.005000 0.004600 0.0003100 0.001100 0.001100 0.0001900 0.0001800 4.500E-05 0.0001400 0.0001300 1.100E-05 3.300E-06 30.00 0.02000 0.005600 0.001200 0.0008700 0.0007600 0.0002300 0.0001900 0.0001700 1.100E-05 9.700E-06 3.400E-05 2.300E-05 2.100E-05 6.200E-07 2.500E-06 40.00 0.008100 0.002800 0.0005600 0.0001900 0.0001500 0.0001100 3.900E-05 3.100E-05 1.100E-06 9.400E-07 1.600E-05 4.700E-06 3.900E-06 6.100E-08 1.200E-06 60.00 0.001800 0.0007000 0.0001200 1.600E-05 1.100E-05 2.200E-05 3.100E-06 2.200E-06 3.400E-08 2.800E-08 3.100E-06 3.800E-07 2.700E-07 2.000E-09 2.300E-07 100.0 0.0001800 7.800E-05 1.000E-05 6.100E-07 3.200E-07 1.900E-06 1.200E-07 6.000E-08 4.400E-10 2.900E-10 2.700E-07 1.400E-08 7.300E-09 2.600E-11 2.000E-08 #S 41 Nb #N 16 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 1 #UBIND 1.898E+04 2695. 2462. 2368. 466.0 376.0 360.0 205.0 202.0 55.00 31.00 31.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 0.000 8.560 0.02030 0.07410 0.03710 0.03810 0.1820 0.1090 0.1120 0.07790 0.07870 0.4800 0.3240 0.3260 0.3850 1.880 0.05000 8.490 0.02030 0.07400 0.03710 0.03810 0.1820 0.1090 0.1120 0.07790 0.07870 0.4790 0.3240 0.3260 0.3850 1.810 0.1000 8.300 0.02030 0.07400 0.03710 0.03810 0.1820 0.1090 0.1120 0.07790 0.07870 0.4750 0.3240 0.3260 0.3850 1.630 0.1500 8.030 0.02030 0.07400 0.03710 0.03810 0.1820 0.1090 0.1120 0.07790 0.07870 0.4690 0.3240 0.3260 0.3850 1.370 0.2000 7.720 0.02030 0.07400 0.03710 0.03810 0.1810 0.1090 0.1120 0.07790 0.07870 0.4600 0.3240 0.3250 0.3840 1.080 0.3000 7.140 0.02030 0.07390 0.03710 0.03810 0.1800 0.1090 0.1120 0.07790 0.07870 0.4360 0.3230 0.3240 0.3830 0.5670 0.4000 6.710 0.02030 0.07380 0.03710 0.03810 0.1780 0.1090 0.1120 0.07790 0.07870 0.4050 0.3200 0.3220 0.3770 0.2480 0.5000 6.410 0.02030 0.07360 0.03710 0.03810 0.1750 0.1090 0.1120 0.07790 0.07870 0.3680 0.3150 0.3160 0.3660 0.1030 0.6000 6.150 0.02030 0.07350 0.03710 0.03810 0.1720 0.1090 0.1110 0.07790 0.07870 0.3280 0.3060 0.3080 0.3480 0.05550 0.7000 5.890 0.02030 0.07320 0.03710 0.03810 0.1690 0.1090 0.1110 0.07790 0.07870 0.2870 0.2940 0.2960 0.3230 0.04570 0.8000 5.590 0.02030 0.07300 0.03710 0.03810 0.1650 0.1090 0.1110 0.07790 0.07870 0.2470 0.2790 0.2810 0.2950 0.04520 1.000 4.950 0.02030 0.07240 0.03710 0.03810 0.1560 0.1090 0.1110 0.07790 0.07870 0.1730 0.2410 0.2430 0.2350 0.04020 1.200 4.300 0.02030 0.07170 0.03710 0.03810 0.1460 0.1080 0.1100 0.07790 0.07860 0.1150 0.1980 0.1980 0.1790 0.02860 1.400 3.720 0.02030 0.07090 0.03710 0.03810 0.1350 0.1070 0.1090 0.07780 0.07850 0.07460 0.1530 0.1530 0.1320 0.01760 1.600 3.240 0.02030 0.06990 0.03710 0.03810 0.1240 0.1050 0.1070 0.07760 0.07840 0.04970 0.1140 0.1120 0.09500 0.009930 1.800 2.860 0.02020 0.06890 0.03700 0.03810 0.1120 0.1030 0.1050 0.07740 0.07810 0.03640 0.08080 0.07910 0.06740 0.005470 2.000 2.570 0.02020 0.06770 0.03700 0.03810 0.09990 0.1010 0.1020 0.07700 0.07770 0.03050 0.05570 0.05400 0.04710 0.003210 2.400 2.180 0.02010 0.06510 0.03700 0.03800 0.07760 0.09420 0.09520 0.07570 0.07630 0.02840 0.02550 0.02440 0.02230 0.001890 3.000 1.850 0.02000 0.06070 0.03680 0.03780 0.04970 0.08110 0.08120 0.07220 0.07270 0.02630 0.01080 0.01090 0.007110 0.001790 4.000 1.450 0.01980 0.05210 0.03630 0.03720 0.02220 0.05540 0.05440 0.06230 0.06240 0.01380 0.009150 0.009520 0.002050 0.001050 5.000 1.110 0.01950 0.04310 0.03530 0.03610 0.01270 0.03270 0.03130 0.04950 0.04930 0.004900 0.007240 0.007260 0.001820 0.0003730 6.000 0.8380 0.01910 0.03430 0.03380 0.03450 0.01110 0.01740 0.01620 0.03690 0.03660 0.002100 0.004260 0.004120 0.001690 0.0001410 7.000 0.6390 0.01870 0.02640 0.03190 0.03250 0.01100 0.008840 0.008180 0.02640 0.02600 0.001710 0.002120 0.001990 0.001370 0.0001050 8.000 0.4990 0.01820 0.01970 0.02960 0.03000 0.01010 0.004900 0.004640 0.01840 0.01810 0.001690 0.001010 0.0009360 0.001020 0.0001040 10.00 0.3200 0.01700 0.010000 0.02400 0.02400 0.006600 0.003000 0.003100 0.008600 0.008400 0.001200 0.0003800 0.0003900 0.0005000 7.600E-05 15.00 0.1400 0.01400 0.002300 0.01200 0.01200 0.0009500 0.002500 0.002500 0.001300 0.001300 0.0001800 0.0003000 0.0003100 7.900E-05 1.100E-05 20.00 0.06700 0.01100 0.001700 0.005300 0.005000 0.0003100 0.001200 0.001200 0.0002400 0.0002200 4.700E-05 0.0001600 0.0001500 1.400E-05 2.900E-06 30.00 0.02100 0.005800 0.001300 0.0009800 0.0008500 0.0002400 0.0002200 0.0001900 1.300E-05 1.200E-05 3.600E-05 2.800E-05 2.500E-05 8.000E-07 2.200E-06 40.00 0.008700 0.002900 0.0006000 0.0002100 0.0001700 0.0001200 4.600E-05 3.700E-05 1.400E-06 1.200E-06 1.800E-05 5.800E-06 4.800E-06 8.100E-08 1.100E-06 60.00 0.001900 0.0007600 0.0001300 1.900E-05 1.300E-05 2.400E-05 3.800E-06 2.700E-06 4.500E-08 3.600E-08 3.600E-06 4.700E-07 3.400E-07 2.600E-09 2.200E-07 100.0 0.0002100 8.700E-05 1.200E-05 7.400E-07 3.800E-07 2.100E-06 1.400E-07 7.300E-08 5.700E-10 3.800E-10 3.200E-07 1.800E-08 9.400E-09 3.500E-11 1.900E-08 #S 42 Mo #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 1 1 #UBIND 2.000E+04 2866. 2625. 2520. 505.0 410.0 393.0 230.0 226.0 62.00 35.00 35.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 8.480 0.01980 0.07200 0.03600 0.03710 0.1760 0.1050 0.1080 0.07470 0.07540 0.4560 0.3060 0.3090 0.3470 0.3570 1.820 0.05000 8.420 0.01980 0.07200 0.03600 0.03710 0.1760 0.1050 0.1080 0.07470 0.07540 0.4550 0.3060 0.3090 0.3470 0.3570 1.760 0.1000 8.240 0.01980 0.07200 0.03600 0.03710 0.1760 0.1050 0.1080 0.07470 0.07540 0.4520 0.3060 0.3090 0.3470 0.3570 1.600 0.1500 7.990 0.01980 0.07200 0.03600 0.03710 0.1750 0.1050 0.1080 0.07470 0.07540 0.4460 0.3060 0.3090 0.3470 0.3570 1.360 0.2000 7.700 0.01980 0.07190 0.03600 0.03710 0.1750 0.1050 0.1080 0.07470 0.07540 0.4390 0.3060 0.3090 0.3470 0.3570 1.090 0.3000 7.150 0.01980 0.07190 0.03600 0.03710 0.1740 0.1050 0.1080 0.07470 0.07540 0.4180 0.3050 0.3080 0.3460 0.3560 0.5900 0.4000 6.740 0.01980 0.07170 0.03600 0.03710 0.1720 0.1050 0.1080 0.07470 0.07540 0.3900 0.3030 0.3060 0.3430 0.3520 0.2690 0.5000 6.460 0.01980 0.07160 0.03600 0.03710 0.1700 0.1050 0.1080 0.07470 0.07540 0.3580 0.2980 0.3020 0.3360 0.3440 0.1140 0.6000 6.220 0.01980 0.07140 0.03600 0.03710 0.1670 0.1050 0.1080 0.07470 0.07540 0.3220 0.2920 0.2950 0.3250 0.3300 0.05760 0.7000 5.980 0.01980 0.07120 0.03600 0.03710 0.1640 0.1050 0.1070 0.07470 0.07540 0.2850 0.2830 0.2850 0.3080 0.3110 0.04350 0.8000 5.720 0.01980 0.07100 0.03600 0.03710 0.1600 0.1050 0.1070 0.07470 0.07540 0.2480 0.2700 0.2730 0.2870 0.2880 0.04200 1.000 5.120 0.01980 0.07050 0.03600 0.03710 0.1520 0.1050 0.1070 0.07470 0.07540 0.1790 0.2390 0.2400 0.2400 0.2370 0.03900 1.200 4.500 0.01980 0.06980 0.03600 0.03710 0.1430 0.1040 0.1060 0.07460 0.07530 0.1230 0.2000 0.2000 0.1910 0.1870 0.02950 1.400 3.910 0.01970 0.06910 0.03600 0.03710 0.1330 0.1030 0.1050 0.07460 0.07530 0.08170 0.1600 0.1590 0.1470 0.1430 0.01920 1.600 3.410 0.01970 0.06820 0.03600 0.03710 0.1220 0.1020 0.1040 0.07440 0.07520 0.05480 0.1230 0.1210 0.1110 0.1060 0.01140 1.800 2.990 0.01970 0.06720 0.03600 0.03710 0.1110 0.1000 0.1020 0.07430 0.07490 0.03930 0.09020 0.08780 0.08150 0.07800 0.006510 2.000 2.670 0.01970 0.06620 0.03600 0.03700 0.1000 0.09810 0.09970 0.07400 0.07460 0.03160 0.06430 0.06180 0.05920 0.05630 0.003790 2.400 2.220 0.01960 0.06380 0.03590 0.03700 0.07900 0.09240 0.09350 0.07300 0.07360 0.02790 0.03080 0.02920 0.03000 0.02830 0.001930 3.000 1.850 0.01950 0.05960 0.03580 0.03680 0.05190 0.08070 0.08100 0.07010 0.07060 0.02680 0.01220 0.01200 0.01030 0.009630 0.001740 4.000 1.460 0.01930 0.05160 0.03530 0.03630 0.02380 0.05690 0.05600 0.06170 0.06190 0.01560 0.009260 0.009590 0.002560 0.002450 0.001150 5.000 1.140 0.01900 0.04310 0.03440 0.03530 0.01310 0.03490 0.03360 0.05020 0.05010 0.005970 0.007870 0.007890 0.002000 0.001970 0.0004510 6.000 0.8670 0.01860 0.03470 0.03310 0.03390 0.01090 0.01920 0.01800 0.03840 0.03810 0.002410 0.004980 0.004800 0.001930 0.001890 0.0001640 7.000 0.6650 0.01820 0.02710 0.03140 0.03200 0.01080 0.010000 0.009260 0.02800 0.02770 0.001760 0.002610 0.002440 0.001630 0.001590 0.0001070 8.000 0.5200 0.01780 0.02050 0.02930 0.02970 0.01020 0.005480 0.005130 0.01990 0.01960 0.001730 0.001270 0.001170 0.001250 0.001210 0.0001040 10.00 0.3400 0.01700 0.01100 0.02400 0.02400 0.007000 0.003000 0.003100 0.009600 0.009400 0.001300 0.0004300 0.0004300 0.0006500 0.0006200 8.200E-05 15.00 0.1400 0.01400 0.002400 0.01300 0.01200 0.001100 0.002500 0.002500 0.001500 0.001500 0.0002200 0.0003300 0.0003400 0.0001100 1.000E-04 1.300E-05 20.00 0.07000 0.01100 0.001700 0.005700 0.005300 0.0003200 0.001300 0.001300 0.0002900 0.0002700 5.000E-05 0.0001800 0.0001700 2.000E-05 1.900E-05 3.000E-06 30.00 0.02300 0.005900 0.001300 0.001100 0.0009500 0.0002500 0.0002500 0.0002200 1.700E-05 1.500E-05 3.900E-05 3.400E-05 3.000E-05 1.200E-06 1.000E-06 2.300E-06 40.00 0.009300 0.003000 0.0006400 0.0002500 0.0002000 0.0001300 5.300E-05 4.300E-05 1.800E-06 1.500E-06 2.000E-05 7.100E-06 5.900E-06 1.200E-07 1.000E-07 1.200E-06 60.00 0.002100 0.0008200 0.0001400 2.200E-05 1.500E-05 2.700E-05 4.500E-06 3.200E-06 5.800E-08 4.600E-08 4.200E-06 6.000E-07 4.300E-07 4.000E-09 3.100E-09 2.500E-07 100.0 0.0002300 9.700E-05 1.300E-05 8.800E-07 4.500E-07 2.500E-06 1.700E-07 8.900E-08 7.400E-10 4.900E-10 3.800E-07 2.300E-08 1.200E-08 5.200E-11 3.400E-11 2.300E-08 #S 43 Tc #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 1 2 #UBIND 2.104E+04 3042. 2793. 2677. 544.0 445.0 425.0 257.0 253.0 68.00 39.00 39.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 9.410 0.01930 0.07000 0.03500 0.03610 0.1700 0.1020 0.1040 0.07170 0.07240 0.4300 0.2870 0.2910 0.2990 0.3050 1.620 0.05000 9.330 0.01930 0.07000 0.03500 0.03610 0.1700 0.1020 0.1040 0.07170 0.07240 0.4290 0.2870 0.2910 0.2990 0.3050 1.580 0.1000 9.080 0.01930 0.07000 0.03500 0.03610 0.1700 0.1020 0.1040 0.07170 0.07240 0.4260 0.2870 0.2910 0.2990 0.3050 1.460 0.1500 8.700 0.01930 0.07000 0.03500 0.03610 0.1690 0.1020 0.1040 0.07170 0.07240 0.4220 0.2870 0.2910 0.2990 0.3050 1.280 0.2000 8.260 0.01930 0.07000 0.03500 0.03610 0.1690 0.1020 0.1040 0.07170 0.07240 0.4150 0.2870 0.2910 0.2990 0.3050 1.060 0.3000 7.380 0.01930 0.06990 0.03500 0.03610 0.1680 0.1020 0.1040 0.07170 0.07240 0.3970 0.2860 0.2900 0.2980 0.3040 0.6430 0.4000 6.700 0.01930 0.06980 0.03500 0.03610 0.1660 0.1020 0.1040 0.07170 0.07240 0.3740 0.2850 0.2890 0.2970 0.3030 0.3340 0.5000 6.250 0.01930 0.06970 0.03500 0.03610 0.1640 0.1020 0.1040 0.07170 0.07240 0.3460 0.2820 0.2850 0.2950 0.3000 0.1580 0.6000 5.970 0.01930 0.06950 0.03500 0.03610 0.1620 0.1020 0.1040 0.07170 0.07240 0.3150 0.2770 0.2800 0.2900 0.2950 0.07840 0.7000 5.760 0.01930 0.06930 0.03500 0.03610 0.1590 0.1020 0.1040 0.07170 0.07240 0.2820 0.2690 0.2730 0.2820 0.2860 0.05050 0.8000 5.550 0.01930 0.06910 0.03500 0.03610 0.1560 0.1010 0.1040 0.07170 0.07240 0.2490 0.2600 0.2630 0.2710 0.2730 0.04410 1.000 5.090 0.01930 0.06860 0.03500 0.03610 0.1490 0.1010 0.1030 0.07170 0.07240 0.1850 0.2340 0.2360 0.2410 0.2410 0.04290 1.200 4.570 0.01930 0.06800 0.03500 0.03610 0.1400 0.1010 0.1030 0.07170 0.07240 0.1310 0.2020 0.2020 0.2040 0.2030 0.03630 1.400 4.030 0.01920 0.06730 0.03500 0.03610 0.1310 0.09990 0.1020 0.07160 0.07230 0.08960 0.1660 0.1650 0.1670 0.1640 0.02600 1.600 3.540 0.01920 0.06650 0.03500 0.03610 0.1210 0.09880 0.1010 0.07150 0.07220 0.06110 0.1310 0.1290 0.1320 0.1290 0.01670 1.800 3.120 0.01920 0.06560 0.03500 0.03610 0.1110 0.09730 0.09920 0.07140 0.07210 0.04330 0.09990 0.09710 0.1020 0.09920 0.010000 2.000 2.770 0.01920 0.06470 0.03500 0.03610 0.1000 0.09540 0.09710 0.07110 0.07180 0.03370 0.07350 0.07070 0.07730 0.07470 0.005900 2.400 2.280 0.01910 0.06240 0.03490 0.03600 0.08020 0.09040 0.09170 0.07040 0.07100 0.02790 0.03710 0.03510 0.04220 0.04040 0.002580 3.000 1.860 0.01900 0.05860 0.03480 0.03590 0.05400 0.08000 0.08050 0.06810 0.06860 0.02710 0.01430 0.01370 0.01570 0.01490 0.002050 4.000 1.470 0.01880 0.05110 0.03440 0.03540 0.02550 0.05820 0.05740 0.06090 0.06120 0.01750 0.009390 0.009720 0.003610 0.003460 0.001530 5.000 1.160 0.01850 0.04300 0.03360 0.03450 0.01370 0.03700 0.03560 0.05070 0.05060 0.007260 0.008470 0.008520 0.002350 0.002340 0.0006590 6.000 0.8930 0.01820 0.03500 0.03250 0.03330 0.01080 0.02110 0.01980 0.03960 0.03930 0.002840 0.005740 0.005560 0.002300 0.002290 0.0002390 7.000 0.6900 0.01790 0.02760 0.03090 0.03150 0.01060 0.01130 0.01040 0.02950 0.02920 0.001850 0.003190 0.002980 0.002040 0.002010 0.0001350 8.000 0.5410 0.01740 0.02120 0.02900 0.02940 0.01020 0.006160 0.005710 0.02140 0.02100 0.001780 0.001600 0.001460 0.001630 0.001590 0.0001260 10.00 0.3500 0.01600 0.01200 0.02400 0.02500 0.007400 0.003100 0.003100 0.01100 0.010000 0.001500 0.0005000 0.0004800 0.0008800 0.0008600 0.0001100 15.00 0.1500 0.01400 0.002500 0.01300 0.01300 0.001300 0.002600 0.002600 0.001800 0.001700 0.0002700 0.0003500 0.0003600 0.0001600 0.0001500 2.000E-05 20.00 0.07400 0.01100 0.001700 0.006100 0.005700 0.0003300 0.001400 0.001400 0.0003400 0.0003200 5.500E-05 0.0002000 0.0002000 3.000E-05 2.800E-05 3.900E-06 30.00 0.02400 0.006000 0.001300 0.001200 0.001100 0.0002600 0.0002800 0.0002500 2.100E-05 1.900E-05 4.200E-05 4.100E-05 3.600E-05 1.800E-06 1.600E-06 3.000E-06 40.00 0.009900 0.003200 0.0006800 0.0002800 0.0002200 0.0001400 6.200E-05 5.000E-05 2.200E-06 1.900E-06 2.200E-05 8.800E-06 7.200E-06 1.900E-07 1.600E-07 1.600E-06 60.00 0.002300 0.0008800 0.0001600 2.600E-05 1.800E-05 3.000E-05 5.400E-06 3.800E-06 7.400E-08 5.900E-08 4.900E-06 7.600E-07 5.400E-07 6.300E-09 5.000E-09 3.400E-07 100.0 0.0002600 0.0001100 1.500E-05 1.000E-06 5.300E-07 2.800E-06 2.100E-07 1.100E-07 9.600E-10 6.300E-10 4.500E-07 2.900E-08 1.500E-08 8.300E-11 5.500E-11 3.200E-08 #S 44 Ru #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 3 1 #UBIND 2.212E+04 3224. 2967. 2838. 585.0 483.0 461.0 284.0 279.0 75.00 43.00 43.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 8.340 0.01880 0.06820 0.03410 0.03520 0.1640 0.09820 0.1010 0.06900 0.06970 0.4160 0.2760 0.2810 0.2960 0.3030 1.740 0.05000 8.290 0.01880 0.06820 0.03410 0.03520 0.1640 0.09820 0.1010 0.06900 0.06970 0.4150 0.2760 0.2810 0.2960 0.3030 1.690 0.1000 8.140 0.01880 0.06820 0.03410 0.03520 0.1640 0.09820 0.1010 0.06900 0.06970 0.4120 0.2760 0.2810 0.2960 0.3030 1.540 0.1500 7.920 0.01880 0.06810 0.03410 0.03520 0.1640 0.09820 0.1010 0.06900 0.06970 0.4080 0.2760 0.2810 0.2960 0.3030 1.330 0.2000 7.660 0.01880 0.06810 0.03410 0.03520 0.1630 0.09820 0.1010 0.06900 0.06970 0.4020 0.2760 0.2810 0.2960 0.3030 1.090 0.3000 7.160 0.01880 0.06810 0.03410 0.03520 0.1620 0.09820 0.1010 0.06900 0.06970 0.3860 0.2750 0.2810 0.2960 0.3020 0.6240 0.4000 6.780 0.01880 0.06800 0.03410 0.03520 0.1610 0.09820 0.1000 0.06900 0.06970 0.3650 0.2740 0.2790 0.2940 0.3010 0.3040 0.5000 6.510 0.01880 0.06780 0.03410 0.03520 0.1590 0.09810 0.1000 0.06900 0.06970 0.3390 0.2710 0.2760 0.2920 0.2980 0.1350 0.6000 6.310 0.01880 0.06770 0.03410 0.03520 0.1570 0.09810 0.1000 0.06900 0.06970 0.3100 0.2670 0.2720 0.2860 0.2910 0.06390 0.7000 6.120 0.01880 0.06750 0.03410 0.03520 0.1540 0.09810 0.1000 0.06900 0.06970 0.2790 0.2610 0.2650 0.2770 0.2810 0.04110 0.8000 5.910 0.01880 0.06730 0.03410 0.03520 0.1520 0.09800 0.1000 0.06900 0.06970 0.2480 0.2520 0.2560 0.2660 0.2690 0.03650 1.000 5.410 0.01880 0.06690 0.03410 0.03520 0.1450 0.09780 0.1000 0.06900 0.06960 0.1880 0.2300 0.2320 0.2360 0.2360 0.03550 1.200 4.840 0.01880 0.06630 0.03410 0.03520 0.1370 0.09730 0.09960 0.06900 0.06960 0.1350 0.2010 0.2010 0.2000 0.1990 0.02960 1.400 4.280 0.01880 0.06570 0.03410 0.03520 0.1290 0.09670 0.09880 0.06890 0.06960 0.09440 0.1680 0.1670 0.1650 0.1620 0.02120 1.600 3.750 0.01870 0.06490 0.03400 0.03520 0.1190 0.09570 0.09780 0.06890 0.06950 0.06500 0.1360 0.1330 0.1320 0.1290 0.01380 1.800 3.300 0.01870 0.06410 0.03400 0.03520 0.1100 0.09450 0.09640 0.06870 0.06940 0.04600 0.1060 0.1020 0.1040 0.1010 0.008410 2.000 2.920 0.01870 0.06320 0.03400 0.03510 0.1000 0.09290 0.09460 0.06860 0.06920 0.03500 0.07960 0.07610 0.07980 0.07700 0.005040 2.400 2.360 0.01870 0.06110 0.03400 0.03510 0.08110 0.08850 0.08980 0.06790 0.06850 0.02740 0.04200 0.03930 0.04550 0.04340 0.002150 3.000 1.880 0.01860 0.05750 0.03390 0.03500 0.05580 0.07920 0.07980 0.06610 0.06660 0.02660 0.01620 0.01520 0.01810 0.01710 0.001570 4.000 1.470 0.01840 0.05060 0.03350 0.03460 0.02710 0.05920 0.05850 0.06000 0.06030 0.01870 0.009230 0.009490 0.004150 0.003930 0.001270 5.000 1.170 0.01810 0.04290 0.03280 0.03380 0.01430 0.03890 0.03750 0.05090 0.05090 0.008390 0.008680 0.008720 0.002270 0.002240 0.0006000 6.000 0.9180 0.01780 0.03530 0.03180 0.03260 0.01080 0.02290 0.02160 0.04060 0.04040 0.003290 0.006290 0.006070 0.002210 0.002190 0.0002250 7.000 0.7150 0.01750 0.02820 0.03040 0.03110 0.01040 0.01260 0.01160 0.03090 0.03060 0.001920 0.003690 0.003430 0.002030 0.002000 0.0001140 8.000 0.5630 0.01710 0.02190 0.02860 0.02910 0.01020 0.006910 0.006360 0.02270 0.02240 0.001770 0.001930 0.001740 0.001680 0.001640 9.950E-05 10.00 0.3700 0.01600 0.01200 0.02400 0.02500 0.007700 0.003200 0.003200 0.01200 0.01100 0.001500 0.0005700 0.0005400 0.0009600 0.0009200 8.900E-05 15.00 0.1600 0.01400 0.002700 0.01400 0.01300 0.001500 0.002600 0.002700 0.002100 0.002000 0.0003200 0.0003600 0.0003700 0.0001800 0.0001700 1.900E-05 20.00 0.07700 0.01100 0.001600 0.006500 0.006000 0.0003400 0.001500 0.001500 0.0004100 0.0003900 6.000E-05 0.0002200 0.0002100 3.600E-05 3.300E-05 3.400E-06 30.00 0.02500 0.006200 0.001300 0.001300 0.001200 0.0002600 0.0003200 0.0002800 2.600E-05 2.300E-05 4.400E-05 4.700E-05 4.200E-05 2.200E-06 2.000E-06 2.400E-06 40.00 0.010000 0.003300 0.0007200 0.0003200 0.0002500 0.0001500 7.200E-05 5.800E-05 2.800E-06 2.400E-06 2.400E-05 1.100E-05 8.500E-06 2.400E-07 2.000E-07 1.400E-06 60.00 0.002500 0.0009400 0.0001700 3.000E-05 2.100E-05 3.300E-05 6.400E-06 4.500E-06 9.400E-08 7.500E-08 5.500E-06 9.300E-07 6.500E-07 8.000E-09 6.300E-09 3.100E-07 100.0 0.0002800 0.0001200 1.700E-05 1.200E-06 6.200E-07 3.200E-06 2.500E-07 1.300E-07 1.200E-09 8.100E-10 5.200E-07 3.600E-08 1.900E-08 1.100E-10 6.900E-11 2.900E-08 #S 45 Rh #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 4 1 #UBIND 2.322E+04 3412. 3146. 3004. 627.0 521.0 496.0 3120. 307.0 81.00 48.00 48.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 8.280 0.01840 0.06640 0.03320 0.03430 0.1590 0.09490 0.09730 0.06650 0.06710 0.3990 0.2630 0.2700 0.2770 0.2830 1.700 0.05000 8.230 0.01840 0.06640 0.03320 0.03430 0.1590 0.09490 0.09730 0.06650 0.06710 0.3980 0.2630 0.2700 0.2770 0.2830 1.650 0.1000 8.090 0.01840 0.06640 0.03320 0.03430 0.1590 0.09490 0.09730 0.06650 0.06710 0.3960 0.2630 0.2700 0.2770 0.2830 1.520 0.1500 7.880 0.01840 0.06640 0.03320 0.03430 0.1590 0.09490 0.09730 0.06650 0.06710 0.3920 0.2630 0.2700 0.2770 0.2830 1.320 0.2000 7.640 0.01840 0.06630 0.03320 0.03430 0.1580 0.09490 0.09730 0.06650 0.06710 0.3870 0.2630 0.2690 0.2770 0.2830 1.090 0.3000 7.160 0.01840 0.06630 0.03320 0.03430 0.1570 0.09490 0.09730 0.06650 0.06710 0.3720 0.2630 0.2690 0.2770 0.2830 0.6380 0.4000 6.780 0.01840 0.06620 0.03320 0.03430 0.1560 0.09490 0.09730 0.06650 0.06710 0.3530 0.2620 0.2680 0.2760 0.2820 0.3190 0.5000 6.530 0.01840 0.06610 0.03320 0.03430 0.1540 0.09490 0.09730 0.06650 0.06710 0.3300 0.2590 0.2650 0.2740 0.2790 0.1450 0.6000 6.340 0.01830 0.06600 0.03320 0.03430 0.1520 0.09490 0.09720 0.06650 0.06710 0.3040 0.2560 0.2620 0.2700 0.2750 0.06770 0.7000 6.160 0.01830 0.06580 0.03320 0.03430 0.1500 0.09490 0.09720 0.06650 0.06710 0.2760 0.2510 0.2560 0.2640 0.2680 0.04090 0.8000 5.970 0.01830 0.06560 0.03320 0.03430 0.1470 0.09480 0.09710 0.06650 0.06710 0.2470 0.2440 0.2480 0.2550 0.2580 0.03450 1.000 5.520 0.01830 0.06520 0.03310 0.03430 0.1410 0.09460 0.09690 0.06650 0.06710 0.1910 0.2250 0.2280 0.2310 0.2320 0.03350 1.200 4.990 0.01830 0.06470 0.03310 0.03430 0.1340 0.09420 0.09650 0.06640 0.06710 0.1400 0.2000 0.2000 0.2010 0.2000 0.02900 1.400 4.450 0.01830 0.06410 0.03310 0.03430 0.1260 0.09370 0.09590 0.06640 0.06710 0.1000 0.1700 0.1690 0.1690 0.1670 0.02160 1.600 3.920 0.01830 0.06340 0.03310 0.03430 0.1180 0.09290 0.09500 0.06640 0.06700 0.07000 0.1400 0.1380 0.1390 0.1370 0.01460 1.800 3.460 0.01830 0.06260 0.03310 0.03430 0.1090 0.09180 0.09380 0.06630 0.06690 0.04970 0.1120 0.1080 0.1120 0.1090 0.009230 2.000 3.050 0.01830 0.06180 0.03310 0.03430 0.09980 0.09040 0.09220 0.06610 0.06680 0.03720 0.08620 0.08240 0.08820 0.08560 0.005660 2.400 2.450 0.01820 0.05990 0.03310 0.03420 0.08180 0.08650 0.08800 0.06560 0.06620 0.02740 0.04760 0.04440 0.05270 0.05060 0.002330 3.000 1.920 0.01810 0.05650 0.03300 0.03410 0.05740 0.07830 0.07900 0.06410 0.06460 0.02620 0.01870 0.01730 0.02250 0.02130 0.001490 4.000 1.480 0.01790 0.05000 0.03270 0.03370 0.02880 0.05990 0.05930 0.05900 0.05930 0.01990 0.009240 0.009430 0.005290 0.005000 0.001280 5.000 1.190 0.01770 0.04280 0.03210 0.03310 0.01510 0.04050 0.03920 0.05090 0.05090 0.009650 0.008870 0.008950 0.002440 0.002400 0.0006670 6.000 0.9390 0.01740 0.03550 0.03110 0.03200 0.01080 0.02470 0.02330 0.04140 0.04120 0.003880 0.006840 0.006620 0.002280 0.002270 0.0002610 7.000 0.7380 0.01710 0.02860 0.02990 0.03060 0.01020 0.01400 0.01290 0.03210 0.03180 0.002050 0.004250 0.003950 0.002170 0.002150 0.0001220 8.000 0.5840 0.01670 0.02250 0.02830 0.02880 0.01010 0.007750 0.007100 0.02400 0.02370 0.001780 0.002320 0.002090 0.001860 0.001830 9.700E-05 10.00 0.3800 0.01600 0.01300 0.02400 0.02500 0.008000 0.003300 0.003300 0.01300 0.01200 0.001600 0.0006700 0.0006100 0.001100 0.001100 9.000E-05 15.00 0.1600 0.01300 0.002900 0.01400 0.01400 0.001700 0.002700 0.002700 0.002400 0.002300 0.0003800 0.0003800 0.0003900 0.0002200 0.0002100 2.100E-05 20.00 0.08100 0.01100 0.001600 0.006800 0.006400 0.0003600 0.001600 0.001600 0.0004800 0.0004500 6.800E-05 0.0002400 0.0002400 4.600E-05 4.300E-05 3.700E-06 30.00 0.02700 0.006300 0.001400 0.001500 0.001300 0.0002700 0.0003600 0.0003100 3.100E-05 2.800E-05 4.500E-05 5.500E-05 4.800E-05 2.900E-06 2.600E-06 2.500E-06 40.00 0.01100 0.003400 0.0007500 0.0003600 0.0002900 0.0001500 8.300E-05 6.700E-05 3.400E-06 3.000E-06 2.600E-05 1.300E-05 1.000E-05 3.200E-07 2.700E-07 1.400E-06 60.00 0.002700 0.001000 0.0001800 3.500E-05 2.400E-05 3.700E-05 7.500E-06 5.200E-06 1.200E-07 9.500E-08 6.300E-06 1.100E-06 7.800E-07 1.100E-08 8.600E-09 3.400E-07 100.0 0.0003100 0.0001300 1.900E-05 1.500E-06 7.300E-07 3.600E-06 3.000E-07 1.500E-07 1.600E-09 1.000E-09 6.100E-07 4.500E-08 2.300E-08 1.400E-10 9.400E-11 3.300E-08 #S 46 Pd #N 16 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 #UBIND 2.435E+04 3605. 3331. 3174. 670.0 559.0 531.0 340.0 335.0 86.00 51.00 51.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 0.000 7.020 0.01790 0.06470 0.03230 0.03350 0.1540 0.09190 0.09430 0.06420 0.06470 0.3860 0.2540 0.2610 0.2780 0.2850 0.05000 7.020 0.01790 0.06470 0.03230 0.03350 0.1540 0.09190 0.09430 0.06420 0.06470 0.3850 0.2540 0.2610 0.2780 0.2850 0.1000 7.020 0.01790 0.06470 0.03230 0.03350 0.1540 0.09190 0.09430 0.06420 0.06470 0.3830 0.2540 0.2610 0.2780 0.2850 0.1500 7.010 0.01790 0.06470 0.03230 0.03350 0.1540 0.09190 0.09430 0.06420 0.06470 0.3800 0.2540 0.2610 0.2780 0.2850 0.2000 7.000 0.01790 0.06460 0.03230 0.03350 0.1530 0.09190 0.09430 0.06420 0.06470 0.3750 0.2540 0.2610 0.2780 0.2850 0.3000 6.960 0.01790 0.06460 0.03230 0.03350 0.1530 0.09190 0.09430 0.06420 0.06470 0.3620 0.2530 0.2610 0.2770 0.2840 0.4000 6.910 0.01790 0.06450 0.03230 0.03350 0.1510 0.09190 0.09430 0.06420 0.06470 0.3440 0.2520 0.2600 0.2760 0.2830 0.5000 6.820 0.01790 0.06440 0.03230 0.03350 0.1500 0.09190 0.09430 0.06420 0.06470 0.3230 0.2510 0.2580 0.2740 0.2800 0.6000 6.690 0.01790 0.06430 0.03230 0.03350 0.1480 0.09190 0.09420 0.06420 0.06470 0.2990 0.2480 0.2540 0.2690 0.2740 0.7000 6.530 0.01790 0.06420 0.03230 0.03350 0.1460 0.09180 0.09420 0.06420 0.06470 0.2720 0.2430 0.2490 0.2610 0.2650 0.8000 6.320 0.01790 0.06400 0.03230 0.03350 0.1440 0.09180 0.09410 0.06420 0.06470 0.2460 0.2370 0.2420 0.2510 0.2540 1.000 5.820 0.01790 0.06360 0.03230 0.03350 0.1380 0.09160 0.09390 0.06410 0.06470 0.1920 0.2200 0.2240 0.2250 0.2260 1.200 5.250 0.01790 0.06310 0.03230 0.03350 0.1310 0.09130 0.09360 0.06410 0.06470 0.1440 0.1980 0.1990 0.1960 0.1950 1.400 4.680 0.01790 0.06260 0.03230 0.03340 0.1240 0.09080 0.09310 0.06410 0.06470 0.1040 0.1710 0.1700 0.1650 0.1630 1.600 4.130 0.01790 0.06190 0.03230 0.03340 0.1160 0.09010 0.09230 0.06410 0.06460 0.07390 0.1430 0.1400 0.1370 0.1340 1.800 3.640 0.01780 0.06120 0.03230 0.03340 0.1080 0.08920 0.09120 0.06400 0.06460 0.05270 0.1160 0.1120 0.1110 0.1080 2.000 3.210 0.01780 0.06040 0.03230 0.03340 0.09930 0.08790 0.08980 0.06390 0.06440 0.03920 0.09110 0.08690 0.08880 0.08610 2.400 2.560 0.01780 0.05870 0.03220 0.03340 0.08230 0.08460 0.08610 0.06350 0.06400 0.02750 0.05240 0.04860 0.05480 0.05260 3.000 1.960 0.01770 0.05550 0.03210 0.03330 0.05890 0.07720 0.07800 0.06230 0.06270 0.02550 0.02120 0.01930 0.02470 0.02340 4.000 1.490 0.01750 0.04940 0.03180 0.03300 0.03040 0.06040 0.05990 0.05790 0.05820 0.02070 0.009240 0.009300 0.006110 0.005720 5.000 1.200 0.01730 0.04260 0.03130 0.03230 0.01590 0.04200 0.04080 0.05070 0.05080 0.01080 0.008860 0.008950 0.002480 0.002390 6.000 0.9580 0.01700 0.03570 0.03050 0.03140 0.01090 0.02640 0.02500 0.04200 0.04190 0.004510 0.007240 0.007010 0.002160 0.002120 7.000 0.7600 0.01670 0.02910 0.02930 0.03010 0.010000 0.01540 0.01420 0.03310 0.03290 0.002230 0.004750 0.004410 0.002110 0.002060 8.000 0.6050 0.01640 0.02310 0.02790 0.02850 0.009960 0.008660 0.007900 0.02520 0.02500 0.001790 0.002710 0.002430 0.001870 0.001810 10.00 0.4000 0.01600 0.01400 0.02400 0.02400 0.008300 0.003500 0.003400 0.01400 0.01300 0.001700 0.0007900 0.0007000 0.001200 0.001100 15.00 0.1700 0.01300 0.003100 0.01400 0.01400 0.002000 0.002700 0.002700 0.002700 0.002600 0.0004400 0.0003800 0.0003900 0.0002500 0.0002300 20.00 0.08400 0.01100 0.001600 0.007200 0.006700 0.0003900 0.001700 0.001600 0.0005600 0.0005300 7.700E-05 0.0002600 0.0002500 5.300E-05 4.900E-05 30.00 0.02800 0.006400 0.001400 0.001600 0.001400 0.0002700 0.0004000 0.0003500 3.800E-05 3.400E-05 4.700E-05 6.300E-05 5.500E-05 3.500E-06 3.100E-06 40.00 0.01200 0.003500 0.0007900 0.0004000 0.0003200 0.0001600 9.500E-05 7.600E-05 4.200E-06 3.700E-06 2.800E-05 1.500E-05 1.200E-05 3.900E-07 3.300E-07 60.00 0.002900 0.001100 0.0002000 4.000E-05 2.700E-05 4.100E-05 8.900E-06 6.100E-06 1.500E-07 1.200E-07 7.000E-06 1.400E-06 9.300E-07 1.400E-08 1.100E-08 100.0 0.0003400 0.0001400 2.100E-05 1.700E-06 8.500E-07 4.100E-06 3.600E-07 1.800E-07 2.000E-09 1.300E-09 6.900E-07 5.500E-08 2.700E-08 1.800E-10 1.200E-10 #S 47 Ag #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 1 #UBIND 2.551E+04 3806. 3524. 3352. 717.0 602.0 573.0 374.0 368.0 95.00 59.00 59.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 8.180 0.01750 0.06310 0.03150 0.03270 0.1490 0.08910 0.09150 0.06200 0.06250 0.3690 0.2420 0.2490 0.2470 0.2510 1.640 0.05000 8.130 0.01750 0.06310 0.03150 0.03270 0.1490 0.08910 0.09150 0.06200 0.06250 0.3680 0.2420 0.2490 0.2470 0.2510 1.600 0.1000 8.010 0.01750 0.06310 0.03150 0.03270 0.1490 0.08910 0.09150 0.06200 0.06250 0.3660 0.2420 0.2490 0.2470 0.2510 1.480 0.1500 7.820 0.01750 0.06310 0.03150 0.03270 0.1490 0.08910 0.09150 0.06200 0.06250 0.3630 0.2420 0.2490 0.2470 0.2510 1.300 0.2000 7.590 0.01750 0.06300 0.03150 0.03270 0.1490 0.08910 0.09150 0.06200 0.06250 0.3590 0.2420 0.2490 0.2470 0.2510 1.080 0.3000 7.140 0.01750 0.06300 0.03150 0.03270 0.1480 0.08900 0.09150 0.06200 0.06250 0.3470 0.2410 0.2490 0.2470 0.2510 0.6600 0.4000 6.790 0.01750 0.06290 0.03150 0.03270 0.1470 0.08900 0.09140 0.06200 0.06250 0.3320 0.2410 0.2480 0.2460 0.2510 0.3460 0.5000 6.540 0.01750 0.06280 0.03150 0.03270 0.1460 0.08900 0.09140 0.06200 0.06250 0.3130 0.2390 0.2460 0.2450 0.2500 0.1640 0.6000 6.370 0.01750 0.06270 0.03150 0.03270 0.1440 0.08900 0.09140 0.06200 0.06250 0.2910 0.2370 0.2430 0.2430 0.2470 0.07630 0.7000 6.220 0.01750 0.06260 0.03150 0.03270 0.1420 0.08900 0.09140 0.06200 0.06250 0.2680 0.2330 0.2390 0.2390 0.2430 0.04200 0.8000 6.070 0.01750 0.06240 0.03150 0.03270 0.1400 0.08890 0.09130 0.06200 0.06250 0.2430 0.2280 0.2340 0.2340 0.2370 0.03160 1.000 5.700 0.01750 0.06210 0.03150 0.03260 0.1350 0.08880 0.09120 0.06200 0.06250 0.1940 0.2140 0.2180 0.2180 0.2200 0.02960 1.200 5.240 0.01750 0.06160 0.03140 0.03260 0.1290 0.08850 0.09090 0.06200 0.06250 0.1480 0.1950 0.1970 0.1970 0.1970 0.02720 1.400 4.750 0.01740 0.06110 0.03140 0.03260 0.1220 0.08810 0.09040 0.06200 0.06250 0.1100 0.1720 0.1710 0.1720 0.1720 0.02180 1.600 4.240 0.01740 0.06050 0.03140 0.03260 0.1140 0.08750 0.08970 0.06190 0.06250 0.07930 0.1460 0.1440 0.1470 0.1460 0.01570 1.800 3.770 0.01740 0.05980 0.03140 0.03260 0.1070 0.08670 0.08880 0.06190 0.06240 0.05710 0.1210 0.1180 0.1230 0.1210 0.01060 2.000 3.350 0.01740 0.05910 0.03140 0.03260 0.09870 0.08560 0.08760 0.06180 0.06230 0.04230 0.09720 0.09320 0.1010 0.09930 0.006820 2.400 2.670 0.01740 0.05750 0.03140 0.03260 0.08270 0.08270 0.08430 0.06150 0.06200 0.02820 0.05830 0.05440 0.06570 0.06370 0.002810 3.000 2.020 0.01730 0.05450 0.03130 0.03250 0.06020 0.07610 0.07700 0.06040 0.06090 0.02510 0.02450 0.02230 0.03150 0.03010 0.001370 4.000 1.500 0.01710 0.04880 0.03110 0.03220 0.03190 0.06070 0.06040 0.05670 0.05710 0.02150 0.009540 0.009500 0.008280 0.007800 0.001240 5.000 1.210 0.01690 0.04240 0.03060 0.03170 0.01680 0.04340 0.04220 0.05040 0.05050 0.01210 0.008920 0.009070 0.003010 0.002900 0.0007710 6.000 0.9760 0.01670 0.03580 0.02980 0.03080 0.01110 0.02800 0.02660 0.04240 0.04230 0.005300 0.007680 0.007500 0.002370 0.002340 0.0003400 7.000 0.7810 0.01640 0.02940 0.02880 0.02960 0.009860 0.01680 0.01550 0.03400 0.03380 0.002500 0.005310 0.004970 0.002340 0.002310 0.0001460 8.000 0.6250 0.01610 0.02360 0.02750 0.02810 0.009800 0.009630 0.008760 0.02630 0.02610 0.001830 0.003170 0.002850 0.002140 0.002100 9.470E-05 10.00 0.4100 0.01500 0.01400 0.02400 0.02400 0.008500 0.003700 0.003600 0.01500 0.01400 0.001700 0.0009500 0.0008300 0.001400 0.001400 8.800E-05 15.00 0.1800 0.01300 0.003300 0.01500 0.01400 0.002200 0.002700 0.002800 0.003000 0.002900 0.0005200 0.0003900 0.0004100 0.0003200 0.0003000 2.700E-05 20.00 0.08800 0.01100 0.001600 0.007600 0.007100 0.0004300 0.001800 0.001700 0.0006600 0.0006200 8.900E-05 0.0002800 0.0002800 7.100E-05 6.500E-05 4.600E-06 30.00 0.02900 0.006500 0.001400 0.001800 0.001500 0.0002700 0.0004400 0.0003900 4.500E-05 4.100E-05 4.800E-05 7.200E-05 6.300E-05 4.900E-06 4.300E-06 2.400E-06 40.00 0.01200 0.003700 0.0008200 0.0004500 0.0003600 0.0001700 0.0001100 8.700E-05 5.200E-06 4.500E-06 3.100E-05 1.700E-05 1.400E-05 5.400E-07 4.700E-07 1.500E-06 60.00 0.003100 0.001100 0.0002200 4.600E-05 3.100E-05 4.500E-05 1.000E-05 7.100E-06 1.800E-07 1.500E-07 7.900E-06 1.600E-06 1.100E-06 1.900E-08 1.500E-08 4.000E-07 100.0 0.0003800 0.0001500 2.300E-05 2.000E-06 9.900E-07 4.600E-06 4.300E-07 2.100E-07 2.500E-09 1.600E-09 8.000E-07 6.800E-08 3.400E-08 2.600E-10 1.700E-10 4.000E-08 #S 48 Cd #N 17 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 #UBIND 2.671E+04 4018. 3727. 3538. 770.0 651.0 617.0 411.0 405.0 108.0 67.00 67.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 0.000 9.040 0.01710 0.06150 0.03070 0.03190 0.1450 0.08640 0.08880 0.06000 0.06050 0.3520 0.2300 0.2370 0.2240 0.2280 1.450 0.05000 8.980 0.01710 0.06150 0.03070 0.03190 0.1450 0.08640 0.08880 0.06000 0.06050 0.3510 0.2300 0.2370 0.2240 0.2280 1.420 0.1000 8.800 0.01710 0.06150 0.03070 0.03190 0.1450 0.08640 0.08880 0.06000 0.06050 0.3500 0.2300 0.2370 0.2240 0.2280 1.330 0.1500 8.530 0.01710 0.06150 0.03070 0.03190 0.1450 0.08640 0.08880 0.06000 0.06050 0.3470 0.2300 0.2370 0.2240 0.2280 1.200 0.2000 8.200 0.01710 0.06150 0.03070 0.03190 0.1440 0.08640 0.08880 0.06000 0.06050 0.3430 0.2300 0.2370 0.2240 0.2280 1.040 0.3000 7.490 0.01710 0.06140 0.03070 0.03190 0.1440 0.08640 0.08880 0.06000 0.06050 0.3330 0.2300 0.2370 0.2240 0.2280 0.6940 0.4000 6.880 0.01710 0.06140 0.03070 0.03190 0.1430 0.08640 0.08880 0.06000 0.06050 0.3200 0.2290 0.2360 0.2240 0.2280 0.4090 0.5000 6.450 0.01710 0.06130 0.03070 0.03190 0.1420 0.08630 0.08880 0.06000 0.06050 0.3030 0.2280 0.2350 0.2230 0.2270 0.2190 0.6000 6.180 0.01710 0.06120 0.03070 0.03190 0.1400 0.08630 0.08870 0.06000 0.06050 0.2840 0.2260 0.2330 0.2220 0.2260 0.1120 0.7000 5.990 0.01710 0.06110 0.03070 0.03190 0.1380 0.08630 0.08870 0.06000 0.06050 0.2630 0.2240 0.2300 0.2200 0.2240 0.06050 0.8000 5.840 0.01710 0.06090 0.03070 0.03190 0.1360 0.08630 0.08870 0.06000 0.06050 0.2400 0.2200 0.2250 0.2170 0.2200 0.03980 1.000 5.540 0.01710 0.06060 0.03070 0.03190 0.1310 0.08610 0.08850 0.06000 0.06050 0.1950 0.2080 0.2120 0.2070 0.2100 0.03250 1.200 5.180 0.01700 0.06020 0.03070 0.03190 0.1260 0.08590 0.08830 0.05990 0.06050 0.1520 0.1910 0.1940 0.1930 0.1940 0.03170 1.400 4.760 0.01700 0.05970 0.03070 0.03190 0.1200 0.08560 0.08790 0.05990 0.06050 0.1150 0.1710 0.1710 0.1740 0.1740 0.02750 1.600 4.310 0.01700 0.05910 0.03060 0.03190 0.1130 0.08500 0.08730 0.05990 0.06050 0.08460 0.1480 0.1470 0.1530 0.1530 0.02130 1.800 3.870 0.01700 0.05850 0.03060 0.03190 0.1060 0.08430 0.08640 0.05990 0.06040 0.06170 0.1250 0.1220 0.1320 0.1310 0.01520 2.000 3.460 0.01700 0.05780 0.03060 0.03190 0.09800 0.08340 0.08540 0.05980 0.06040 0.04570 0.1030 0.09890 0.1110 0.1100 0.01020 2.400 2.770 0.01700 0.05630 0.03060 0.03180 0.08290 0.08080 0.08250 0.05950 0.06010 0.02940 0.06410 0.06010 0.07560 0.07380 0.004320 3.000 2.080 0.01690 0.05360 0.03050 0.03170 0.06130 0.07490 0.07600 0.05870 0.05920 0.02480 0.02820 0.02560 0.03850 0.03710 0.001790 4.000 1.520 0.01670 0.04820 0.03030 0.03150 0.03340 0.06080 0.06070 0.05550 0.05590 0.02230 0.010000 0.009860 0.01090 0.01030 0.001560 5.000 1.220 0.01650 0.04210 0.02990 0.03100 0.01780 0.04450 0.04340 0.05000 0.05010 0.01350 0.008940 0.009150 0.003690 0.003540 0.001060 6.000 0.9910 0.01630 0.03580 0.02920 0.03020 0.01130 0.02960 0.02810 0.04260 0.04260 0.006190 0.008050 0.007940 0.002560 0.002540 0.0004980 7.000 0.8000 0.01610 0.02970 0.02830 0.02910 0.009730 0.01820 0.01680 0.03480 0.03460 0.002840 0.005870 0.005530 0.002520 0.002510 0.0002130 8.000 0.6440 0.01580 0.02410 0.02710 0.02770 0.009620 0.01070 0.009670 0.02740 0.02710 0.001900 0.003650 0.003310 0.002380 0.002350 0.0001240 10.00 0.4300 0.01500 0.01500 0.02400 0.02400 0.008600 0.004000 0.003800 0.01600 0.01500 0.001800 0.001100 0.0009900 0.001700 0.001600 0.0001100 15.00 0.1800 0.01300 0.003600 0.01500 0.01500 0.002500 0.002600 0.002800 0.003400 0.003300 0.0006000 0.0004000 0.0004200 0.0004000 0.0003800 3.900E-05 20.00 0.09200 0.01100 0.001600 0.007900 0.007400 0.0004800 0.001900 0.001800 0.0007600 0.0007100 0.0001100 0.0003100 0.0003000 9.100E-05 8.500E-05 6.700E-06 30.00 0.03100 0.006600 0.001400 0.001900 0.001700 0.0002700 0.0004900 0.0004300 5.400E-05 4.900E-05 4.900E-05 8.300E-05 7.200E-05 6.500E-06 5.800E-06 3.000E-06 40.00 0.01300 0.003800 0.0008500 0.0005000 0.0004000 0.0001800 0.0001200 9.800E-05 6.300E-06 5.500E-06 3.300E-05 2.000E-05 1.600E-05 7.400E-07 6.400E-07 2.000E-06 60.00 0.003300 0.001200 0.0002300 5.300E-05 3.500E-05 4.900E-05 1.200E-05 8.200E-06 2.300E-07 1.800E-07 8.900E-06 2.000E-06 1.400E-06 2.700E-08 2.100E-08 5.500E-07 100.0 0.0004100 0.0001700 2.600E-05 2.400E-06 1.100E-06 5.100E-06 5.100E-07 2.500E-07 3.100E-09 2.000E-09 9.300E-07 8.400E-08 4.100E-08 3.600E-10 2.300E-10 5.700E-08 #S 49 In #N 18 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 1 #UBIND 2.794E+04 4238. 3938. 3730. 826.0 702.0 664.0 451.0 444.0 122.0 77.00 77.00 0.000 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 0.000 9.250 0.01670 0.06000 0.02990 0.03110 0.1410 0.08380 0.08630 0.05810 0.05860 0.3360 0.2190 0.2260 0.2060 0.2080 1.240 0.9620 0.05000 9.210 0.01670 0.06000 0.02990 0.03110 0.1410 0.08380 0.08630 0.05810 0.05860 0.3360 0.2190 0.2260 0.2060 0.2080 1.220 0.9610 0.1000 9.090 0.01670 0.06000 0.02990 0.03110 0.1410 0.08380 0.08630 0.05810 0.05860 0.3350 0.2190 0.2260 0.2060 0.2080 1.160 0.9580 0.1500 8.890 0.01670 0.06000 0.02990 0.03110 0.1410 0.08380 0.08630 0.05810 0.05860 0.3320 0.2190 0.2260 0.2060 0.2080 1.070 0.9440 0.2000 8.630 0.01670 0.06000 0.02990 0.03110 0.1400 0.08380 0.08630 0.05810 0.05860 0.3290 0.2190 0.2260 0.2060 0.2080 0.9610 0.9140 0.3000 7.970 0.01670 0.06000 0.02990 0.03110 0.1400 0.08380 0.08630 0.05810 0.05860 0.3200 0.2190 0.2260 0.2060 0.2080 0.7060 0.7920 0.4000 7.290 0.01670 0.05990 0.02990 0.03110 0.1390 0.08380 0.08620 0.05810 0.05860 0.3080 0.2190 0.2260 0.2060 0.2080 0.4670 0.6140 0.5000 6.700 0.01670 0.05980 0.02990 0.03110 0.1380 0.08380 0.08620 0.05810 0.05860 0.2930 0.2180 0.2250 0.2060 0.2080 0.2830 0.4300 0.6000 6.250 0.01670 0.05970 0.02990 0.03110 0.1360 0.08380 0.08620 0.05810 0.05860 0.2760 0.2160 0.2230 0.2050 0.2070 0.1620 0.2790 0.7000 5.930 0.01670 0.05960 0.02990 0.03110 0.1350 0.08380 0.08620 0.05810 0.05860 0.2570 0.2140 0.2200 0.2040 0.2060 0.09190 0.1700 0.8000 5.710 0.01670 0.05950 0.02990 0.03110 0.1330 0.08370 0.08620 0.05810 0.05860 0.2370 0.2110 0.2160 0.2020 0.2040 0.05680 0.09990 1.000 5.380 0.01670 0.05920 0.02990 0.03110 0.1280 0.08360 0.08600 0.05810 0.05860 0.1950 0.2010 0.2060 0.1960 0.1980 0.03740 0.03260 1.200 5.070 0.01670 0.05880 0.02990 0.03110 0.1230 0.08340 0.08580 0.05810 0.05860 0.1550 0.1870 0.1900 0.1860 0.1870 0.03650 0.01260 1.400 4.720 0.01660 0.05830 0.02990 0.03110 0.1170 0.08310 0.08540 0.05800 0.05860 0.1190 0.1700 0.1710 0.1720 0.1730 0.03410 0.008240 1.600 4.340 0.01660 0.05780 0.02990 0.03110 0.1110 0.08270 0.08490 0.05800 0.05860 0.08950 0.1500 0.1490 0.1550 0.1560 0.02840 0.007790 1.800 3.940 0.01660 0.05720 0.02990 0.03110 0.1040 0.08200 0.08420 0.05800 0.05860 0.06630 0.1290 0.1260 0.1370 0.1370 0.02150 0.007700 2.000 3.550 0.01660 0.05660 0.02990 0.03110 0.09730 0.08120 0.08330 0.05790 0.05850 0.04940 0.1070 0.1040 0.1190 0.1180 0.01520 0.007190 2.400 2.870 0.01660 0.05520 0.02990 0.03110 0.08290 0.07890 0.08070 0.05770 0.05830 0.03090 0.06990 0.06580 0.08430 0.08310 0.006760 0.005260 3.000 2.150 0.01650 0.05260 0.02980 0.03100 0.06230 0.07370 0.07480 0.05700 0.05750 0.02460 0.03230 0.02930 0.04570 0.04450 0.002460 0.002480 4.000 1.540 0.01640 0.04760 0.02960 0.03080 0.03490 0.06080 0.06080 0.05430 0.05470 0.02280 0.01080 0.01040 0.01390 0.01330 0.001970 0.0005940 5.000 1.230 0.01620 0.04180 0.02920 0.03030 0.01870 0.04550 0.04440 0.04940 0.04960 0.01470 0.008940 0.009180 0.004550 0.004380 0.001450 0.0003590 6.000 1.000 0.01600 0.03580 0.02860 0.02960 0.01170 0.03100 0.02950 0.04280 0.04280 0.007150 0.008360 0.008310 0.002780 0.002770 0.0007340 0.0003470 7.000 0.8170 0.01570 0.03000 0.02780 0.02860 0.009650 0.01960 0.01810 0.03540 0.03530 0.003270 0.006400 0.006080 0.002670 0.002680 0.0003190 0.0002790 8.000 0.6630 0.01550 0.02450 0.02660 0.02740 0.009440 0.01170 0.01060 0.02830 0.02800 0.002010 0.004170 0.003800 0.002580 0.002570 0.0001710 0.0001870 10.00 0.4400 0.01500 0.01500 0.02400 0.02400 0.008700 0.004400 0.004100 0.01700 0.01600 0.001800 0.001400 0.001200 0.001900 0.001900 0.0001400 6.200E-05 15.00 0.1900 0.01300 0.003900 0.01500 0.01500 0.002800 0.002600 0.002700 0.003800 0.003600 0.0006900 0.0004000 0.0004300 0.0005000 0.0004700 5.600E-05 1.600E-05 20.00 0.09600 0.01100 0.001600 0.008200 0.007700 0.0005400 0.001900 0.001900 0.0008700 0.0008200 0.0001200 0.0003300 0.0003200 0.0001200 0.0001100 1.000E-05 1.300E-05 30.00 0.03200 0.006700 0.001400 0.002100 0.001800 0.0002700 0.0005400 0.0004700 6.400E-05 5.900E-05 5.000E-05 9.400E-05 8.300E-05 8.500E-06 7.700E-06 3.900E-06 3.900E-06 40.00 0.01400 0.003900 0.0008800 0.0005600 0.0004400 0.0001900 0.0001400 0.0001100 7.600E-06 6.600E-06 3.500E-05 2.400E-05 1.900E-05 9.900E-07 8.600E-07 2.700E-06 9.800E-07 60.00 0.003500 0.001300 0.0002500 6.000E-05 4.000E-05 5.300E-05 1.400E-05 9.500E-06 2.800E-07 2.200E-07 9.900E-06 2.400E-06 1.600E-06 3.600E-08 2.900E-08 7.700E-07 9.800E-08 100.0 0.0004500 0.0001800 2.800E-05 2.700E-06 1.300E-06 5.700E-06 6.000E-07 2.900E-07 3.800E-09 2.400E-09 1.100E-06 1.000E-07 5.000E-08 4.900E-10 3.200E-10 8.200E-08 4.200E-09 #S 50 Sn #N 18 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 #UBIND 2.920E+04 4465. 4156. 3929. 884.0 757.0 715.0 496.0 485.0 137.0 89.00 89.00 25.00 24.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 0.000 9.400 0.01630 0.05860 0.02920 0.03040 0.1370 0.08140 0.08390 0.05630 0.05690 0.3220 0.2090 0.2160 0.1920 0.1930 1.100 0.8360 0.05000 9.370 0.01630 0.05860 0.02920 0.03040 0.1370 0.08140 0.08390 0.05630 0.05690 0.3220 0.2090 0.2160 0.1920 0.1930 1.090 0.8360 0.1000 9.280 0.01630 0.05860 0.02920 0.03040 0.1370 0.08140 0.08390 0.05630 0.05690 0.3200 0.2090 0.2160 0.1920 0.1930 1.050 0.8340 0.1500 9.130 0.01630 0.05860 0.02920 0.03040 0.1370 0.08140 0.08390 0.05630 0.05690 0.3180 0.2090 0.2160 0.1920 0.1930 0.9810 0.8270 0.2000 8.920 0.01630 0.05860 0.02920 0.03040 0.1360 0.08140 0.08390 0.05630 0.05690 0.3160 0.2090 0.2160 0.1920 0.1930 0.8970 0.8100 0.3000 8.350 0.01630 0.05850 0.02920 0.03040 0.1360 0.08140 0.08390 0.05630 0.05690 0.3080 0.2090 0.2160 0.1920 0.1930 0.6970 0.7360 0.4000 7.680 0.01630 0.05850 0.02920 0.03040 0.1350 0.08140 0.08390 0.05630 0.05690 0.2970 0.2080 0.2160 0.1920 0.1930 0.4950 0.6140 0.5000 7.020 0.01630 0.05840 0.02920 0.03040 0.1340 0.08140 0.08380 0.05630 0.05690 0.2830 0.2080 0.2150 0.1910 0.1930 0.3260 0.4720 0.6000 6.460 0.01630 0.05830 0.02920 0.03040 0.1330 0.08140 0.08380 0.05630 0.05690 0.2680 0.2070 0.2130 0.1910 0.1920 0.2020 0.3370 0.7000 6.020 0.01630 0.05820 0.02920 0.03040 0.1310 0.08130 0.08380 0.05630 0.05690 0.2510 0.2050 0.2110 0.1900 0.1920 0.1220 0.2280 0.8000 5.700 0.01630 0.05810 0.02920 0.03040 0.1290 0.08130 0.08380 0.05630 0.05690 0.2330 0.2020 0.2080 0.1890 0.1910 0.07540 0.1480 1.000 5.280 0.01630 0.05780 0.02920 0.03040 0.1250 0.08120 0.08370 0.05630 0.05690 0.1950 0.1940 0.1990 0.1850 0.1870 0.04240 0.05670 1.200 4.970 0.01630 0.05740 0.02920 0.03040 0.1210 0.08110 0.08350 0.05630 0.05680 0.1570 0.1830 0.1860 0.1780 0.1790 0.03890 0.02220 1.400 4.660 0.01630 0.05700 0.02920 0.03040 0.1150 0.08080 0.08320 0.05630 0.05680 0.1230 0.1680 0.1690 0.1670 0.1690 0.03800 0.01170 1.600 4.320 0.01630 0.05650 0.02920 0.03040 0.1090 0.08040 0.08270 0.05630 0.05680 0.09410 0.1500 0.1500 0.1540 0.1550 0.03380 0.009630 1.800 3.970 0.01620 0.05600 0.02920 0.03040 0.1030 0.07980 0.08210 0.05620 0.05680 0.07070 0.1310 0.1290 0.1390 0.1400 0.02720 0.009500 2.000 3.620 0.01620 0.05540 0.02920 0.03040 0.09640 0.07910 0.08120 0.05620 0.05670 0.05310 0.1110 0.1080 0.1230 0.1230 0.02020 0.009290 2.400 2.960 0.01620 0.05410 0.02910 0.03040 0.08290 0.07710 0.07890 0.05600 0.05660 0.03270 0.07530 0.07120 0.09130 0.09080 0.009650 0.007480 3.000 2.230 0.01610 0.05170 0.02910 0.03030 0.06310 0.07240 0.07370 0.05540 0.05600 0.02450 0.03660 0.03330 0.05250 0.05150 0.003280 0.003900 4.000 1.560 0.01600 0.04690 0.02890 0.03010 0.03630 0.06070 0.06080 0.05310 0.05360 0.02310 0.01180 0.01120 0.01720 0.01660 0.002300 0.0009530 5.000 1.240 0.01580 0.04150 0.02860 0.02970 0.01980 0.04630 0.04540 0.04880 0.04910 0.01590 0.008950 0.009190 0.005630 0.005420 0.001830 0.0004810 6.000 1.020 0.01560 0.03580 0.02800 0.02910 0.01210 0.03230 0.03080 0.04280 0.04290 0.008180 0.008600 0.008620 0.003040 0.003020 0.0009910 0.0004650 7.000 0.8330 0.01540 0.03020 0.02720 0.02820 0.009620 0.02090 0.01940 0.03590 0.03580 0.003780 0.006910 0.006610 0.002790 0.002810 0.0004440 0.0003950 8.000 0.6800 0.01520 0.02490 0.02620 0.02700 0.009260 0.01280 0.01160 0.02910 0.02890 0.002160 0.004700 0.004300 0.002740 0.002750 0.0002230 0.0002780 10.00 0.4600 0.01500 0.01600 0.02400 0.02400 0.008700 0.004800 0.004400 0.01800 0.01700 0.001800 0.001600 0.001400 0.002100 0.002100 0.0001600 9.800E-05 15.00 0.2000 0.01300 0.004200 0.01500 0.01500 0.003100 0.002600 0.002700 0.004200 0.004000 0.0007800 0.0004100 0.0004400 0.0005900 0.0005700 7.400E-05 2.200E-05 20.00 0.1000 0.01100 0.001600 0.008600 0.008000 0.0006100 0.002000 0.002000 0.0009900 0.0009300 0.0001500 0.0003500 0.0003500 0.0001400 0.0001400 1.400E-05 1.900E-05 30.00 0.03400 0.006800 0.001400 0.002300 0.001900 0.0002700 0.0005900 0.0005200 7.600E-05 6.900E-05 5.100E-05 0.0001100 9.400E-05 1.100E-05 9.900E-06 4.600E-06 5.800E-06 40.00 0.01400 0.004000 0.0009100 0.0006200 0.0004800 0.0002000 0.0001600 0.0001200 9.100E-06 7.900E-06 3.800E-05 2.800E-05 2.200E-05 1.300E-06 1.100E-06 3.400E-06 1.500E-06 60.00 0.003800 0.001300 0.0002700 6.800E-05 4.500E-05 5.700E-05 1.600E-05 1.100E-05 3.400E-07 2.700E-07 1.100E-05 2.900E-06 1.900E-06 4.800E-08 3.800E-08 1.000E-06 1.500E-07 100.0 0.0004900 0.0002000 3.100E-05 3.200E-06 1.500E-06 6.400E-06 7.100E-07 3.400E-07 4.700E-09 3.000E-09 1.200E-06 1.200E-07 6.000E-08 6.600E-10 4.200E-10 1.100E-07 6.700E-09 #S 51 Sb #N 19 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 1 #UBIND 3.049E+04 4699. 4381. 4132. 944.0 812.0 766.0 537.0 528.0 152.0 99.00 99.00 33.00 32.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 0.000 9.510 0.01590 0.05720 0.02850 0.02980 0.1330 0.07910 0.08160 0.05460 0.05520 0.3090 0.2000 0.2070 0.1790 0.1810 1.000 0.7370 0.7640 0.05000 9.480 0.01590 0.05720 0.02850 0.02980 0.1330 0.07910 0.08160 0.05460 0.05520 0.3080 0.2000 0.2070 0.1790 0.1810 0.9900 0.7370 0.7640 0.1000 9.410 0.01590 0.05720 0.02850 0.02980 0.1330 0.07910 0.08160 0.05460 0.05520 0.3070 0.2000 0.2070 0.1790 0.1810 0.9580 0.7360 0.7630 0.1500 9.300 0.01590 0.05720 0.02850 0.02980 0.1330 0.07910 0.08160 0.05460 0.05520 0.3050 0.2000 0.2070 0.1790 0.1810 0.9070 0.7320 0.7580 0.2000 9.130 0.01590 0.05720 0.02850 0.02980 0.1330 0.07910 0.08160 0.05460 0.05520 0.3030 0.2000 0.2070 0.1790 0.1810 0.8410 0.7230 0.7470 0.3000 8.650 0.01590 0.05720 0.02850 0.02980 0.1320 0.07910 0.08160 0.05460 0.05520 0.2960 0.2000 0.2070 0.1790 0.1800 0.6790 0.6780 0.6960 0.4000 8.030 0.01590 0.05710 0.02850 0.02980 0.1310 0.07910 0.08160 0.05460 0.05520 0.2860 0.1990 0.2060 0.1790 0.1800 0.5080 0.5960 0.6040 0.5000 7.360 0.01590 0.05710 0.02850 0.02980 0.1300 0.07910 0.08160 0.05460 0.05520 0.2740 0.1990 0.2060 0.1790 0.1800 0.3550 0.4890 0.4870 0.6000 6.730 0.01590 0.05700 0.02850 0.02980 0.1290 0.07910 0.08160 0.05460 0.05520 0.2610 0.1980 0.2040 0.1790 0.1800 0.2340 0.3770 0.3670 0.7000 6.210 0.01590 0.05690 0.02850 0.02980 0.1280 0.07910 0.08150 0.05460 0.05520 0.2450 0.1960 0.2030 0.1780 0.1800 0.1490 0.2750 0.2620 0.8000 5.790 0.01590 0.05680 0.02850 0.02980 0.1260 0.07900 0.08150 0.05460 0.05520 0.2290 0.1940 0.2000 0.1780 0.1790 0.09470 0.1920 0.1780 1.000 5.230 0.01590 0.05650 0.02850 0.02980 0.1220 0.07900 0.08140 0.05460 0.05520 0.1940 0.1880 0.1930 0.1750 0.1760 0.04850 0.08490 0.07460 1.200 4.880 0.01590 0.05620 0.02850 0.02980 0.1180 0.07880 0.08120 0.05460 0.05520 0.1590 0.1780 0.1820 0.1700 0.1710 0.04040 0.03560 0.03030 1.400 4.590 0.01590 0.05580 0.02850 0.02980 0.1130 0.07860 0.08100 0.05460 0.05520 0.1270 0.1650 0.1670 0.1620 0.1630 0.04010 0.01710 0.01530 1.600 4.290 0.01590 0.05530 0.02850 0.02980 0.1070 0.07820 0.08060 0.05460 0.05520 0.09820 0.1500 0.1500 0.1520 0.1530 0.03750 0.01190 0.01160 1.800 3.980 0.01590 0.05480 0.02850 0.02980 0.1020 0.07770 0.08000 0.05460 0.05510 0.07490 0.1320 0.1310 0.1400 0.1400 0.03190 0.01100 0.01130 2.000 3.660 0.01590 0.05430 0.02850 0.02970 0.09550 0.07710 0.07930 0.05450 0.05510 0.05680 0.1150 0.1120 0.1260 0.1260 0.02500 0.01100 0.01110 2.400 3.040 0.01580 0.05300 0.02840 0.02970 0.08270 0.07530 0.07720 0.05440 0.05490 0.03480 0.08010 0.07620 0.09710 0.09660 0.01290 0.009610 0.009260 3.000 2.300 0.01580 0.05080 0.02840 0.02970 0.06380 0.07110 0.07250 0.05390 0.05440 0.02450 0.04090 0.03740 0.05900 0.05800 0.004330 0.005580 0.004970 4.000 1.590 0.01570 0.04630 0.02820 0.02950 0.03760 0.06050 0.06070 0.05200 0.05240 0.02320 0.01310 0.01220 0.02100 0.02020 0.002560 0.001450 0.001220 5.000 1.250 0.01550 0.04120 0.02790 0.02910 0.02080 0.04700 0.04610 0.04820 0.04840 0.01700 0.008970 0.009210 0.006960 0.006670 0.002180 0.0006170 0.0006000 6.000 1.030 0.01530 0.03580 0.02740 0.02850 0.01250 0.03350 0.03210 0.04270 0.04280 0.009250 0.008750 0.008850 0.003390 0.003340 0.001270 0.0005790 0.0005790 7.000 0.8480 0.01510 0.03040 0.02670 0.02770 0.009640 0.02220 0.02070 0.03630 0.03630 0.004370 0.007350 0.007100 0.002900 0.002920 0.0005920 0.0005170 0.0004920 8.000 0.6960 0.01490 0.02520 0.02580 0.02660 0.009090 0.01390 0.01260 0.02980 0.02960 0.002370 0.005220 0.004820 0.002870 0.002890 0.0002850 0.0003820 0.0003460 10.00 0.4700 0.01400 0.01600 0.02300 0.02400 0.008700 0.005300 0.004800 0.01900 0.01800 0.001800 0.001900 0.001700 0.002300 0.002300 0.0001900 0.0001500 0.0001200 15.00 0.2100 0.01300 0.004500 0.01600 0.01500 0.003300 0.002500 0.002700 0.004600 0.004400 0.0008700 0.0004200 0.0004400 0.0007000 0.0006800 9.400E-05 2.800E-05 2.800E-05 20.00 0.1000 0.01100 0.001600 0.008900 0.008300 0.0007000 0.002100 0.002000 0.001100 0.001100 0.0001700 0.0003700 0.0003700 0.0001800 0.0001700 1.900E-05 2.400E-05 2.400E-05 30.00 0.03500 0.006800 0.001300 0.002400 0.002100 0.0002700 0.0006400 0.0005700 8.900E-05 8.100E-05 5.200E-05 0.0001200 0.0001100 1.400E-05 1.300E-05 5.300E-06 8.100E-06 6.900E-06 40.00 0.01500 0.004100 0.0009300 0.0006800 0.0005300 0.0002000 0.0001700 0.0001400 1.100E-05 9.400E-06 4.000E-05 3.200E-05 2.600E-05 1.700E-06 1.400E-06 4.100E-06 2.200E-06 1.700E-06 60.00 0.004000 0.001400 0.0002900 7.700E-05 5.100E-05 6.200E-05 1.800E-05 1.200E-05 4.200E-07 3.300E-07 1.200E-05 3.400E-06 2.300E-06 6.300E-08 5.000E-08 1.200E-06 2.300E-07 1.500E-07 100.0 0.0005300 0.0002100 3.400E-05 3.700E-06 1.700E-06 7.100E-06 8.300E-07 4.000E-07 5.700E-09 3.600E-09 1.400E-06 1.500E-07 7.300E-08 8.700E-10 5.600E-10 1.400E-07 1.000E-08 4.700E-09 #S 52 Te #N 19 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 2 #UBIND 3.181E+04 4939. 4612. 4341. 1006. 870.0 819.0 583.0 573.0 169.0 110.0 110.0 42.00 40.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 0.000 9.600 0.01560 0.05590 0.02780 0.02910 0.1300 0.07690 0.07940 0.05300 0.05360 0.2960 0.1910 0.1980 0.1680 0.1700 0.9200 0.6650 0.6930 0.05000 9.580 0.01560 0.05590 0.02780 0.02910 0.1300 0.07690 0.07940 0.05300 0.05360 0.2960 0.1910 0.1980 0.1680 0.1700 0.9110 0.6650 0.6930 0.1000 9.520 0.01560 0.05590 0.02780 0.02910 0.1290 0.07690 0.07940 0.05300 0.05360 0.2950 0.1910 0.1980 0.1680 0.1700 0.8860 0.6640 0.6930 0.1500 9.430 0.01560 0.05590 0.02780 0.02910 0.1290 0.07690 0.07940 0.05300 0.05360 0.2930 0.1910 0.1980 0.1680 0.1700 0.8450 0.6620 0.6900 0.2000 9.290 0.01560 0.05590 0.02780 0.02910 0.1290 0.07690 0.07940 0.05300 0.05360 0.2910 0.1910 0.1980 0.1680 0.1700 0.7920 0.6560 0.6820 0.3000 8.880 0.01560 0.05590 0.02780 0.02910 0.1290 0.07690 0.07940 0.05300 0.05360 0.2850 0.1910 0.1980 0.1680 0.1700 0.6590 0.6260 0.6470 0.4000 8.320 0.01560 0.05580 0.02780 0.02910 0.1280 0.07690 0.07940 0.05300 0.05360 0.2760 0.1910 0.1980 0.1680 0.1700 0.5120 0.5690 0.5810 0.5000 7.670 0.01560 0.05580 0.02780 0.02910 0.1270 0.07690 0.07940 0.05300 0.05360 0.2660 0.1910 0.1970 0.1680 0.1700 0.3740 0.4890 0.4900 0.6000 7.030 0.01560 0.05570 0.02780 0.02910 0.1260 0.07690 0.07940 0.05300 0.05360 0.2530 0.1900 0.1960 0.1680 0.1700 0.2590 0.3970 0.3900 0.7000 6.440 0.01560 0.05560 0.02780 0.02910 0.1250 0.07690 0.07940 0.05300 0.05360 0.2390 0.1890 0.1950 0.1680 0.1700 0.1730 0.3070 0.2940 0.8000 5.950 0.01560 0.05550 0.02780 0.02910 0.1230 0.07690 0.07940 0.05300 0.05360 0.2240 0.1870 0.1930 0.1670 0.1690 0.1140 0.2270 0.2120 1.000 5.250 0.01560 0.05520 0.02780 0.02910 0.1200 0.07680 0.07930 0.05300 0.05360 0.1920 0.1810 0.1860 0.1660 0.1670 0.05600 0.1130 0.09980 1.200 4.820 0.01560 0.05490 0.02780 0.02910 0.1160 0.07670 0.07910 0.05300 0.05360 0.1600 0.1730 0.1770 0.1620 0.1630 0.04200 0.05130 0.04370 1.400 4.520 0.01560 0.05460 0.02780 0.02910 0.1110 0.07650 0.07890 0.05300 0.05360 0.1290 0.1620 0.1640 0.1560 0.1580 0.04090 0.02430 0.02100 1.600 4.250 0.01560 0.05410 0.02780 0.02910 0.1060 0.07610 0.07850 0.05300 0.05360 0.1020 0.1490 0.1490 0.1480 0.1490 0.03970 0.01470 0.01380 1.800 3.970 0.01550 0.05370 0.02780 0.02910 0.1000 0.07570 0.07800 0.05300 0.05360 0.07880 0.1330 0.1320 0.1380 0.1390 0.03550 0.01230 0.01240 2.000 3.680 0.01550 0.05310 0.02780 0.02910 0.09450 0.07520 0.07740 0.05300 0.05350 0.06050 0.1170 0.1150 0.1270 0.1270 0.02920 0.01210 0.01230 2.400 3.110 0.01550 0.05200 0.02780 0.02910 0.08240 0.07360 0.07550 0.05280 0.05340 0.03700 0.08430 0.08070 0.1010 0.1010 0.01640 0.01130 0.01110 3.000 2.370 0.01540 0.04990 0.02770 0.02900 0.06440 0.06980 0.07130 0.05240 0.05300 0.02470 0.04520 0.04150 0.06490 0.06390 0.005650 0.007320 0.006600 4.000 1.630 0.01530 0.04570 0.02760 0.02890 0.03880 0.06010 0.06040 0.05080 0.05120 0.02320 0.01460 0.01340 0.02490 0.02400 0.002770 0.002080 0.001730 5.000 1.270 0.01520 0.04080 0.02730 0.02850 0.02190 0.04750 0.04680 0.04740 0.04780 0.01800 0.009040 0.009240 0.008540 0.008160 0.002500 0.0007710 0.0007260 6.000 1.040 0.01500 0.03570 0.02690 0.02800 0.01310 0.03460 0.03320 0.04250 0.04270 0.01030 0.008830 0.009010 0.003820 0.003740 0.001560 0.0006780 0.0006800 7.000 0.8620 0.01480 0.03050 0.02620 0.02720 0.009710 0.02340 0.02190 0.03660 0.03660 0.005030 0.007720 0.007540 0.003010 0.003020 0.0007630 0.0006300 0.0006050 8.000 0.7120 0.01460 0.02550 0.02540 0.02630 0.008950 0.01500 0.01360 0.03040 0.03020 0.002630 0.005720 0.005330 0.002970 0.002990 0.0003610 0.0004890 0.0004460 10.00 0.4900 0.01400 0.01700 0.02300 0.02400 0.008700 0.005800 0.005200 0.01900 0.01900 0.001800 0.002300 0.002000 0.002500 0.002500 0.0002100 0.0002000 0.0001700 15.00 0.2100 0.01200 0.004800 0.01600 0.01500 0.003600 0.002500 0.002700 0.005000 0.004900 0.0009700 0.0004300 0.0004500 0.0008200 0.0007900 0.0001100 3.300E-05 3.400E-05 20.00 0.1100 0.010000 0.001700 0.009200 0.008600 0.0007900 0.002100 0.002100 0.001300 0.001200 0.0002100 0.0003800 0.0003900 0.0002100 0.0002000 2.400E-05 3.000E-05 2.900E-05 30.00 0.03700 0.006900 0.001300 0.002600 0.002200 0.0002600 0.0006900 0.0006100 1.000E-04 9.400E-05 5.200E-05 0.0001300 0.0001200 1.700E-05 1.600E-05 5.800E-06 1.100E-05 9.100E-06 40.00 0.01600 0.004200 0.0009600 0.0007400 0.0005800 0.0002100 0.0001900 0.0001600 1.300E-05 1.100E-05 4.200E-05 3.700E-05 3.000E-05 2.100E-06 1.800E-06 4.700E-06 2.900E-06 2.300E-06 60.00 0.004300 0.001500 0.0003100 8.700E-05 5.700E-05 6.700E-05 2.100E-05 1.400E-05 5.000E-07 4.000E-07 1.400E-05 4.000E-06 2.700E-06 8.200E-08 6.400E-08 1.500E-06 3.100E-07 2.100E-07 100.0 0.0005800 0.0002300 3.800E-05 4.200E-06 2.000E-06 7.800E-06 9.700E-07 4.600E-07 7.000E-09 4.400E-09 1.600E-06 1.800E-07 8.700E-08 1.100E-09 7.200E-10 1.800E-07 1.400E-08 6.600E-09 #S 53 I #N 19 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 3 #UBIND 3.317E+04 5188. 4852. 4557. 1072. 931.0 875.0 631.0 620.0 186.0 123.0 123.0 50.00 50.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 0.000 9.670 0.01530 0.05470 0.02720 0.02850 0.1260 0.07480 0.07740 0.05150 0.05210 0.2850 0.1840 0.1900 0.1590 0.1610 0.8530 0.6080 0.6370 0.05000 9.660 0.01530 0.05470 0.02720 0.02850 0.1260 0.07480 0.07740 0.05150 0.05210 0.2840 0.1840 0.1900 0.1590 0.1610 0.8460 0.6080 0.6370 0.1000 9.610 0.01530 0.05470 0.02720 0.02850 0.1260 0.07480 0.07740 0.05150 0.05210 0.2840 0.1840 0.1900 0.1590 0.1610 0.8260 0.6080 0.6370 0.1500 9.530 0.01530 0.05470 0.02720 0.02850 0.1260 0.07480 0.07740 0.05150 0.05210 0.2820 0.1840 0.1900 0.1590 0.1610 0.7920 0.6060 0.6350 0.2000 9.420 0.01530 0.05470 0.02720 0.02850 0.1260 0.07480 0.07740 0.05150 0.05210 0.2800 0.1840 0.1900 0.1590 0.1610 0.7480 0.6020 0.6300 0.3000 9.070 0.01530 0.05460 0.02720 0.02850 0.1250 0.07480 0.07740 0.05150 0.05210 0.2750 0.1830 0.1900 0.1590 0.1610 0.6370 0.5820 0.6050 0.4000 8.570 0.01530 0.05460 0.02720 0.02850 0.1250 0.07480 0.07740 0.05150 0.05210 0.2670 0.1830 0.1900 0.1590 0.1610 0.5100 0.5410 0.5560 0.5000 7.960 0.01530 0.05450 0.02720 0.02850 0.1240 0.07480 0.07740 0.05150 0.05210 0.2570 0.1830 0.1890 0.1590 0.1610 0.3870 0.4790 0.4850 0.6000 7.310 0.01530 0.05440 0.02720 0.02850 0.1230 0.07480 0.07740 0.05150 0.05210 0.2460 0.1820 0.1880 0.1590 0.1610 0.2790 0.4050 0.4010 0.7000 6.700 0.01530 0.05440 0.02720 0.02850 0.1220 0.07480 0.07730 0.05150 0.05210 0.2330 0.1810 0.1870 0.1590 0.1610 0.1940 0.3270 0.3170 0.8000 6.150 0.01530 0.05430 0.02720 0.02850 0.1200 0.07480 0.07730 0.05150 0.05210 0.2200 0.1800 0.1860 0.1590 0.1600 0.1310 0.2540 0.2390 1.000 5.330 0.01520 0.05400 0.02720 0.02850 0.1170 0.07470 0.07720 0.05150 0.05210 0.1900 0.1750 0.1800 0.1570 0.1590 0.06450 0.1380 0.1240 1.200 4.810 0.01520 0.05370 0.02720 0.02850 0.1130 0.07460 0.07710 0.05150 0.05210 0.1600 0.1680 0.1720 0.1550 0.1560 0.04410 0.06830 0.05840 1.400 4.470 0.01520 0.05340 0.02720 0.02850 0.1090 0.07440 0.07690 0.05150 0.05210 0.1310 0.1590 0.1610 0.1500 0.1520 0.04130 0.03330 0.02810 1.600 4.200 0.01520 0.05300 0.02720 0.02850 0.1040 0.07420 0.07660 0.05150 0.05210 0.1050 0.1470 0.1480 0.1440 0.1450 0.04090 0.01870 0.01670 1.800 3.950 0.01520 0.05260 0.02720 0.02850 0.09890 0.07380 0.07610 0.05150 0.05210 0.08240 0.1330 0.1330 0.1360 0.1370 0.03810 0.01390 0.01350 2.000 3.690 0.01520 0.05210 0.02710 0.02850 0.09340 0.07330 0.07560 0.05150 0.05210 0.06400 0.1180 0.1170 0.1260 0.1270 0.03270 0.01300 0.01310 2.400 3.160 0.01520 0.05100 0.02710 0.02850 0.08210 0.07190 0.07390 0.05140 0.05200 0.03940 0.08790 0.08460 0.1040 0.1040 0.01990 0.01260 0.01240 3.000 2.440 0.01510 0.04900 0.02710 0.02840 0.06490 0.06850 0.07010 0.05110 0.05160 0.02510 0.04930 0.04560 0.06990 0.06900 0.007250 0.009020 0.008230 4.000 1.670 0.01500 0.04500 0.02700 0.02830 0.04000 0.05970 0.06010 0.04960 0.05010 0.02310 0.01630 0.01490 0.02890 0.02790 0.002980 0.002860 0.002370 5.000 1.280 0.01490 0.04050 0.02670 0.02800 0.02290 0.04800 0.04730 0.04670 0.04700 0.01880 0.009190 0.009320 0.01030 0.009870 0.002770 0.0009580 0.0008720 6.000 1.050 0.01470 0.03560 0.02630 0.02750 0.01370 0.03560 0.03430 0.04230 0.04240 0.01140 0.008850 0.009100 0.004380 0.004250 0.001860 0.0007640 0.0007670 7.000 0.8740 0.01450 0.03060 0.02570 0.02680 0.009830 0.02460 0.02310 0.03680 0.03680 0.005760 0.008030 0.007930 0.003130 0.003140 0.0009580 0.0007340 0.0007110 8.000 0.7260 0.01430 0.02580 0.02500 0.02590 0.008830 0.01610 0.01460 0.03090 0.03080 0.002950 0.006190 0.005830 0.003040 0.003070 0.0004540 0.0005970 0.0005480 10.00 0.5000 0.01400 0.01700 0.02300 0.02300 0.008600 0.006300 0.005700 0.02000 0.02000 0.001800 0.002600 0.002300 0.002700 0.002700 0.0002200 0.0002600 0.0002200 15.00 0.2200 0.01200 0.005100 0.01600 0.01600 0.003900 0.002500 0.002600 0.005500 0.005300 0.001100 0.0004400 0.0004600 0.0009500 0.0009100 0.0001400 3.900E-05 3.900E-05 20.00 0.1100 0.010000 0.001700 0.009500 0.008900 0.0008900 0.002100 0.002200 0.001400 0.001300 0.0002400 0.0004000 0.0004100 0.0002500 0.0002400 3.100E-05 3.500E-05 3.500E-05 30.00 0.03900 0.006900 0.001300 0.002800 0.002400 0.0002600 0.0007500 0.0006600 0.0001200 0.0001100 5.300E-05 0.0001500 0.0001300 2.100E-05 1.900E-05 6.300E-06 1.300E-05 1.100E-05 40.00 0.01700 0.004300 0.0009800 0.0008100 0.0006400 0.0002100 0.0002100 0.0001700 1.500E-05 1.300E-05 4.400E-05 4.300E-05 3.400E-05 2.700E-06 2.300E-06 5.300E-06 3.800E-06 2.900E-06 60.00 0.004500 0.001500 0.0003300 9.800E-05 6.400E-05 7.200E-05 2.400E-05 1.600E-05 6.100E-07 4.800E-07 1.500E-05 4.700E-06 3.200E-06 1.000E-07 8.200E-08 1.800E-06 4.200E-07 2.700E-07 100.0 0.0006200 0.0002500 4.100E-05 4.900E-06 2.300E-06 8.700E-06 1.100E-06 5.400E-07 8.500E-09 5.300E-09 1.800E-06 2.200E-07 1.000E-07 1.500E-09 9.300E-10 2.100E-07 1.900E-08 8.900E-09 #S 54 Xe #N 19 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 4 #UBIND 3.457E+04 5453. 5107. 4787. 1149. 1002. 941.0 689.0 677.0 213.0 146.0 146.0 69.00 68.00 23.00 13.00 12.00 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 0.000 9.740 0.01490 0.05350 0.02650 0.02790 0.1230 0.07290 0.07540 0.05010 0.05070 0.2740 0.1760 0.1820 0.1510 0.1530 0.7970 0.5620 0.5920 0.05000 9.720 0.01490 0.05350 0.02650 0.02790 0.1230 0.07290 0.07540 0.05010 0.05070 0.2740 0.1760 0.1820 0.1510 0.1530 0.7910 0.5620 0.5920 0.1000 9.690 0.01490 0.05350 0.02650 0.02790 0.1230 0.07290 0.07540 0.05010 0.05070 0.2730 0.1760 0.1820 0.1510 0.1530 0.7740 0.5610 0.5910 0.1500 9.620 0.01490 0.05350 0.02650 0.02790 0.1230 0.07290 0.07540 0.05010 0.05070 0.2720 0.1760 0.1820 0.1510 0.1530 0.7470 0.5600 0.5900 0.2000 9.520 0.01490 0.05340 0.02650 0.02790 0.1230 0.07290 0.07540 0.05010 0.05070 0.2700 0.1760 0.1820 0.1510 0.1530 0.7100 0.5580 0.5860 0.3000 9.220 0.01490 0.05340 0.02650 0.02790 0.1220 0.07290 0.07540 0.05010 0.05070 0.2650 0.1760 0.1820 0.1510 0.1530 0.6150 0.5430 0.5680 0.4000 8.770 0.01490 0.05340 0.02650 0.02790 0.1210 0.07290 0.07540 0.05010 0.05070 0.2580 0.1760 0.1820 0.1510 0.1530 0.5050 0.5130 0.5310 0.5000 8.210 0.01490 0.05330 0.02650 0.02790 0.1210 0.07290 0.07540 0.05010 0.05070 0.2490 0.1760 0.1820 0.1510 0.1530 0.3940 0.4660 0.4750 0.6000 7.590 0.01490 0.05320 0.02650 0.02790 0.1200 0.07290 0.07540 0.05010 0.05070 0.2390 0.1750 0.1810 0.1510 0.1530 0.2940 0.4060 0.4050 0.7000 6.960 0.01490 0.05320 0.02650 0.02790 0.1190 0.07280 0.07540 0.05010 0.05070 0.2280 0.1750 0.1800 0.1510 0.1530 0.2110 0.3390 0.3310 0.8000 6.380 0.01490 0.05310 0.02650 0.02790 0.1170 0.07280 0.07540 0.05010 0.05070 0.2150 0.1730 0.1790 0.1510 0.1520 0.1480 0.2730 0.2600 1.000 5.450 0.01490 0.05280 0.02650 0.02790 0.1140 0.07280 0.07530 0.05010 0.05070 0.1880 0.1700 0.1740 0.1500 0.1510 0.07380 0.1600 0.1450 1.200 4.840 0.01490 0.05260 0.02650 0.02790 0.1110 0.07270 0.07520 0.05010 0.05070 0.1600 0.1640 0.1680 0.1480 0.1490 0.04700 0.08550 0.07370 1.400 4.440 0.01490 0.05230 0.02650 0.02790 0.1070 0.07250 0.07500 0.05010 0.05070 0.1330 0.1550 0.1580 0.1450 0.1460 0.04150 0.04360 0.03660 1.600 4.160 0.01490 0.05190 0.02650 0.02790 0.1020 0.07230 0.07470 0.05010 0.05070 0.1080 0.1450 0.1460 0.1400 0.1410 0.04120 0.02370 0.02040 1.800 3.910 0.01490 0.05150 0.02650 0.02790 0.09750 0.07190 0.07430 0.05010 0.05070 0.08570 0.1330 0.1330 0.1330 0.1340 0.03960 0.01600 0.01480 2.000 3.680 0.01490 0.05100 0.02650 0.02790 0.09240 0.07150 0.07380 0.05010 0.05070 0.06730 0.1190 0.1180 0.1250 0.1260 0.03540 0.01380 0.01360 2.400 3.190 0.01490 0.05000 0.02650 0.02790 0.08160 0.07020 0.07230 0.05000 0.05060 0.04180 0.09090 0.08800 0.1060 0.1060 0.02330 0.01350 0.01330 3.000 2.500 0.01480 0.04810 0.02650 0.02780 0.06530 0.06720 0.06890 0.04970 0.05030 0.02560 0.05320 0.04960 0.07420 0.07330 0.009110 0.01060 0.009720 4.000 1.710 0.01470 0.04440 0.02640 0.02770 0.04110 0.05920 0.05980 0.04850 0.04900 0.02280 0.01830 0.01660 0.03290 0.03190 0.003190 0.003760 0.003120 5.000 1.300 0.01460 0.04010 0.02610 0.02740 0.02400 0.04830 0.04780 0.04590 0.04630 0.01950 0.009440 0.009470 0.01240 0.01180 0.003000 0.001200 0.001040 6.000 1.060 0.01440 0.03540 0.02580 0.02700 0.01430 0.03650 0.03520 0.04190 0.04210 0.01250 0.008820 0.009150 0.005070 0.004880 0.002160 0.0008430 0.0008360 7.000 0.8860 0.01420 0.03060 0.02530 0.02630 0.010000 0.02580 0.02420 0.03690 0.03690 0.006550 0.008260 0.008250 0.003300 0.003280 0.001170 0.0008240 0.0008000 8.000 0.7400 0.01400 0.02600 0.02450 0.02550 0.008740 0.01720 0.01570 0.03140 0.03130 0.003330 0.006620 0.006310 0.003100 0.003120 0.0005650 0.0007010 0.0006450 10.00 0.5100 0.01400 0.01800 0.02300 0.02300 0.008500 0.006900 0.006200 0.02100 0.02100 0.001900 0.003000 0.002600 0.002900 0.002900 0.0002400 0.0003300 0.0002800 15.00 0.2300 0.01200 0.005400 0.01600 0.01600 0.004200 0.002400 0.002600 0.006000 0.005700 0.001200 0.0004600 0.0004700 0.001100 0.001000 0.0001600 4.500E-05 4.400E-05 20.00 0.1200 0.010000 0.001800 0.009700 0.009200 0.001000 0.002200 0.002200 0.001600 0.001500 0.0002800 0.0004100 0.0004300 0.0003000 0.0002800 3.900E-05 4.000E-05 4.000E-05 30.00 0.04000 0.007000 0.001300 0.003000 0.002500 0.0002600 0.0008000 0.0007100 0.0001400 0.0001300 5.300E-05 0.0001600 0.0001500 2.600E-05 2.400E-05 6.800E-06 1.600E-05 1.400E-05 40.00 0.01800 0.004400 0.0009900 0.0008900 0.0006900 0.0002200 0.0002400 0.0001900 1.800E-05 1.500E-05 4.600E-05 4.800E-05 3.900E-05 3.300E-06 2.900E-06 5.900E-06 4.800E-06 3.600E-06 60.00 0.004800 0.001600 0.0003400 0.0001100 7.200E-05 7.700E-05 2.700E-05 1.800E-05 7.300E-07 5.700E-07 1.600E-05 5.500E-06 3.700E-06 1.300E-07 1.000E-07 2.100E-06 5.400E-07 3.500E-07 100.0 0.0006700 0.0002700 4.500E-05 5.600E-06 2.600E-06 9.500E-06 1.300E-06 6.200E-07 1.000E-08 6.400E-09 2.000E-06 2.600E-07 1.200E-07 1.900E-09 1.200E-09 2.600E-07 2.500E-08 1.200E-08 #S 55 Cs #N 20 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 4 1 #UBIND 3.599E+07 5713. 5360. 5012. 1217. 1065. 998.0 740.0 724.0 231.0 162.0 162.0 77.00 75.00 23.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 0.000 11.70 0.01460 0.05230 0.02590 0.02730 0.1200 0.07100 0.07350 0.04880 0.04940 0.2640 0.1700 0.1760 0.1440 0.1450 0.7200 0.4980 0.5220 2.740 0.05000 11.50 0.01460 0.05230 0.02590 0.02730 0.1200 0.07100 0.07350 0.04880 0.04940 0.2640 0.1700 0.1760 0.1440 0.1450 0.7150 0.4980 0.5220 2.550 0.1000 11.00 0.01460 0.05230 0.02590 0.02730 0.1200 0.07100 0.07350 0.04880 0.04940 0.2630 0.1700 0.1760 0.1440 0.1450 0.7020 0.4980 0.5220 2.050 0.1500 10.40 0.01460 0.05230 0.02590 0.02730 0.1200 0.07100 0.07350 0.04880 0.04940 0.2620 0.1700 0.1760 0.1440 0.1450 0.6820 0.4970 0.5210 1.440 0.2000 9.740 0.01460 0.05230 0.02590 0.02730 0.1190 0.07100 0.07350 0.04880 0.04940 0.2600 0.1700 0.1760 0.1440 0.1450 0.6540 0.4960 0.5190 0.8910 0.3000 8.890 0.01460 0.05220 0.02590 0.02730 0.1190 0.07100 0.07350 0.04880 0.04940 0.2560 0.1700 0.1750 0.1440 0.1450 0.5800 0.4880 0.5090 0.2540 0.4000 8.400 0.01460 0.05220 0.02590 0.02730 0.1180 0.07100 0.07350 0.04880 0.04940 0.2490 0.1690 0.1750 0.1440 0.1450 0.4920 0.4700 0.4870 0.08670 0.5000 7.980 0.01460 0.05220 0.02590 0.02730 0.1180 0.07100 0.07350 0.04880 0.04940 0.2420 0.1690 0.1750 0.1440 0.1450 0.3990 0.4390 0.4510 0.07100 0.6000 7.500 0.01460 0.05210 0.02590 0.02730 0.1170 0.07100 0.07350 0.04880 0.04940 0.2320 0.1690 0.1750 0.1440 0.1450 0.3110 0.3980 0.4020 0.06810 0.7000 6.980 0.01460 0.05200 0.02590 0.02730 0.1160 0.07100 0.07350 0.04880 0.04940 0.2220 0.1680 0.1740 0.1440 0.1450 0.2340 0.3480 0.3460 0.05610 0.8000 6.460 0.01460 0.05190 0.02590 0.02730 0.1150 0.07090 0.07350 0.04880 0.04940 0.2110 0.1670 0.1730 0.1430 0.1450 0.1710 0.2950 0.2870 0.04070 1.000 5.560 0.01460 0.05170 0.02590 0.02730 0.1120 0.07090 0.07340 0.04880 0.04940 0.1860 0.1640 0.1690 0.1430 0.1440 0.08960 0.1920 0.1790 0.01720 1.200 4.890 0.01460 0.05150 0.02590 0.02730 0.1090 0.07080 0.07330 0.04880 0.04940 0.1600 0.1590 0.1630 0.1410 0.1430 0.05410 0.1130 0.1010 0.006660 1.400 4.440 0.01460 0.05120 0.02590 0.02730 0.1050 0.07070 0.07320 0.04880 0.04940 0.1340 0.1520 0.1550 0.1390 0.1400 0.04390 0.06150 0.05310 0.003220 1.600 4.130 0.01460 0.05080 0.02590 0.02730 0.1010 0.07050 0.07290 0.04880 0.04940 0.1100 0.1430 0.1450 0.1350 0.1360 0.04280 0.03370 0.02900 0.002480 1.800 3.890 0.01460 0.05040 0.02590 0.02730 0.09610 0.07020 0.07260 0.04880 0.04940 0.08870 0.1320 0.1320 0.1300 0.1310 0.04220 0.02090 0.01890 0.002430 2.000 3.660 0.01460 0.05000 0.02590 0.02730 0.09130 0.06980 0.07210 0.04880 0.04930 0.07050 0.1200 0.1190 0.1230 0.1240 0.03930 0.01620 0.01580 0.002370 2.400 3.220 0.01450 0.04900 0.02590 0.02730 0.08110 0.06870 0.07080 0.04870 0.04930 0.04430 0.09340 0.09080 0.1070 0.1070 0.02800 0.01510 0.01530 0.001810 3.000 2.560 0.01450 0.04730 0.02590 0.02730 0.06550 0.06600 0.06770 0.04850 0.04900 0.02640 0.05690 0.05330 0.07780 0.07700 0.01190 0.01290 0.01230 0.0007930 4.000 1.750 0.01440 0.04380 0.02580 0.02710 0.04210 0.05860 0.05930 0.04740 0.04790 0.02250 0.02040 0.01840 0.03680 0.03580 0.003660 0.005220 0.004430 0.0002060 5.000 1.320 0.01430 0.03970 0.02560 0.02690 0.02500 0.04850 0.04810 0.04520 0.04550 0.02010 0.009820 0.009710 0.01460 0.01390 0.003360 0.001630 0.001400 0.0001810 6.000 1.070 0.01410 0.03520 0.02530 0.02650 0.01490 0.03730 0.03610 0.04160 0.04180 0.01350 0.008770 0.009140 0.005910 0.005660 0.002590 0.0009990 0.0009950 0.0001430 7.000 0.8970 0.01400 0.03070 0.02480 0.02590 0.01020 0.02680 0.02520 0.03690 0.03700 0.007380 0.008420 0.008500 0.003510 0.003480 0.001490 0.0009760 0.0009710 8.290E-05 8.000 0.7530 0.01380 0.02620 0.02410 0.02510 0.008680 0.01820 0.01670 0.03170 0.03160 0.003780 0.007010 0.006740 0.003150 0.003180 0.0007340 0.0008670 0.0008190 4.080E-05 10.00 0.5300 0.01300 0.01800 0.02200 0.02300 0.008400 0.007600 0.006700 0.02200 0.02100 0.001900 0.003400 0.003000 0.003000 0.003000 0.0002800 0.0004500 0.0003800 1.500E-05 15.00 0.2400 0.01200 0.005800 0.01600 0.01600 0.004400 0.002400 0.002600 0.006400 0.006200 0.001300 0.0004800 0.0004900 0.001200 0.001200 0.0001900 5.600E-05 5.400E-05 1.000E-05 20.00 0.1200 0.010000 0.001900 0.010000 0.009400 0.001100 0.002200 0.002200 0.001800 0.001700 0.0003300 0.0004200 0.0004400 0.0003500 0.0003300 5.000E-05 4.800E-05 4.900E-05 2.700E-06 30.00 0.04200 0.007000 0.001300 0.003100 0.002700 0.0002500 0.0008600 0.0007700 0.0001600 0.0001400 5.400E-05 0.0001800 0.0001600 3.200E-05 2.900E-05 7.600E-06 2.100E-05 1.800E-05 4.100E-07 40.00 0.01800 0.004500 0.001000 0.0009700 0.0007500 0.0002200 0.0002600 0.0002100 2.100E-05 1.800E-05 4.800E-05 5.500E-05 4.400E-05 4.100E-06 3.500E-06 6.800E-06 6.300E-06 4.900E-06 3.600E-07 60.00 0.005000 0.001700 0.0003600 0.0001200 7.900E-05 8.200E-05 3.100E-05 2.100E-05 8.600E-07 6.800E-07 1.800E-05 6.400E-06 4.300E-06 1.700E-07 1.300E-07 2.500E-06 7.400E-07 4.800E-07 1.300E-07 100.0 0.0007300 0.0002900 4.900E-05 6.400E-06 2.900E-06 1.000E-05 1.500E-06 7.100E-07 1.200E-08 7.700E-09 2.300E-06 3.100E-07 1.500E-07 2.400E-09 1.500E-09 3.200E-07 3.500E-08 1.600E-08 1.700E-08 #S 56 Ba #N 20 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 4 2 #UBIND 3.744E+04 5987. 5624. 5247. 1293. 1137. 1063. 796.0 781.0 253.0 180.0 180.0 93.00 90.00 40.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 0.000 12.90 0.01430 0.05120 0.02540 0.02680 0.1170 0.06920 0.07180 0.04750 0.04810 0.2550 0.1630 0.1690 0.1370 0.1390 0.6570 0.4520 0.4730 2.230 0.05000 12.70 0.01430 0.05120 0.02540 0.02680 0.1170 0.06920 0.07180 0.04750 0.04810 0.2540 0.1630 0.1690 0.1370 0.1390 0.6530 0.4520 0.4730 2.120 0.1000 12.00 0.01430 0.05120 0.02540 0.02680 0.1170 0.06920 0.07180 0.04750 0.04810 0.2540 0.1630 0.1690 0.1370 0.1390 0.6430 0.4520 0.4730 1.820 0.1500 11.20 0.01430 0.05120 0.02540 0.02680 0.1170 0.06920 0.07180 0.04750 0.04810 0.2530 0.1630 0.1690 0.1370 0.1390 0.6270 0.4520 0.4720 1.420 0.2000 10.30 0.01430 0.05120 0.02540 0.02680 0.1160 0.06920 0.07180 0.04750 0.04810 0.2510 0.1630 0.1690 0.1370 0.1390 0.6050 0.4510 0.4710 1.010 0.3000 8.950 0.01430 0.05110 0.02540 0.02680 0.1160 0.06920 0.07180 0.04750 0.04810 0.2470 0.1630 0.1690 0.1370 0.1390 0.5470 0.4460 0.4640 0.4010 0.4000 8.200 0.01430 0.05110 0.02540 0.02680 0.1160 0.06920 0.07180 0.04750 0.04810 0.2410 0.1630 0.1690 0.1370 0.1390 0.4750 0.4330 0.4500 0.1440 0.5000 7.760 0.01430 0.05100 0.02540 0.02680 0.1150 0.06920 0.07180 0.04750 0.04810 0.2340 0.1630 0.1690 0.1370 0.1390 0.3970 0.4130 0.4250 0.08240 0.6000 7.370 0.01430 0.05100 0.02540 0.02680 0.1140 0.06920 0.07180 0.04750 0.04810 0.2260 0.1630 0.1680 0.1370 0.1390 0.3200 0.3830 0.3900 0.07740 0.7000 6.950 0.01430 0.05090 0.02540 0.02680 0.1130 0.06920 0.07170 0.04750 0.04810 0.2170 0.1620 0.1680 0.1370 0.1390 0.2500 0.3450 0.3470 0.07450 0.8000 6.500 0.01430 0.05080 0.02540 0.02680 0.1120 0.06920 0.07170 0.04750 0.04810 0.2060 0.1610 0.1670 0.1370 0.1390 0.1900 0.3020 0.2990 0.06400 1.000 5.640 0.01430 0.05060 0.02540 0.02680 0.1090 0.06910 0.07170 0.04750 0.04810 0.1830 0.1590 0.1640 0.1360 0.1380 0.1050 0.2130 0.2030 0.03560 1.200 4.950 0.01430 0.05040 0.02540 0.02680 0.1060 0.06900 0.07160 0.04750 0.04810 0.1590 0.1540 0.1590 0.1350 0.1370 0.06240 0.1360 0.1240 0.01590 1.400 4.460 0.01430 0.05010 0.02540 0.02680 0.1030 0.06890 0.07140 0.04750 0.04810 0.1350 0.1480 0.1510 0.1340 0.1350 0.04690 0.07970 0.07030 0.007020 1.600 4.120 0.01430 0.04980 0.02540 0.02680 0.09890 0.06870 0.07120 0.04750 0.04810 0.1120 0.1400 0.1420 0.1310 0.1320 0.04390 0.04530 0.03920 0.004200 1.800 3.860 0.01430 0.04940 0.02540 0.02680 0.09470 0.06850 0.07090 0.04750 0.04810 0.09130 0.1300 0.1310 0.1260 0.1280 0.04370 0.02720 0.02410 0.003690 2.000 3.640 0.01430 0.04900 0.02540 0.02680 0.09020 0.06810 0.07050 0.04750 0.04810 0.07340 0.1200 0.1190 0.1210 0.1220 0.04210 0.01930 0.01830 0.003670 2.400 3.230 0.01420 0.04810 0.02540 0.02680 0.08060 0.06710 0.06930 0.04740 0.04800 0.04680 0.09530 0.09310 0.1070 0.1070 0.03250 0.01630 0.01660 0.003120 3.000 2.610 0.01420 0.04650 0.02530 0.02670 0.06570 0.06470 0.06650 0.04720 0.04780 0.02730 0.06030 0.05670 0.08070 0.08000 0.01520 0.01490 0.01450 0.001540 4.000 1.790 0.01410 0.04320 0.02520 0.02660 0.04300 0.05800 0.05880 0.04630 0.04690 0.02220 0.02270 0.02040 0.04060 0.03960 0.004240 0.006850 0.005920 0.0003670 5.000 1.340 0.01400 0.03930 0.02510 0.02640 0.02600 0.04860 0.04830 0.04440 0.04480 0.02040 0.01030 0.01010 0.01690 0.01620 0.003660 0.002190 0.001850 0.0002880 6.000 1.080 0.01390 0.03500 0.02480 0.02600 0.01560 0.03800 0.03680 0.04110 0.04140 0.01450 0.008720 0.009100 0.006900 0.006590 0.003020 0.001160 0.001140 0.0002440 7.000 0.9070 0.01370 0.03070 0.02430 0.02550 0.01050 0.02780 0.02620 0.03690 0.03700 0.008250 0.008520 0.008680 0.003800 0.003730 0.001840 0.001100 0.001120 0.0001510 8.000 0.7640 0.01350 0.02640 0.02370 0.02480 0.008660 0.01920 0.01760 0.03200 0.03190 0.004290 0.007340 0.007140 0.003210 0.003230 0.0009390 0.001020 0.0009840 7.700E-05 10.00 0.5400 0.01300 0.01900 0.02200 0.02300 0.008300 0.008300 0.007300 0.02200 0.02200 0.001900 0.003800 0.003300 0.003100 0.003100 0.0003200 0.0005700 0.0004900 2.600E-05 15.00 0.2400 0.01200 0.006100 0.01600 0.01600 0.004700 0.002400 0.002500 0.006900 0.006700 0.001300 0.0005100 0.0005100 0.001300 0.001300 0.0002200 6.900E-05 6.500E-05 1.700E-05 20.00 0.1300 0.010000 0.002000 0.010000 0.009700 0.001300 0.002200 0.002300 0.001900 0.001800 0.0003800 0.0004300 0.0004600 0.0004000 0.0003800 6.300E-05 5.500E-05 5.700E-05 5.000E-06 30.00 0.04400 0.007000 0.001200 0.003300 0.002900 0.0002500 0.0009100 0.0008200 0.0001800 0.0001600 5.500E-05 0.0002000 0.0001800 3.800E-05 3.400E-05 8.500E-06 2.600E-05 2.300E-05 6.600E-07 40.00 0.01900 0.004500 0.001000 0.001000 0.0008100 0.0002300 0.0002900 0.0002300 2.400E-05 2.100E-05 4.900E-05 6.200E-05 5.000E-05 5.000E-06 4.300E-06 7.700E-06 8.100E-06 6.400E-06 6.000E-07 60.00 0.005300 0.001800 0.0003800 0.0001400 8.800E-05 8.700E-05 3.500E-05 2.300E-05 1.000E-06 8.000E-07 1.900E-05 7.400E-06 5.000E-06 2.100E-07 1.600E-07 3.000E-06 9.700E-07 6.300E-07 2.300E-07 100.0 0.0007800 0.0003100 5.300E-05 7.300E-06 3.300E-06 1.100E-05 1.700E-06 8.100E-07 1.500E-08 9.200E-09 2.500E-06 3.600E-07 1.700E-07 3.000E-09 1.900E-09 3.900E-07 4.800E-08 2.200E-08 3.100E-08 #S 57 La #N 21 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 2 2 4 1 2 #UBIND 3.892E+04 6267. 5891. 5483. 1362. 1205. 1124. 852.0 835.0 271.0 196.0 196.0 103.0 103.0 36.00 18.00 18.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 0.000 12.70 0.01400 0.05010 0.02480 0.02630 0.1140 0.06750 0.07010 0.04630 0.04690 0.2460 0.1580 0.1630 0.1310 0.1330 0.6160 0.4240 0.4400 0.5090 2.070 0.05000 12.50 0.01400 0.05010 0.02480 0.02630 0.1140 0.06750 0.07010 0.04630 0.04690 0.2460 0.1580 0.1630 0.1310 0.1330 0.6130 0.4240 0.4400 0.5090 1.980 0.1000 12.00 0.01400 0.05010 0.02480 0.02630 0.1140 0.06750 0.07010 0.04630 0.04690 0.2450 0.1580 0.1630 0.1310 0.1330 0.6040 0.4240 0.4400 0.5090 1.730 0.1500 11.30 0.01400 0.05010 0.02480 0.02630 0.1140 0.06750 0.07010 0.04630 0.04690 0.2440 0.1580 0.1630 0.1310 0.1330 0.5910 0.4240 0.4400 0.5090 1.390 0.2000 10.50 0.01400 0.05010 0.02480 0.02630 0.1140 0.06750 0.07010 0.04630 0.04690 0.2430 0.1580 0.1630 0.1310 0.1330 0.5720 0.4230 0.4390 0.5090 1.030 0.3000 9.200 0.01400 0.05000 0.02480 0.02630 0.1130 0.06750 0.07010 0.04630 0.04690 0.2390 0.1580 0.1630 0.1310 0.1330 0.5220 0.4190 0.4340 0.5040 0.4570 0.4000 8.420 0.01400 0.05000 0.02480 0.02630 0.1130 0.06750 0.07010 0.04630 0.04690 0.2340 0.1570 0.1630 0.1310 0.1330 0.4600 0.4100 0.4230 0.4880 0.1750 0.5000 7.950 0.01400 0.05000 0.02480 0.02630 0.1120 0.06750 0.07010 0.04630 0.04690 0.2270 0.1570 0.1630 0.1310 0.1330 0.3920 0.3930 0.4040 0.4570 0.08800 0.6000 7.570 0.01400 0.04990 0.02480 0.02630 0.1110 0.06750 0.07010 0.04630 0.04690 0.2200 0.1570 0.1630 0.1310 0.1330 0.3230 0.3690 0.3770 0.4120 0.07480 0.7000 7.160 0.01400 0.04980 0.02480 0.02630 0.1110 0.06740 0.07010 0.04630 0.04690 0.2110 0.1560 0.1620 0.1310 0.1330 0.2580 0.3380 0.3420 0.3580 0.07400 0.8000 6.710 0.01400 0.04980 0.02480 0.02630 0.1090 0.06740 0.07000 0.04630 0.04690 0.2020 0.1560 0.1610 0.1310 0.1330 0.2010 0.3020 0.3020 0.3010 0.06790 1.000 5.830 0.01400 0.04960 0.02480 0.02630 0.1070 0.06740 0.07000 0.04630 0.04690 0.1810 0.1540 0.1580 0.1310 0.1320 0.1160 0.2230 0.2170 0.1980 0.04340 1.200 5.080 0.01400 0.04930 0.02480 0.02630 0.1040 0.06730 0.06990 0.04630 0.04690 0.1580 0.1500 0.1540 0.1300 0.1310 0.06940 0.1500 0.1410 0.1210 0.02170 1.400 4.520 0.01400 0.04910 0.02480 0.02630 0.1010 0.06720 0.06980 0.04630 0.04690 0.1350 0.1440 0.1480 0.1280 0.1300 0.04960 0.09330 0.08460 0.06950 0.009930 1.600 4.130 0.01400 0.04880 0.02480 0.02630 0.09720 0.06710 0.06960 0.04630 0.04690 0.1140 0.1370 0.1400 0.1260 0.1270 0.04420 0.05530 0.04900 0.03860 0.005300 1.800 3.850 0.01400 0.04840 0.02480 0.02620 0.09330 0.06680 0.06930 0.04630 0.04690 0.09360 0.1290 0.1300 0.1230 0.1240 0.04370 0.03330 0.02960 0.02100 0.004070 2.000 3.620 0.01400 0.04810 0.02480 0.02620 0.08900 0.06650 0.06890 0.04630 0.04690 0.07610 0.1190 0.1190 0.1180 0.1190 0.04300 0.02230 0.02080 0.01180 0.003940 2.400 3.230 0.01400 0.04720 0.02480 0.02620 0.08000 0.06560 0.06790 0.04630 0.04680 0.04930 0.09690 0.09500 0.1060 0.1070 0.03550 0.01670 0.01720 0.005400 0.003620 3.000 2.650 0.01390 0.04570 0.02480 0.02620 0.06580 0.06340 0.06530 0.04610 0.04670 0.02830 0.06340 0.06000 0.08290 0.08240 0.01830 0.01590 0.01590 0.004550 0.002020 4.000 1.840 0.01380 0.04250 0.02470 0.02610 0.04380 0.05730 0.05830 0.04530 0.04580 0.02190 0.02510 0.02250 0.04430 0.04320 0.004890 0.008290 0.007370 0.003520 0.0004780 5.000 1.360 0.01370 0.03890 0.02450 0.02590 0.02700 0.04860 0.04850 0.04360 0.04400 0.02070 0.01100 0.01050 0.01950 0.01860 0.003850 0.002770 0.002350 0.001750 0.0003240 6.000 1.090 0.01360 0.03480 0.02430 0.02560 0.01630 0.03850 0.03750 0.04070 0.04100 0.01530 0.008680 0.009060 0.008060 0.007670 0.003360 0.001300 0.001270 0.0007120 0.0002910 7.000 0.9160 0.01340 0.03070 0.02390 0.02510 0.01080 0.02870 0.02720 0.03680 0.03690 0.009130 0.008560 0.008800 0.004170 0.004060 0.002170 0.001180 0.001220 0.0003130 0.0001920 8.000 0.7750 0.01330 0.02650 0.02330 0.02440 0.008680 0.02020 0.01860 0.03220 0.03220 0.004850 0.007620 0.007490 0.003280 0.003300 0.001150 0.001120 0.001110 0.0002050 0.0001020 10.00 0.5500 0.01300 0.01900 0.02200 0.02300 0.008100 0.008900 0.007800 0.02300 0.02300 0.002000 0.004200 0.003700 0.003200 0.003200 0.0003700 0.0006700 0.0006000 0.0001900 3.200E-05 15.00 0.2500 0.01200 0.006400 0.01600 0.01600 0.004900 0.002300 0.002500 0.007400 0.007100 0.001400 0.0005500 0.0005300 0.001500 0.001400 0.0002500 8.200E-05 7.600E-05 9.600E-05 2.100E-05 20.00 0.1300 0.010000 0.002100 0.010000 0.009900 0.001400 0.002200 0.002300 0.002100 0.002000 0.0004300 0.0004400 0.0004700 0.0004600 0.0004400 7.700E-05 6.000E-05 6.400E-05 3.000E-05 6.600E-06 30.00 0.04500 0.007100 0.001200 0.003500 0.003000 0.0002500 0.0009700 0.0008700 0.0002100 0.0001900 5.600E-05 0.0002100 0.0001900 4.500E-05 4.100E-05 9.300E-06 3.000E-05 2.700E-05 2.900E-06 7.800E-07 40.00 0.02000 0.004400 0.001000 0.001100 0.0008800 0.0002300 0.0003100 0.0002500 2.800E-05 2.400E-05 5.100E-05 6.900E-05 5.600E-05 6.100E-06 5.200E-06 8.400E-06 9.700E-06 7.800E-06 3.900E-07 7.000E-07 60.00 0.005600 0.001800 0.0004000 0.0001500 9.700E-05 9.200E-05 3.900E-05 2.600E-05 1.200E-06 9.400E-07 2.100E-05 8.500E-06 5.700E-06 2.500E-07 2.000E-07 3.400E-06 1.200E-06 7.900E-07 1.600E-08 2.900E-07 100.0 0.0008400 0.0003300 5.800E-05 8.200E-06 3.700E-06 1.300E-05 2.000E-06 9.200E-07 1.800E-08 1.100E-08 2.800E-06 4.300E-07 2.000E-07 3.700E-09 2.300E-09 4.600E-07 6.000E-08 2.800E-08 2.400E-10 3.900E-08 #S 58 Ce #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 1 2 2 4 1 2 #UBIND 4.044E+04 6549. 6165. 5724. 1435. 1273. 1186. 901.0 883.0 289.0 207.0 207.0 112.0 108.0 0.000 38.00 18.00 18.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 12.60 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2390 0.1530 0.1590 0.1270 0.1290 0.1420 0.6020 0.4140 0.4310 0.4980 2.050 0.05000 12.40 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2390 0.1530 0.1590 0.1270 0.1290 0.1420 0.5990 0.4140 0.4310 0.4980 1.960 0.1000 11.90 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2380 0.1530 0.1590 0.1270 0.1290 0.1420 0.5910 0.4140 0.4310 0.4980 1.720 0.1500 11.20 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2380 0.1530 0.1590 0.1270 0.1290 0.1420 0.5790 0.4130 0.4310 0.4980 1.390 0.2000 10.40 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2360 0.1530 0.1590 0.1270 0.1290 0.1420 0.5610 0.4130 0.4300 0.4980 1.040 0.3000 9.170 0.01370 0.04900 0.02430 0.02570 0.1110 0.06580 0.06840 0.04520 0.04580 0.2330 0.1530 0.1590 0.1270 0.1290 0.1420 0.5150 0.4090 0.4260 0.4930 0.4690 0.4000 8.400 0.01370 0.04900 0.02430 0.02570 0.1100 0.06580 0.06840 0.04520 0.04580 0.2280 0.1530 0.1590 0.1270 0.1290 0.1420 0.4560 0.4010 0.4160 0.4790 0.1810 0.5000 7.940 0.01370 0.04890 0.02430 0.02570 0.1100 0.06580 0.06840 0.04520 0.04580 0.2220 0.1530 0.1590 0.1270 0.1290 0.1420 0.3910 0.3860 0.3980 0.4500 0.08800 0.6000 7.570 0.01370 0.04890 0.02430 0.02570 0.1090 0.06580 0.06840 0.04520 0.04580 0.2150 0.1530 0.1580 0.1270 0.1290 0.1420 0.3250 0.3640 0.3730 0.4080 0.07210 0.7000 7.180 0.01370 0.04880 0.02430 0.02570 0.1080 0.06580 0.06840 0.04520 0.04580 0.2070 0.1520 0.1580 0.1270 0.1290 0.1420 0.2620 0.3360 0.3400 0.3580 0.07150 0.8000 6.760 0.01370 0.04870 0.02430 0.02570 0.1070 0.06580 0.06840 0.04520 0.04580 0.1980 0.1520 0.1570 0.1270 0.1290 0.1420 0.2060 0.3020 0.3020 0.3040 0.06660 1.000 5.910 0.01370 0.04860 0.02430 0.02570 0.1050 0.06580 0.06840 0.04520 0.04580 0.1790 0.1500 0.1550 0.1270 0.1280 0.1400 0.1210 0.2270 0.2210 0.2030 0.04410 1.200 5.170 0.01370 0.04830 0.02430 0.02570 0.1020 0.06570 0.06830 0.04520 0.04580 0.1570 0.1460 0.1510 0.1260 0.1280 0.1380 0.07220 0.1560 0.1460 0.1270 0.02280 1.400 4.610 0.01370 0.04810 0.02430 0.02570 0.09900 0.06560 0.06820 0.04520 0.04580 0.1360 0.1420 0.1450 0.1250 0.1260 0.1340 0.05010 0.09930 0.08970 0.07470 0.01060 1.600 4.210 0.01370 0.04780 0.02430 0.02570 0.09560 0.06550 0.06800 0.04520 0.04580 0.1150 0.1350 0.1380 0.1230 0.1240 0.1280 0.04330 0.06000 0.05260 0.04240 0.005470 1.800 3.920 0.01370 0.04750 0.02430 0.02570 0.09190 0.06530 0.06780 0.04520 0.04580 0.09540 0.1270 0.1290 0.1200 0.1210 0.1210 0.04240 0.03620 0.03160 0.02360 0.003950 2.000 3.690 0.01370 0.04710 0.02430 0.02570 0.08790 0.06500 0.06740 0.04520 0.04570 0.07820 0.1180 0.1190 0.1160 0.1170 0.1140 0.04210 0.02370 0.02150 0.01330 0.003730 2.400 3.300 0.01370 0.04630 0.02430 0.02570 0.07930 0.06420 0.06650 0.04510 0.04570 0.05130 0.09780 0.09620 0.1050 0.1060 0.09780 0.03600 0.01640 0.01660 0.005640 0.003520 3.000 2.730 0.01360 0.04490 0.02420 0.02570 0.06590 0.06220 0.06410 0.04500 0.04550 0.02930 0.06580 0.06240 0.08390 0.08350 0.07430 0.01960 0.01570 0.01570 0.004370 0.002100 4.000 1.910 0.01360 0.04190 0.02420 0.02560 0.04460 0.05660 0.05770 0.04430 0.04480 0.02150 0.02710 0.02440 0.04680 0.04580 0.04380 0.005220 0.008900 0.007900 0.003580 0.0005090 5.000 1.400 0.01350 0.03840 0.02400 0.02540 0.02790 0.04850 0.04850 0.04280 0.04320 0.02060 0.01160 0.01090 0.02150 0.02060 0.02470 0.003750 0.003130 0.002600 0.001900 0.0003080 6.000 1.110 0.01330 0.03460 0.02380 0.02510 0.01710 0.03900 0.03810 0.04020 0.04050 0.01590 0.008600 0.008920 0.009090 0.008650 0.01380 0.003410 0.001360 0.001280 0.0008070 0.0002870 7.000 0.9300 0.01320 0.03060 0.02340 0.02470 0.01120 0.02960 0.02810 0.03660 0.03670 0.009900 0.008460 0.008770 0.004510 0.004370 0.007700 0.002330 0.001150 0.001190 0.0003500 0.0002000 8.000 0.7870 0.01300 0.02660 0.02290 0.02400 0.008740 0.02120 0.01950 0.03230 0.03230 0.005400 0.007760 0.007690 0.003320 0.003320 0.004340 0.001290 0.001120 0.001120 0.0002120 0.0001110 10.00 0.5700 0.01300 0.01900 0.02200 0.02200 0.008000 0.009700 0.008500 0.02300 0.02300 0.002100 0.004500 0.004100 0.003200 0.003200 0.001400 0.0003900 0.0007200 0.0006400 0.0001900 3.300E-05 15.00 0.2600 0.01100 0.006800 0.01600 0.01600 0.005200 0.002400 0.002500 0.007900 0.007600 0.001500 0.0005900 0.0005600 0.001600 0.001600 0.0001200 0.0002600 9.000E-05 8.000E-05 1.000E-04 2.100E-05 20.00 0.1400 0.010000 0.002200 0.01100 0.010000 0.001600 0.002200 0.002300 0.002300 0.002200 0.0004900 0.0004300 0.0004700 0.0005200 0.0004900 1.400E-05 8.700E-05 6.000E-05 6.300E-05 3.300E-05 7.100E-06 30.00 0.04700 0.007100 0.001200 0.003700 0.003200 0.0002500 0.001000 0.0009300 0.0002400 0.0002100 5.700E-05 0.0002300 0.0002100 5.200E-05 4.700E-05 4.700E-07 9.400E-06 3.200E-05 2.900E-05 3.400E-06 7.700E-07 40.00 0.02100 0.004700 0.001000 0.001200 0.0009500 0.0002300 0.0003400 0.0002700 3.300E-05 2.800E-05 5.100E-05 7.600E-05 6.200E-05 7.200E-06 6.100E-06 3.100E-08 8.500E-06 1.100E-05 8.500E-06 4.600E-07 6.900E-07 60.00 0.005900 0.001900 0.0004200 0.0001700 0.0001100 9.700E-05 4.400E-05 2.900E-05 1.400E-06 1.100E-06 2.200E-05 9.700E-06 6.400E-06 3.100E-07 2.400E-07 5.100E-10 3.700E-06 1.400E-06 8.900E-07 2.000E-08 3.000E-07 100.0 0.0009000 0.0003500 6.200E-05 9.400E-06 4.200E-06 1.400E-05 2.300E-06 1.000E-06 2.100E-08 1.300E-08 3.100E-06 5.000E-07 2.300E-07 4.500E-09 2.800E-09 3.500E-12 5.100E-07 6.900E-08 3.100E-08 2.900E-10 4.200E-08 #S 59 Pr #N 21 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 3 2 2 4 2 #UBIND 4.199E+04 6835. 6441. 5965. 1511. 1338. 1243. 951.0 931.0 304.0 218.0 218.0 118.0 115.0 0.000 38.00 23.00 23.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 0.000 12.50 0.01350 0.04800 0.02380 0.02520 0.1090 0.06430 0.06690 0.04410 0.04470 0.2340 0.1500 0.1560 0.1250 0.1260 0.1490 0.6090 0.4170 0.4420 2.150 0.05000 12.30 0.01350 0.04800 0.02380 0.02520 0.1090 0.06430 0.06690 0.04410 0.04470 0.2340 0.1500 0.1560 0.1250 0.1260 0.1490 0.6070 0.4170 0.4420 2.050 0.1000 11.80 0.01350 0.04800 0.02380 0.02520 0.1090 0.06430 0.06690 0.04410 0.04470 0.2330 0.1500 0.1560 0.1250 0.1260 0.1490 0.5990 0.4160 0.4420 1.780 0.1500 11.00 0.01350 0.04800 0.02380 0.02520 0.1080 0.06430 0.06690 0.04410 0.04470 0.2330 0.1500 0.1560 0.1250 0.1260 0.1490 0.5860 0.4160 0.4410 1.420 0.2000 10.20 0.01350 0.04800 0.02380 0.02520 0.1080 0.06430 0.06690 0.04410 0.04470 0.2320 0.1500 0.1560 0.1250 0.1260 0.1490 0.5680 0.4160 0.4400 1.030 0.3000 8.900 0.01350 0.04800 0.02380 0.02520 0.1080 0.06430 0.06690 0.04410 0.04470 0.2280 0.1500 0.1560 0.1250 0.1260 0.1490 0.5200 0.4120 0.4360 0.4410 0.4000 8.150 0.01350 0.04790 0.02380 0.02520 0.1080 0.06430 0.06690 0.04410 0.04470 0.2240 0.1500 0.1560 0.1250 0.1260 0.1480 0.4610 0.4030 0.4250 0.1620 0.5000 7.730 0.01350 0.04790 0.02380 0.02520 0.1070 0.06430 0.06690 0.04410 0.04470 0.2180 0.1500 0.1550 0.1250 0.1260 0.1480 0.3950 0.3880 0.4050 0.07990 0.6000 7.400 0.01350 0.04790 0.02380 0.02520 0.1060 0.06430 0.06690 0.04410 0.04470 0.2110 0.1490 0.1550 0.1250 0.1260 0.1480 0.3270 0.3660 0.3780 0.06860 0.7000 7.060 0.01350 0.04780 0.02380 0.02520 0.1060 0.06420 0.06690 0.04410 0.04470 0.2040 0.1490 0.1550 0.1250 0.1260 0.1480 0.2640 0.3370 0.3430 0.06770 0.8000 6.680 0.01350 0.04770 0.02380 0.02520 0.1050 0.06420 0.06690 0.04410 0.04470 0.1950 0.1480 0.1540 0.1250 0.1260 0.1470 0.2070 0.3030 0.3030 0.06160 1.000 5.900 0.01350 0.04760 0.02380 0.02520 0.1030 0.06420 0.06680 0.04410 0.04470 0.1770 0.1470 0.1520 0.1240 0.1260 0.1450 0.1210 0.2280 0.2180 0.03870 1.200 5.230 0.01340 0.04740 0.02380 0.02520 0.1000 0.06420 0.06680 0.04410 0.04470 0.1570 0.1440 0.1480 0.1240 0.1250 0.1400 0.07090 0.1560 0.1430 0.01920 1.400 4.720 0.01340 0.04710 0.02380 0.02520 0.09720 0.06410 0.06670 0.04410 0.04470 0.1360 0.1390 0.1430 0.1230 0.1240 0.1350 0.04830 0.09900 0.08650 0.008730 1.600 4.350 0.01340 0.04690 0.02380 0.02520 0.09400 0.06390 0.06650 0.04410 0.04470 0.1160 0.1330 0.1360 0.1210 0.1220 0.1280 0.04110 0.05960 0.05020 0.004500 1.800 4.070 0.01340 0.04660 0.02380 0.02520 0.09050 0.06380 0.06630 0.04410 0.04470 0.09680 0.1260 0.1280 0.1180 0.1190 0.1200 0.04010 0.03570 0.02970 0.003280 2.000 3.840 0.01340 0.04620 0.02380 0.02520 0.08670 0.06350 0.06600 0.04410 0.04460 0.07990 0.1180 0.1190 0.1150 0.1150 0.1120 0.03990 0.02300 0.01990 0.003110 2.400 3.440 0.01340 0.04550 0.02370 0.02520 0.07860 0.06280 0.06510 0.04410 0.04460 0.05290 0.09820 0.09690 0.1040 0.1050 0.09460 0.03450 0.01530 0.01510 0.002930 3.000 2.850 0.01340 0.04410 0.02370 0.02520 0.06580 0.06100 0.06300 0.04390 0.04450 0.03000 0.06750 0.06420 0.08420 0.08400 0.07120 0.01940 0.01470 0.01430 0.001780 4.000 1.990 0.01330 0.04130 0.02370 0.02510 0.04530 0.05590 0.05710 0.04340 0.04390 0.02100 0.02890 0.02590 0.04840 0.04750 0.04210 0.005170 0.008740 0.007500 0.0004420 5.000 1.450 0.01320 0.03800 0.02350 0.02490 0.02880 0.04840 0.04850 0.04200 0.04240 0.02040 0.01220 0.01130 0.02300 0.02220 0.02410 0.003450 0.003210 0.002550 0.0002490 6.000 1.140 0.01310 0.03440 0.02330 0.02470 0.01780 0.03940 0.03860 0.03960 0.04000 0.01630 0.008480 0.008740 0.009970 0.009510 0.01370 0.003220 0.001330 0.001180 0.0002370 7.000 0.9480 0.01290 0.03050 0.02300 0.02420 0.01160 0.03030 0.02890 0.03640 0.03660 0.01060 0.008280 0.008620 0.004820 0.004650 0.007780 0.002310 0.001060 0.001050 0.0001730 8.000 0.8010 0.01280 0.02670 0.02250 0.02370 0.008840 0.02210 0.02040 0.03240 0.03240 0.005920 0.007770 0.007770 0.003340 0.003310 0.004450 0.001330 0.001040 0.001010 1.000E-04 10.00 0.5800 0.01200 0.01900 0.02100 0.02200 0.007800 0.010000 0.009100 0.02400 0.02400 0.002200 0.004800 0.004400 0.003100 0.003100 0.001500 0.0004000 0.0007200 0.0006200 3.000E-05 15.00 0.2700 0.01100 0.007100 0.01600 0.01600 0.005400 0.002400 0.002500 0.008400 0.008100 0.001500 0.0006400 0.0005900 0.001700 0.001600 0.0001300 0.0002500 9.300E-05 7.700E-05 1.800E-05 20.00 0.1400 0.009900 0.002400 0.01100 0.010000 0.001700 0.002200 0.002300 0.002600 0.002400 0.0005400 0.0004300 0.0004700 0.0005600 0.0005300 1.600E-05 9.100E-05 5.500E-05 5.700E-05 6.600E-06 30.00 0.04900 0.007100 0.001200 0.003900 0.003300 0.0002500 0.001100 0.0009800 0.0002700 0.0002400 5.900E-05 0.0002400 0.0002200 5.900E-05 5.300E-05 5.400E-07 9.200E-06 3.200E-05 2.700E-05 6.600E-07 40.00 0.02200 0.004700 0.001000 0.001300 0.001000 0.0002300 0.0003700 0.0003000 3.800E-05 3.200E-05 5.200E-05 8.300E-05 6.700E-05 8.300E-06 7.100E-06 3.700E-08 8.000E-06 1.100E-05 8.400E-06 5.700E-07 60.00 0.006200 0.002000 0.0004400 0.0001900 0.0001200 1.000E-04 4.900E-05 3.200E-05 1.700E-06 1.300E-06 2.300E-05 1.100E-05 7.200E-06 3.600E-07 2.800E-07 6.000E-10 3.600E-06 1.400E-06 9.000E-07 2.600E-07 100.0 0.0009600 0.0003700 6.700E-05 1.100E-05 4.700E-06 1.500E-05 2.600E-06 1.200E-06 2.500E-08 1.500E-08 3.400E-06 5.700E-07 2.600E-07 5.400E-09 3.300E-09 3.800E-12 5.300E-07 7.500E-08 3.200E-08 3.800E-08 #S 60 Nd #N 21 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 4 2 2 4 2 #UBIND 4.357E+04 7126. 6722. 6208. 1576. 1403. 1298. 1000. 978.0 321.0 230.0 230.0 124.0 121.0 0.000 38.00 22.00 22.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 0.000 12.40 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2280 0.1460 0.1520 0.1210 0.1230 0.1410 0.5970 0.4070 0.4340 2.130 0.05000 12.20 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2280 0.1460 0.1520 0.1210 0.1230 0.1410 0.5950 0.4070 0.4340 2.030 0.1000 11.70 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2280 0.1460 0.1520 0.1210 0.1230 0.1410 0.5870 0.4070 0.4340 1.770 0.1500 11.00 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2270 0.1460 0.1520 0.1210 0.1230 0.1410 0.5750 0.4070 0.4340 1.420 0.2000 10.20 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2260 0.1460 0.1520 0.1210 0.1230 0.1410 0.5580 0.4060 0.4330 1.040 0.3000 8.880 0.01320 0.04700 0.02330 0.02480 0.1060 0.06280 0.06540 0.04310 0.04360 0.2230 0.1460 0.1520 0.1210 0.1230 0.1410 0.5130 0.4030 0.4280 0.4520 0.4000 8.130 0.01320 0.04700 0.02330 0.02480 0.1050 0.06280 0.06540 0.04310 0.04360 0.2190 0.1460 0.1520 0.1210 0.1230 0.1410 0.4570 0.3950 0.4180 0.1670 0.5000 7.720 0.01320 0.04690 0.02330 0.02480 0.1050 0.06280 0.06540 0.04310 0.04360 0.2130 0.1460 0.1520 0.1210 0.1230 0.1410 0.3930 0.3820 0.4000 0.07990 0.6000 7.400 0.01320 0.04690 0.02330 0.02480 0.1040 0.06280 0.06540 0.04310 0.04360 0.2070 0.1460 0.1510 0.1210 0.1230 0.1410 0.3290 0.3610 0.3740 0.06640 0.7000 7.070 0.01320 0.04680 0.02330 0.02480 0.1030 0.06280 0.06540 0.04310 0.04360 0.2000 0.1450 0.1510 0.1210 0.1230 0.1400 0.2670 0.3340 0.3420 0.06570 0.8000 6.710 0.01320 0.04680 0.02330 0.02480 0.1020 0.06270 0.06540 0.04310 0.04360 0.1920 0.1450 0.1500 0.1210 0.1230 0.1400 0.2110 0.3030 0.3030 0.06060 1.000 5.960 0.01320 0.04660 0.02330 0.02480 0.1010 0.06270 0.06530 0.04310 0.04360 0.1750 0.1430 0.1480 0.1210 0.1220 0.1380 0.1250 0.2310 0.2220 0.03940 1.200 5.310 0.01320 0.04640 0.02330 0.02480 0.09820 0.06270 0.06530 0.04310 0.04360 0.1560 0.1400 0.1450 0.1200 0.1220 0.1350 0.07380 0.1610 0.1480 0.02010 1.400 4.800 0.01320 0.04620 0.02330 0.02480 0.09540 0.06260 0.06520 0.04310 0.04360 0.1360 0.1360 0.1410 0.1190 0.1210 0.1300 0.04910 0.1040 0.09100 0.009290 1.600 4.420 0.01320 0.04600 0.02330 0.02480 0.09240 0.06250 0.06510 0.04310 0.04360 0.1160 0.1310 0.1340 0.1180 0.1190 0.1240 0.04050 0.06410 0.05350 0.004670 1.800 4.130 0.01320 0.04570 0.02330 0.02480 0.08910 0.06230 0.06490 0.04310 0.04360 0.09820 0.1250 0.1270 0.1160 0.1170 0.1180 0.03900 0.03870 0.03170 0.003210 2.000 3.910 0.01320 0.04540 0.02330 0.02480 0.08560 0.06210 0.06460 0.04310 0.04360 0.08160 0.1170 0.1180 0.1120 0.1130 0.1100 0.03890 0.02450 0.02060 0.002950 2.400 3.510 0.01310 0.04460 0.02320 0.02480 0.07790 0.06140 0.06380 0.04310 0.04360 0.05480 0.09870 0.09770 0.1030 0.1040 0.09510 0.03460 0.01520 0.01460 0.002840 3.000 2.920 0.01310 0.04340 0.02320 0.02470 0.06570 0.05980 0.06190 0.04290 0.04350 0.03110 0.06930 0.06620 0.08470 0.08470 0.07330 0.02050 0.01430 0.01400 0.001820 4.000 2.060 0.01300 0.04070 0.02320 0.02470 0.04590 0.05520 0.05640 0.04240 0.04290 0.02070 0.03090 0.02770 0.05050 0.04970 0.04480 0.005580 0.009190 0.007880 0.0004740 5.000 1.500 0.01290 0.03760 0.02300 0.02450 0.02970 0.04820 0.04840 0.04120 0.04170 0.02020 0.01310 0.01190 0.02490 0.02410 0.02640 0.003370 0.003570 0.002790 0.0002390 6.000 1.170 0.01280 0.03410 0.02280 0.02420 0.01860 0.03980 0.03900 0.03910 0.03940 0.01680 0.008500 0.008670 0.01110 0.01060 0.01530 0.003220 0.001430 0.001210 0.0002290 7.000 0.9650 0.01270 0.03040 0.02250 0.02390 0.01210 0.03100 0.02960 0.03610 0.03630 0.01120 0.008140 0.008520 0.005290 0.005070 0.008890 0.002420 0.001040 0.001020 0.0001760 8.000 0.8140 0.01260 0.02680 0.02210 0.02330 0.008970 0.02290 0.02130 0.03240 0.03250 0.006510 0.007800 0.007860 0.003440 0.003390 0.005180 0.001460 0.001030 0.0009960 0.0001070 10.00 0.5900 0.01200 0.02000 0.02100 0.02200 0.007700 0.01100 0.009800 0.02400 0.02400 0.002300 0.005100 0.004700 0.003100 0.003100 0.001800 0.0004400 0.0007500 0.0006500 3.200E-05 15.00 0.2700 0.01100 0.007500 0.01600 0.01600 0.005500 0.002400 0.002500 0.008900 0.008600 0.001600 0.0007100 0.0006300 0.001800 0.001700 0.0001600 0.0002500 1.000E-04 8.300E-05 1.800E-05 20.00 0.1400 0.009800 0.002500 0.01100 0.01100 0.001900 0.002200 0.002300 0.002800 0.002600 0.0006000 0.0004200 0.0004700 0.0006200 0.0005800 2.100E-05 1.000E-04 5.500E-05 5.600E-05 7.100E-06 30.00 0.05100 0.007100 0.001200 0.004100 0.003500 0.0002600 0.001100 0.001000 0.0003000 0.0002700 6.100E-05 0.0002500 0.0002300 6.800E-05 6.100E-05 7.100E-07 9.500E-06 3.300E-05 2.900E-05 6.700E-07 40.00 0.02300 0.004800 0.001000 0.001400 0.001100 0.0002300 0.0004000 0.0003200 4.300E-05 3.700E-05 5.200E-05 9.000E-05 7.300E-05 9.700E-06 8.300E-06 4.900E-08 8.000E-06 1.200E-05 9.100E-06 5.600E-07 60.00 0.006500 0.002000 0.0004600 0.0002000 0.0001300 0.0001100 5.500E-05 3.600E-05 2.000E-06 1.500E-06 2.500E-05 1.200E-05 8.100E-06 4.300E-07 3.300E-07 8.200E-10 3.800E-06 1.600E-06 9.900E-07 2.700E-07 100.0 0.001000 0.0003900 7.200E-05 1.200E-05 5.200E-06 1.600E-05 3.000E-06 1.300E-06 3.000E-08 1.800E-08 3.700E-06 6.500E-07 3.000E-07 6.400E-09 3.900E-09 4.800E-12 5.700E-07 8.600E-08 3.700E-08 4.000E-08 #S 61 Pm #N 21 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 5 2 2 4 2 #UBIND 4.518E+04 7430. 7015. 6465. 1656. 1478. 1364. 1060. 1034. 337.0 242.0 242.0 133.0 129.0 0.000 38.00 22.00 22.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 0.000 12.30 0.01290 0.04610 0.02280 0.02430 0.1040 0.06130 0.06400 0.04210 0.04260 0.2230 0.1420 0.1480 0.1180 0.1190 0.1340 0.5860 0.3990 0.4270 2.110 0.05000 12.20 0.01290 0.04610 0.02280 0.02430 0.1040 0.06130 0.06400 0.04210 0.04260 0.2230 0.1420 0.1480 0.1180 0.1190 0.1340 0.5840 0.3990 0.4270 2.010 0.1000 11.60 0.01290 0.04610 0.02280 0.02430 0.1040 0.06130 0.06400 0.04210 0.04260 0.2220 0.1420 0.1480 0.1180 0.1190 0.1340 0.5760 0.3990 0.4270 1.760 0.1500 10.90 0.01290 0.04610 0.02280 0.02430 0.1040 0.06130 0.06400 0.04210 0.04260 0.2210 0.1420 0.1480 0.1180 0.1190 0.1340 0.5650 0.3990 0.4270 1.410 0.2000 10.10 0.01290 0.04610 0.02280 0.02430 0.1040 0.06130 0.06400 0.04210 0.04260 0.2200 0.1420 0.1480 0.1180 0.1190 0.1340 0.5490 0.3980 0.4260 1.050 0.3000 8.860 0.01290 0.04600 0.02280 0.02430 0.1030 0.06130 0.06400 0.04210 0.04260 0.2180 0.1420 0.1480 0.1180 0.1190 0.1340 0.5060 0.3950 0.4220 0.4620 0.4000 8.110 0.01290 0.04600 0.02280 0.02430 0.1030 0.06130 0.06400 0.04210 0.04260 0.2140 0.1420 0.1480 0.1180 0.1190 0.1340 0.4530 0.3880 0.4120 0.1730 0.5000 7.700 0.01290 0.04600 0.02280 0.02430 0.1020 0.06130 0.06400 0.04210 0.04260 0.2090 0.1420 0.1480 0.1180 0.1190 0.1340 0.3920 0.3750 0.3960 0.08020 0.6000 7.390 0.01290 0.04590 0.02280 0.02430 0.1020 0.06130 0.06400 0.04210 0.04260 0.2030 0.1420 0.1480 0.1180 0.1190 0.1340 0.3300 0.3570 0.3710 0.06440 0.7000 7.080 0.01290 0.04590 0.02280 0.02430 0.1010 0.06130 0.06400 0.04210 0.04260 0.1960 0.1420 0.1470 0.1180 0.1190 0.1340 0.2700 0.3320 0.3400 0.06370 0.8000 6.740 0.01290 0.04580 0.02280 0.02430 0.1000 0.06130 0.06400 0.04210 0.04260 0.1890 0.1410 0.1470 0.1180 0.1190 0.1340 0.2160 0.3020 0.3040 0.05960 1.000 6.020 0.01290 0.04570 0.02280 0.02430 0.09850 0.06130 0.06390 0.04210 0.04260 0.1720 0.1400 0.1450 0.1180 0.1190 0.1320 0.1300 0.2330 0.2250 0.03990 1.200 5.370 0.01290 0.04550 0.02280 0.02430 0.09630 0.06120 0.06390 0.04210 0.04260 0.1540 0.1370 0.1420 0.1170 0.1190 0.1300 0.07670 0.1660 0.1520 0.02100 1.400 4.860 0.01290 0.04530 0.02280 0.02430 0.09370 0.06120 0.06380 0.04210 0.04260 0.1360 0.1340 0.1380 0.1170 0.1180 0.1260 0.05020 0.1100 0.09530 0.009860 1.600 4.480 0.01290 0.04510 0.02280 0.02430 0.09090 0.06110 0.06370 0.04210 0.04260 0.1170 0.1290 0.1320 0.1150 0.1160 0.1210 0.04020 0.06840 0.05670 0.004880 1.800 4.190 0.01290 0.04480 0.02280 0.02430 0.08780 0.06090 0.06350 0.04210 0.04260 0.09950 0.1230 0.1250 0.1130 0.1140 0.1150 0.03790 0.04170 0.03370 0.003180 2.000 3.960 0.01290 0.04450 0.02280 0.02430 0.08440 0.06070 0.06320 0.04210 0.04260 0.08330 0.1160 0.1170 0.1100 0.1110 0.1090 0.03780 0.02620 0.02150 0.002820 2.400 3.570 0.01290 0.04380 0.02280 0.02430 0.07720 0.06010 0.06250 0.04210 0.04260 0.05670 0.09890 0.09820 0.1020 0.1030 0.09500 0.03460 0.01520 0.01430 0.002730 3.000 3.000 0.01280 0.04260 0.02270 0.02430 0.06550 0.05870 0.06080 0.04200 0.04250 0.03220 0.07100 0.06800 0.08510 0.08510 0.07470 0.02150 0.01400 0.01370 0.001860 4.000 2.140 0.01280 0.04010 0.02270 0.02420 0.04650 0.05440 0.05580 0.04150 0.04200 0.02040 0.03290 0.02960 0.05240 0.05160 0.04700 0.006050 0.009560 0.008200 0.0005080 5.000 1.550 0.01270 0.03710 0.02260 0.02410 0.03060 0.04800 0.04830 0.04050 0.04090 0.01990 0.01400 0.01260 0.02680 0.02600 0.02840 0.003300 0.003940 0.003050 0.0002310 6.000 1.200 0.01260 0.03380 0.02240 0.02380 0.01930 0.04000 0.03940 0.03860 0.03890 0.01710 0.008570 0.008630 0.01230 0.01170 0.01690 0.003180 0.001540 0.001260 0.0002210 7.000 0.9840 0.01250 0.03030 0.02210 0.02350 0.01250 0.03160 0.03030 0.03580 0.03610 0.01190 0.007990 0.008390 0.005820 0.005560 0.009980 0.002510 0.001030 0.0009900 0.0001780 8.000 0.8280 0.01230 0.02680 0.02170 0.02300 0.009140 0.02380 0.02210 0.03240 0.03250 0.007100 0.007780 0.007910 0.003580 0.003500 0.005920 0.001580 0.001010 0.0009750 0.0001130 10.00 0.6000 0.01200 0.02000 0.02100 0.02200 0.007600 0.01200 0.010000 0.02500 0.02400 0.002400 0.005400 0.005000 0.003000 0.003000 0.002100 0.0004800 0.0007800 0.0006800 3.400E-05 15.00 0.2800 0.01100 0.007800 0.01600 0.01600 0.005700 0.002500 0.002500 0.009300 0.009100 0.001600 0.0007900 0.0006800 0.001900 0.001800 0.0002000 0.0002500 0.0001200 8.900E-05 1.700E-05 20.00 0.1500 0.009700 0.002700 0.01100 0.01100 0.002100 0.002100 0.002300 0.003000 0.002900 0.0006600 0.0004200 0.0004700 0.0006800 0.0006400 2.600E-05 0.0001100 5.400E-05 5.500E-05 7.500E-06 30.00 0.05300 0.007100 0.001100 0.004300 0.003700 0.0002600 0.001200 0.001100 0.0003400 0.0003000 6.400E-05 0.0002600 0.0002500 7.800E-05 6.900E-05 9.200E-07 1.000E-05 3.500E-05 3.000E-05 6.900E-07 40.00 0.02300 0.004900 0.001000 0.001500 0.001200 0.0002300 0.0004300 0.0003500 4.900E-05 4.200E-05 5.200E-05 9.800E-05 8.000E-05 1.100E-05 9.600E-06 6.400E-08 7.900E-06 1.300E-05 9.700E-06 5.400E-07 60.00 0.006800 0.002100 0.0004800 0.0002200 0.0001400 0.0001100 6.100E-05 4.000E-05 2.300E-06 1.800E-06 2.600E-05 1.400E-05 9.000E-06 5.100E-07 3.900E-07 1.100E-09 4.000E-06 1.800E-06 1.100E-06 2.800E-07 100.0 0.001100 0.0004200 7.800E-05 1.300E-05 5.800E-06 1.800E-05 3.400E-06 1.500E-06 3.500E-08 2.100E-08 4.000E-06 7.500E-07 3.400E-07 7.700E-09 4.700E-09 6.100E-12 6.200E-07 9.800E-08 4.100E-08 4.200E-08 #S 62 Sm #N 21 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 2 2 4 2 #UBIND 4.684E+04 7737. 7312. 6717. 1728. 1546. 1425. 1111. 1085. 351.0 251.0 251.0 137.0 132.0 0.000 33.00 26.00 26.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 0.000 12.30 0.01270 0.04520 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2170 0.1390 0.1450 0.1150 0.1160 0.1290 0.5750 0.3910 0.4200 2.090 0.05000 12.10 0.01270 0.04520 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2170 0.1390 0.1450 0.1150 0.1160 0.1290 0.5730 0.3910 0.4200 2.000 0.1000 11.60 0.01270 0.04520 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2170 0.1390 0.1450 0.1150 0.1160 0.1290 0.5660 0.3910 0.4200 1.750 0.1500 10.90 0.01270 0.04520 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2160 0.1390 0.1450 0.1150 0.1160 0.1290 0.5550 0.3910 0.4200 1.410 0.2000 10.10 0.01270 0.04520 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2150 0.1390 0.1450 0.1150 0.1160 0.1290 0.5400 0.3900 0.4190 1.050 0.3000 8.840 0.01270 0.04510 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2130 0.1390 0.1450 0.1150 0.1160 0.1290 0.5000 0.3870 0.4160 0.4720 0.4000 8.090 0.01270 0.04510 0.02230 0.02390 0.1010 0.05990 0.06260 0.04120 0.04170 0.2090 0.1390 0.1450 0.1150 0.1160 0.1290 0.4490 0.3810 0.4070 0.1780 0.5000 7.680 0.01270 0.04510 0.02230 0.02390 0.1000 0.05990 0.06260 0.04120 0.04170 0.2040 0.1390 0.1450 0.1150 0.1160 0.1290 0.3910 0.3690 0.3910 0.08080 0.6000 7.390 0.01270 0.04500 0.02230 0.02390 0.09960 0.05990 0.06260 0.04120 0.04170 0.1990 0.1390 0.1440 0.1150 0.1160 0.1290 0.3310 0.3520 0.3680 0.06260 0.7000 7.090 0.01270 0.04500 0.02230 0.02390 0.09900 0.05990 0.06260 0.04120 0.04170 0.1930 0.1380 0.1440 0.1150 0.1160 0.1290 0.2730 0.3290 0.3380 0.06180 0.8000 6.760 0.01270 0.04490 0.02230 0.02390 0.09830 0.05990 0.06260 0.04120 0.04170 0.1860 0.1380 0.1440 0.1150 0.1160 0.1280 0.2190 0.3010 0.3040 0.05850 1.000 6.060 0.01270 0.04480 0.02230 0.02390 0.09650 0.05990 0.06260 0.04120 0.04170 0.1700 0.1370 0.1420 0.1150 0.1160 0.1270 0.1340 0.2360 0.2280 0.04030 1.200 5.430 0.01270 0.04460 0.02230 0.02390 0.09440 0.05990 0.06250 0.04120 0.04170 0.1530 0.1340 0.1390 0.1140 0.1160 0.1250 0.07970 0.1700 0.1560 0.02180 1.400 4.920 0.01270 0.04440 0.02230 0.02390 0.09200 0.05980 0.06250 0.04120 0.04170 0.1350 0.1310 0.1360 0.1140 0.1150 0.1220 0.05140 0.1140 0.09930 0.01040 1.600 4.540 0.01270 0.04420 0.02230 0.02390 0.08940 0.05970 0.06230 0.04120 0.04170 0.1180 0.1270 0.1300 0.1120 0.1140 0.1180 0.04000 0.07260 0.05990 0.005120 1.800 4.250 0.01270 0.04400 0.02230 0.02390 0.08640 0.05960 0.06220 0.04120 0.04170 0.1010 0.1210 0.1240 0.1110 0.1120 0.1130 0.03700 0.04480 0.03570 0.003180 2.000 4.020 0.01260 0.04370 0.02230 0.02390 0.08330 0.05940 0.06200 0.04120 0.04170 0.08480 0.1150 0.1160 0.1080 0.1090 0.1070 0.03680 0.02810 0.02250 0.002710 2.400 3.630 0.01260 0.04300 0.02230 0.02390 0.07640 0.05890 0.06130 0.04120 0.04170 0.05840 0.09900 0.09860 0.1010 0.1020 0.09460 0.03430 0.01530 0.01400 0.002630 3.000 3.070 0.01260 0.04190 0.02230 0.02380 0.06530 0.05750 0.05970 0.04110 0.04160 0.03330 0.07250 0.06960 0.08520 0.08530 0.07560 0.02240 0.01360 0.01330 0.001880 4.000 2.210 0.01250 0.03950 0.02220 0.02380 0.04700 0.05370 0.05510 0.04070 0.04120 0.02020 0.03480 0.03130 0.05410 0.05330 0.04890 0.006550 0.009870 0.008460 0.0005450 5.000 1.600 0.01250 0.03670 0.02210 0.02360 0.03130 0.04770 0.04810 0.03970 0.04020 0.01960 0.01500 0.01340 0.02870 0.02780 0.03020 0.003250 0.004310 0.003300 0.0002260 6.000 1.230 0.01240 0.03360 0.02200 0.02340 0.02000 0.04020 0.03970 0.03800 0.03840 0.01730 0.008710 0.008640 0.01350 0.01290 0.01840 0.003130 0.001670 0.001320 0.0002130 7.000 1.000 0.01220 0.03020 0.02170 0.02310 0.01300 0.03220 0.03090 0.03550 0.03580 0.01250 0.007850 0.008250 0.006420 0.006100 0.01110 0.002570 0.001030 0.0009650 0.0001790 8.000 0.8430 0.01210 0.02680 0.02140 0.02260 0.009350 0.02450 0.02290 0.03230 0.03240 0.007700 0.007730 0.007920 0.003770 0.003650 0.006660 0.001690 0.0009850 0.0009500 0.0001190 10.00 0.6100 0.01200 0.02000 0.02000 0.02100 0.007500 0.01300 0.01100 0.02500 0.02500 0.002600 0.005600 0.005200 0.003000 0.003000 0.002500 0.0005300 0.0008100 0.0007000 3.700E-05 15.00 0.2900 0.01100 0.008100 0.01600 0.01600 0.005800 0.002500 0.002500 0.009800 0.009500 0.001600 0.0008800 0.0007300 0.002000 0.001900 0.0002400 0.0002500 0.0001300 9.700E-05 1.700E-05 20.00 0.1500 0.009700 0.002800 0.01100 0.01100 0.002300 0.002100 0.002300 0.003300 0.003100 0.0007200 0.0004200 0.0004600 0.0007400 0.0007000 3.200E-05 0.0001200 5.300E-05 5.400E-05 8.000E-06 30.00 0.05500 0.007100 0.001100 0.004500 0.003800 0.0002700 0.001200 0.001100 0.0003800 0.0003400 6.900E-05 0.0002800 0.0002600 8.800E-05 7.800E-05 1.200E-06 1.100E-05 3.600E-05 3.100E-05 7.200E-07 40.00 0.02400 0.004900 0.001000 0.001600 0.001200 0.0002300 0.0004600 0.0003800 5.600E-05 4.800E-05 5.200E-05 0.0001100 8.700E-05 1.300E-05 1.100E-05 8.200E-08 7.800E-06 1.400E-05 1.000E-05 5.200E-07 60.00 0.007100 0.002100 0.0005000 0.0002500 0.0001600 0.0001200 6.700E-05 4.400E-05 2.600E-06 2.000E-06 2.700E-05 1.500E-05 1.000E-05 6.000E-07 4.500E-07 1.400E-09 4.200E-06 2.000E-06 1.200E-06 2.800E-07 100.0 0.001200 0.0004400 8.400E-05 1.500E-05 6.500E-06 1.900E-05 3.800E-06 1.700E-06 4.100E-08 2.500E-08 4.400E-06 8.600E-07 3.800E-07 9.200E-09 5.500E-09 7.800E-12 6.700E-07 1.100E-07 4.600E-08 4.500E-08 #S 63 Eu #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 1 2 2 4 2 #UBIND 4.852E+04 8052. 7618. 6977. 1805. 1619. 1486. 1166. 1136. 366.0 261.0 261.0 141.0 136.0 0.000 0.000 37.00 27.00 27.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 12.20 0.01240 0.04430 0.02190 0.02340 0.09920 0.05860 0.06130 0.04030 0.04080 0.2120 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.5640 0.3840 0.4130 2.070 0.05000 12.00 0.01240 0.04430 0.02190 0.02340 0.09920 0.05860 0.06130 0.04030 0.04080 0.2120 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.5620 0.3840 0.4130 1.980 0.1000 11.50 0.01240 0.04430 0.02190 0.02340 0.09920 0.05860 0.06130 0.04030 0.04080 0.2120 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.5560 0.3840 0.4130 1.740 0.1500 10.80 0.01240 0.04430 0.02190 0.02340 0.09910 0.05860 0.06130 0.04030 0.04080 0.2110 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.5460 0.3830 0.4130 1.410 0.2000 10.10 0.01240 0.04430 0.02190 0.02340 0.09900 0.05860 0.06130 0.04030 0.04080 0.2100 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.5310 0.3830 0.4120 1.060 0.3000 8.820 0.01240 0.04420 0.02190 0.02340 0.09880 0.05860 0.06130 0.04030 0.04080 0.2080 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.4930 0.3810 0.4090 0.4820 0.4000 8.070 0.01240 0.04420 0.02190 0.02340 0.09850 0.05860 0.06130 0.04030 0.04080 0.2050 0.1350 0.1420 0.1120 0.1140 0.1240 0.1300 0.4440 0.3750 0.4000 0.1840 0.5000 7.670 0.01240 0.04420 0.02190 0.02340 0.09800 0.05860 0.06130 0.04030 0.04080 0.2000 0.1350 0.1410 0.1120 0.1140 0.1240 0.1300 0.3890 0.3640 0.3860 0.08170 0.6000 7.380 0.01240 0.04410 0.02190 0.02340 0.09750 0.05860 0.06130 0.04030 0.04080 0.1950 0.1350 0.1410 0.1120 0.1140 0.1240 0.1300 0.3310 0.3480 0.3640 0.06100 0.7000 7.090 0.01240 0.04410 0.02190 0.02340 0.09690 0.05860 0.06130 0.04030 0.04080 0.1890 0.1350 0.1410 0.1120 0.1140 0.1240 0.1300 0.2750 0.3260 0.3360 0.05990 0.8000 6.780 0.01240 0.04410 0.02190 0.02340 0.09620 0.05860 0.06130 0.04030 0.04080 0.1830 0.1350 0.1400 0.1120 0.1140 0.1240 0.1300 0.2230 0.2990 0.3030 0.05730 1.000 6.110 0.01240 0.04390 0.02190 0.02340 0.09460 0.05860 0.06130 0.04030 0.04080 0.1680 0.1330 0.1390 0.1120 0.1130 0.1230 0.1280 0.1380 0.2370 0.2300 0.04060 1.200 5.490 0.01240 0.04380 0.02190 0.02340 0.09270 0.05850 0.06120 0.04030 0.04080 0.1520 0.1310 0.1370 0.1120 0.1130 0.1210 0.1250 0.08290 0.1740 0.1600 0.02260 1.400 4.990 0.01240 0.04360 0.02190 0.02340 0.09040 0.05850 0.06120 0.04030 0.04080 0.1350 0.1290 0.1330 0.1110 0.1120 0.1180 0.1220 0.05290 0.1190 0.1040 0.01100 1.600 4.600 0.01240 0.04340 0.02190 0.02340 0.08790 0.05840 0.06110 0.04030 0.04080 0.1180 0.1250 0.1280 0.1100 0.1110 0.1150 0.1170 0.04000 0.07670 0.06350 0.005410 1.800 4.310 0.01240 0.04310 0.02180 0.02340 0.08510 0.05830 0.06090 0.04030 0.04080 0.1010 0.1200 0.1230 0.1080 0.1100 0.1100 0.1120 0.03620 0.04780 0.03810 0.003220 2.000 4.080 0.01240 0.04290 0.02180 0.02340 0.08210 0.05810 0.06070 0.04030 0.04080 0.08610 0.1140 0.1150 0.1060 0.1070 0.1050 0.1060 0.03580 0.03000 0.02380 0.002620 2.400 3.690 0.01240 0.04230 0.02180 0.02340 0.07560 0.05760 0.06010 0.04020 0.04080 0.06010 0.09890 0.09880 0.09960 0.1000 0.09390 0.09300 0.03400 0.01550 0.01390 0.002520 3.000 3.140 0.01240 0.04120 0.02180 0.02340 0.06510 0.05640 0.05870 0.04020 0.04070 0.03460 0.07380 0.07110 0.08520 0.08540 0.07620 0.07440 0.02320 0.01310 0.01290 0.001890 4.000 2.280 0.01230 0.03900 0.02180 0.02330 0.04740 0.05290 0.05440 0.03980 0.04030 0.02010 0.03680 0.03310 0.05570 0.05490 0.05050 0.04840 0.007120 0.01010 0.008750 0.0005840 5.000 1.660 0.01220 0.03630 0.02170 0.02320 0.03210 0.04730 0.04790 0.03900 0.03940 0.01920 0.01610 0.01420 0.03050 0.02960 0.03190 0.03030 0.003240 0.004650 0.003580 0.0002230 6.000 1.270 0.01210 0.03330 0.02150 0.02300 0.02080 0.04030 0.03990 0.03740 0.03780 0.01740 0.008930 0.008700 0.01480 0.01410 0.01980 0.01860 0.003080 0.001820 0.001410 0.0002040 7.000 1.030 0.01200 0.03010 0.02130 0.02270 0.01350 0.03260 0.03150 0.03510 0.03540 0.01300 0.007720 0.008110 0.007080 0.006700 0.01210 0.01140 0.002630 0.001040 0.0009520 0.0001780 8.000 0.8590 0.01190 0.02680 0.02100 0.02230 0.009590 0.02520 0.02360 0.03220 0.03240 0.008290 0.007640 0.007900 0.004000 0.003850 0.007420 0.006930 0.001800 0.0009550 0.0009300 0.0001230 10.00 0.6200 0.01190 0.02000 0.02000 0.02100 0.007400 0.01300 0.01200 0.02500 0.02500 0.002900 0.005900 0.005500 0.002900 0.003000 0.002800 0.002600 0.0005900 0.0008200 0.0007200 4.000E-05 15.00 0.3000 0.01100 0.008500 0.01600 0.01600 0.006000 0.002600 0.002600 0.010000 0.010000 0.001600 0.0009800 0.0008000 0.002100 0.002000 0.0002900 0.0002600 0.0002500 0.0001500 0.0001100 1.700E-05 20.00 0.1600 0.009600 0.003000 0.01100 0.01100 0.002400 0.002100 0.002300 0.003500 0.003300 0.0007800 0.0004100 0.0004600 0.0008100 0.0007600 3.900E-05 3.500E-05 0.0001300 5.200E-05 5.300E-05 8.400E-06 30.00 0.05700 0.007100 0.001100 4.610 0.004000 0.0002800 0.001300 0.001200 0.0004200 0.0003800 7.400E-05 0.0002900 0.0002700 9.900E-05 8.800E-05 1.500E-06 1.300E-06 1.200E-05 3.700E-05 3.200E-05 7.600E-07 40.00 0.02500 0.005000 0.001000 0.001700 0.001300 0.0002200 0.0005000 0.0004000 6.400E-05 5.500E-05 5.100E-05 0.0001100 9.400E-05 1.500E-05 1.300E-05 1.100E-07 8.900E-08 7.800E-06 1.500E-05 1.100E-05 5.100E-07 60.00 0.007500 0.002200 0.0005200 0.0002700 0.0001700 0.0001200 7.400E-05 4.900E-05 3.100E-06 2.400E-06 2.900E-05 1.700E-05 1.100E-05 7.000E-07 5.300E-07 1.800E-09 1.400E-09 4.400E-06 2.200E-06 1.300E-06 2.900E-07 100.0 0.001200 0.0004600 9.000E-05 1.700E-05 7.200E-06 2.100E-05 4.300E-06 1.900E-06 4.900E-08 2.900E-08 4.800E-06 9.800E-07 4.300E-07 1.100E-08 6.500E-09 9.900E-12 6.600E-12 7.300E-07 1.300E-07 5.100E-08 4.800E-08 #S 64 Gd #N 23 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 1 2 2 4 1 2 #UBIND 5.024E+04 8376. 7931. 7243. 1888. 1695. 1551. 1225. 1193. 383.0 311.0 272.0 147.0 142.0 0.000 0.000 43.00 28.00 28.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 0.000 12.00 0.01220 0.04340 0.02140 0.02300 0.09710 0.05730 0.06010 0.03940 0.03990 0.2070 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.5380 0.3660 0.3910 0.4680 1.930 0.05000 11.90 0.01220 0.04340 0.02140 0.02300 0.09710 0.05730 0.06010 0.03940 0.03990 0.2070 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.5360 0.3660 0.3910 0.4680 1.850 0.1000 11.40 0.01220 0.04340 0.02140 0.02300 0.09710 0.05730 0.06010 0.03940 0.03990 0.2060 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.5310 0.3660 0.3910 0.4680 1.660 0.1500 10.90 0.01220 0.04340 0.02140 0.02300 0.09700 0.05730 0.06010 0.03940 0.03990 0.2060 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.5220 0.3660 0.3910 0.4670 1.370 0.2000 10.20 0.01220 0.04340 0.02140 0.02300 0.09690 0.05730 0.06010 0.03940 0.03990 0.2050 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.5090 0.3660 0.3900 0.4670 1.060 0.3000 9.050 0.01220 0.04340 0.02140 0.02300 0.09670 0.05730 0.06010 0.03940 0.03990 0.2030 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.4750 0.3640 0.3880 0.4630 0.5300 0.4000 8.290 0.01220 0.04340 0.02140 0.02300 0.09640 0.05730 0.06010 0.03940 0.03990 0.2000 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.4320 0.3590 0.3810 0.4520 0.2210 0.5000 7.850 0.01220 0.04330 0.02140 0.02300 0.09600 0.05730 0.06010 0.03940 0.03990 0.1960 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.3820 0.3500 0.3700 0.4280 0.09630 0.6000 7.530 0.01220 0.04330 0.02140 0.02300 0.09550 0.05730 0.06010 0.03940 0.03990 0.1910 0.1320 0.1380 0.1090 0.1100 0.1130 0.1160 0.3300 0.3360 0.3520 0.3930 0.06330 0.7000 7.240 0.01220 0.04330 0.02140 0.02300 0.09500 0.05730 0.06010 0.03940 0.03990 0.1850 0.1310 0.1370 0.1080 0.1100 0.1130 0.1160 0.2780 0.3180 0.3290 0.3510 0.05950 0.8000 6.910 0.01220 0.04320 0.02140 0.02300 0.09430 0.05730 0.06010 0.03940 0.03990 0.1790 0.1310 0.1370 0.1080 0.1100 0.1130 0.1160 0.2290 0.2950 0.3010 0.3050 0.05870 1.000 6.220 0.01220 0.04310 0.02140 0.02300 0.09280 0.05730 0.06000 0.03940 0.03990 0.1660 0.1300 0.1360 0.1080 0.1100 0.1130 0.1150 0.1460 0.2400 0.2360 0.2160 0.04610 1.200 5.570 0.01220 0.04290 0.02140 0.02300 0.09090 0.05730 0.06000 0.03940 0.03990 0.1500 0.1280 0.1340 0.1080 0.1100 0.1120 0.1140 0.09000 0.1810 0.1700 0.1440 0.02810 1.400 5.030 0.01220 0.04280 0.02140 0.02300 0.08880 0.05720 0.05990 0.03940 0.03990 0.1340 0.1260 0.1300 0.1080 0.1090 0.1100 0.1130 0.05750 0.1280 0.1150 0.09240 0.01480 1.600 4.610 0.01220 0.04260 0.02140 0.02300 0.08640 0.05710 0.05980 0.03940 0.03990 0.1180 0.1220 0.1260 0.1070 0.1080 0.1080 0.1100 0.04210 0.08550 0.07300 0.05740 0.007440 1.800 4.290 0.01220 0.04230 0.02140 0.02300 0.08380 0.05700 0.05970 0.03940 0.03990 0.1020 0.1180 0.1210 0.1050 0.1070 0.1050 0.1070 0.03670 0.05490 0.04500 0.03480 0.004160 2.000 4.050 0.01220 0.04210 0.02140 0.02300 0.08100 0.05690 0.05950 0.03940 0.03990 0.08750 0.1120 0.1140 0.1040 0.1050 0.1020 0.1030 0.03570 0.03490 0.02820 0.02080 0.003040 2.400 3.670 0.01220 0.04150 0.02140 0.02300 0.07480 0.05650 0.05900 0.03940 0.03990 0.06190 0.09870 0.09890 0.09800 0.09900 0.09340 0.09370 0.03470 0.01710 0.01520 0.007770 0.002800 3.000 3.160 0.01210 0.04050 0.02140 0.02300 0.06480 0.05540 0.05770 0.03930 0.03990 0.03600 0.07510 0.07260 0.08510 0.08540 0.07850 0.07800 0.02520 0.01330 0.01340 0.003370 0.002270 4.000 2.330 0.01210 0.03840 0.02140 0.02290 0.04780 0.05210 0.05370 0.03900 0.03950 0.02020 0.03880 0.03510 0.05740 0.05670 0.05440 0.05330 0.008280 0.01090 0.009770 0.003050 0.0007700 5.000 1.700 0.01200 0.03580 0.02130 0.02280 0.03280 0.04700 0.04770 0.03830 0.03870 0.01890 0.01740 0.01520 0.03260 0.03160 0.03560 0.03450 0.003450 0.005380 0.004260 0.002210 0.0002690 6.000 1.300 0.01190 0.03300 0.02110 0.02260 0.02150 0.04040 0.04010 0.03690 0.03730 0.01760 0.009280 0.008880 0.01640 0.01560 0.02260 0.02180 0.003160 0.002150 0.001660 0.001220 0.0002310 7.000 1.050 0.01180 0.02990 0.02090 0.02240 0.01410 0.03310 0.03200 0.03480 0.03510 0.01360 0.007650 0.008010 0.007910 0.007450 0.01410 0.01350 0.002810 0.001130 0.001020 0.0005860 0.0002100 8.000 0.8750 0.01170 0.02680 0.02060 0.02200 0.009860 0.02590 0.02430 0.03200 0.03220 0.008910 0.007580 0.007890 0.004330 0.004130 0.008800 0.008390 0.002000 0.0009850 0.0009800 0.0002860 0.0001520 10.00 0.6300 0.01100 0.02100 0.02000 0.02100 0.007300 0.01400 0.01200 0.02500 0.02500 0.003100 0.006100 0.005700 0.002900 0.002900 0.003400 0.003200 0.0006900 0.0008800 0.0008000 0.0001500 5.200E-05 15.00 0.3000 0.01100 0.008800 0.01600 0.01600 0.006100 0.002800 0.002600 0.01100 0.010000 0.001600 0.001100 0.0008900 0.002200 0.002100 0.0003700 0.0003400 0.0002600 0.0001700 0.0001300 0.0001100 1.900E-05 20.00 0.1600 0.009500 0.003200 0.01200 0.01100 0.002600 0.002000 0.002300 0.003800 0.003600 0.0008500 0.0004100 0.0004600 0.0008800 0.0008300 5.100E-05 4.700E-05 0.0001400 5.500E-05 5.600E-05 4.700E-05 1.000E-05 30.00 0.05800 0.007100 0.001100 0.004800 0.004200 0.0003000 0.001300 0.001200 0.0004700 0.0004200 8.100E-05 0.0003000 0.0002900 0.0001100 1.000E-04 2.000E-06 1.800E-06 1.300E-05 4.000E-05 3.600E-05 6.100E-06 9.700E-07 40.00 0.02600 0.005000 0.001000 0.001800 0.001400 0.0002200 0.0005300 0.0004300 7.200E-05 6.200E-05 5.100E-05 0.0001200 1.000E-04 1.700E-05 1.500E-05 1.400E-07 1.200E-07 8.000E-06 1.700E-05 1.300E-05 9.300E-07 5.800E-07 60.00 0.007800 0.002300 0.0005400 0.0002900 0.0001800 0.0001300 8.200E-05 5.300E-05 3.500E-06 2.700E-06 3.000E-05 1.900E-05 1.200E-05 8.300E-07 6.200E-07 2.500E-09 2.000E-09 4.800E-06 2.600E-06 1.600E-06 4.400E-08 3.500E-07 100.0 0.001300 0.0004900 9.600E-05 1.900E-05 8.000E-06 2.200E-05 4.900E-06 2.200E-06 5.700E-08 3.400E-08 5.200E-06 1.100E-06 4.900E-07 1.300E-08 7.700E-09 1.400E-11 9.100E-12 8.200E-07 1.500E-07 6.200E-08 6.900E-10 5.900E-08 #S 65 Tb #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 3 2 2 4 2 #UBIND 5.200E+04 8708. 8252. 7515. 1970. 1770. 1614. 1278. 1244. 400.0 322.0 284.0 152.0 148.0 0.000 0.000 42.00 28.00 28.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 12.00 0.01200 0.04260 0.02100 0.02260 0.09500 0.05610 0.05890 0.03860 0.03910 0.2030 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.5440 0.3700 0.4000 2.030 0.05000 11.80 0.01200 0.04260 0.02100 0.02260 0.09500 0.05610 0.05890 0.03860 0.03910 0.2030 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.5420 0.3700 0.4000 1.940 0.1000 11.40 0.01200 0.04260 0.02100 0.02260 0.09500 0.05610 0.05890 0.03860 0.03910 0.2030 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.5370 0.3700 0.4000 1.720 0.1500 10.70 0.01200 0.04260 0.02100 0.02260 0.09500 0.05610 0.05890 0.03860 0.03910 0.2020 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.5280 0.3700 0.4000 1.410 0.2000 10.00 0.01200 0.04260 0.02100 0.02260 0.09490 0.05610 0.05890 0.03860 0.03910 0.2010 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.5150 0.3700 0.3990 1.070 0.3000 8.790 0.01200 0.04260 0.02100 0.02260 0.09470 0.05610 0.05890 0.03860 0.03910 0.1990 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.4800 0.3680 0.3960 0.5020 0.4000 8.040 0.01200 0.04250 0.02100 0.02260 0.09440 0.05610 0.05890 0.03860 0.03910 0.1960 0.1290 0.1360 0.1070 0.1080 0.1160 0.1210 0.4360 0.3630 0.3890 0.1970 0.5000 7.640 0.01200 0.04250 0.02100 0.02260 0.09400 0.05610 0.05890 0.03860 0.03910 0.1920 0.1290 0.1350 0.1070 0.1080 0.1160 0.1210 0.3850 0.3540 0.3760 0.08430 0.6000 7.360 0.01200 0.04250 0.02100 0.02260 0.09360 0.05610 0.05890 0.03860 0.03910 0.1880 0.1290 0.1350 0.1070 0.1080 0.1160 0.1210 0.3320 0.3390 0.3570 0.05840 0.7000 7.100 0.01200 0.04240 0.02100 0.02260 0.09300 0.05610 0.05890 0.03860 0.03910 0.1830 0.1290 0.1350 0.1070 0.1080 0.1160 0.1210 0.2790 0.3200 0.3320 0.05640 0.8000 6.810 0.01200 0.04240 0.02100 0.02260 0.09240 0.05610 0.05890 0.03860 0.03910 0.1770 0.1280 0.1350 0.1070 0.1080 0.1160 0.1200 0.2290 0.2960 0.3020 0.05480 1.000 6.190 0.01200 0.04230 0.02100 0.02260 0.09100 0.05610 0.05890 0.03860 0.03910 0.1640 0.1280 0.1340 0.1070 0.1080 0.1150 0.1190 0.1460 0.2400 0.2340 0.04100 1.200 5.600 0.01200 0.04210 0.02100 0.02260 0.08930 0.05600 0.05880 0.03860 0.03910 0.1490 0.1260 0.1320 0.1060 0.1080 0.1140 0.1180 0.08930 0.1810 0.1680 0.02400 1.400 5.100 0.01200 0.04200 0.02100 0.02260 0.08730 0.05600 0.05880 0.03860 0.03910 0.1340 0.1240 0.1290 0.1060 0.1070 0.1120 0.1150 0.05640 0.1270 0.1120 0.01230 1.600 4.710 0.01200 0.04180 0.02100 0.02260 0.08500 0.05590 0.05870 0.03860 0.03910 0.1180 0.1200 0.1250 0.1050 0.1070 0.1090 0.1110 0.04070 0.08450 0.07040 0.006050 1.800 4.410 0.01190 0.04160 0.02100 0.02260 0.08250 0.05580 0.05860 0.03860 0.03910 0.1030 0.1160 0.1200 0.1040 0.1050 0.1060 0.1070 0.03510 0.05400 0.04300 0.003390 2.000 4.180 0.01190 0.04130 0.02100 0.02260 0.07990 0.05570 0.05840 0.03860 0.03910 0.08850 0.1110 0.1130 0.1020 0.1030 0.1010 0.1020 0.03400 0.03420 0.02670 0.002510 2.400 3.800 0.01190 0.04080 0.02100 0.02260 0.07400 0.05530 0.05790 0.03850 0.03910 0.06320 0.09840 0.09880 0.09680 0.09780 0.09200 0.09180 0.03310 0.01630 0.01400 0.002320 3.000 3.270 0.01190 0.03980 0.02100 0.02260 0.06440 0.05430 0.05670 0.03850 0.03910 0.03700 0.07590 0.07350 0.08470 0.08500 0.07660 0.07540 0.02450 0.01230 0.01220 0.001890 4.000 2.420 0.01180 0.03780 0.02090 0.02250 0.04810 0.05130 0.05300 0.03820 0.03880 0.02020 0.04040 0.03650 0.05830 0.05760 0.05290 0.05130 0.008350 0.01030 0.009150 0.0006640 5.000 1.770 0.01180 0.03540 0.02090 0.02240 0.03350 0.04660 0.04740 0.03760 0.03810 0.01850 0.01850 0.01600 0.03390 0.03290 0.03480 0.03340 0.003310 0.005310 0.004140 0.0002270 6.000 1.350 0.01170 0.03270 0.02070 0.02230 0.02220 0.04040 0.04020 0.03630 0.03670 0.01750 0.009590 0.009000 0.01750 0.01660 0.02230 0.02130 0.002940 0.002170 0.001620 0.0001870 7.000 1.080 0.01160 0.02970 0.02050 0.02200 0.01460 0.03340 0.03240 0.03440 0.03470 0.01390 0.007530 0.007850 0.008560 0.008050 0.01410 0.01340 0.002680 0.001090 0.0009470 0.0001740 8.000 0.8940 0.01150 0.02670 0.02030 0.02170 0.01020 0.02650 0.02490 0.03190 0.03210 0.009430 0.007410 0.007770 0.004600 0.004370 0.008920 0.008420 0.001990 0.0009020 0.0008830 0.0001310 10.00 0.6500 0.01100 0.02100 0.01900 0.02100 0.007200 0.01500 0.01300 0.02600 0.02600 0.003400 0.006200 0.005900 0.002900 0.002900 0.003600 0.003300 0.0007200 0.0008300 0.0007500 4.800E-05 15.00 0.3100 0.010000 0.009100 0.01600 0.01600 0.006100 0.002900 0.002700 0.01100 0.01100 0.001600 0.001200 0.0009700 0.002200 0.002100 0.0004000 0.0003700 0.0002500 0.0001800 0.0001300 1.600E-05 20.00 0.1700 0.009400 0.003400 0.01200 0.01100 0.002800 0.002000 0.002200 0.004100 0.003800 0.0009000 0.0004100 0.0004500 0.0009400 0.0008800 5.700E-05 5.200E-05 0.0001400 5.100E-05 5.100E-05 9.200E-06 30.00 0.06000 0.007100 0.001100 0.005000 0.004300 0.0003200 0.001400 0.001300 0.0005200 0.0004600 8.900E-05 0.0003100 0.0003000 0.0001200 0.0001100 2.200E-06 2.000E-06 1.400E-05 3.900E-05 3.400E-05 8.900E-07 40.00 0.02700 0.005100 0.001000 0.001900 0.001500 0.0002200 0.0005700 0.0004600 8.100E-05 7.000E-05 5.000E-05 0.0001300 0.0001100 2.000E-05 1.600E-05 1.700E-07 1.400E-07 7.500E-06 1.700E-05 1.300E-05 4.700E-07 60.00 0.008100 0.002300 0.0005500 0.0003200 0.0002000 0.0001300 9.000E-05 5.900E-05 4.100E-06 3.100E-06 3.100E-05 2.100E-05 1.400E-05 9.500E-07 7.200E-07 3.000E-09 2.300E-09 4.700E-06 2.600E-06 1.600E-06 3.000E-07 100.0 0.001400 0.0005100 1.000E-04 2.100E-05 8.900E-06 2.400E-05 5.500E-06 2.400E-06 6.600E-08 4.000E-08 5.600E-06 1.300E-06 5.500E-07 1.500E-08 9.000E-09 1.600E-11 1.000E-11 8.400E-07 1.600E-07 6.400E-08 5.300E-08 #S 66 Dy #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 4 2 2 4 2 #UBIND 5.379E+04 9047. 8581. 7790. 2050. 1845. 1679. 1335. 1298. 419.0 339.0 297.0 158.0 155.0 0.000 0.000 66.00 29.00 29.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 11.90 0.01170 0.04180 0.02060 0.02220 0.09310 0.05490 0.05770 0.03780 0.03840 0.1990 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.5350 0.3640 0.3940 2.010 0.05000 11.80 0.01170 0.04180 0.02060 0.02220 0.09310 0.05490 0.05770 0.03780 0.03840 0.1980 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.5330 0.3640 0.3940 1.930 0.1000 11.30 0.01170 0.04180 0.02060 0.02220 0.09300 0.05490 0.05770 0.03780 0.03840 0.1980 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.5280 0.3640 0.3940 1.710 0.1500 10.70 0.01170 0.04180 0.02060 0.02220 0.09300 0.05490 0.05770 0.03780 0.03840 0.1980 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.5190 0.3640 0.3940 1.400 0.2000 9.970 0.01170 0.04180 0.02060 0.02220 0.09290 0.05490 0.05770 0.03780 0.03840 0.1970 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.5070 0.3640 0.3930 1.070 0.3000 8.770 0.01170 0.04170 0.02060 0.02220 0.09270 0.05490 0.05770 0.03780 0.03840 0.1950 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.4740 0.3620 0.3900 0.5110 0.4000 8.030 0.01170 0.04170 0.02060 0.02220 0.09250 0.05490 0.05770 0.03780 0.03840 0.1920 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.4320 0.3570 0.3840 0.2030 0.5000 7.620 0.01170 0.04170 0.02060 0.02220 0.09210 0.05490 0.05770 0.03780 0.03840 0.1890 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.3830 0.3490 0.3720 0.08590 0.6000 7.350 0.01170 0.04170 0.02060 0.02220 0.09170 0.05490 0.05770 0.03780 0.03840 0.1840 0.1260 0.1330 0.1040 0.1060 0.1130 0.1170 0.3320 0.3350 0.3540 0.05740 0.7000 7.100 0.01170 0.04160 0.02060 0.02220 0.09120 0.05490 0.05770 0.03780 0.03840 0.1800 0.1260 0.1320 0.1040 0.1060 0.1130 0.1170 0.2810 0.3170 0.3300 0.05470 0.8000 6.830 0.01170 0.04160 0.02060 0.02220 0.09060 0.05490 0.05770 0.03780 0.03840 0.1740 0.1260 0.1320 0.1040 0.1060 0.1130 0.1170 0.2320 0.2950 0.3010 0.05360 1.000 6.230 0.01170 0.04150 0.02060 0.02220 0.08930 0.05490 0.05770 0.03780 0.03840 0.1620 0.1250 0.1310 0.1040 0.1060 0.1120 0.1160 0.1500 0.2410 0.2360 0.04100 1.200 5.650 0.01170 0.04130 0.02060 0.02220 0.08760 0.05480 0.05770 0.03780 0.03840 0.1480 0.1230 0.1290 0.1040 0.1060 0.1110 0.1140 0.09250 0.1830 0.1710 0.02460 1.400 5.160 0.01170 0.04120 0.02060 0.02220 0.08570 0.05480 0.05760 0.03780 0.03840 0.1330 0.1210 0.1260 0.1030 0.1050 0.1090 0.1120 0.05830 0.1310 0.1150 0.01280 1.600 4.770 0.01170 0.04100 0.02060 0.02220 0.08360 0.05470 0.05750 0.03780 0.03840 0.1180 0.1180 0.1230 0.1030 0.1040 0.1070 0.1090 0.04120 0.08820 0.07370 0.006400 1.800 4.460 0.01170 0.04080 0.02060 0.02220 0.08130 0.05460 0.05740 0.03780 0.03840 0.1040 0.1140 0.1180 0.1020 0.1030 0.1030 0.1050 0.03470 0.05710 0.04550 0.003510 2.000 4.230 0.01170 0.04060 0.02060 0.02220 0.07880 0.05450 0.05730 0.03780 0.03830 0.08940 0.1100 0.1120 0.1000 0.1020 0.09960 0.1010 0.03320 0.03640 0.02830 0.002480 2.400 3.850 0.01170 0.04010 0.02060 0.02220 0.07320 0.05420 0.05680 0.03770 0.03830 0.06470 0.09800 0.09860 0.09540 0.09650 0.09090 0.09090 0.03260 0.01690 0.01420 0.002220 3.000 3.330 0.01170 0.03920 0.02060 0.02220 0.06410 0.05330 0.05570 0.03770 0.03830 0.03830 0.07670 0.07450 0.08430 0.08470 0.07650 0.07560 0.02500 0.01190 0.01190 0.001880 4.000 2.490 0.01160 0.03730 0.02050 0.02220 0.04830 0.05060 0.05230 0.03750 0.03800 0.02030 0.04210 0.03810 0.05940 0.05860 0.05380 0.05240 0.008990 0.01030 0.009280 0.0007030 5.000 1.830 0.01160 0.03500 0.02050 0.02210 0.03410 0.04610 0.04700 0.03690 0.03740 0.01810 0.01970 0.01700 0.03550 0.03440 0.03600 0.03480 0.003400 0.005610 0.004410 0.0002320 6.000 1.390 0.01150 0.03240 0.02030 0.02190 0.02290 0.04030 0.04030 0.03570 0.03620 0.01740 0.010000 0.009230 0.01880 0.01790 0.02350 0.02250 0.002860 0.002360 0.001740 0.0001790 7.000 1.100 0.01140 0.02960 0.02020 0.02170 0.01510 0.03370 0.03280 0.03400 0.03430 0.01430 0.007490 0.007740 0.009370 0.008790 0.01510 0.01440 0.002690 0.001130 0.0009560 0.0001700 8.000 0.9130 0.01130 0.02670 0.01990 0.02130 0.01050 0.02700 0.02550 0.03170 0.03190 0.009960 0.007280 0.007670 0.004960 0.004690 0.009660 0.009160 0.002060 0.0008820 0.0008590 0.0001330 10.00 0.6600 0.01100 0.02100 0.01900 0.02000 0.007200 0.01500 0.01400 0.02600 0.02600 0.003700 0.006300 0.006100 0.002900 0.002900 0.003900 0.003700 0.0007900 0.0008200 0.0007500 5.100E-05 15.00 0.3200 0.010000 0.009400 0.01600 0.01600 0.006200 0.003100 0.002800 0.01200 0.01100 0.001600 0.001400 0.001100 0.002300 0.002200 0.0004600 0.0004300 0.0002400 0.0002000 0.0001400 1.500E-05 20.00 0.1700 0.009300 0.003600 0.01200 0.01100 0.003000 0.002000 0.002200 0.004300 0.004100 0.0009600 0.0004100 0.0004500 0.001000 0.0009400 6.700E-05 6.100E-05 0.0001500 5.100E-05 5.100E-05 9.500E-06 30.00 0.06200 0.007100 0.001100 0.005200 0.004500 0.0003400 0.001400 0.001300 0.0005700 0.0005100 9.900E-05 0.0003200 0.0003100 0.0001400 0.0001200 2.700E-06 2.400E-06 1.500E-05 3.900E-05 3.500E-05 9.600E-07 40.00 0.02800 0.005100 0.001000 0.002000 0.001600 0.0002200 0.0006000 0.0004900 9.200E-05 7.800E-05 5.000E-05 0.0001400 0.0001200 2.200E-05 1.900E-05 2.000E-07 1.700E-07 7.400E-06 1.800E-05 1.300E-05 4.600E-07 60.00 0.008500 0.002400 0.0005700 0.0003500 0.0002200 0.0001400 9.900E-05 6.400E-05 4.700E-06 3.600E-06 3.200E-05 2.300E-05 1.500E-05 1.100E-06 8.300E-07 3.800E-09 3.000E-09 4.900E-06 2.900E-06 1.700E-06 3.000E-07 100.0 0.001500 0.0005400 0.0001100 2.400E-05 9.800E-06 2.600E-05 6.200E-06 2.700E-06 7.700E-08 4.600E-08 6.000E-06 1.400E-06 6.200E-07 1.800E-08 1.100E-08 2.000E-11 1.300E-11 9.000E-07 1.800E-07 7.100E-08 5.600E-08 #S 67 Ho #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 5 2 2 4 2 #UBIND 5.562E+04 9395. 8918. 8071. 2123. 1918. 1736. 1386. 1346. 431.0 349.0 309.0 164.0 161.0 0.000 0.000 51.00 20.00 20.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 11.80 0.01150 0.04100 0.02020 0.02190 0.09120 0.05370 0.05660 0.03700 0.03760 0.1940 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.5260 0.3580 0.3880 1.990 0.05000 11.70 0.01150 0.04100 0.02020 0.02190 0.09120 0.05370 0.05660 0.03700 0.03760 0.1940 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.5240 0.3580 0.3880 1.910 0.1000 11.20 0.01150 0.04100 0.02020 0.02190 0.09110 0.05370 0.05660 0.03700 0.03760 0.1940 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.5190 0.3580 0.3880 1.700 0.1500 10.60 0.01150 0.04100 0.02020 0.02190 0.09110 0.05370 0.05660 0.03700 0.03760 0.1940 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.5110 0.3580 0.3880 1.400 0.2000 9.940 0.01150 0.04100 0.02020 0.02190 0.09100 0.05370 0.05660 0.03700 0.03760 0.1930 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.4990 0.3580 0.3880 1.070 0.3000 8.750 0.01150 0.04100 0.02020 0.02190 0.09080 0.05370 0.05660 0.03700 0.03760 0.1910 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.4680 0.3560 0.3850 0.5200 0.4000 8.010 0.01150 0.04090 0.02020 0.02190 0.09060 0.05370 0.05660 0.03700 0.03760 0.1880 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.4280 0.3520 0.3790 0.2090 0.5000 7.610 0.01150 0.04090 0.02020 0.02190 0.09030 0.05370 0.05660 0.03700 0.03760 0.1850 0.1230 0.1300 0.1020 0.1040 0.1100 0.1140 0.3810 0.3440 0.3670 0.08770 0.6000 7.340 0.01150 0.04090 0.02020 0.02190 0.08990 0.05370 0.05660 0.03700 0.03760 0.1810 0.1230 0.1300 0.1020 0.1040 0.1100 0.1130 0.3320 0.3310 0.3500 0.05660 0.7000 7.100 0.01150 0.04080 0.02020 0.02190 0.08940 0.05370 0.05660 0.03700 0.03760 0.1760 0.1230 0.1300 0.1020 0.1040 0.1100 0.1130 0.2820 0.3140 0.3280 0.05320 0.8000 6.840 0.01150 0.04080 0.02020 0.02190 0.08890 0.05370 0.05660 0.03700 0.03760 0.1710 0.1230 0.1290 0.1020 0.1040 0.1100 0.1130 0.2350 0.2930 0.3000 0.05240 1.000 6.260 0.01150 0.04070 0.02020 0.02190 0.08760 0.05370 0.05660 0.03700 0.03760 0.1600 0.1220 0.1280 0.1020 0.1040 0.1090 0.1120 0.1540 0.2420 0.2370 0.04100 1.200 5.690 0.01150 0.04060 0.02020 0.02190 0.08600 0.05370 0.05660 0.03700 0.03760 0.1470 0.1210 0.1270 0.1020 0.1030 0.1080 0.1110 0.09570 0.1860 0.1740 0.02520 1.400 5.210 0.01150 0.04040 0.02020 0.02190 0.08430 0.05370 0.05650 0.03700 0.03760 0.1330 0.1190 0.1240 0.1010 0.1030 0.1060 0.1090 0.06030 0.1340 0.1190 0.01340 1.600 4.820 0.01150 0.04030 0.02020 0.02190 0.08230 0.05360 0.05650 0.03700 0.03760 0.1180 0.1160 0.1210 0.1010 0.1020 0.1040 0.1060 0.04200 0.09180 0.07690 0.006760 1.800 4.510 0.01150 0.04010 0.02020 0.02190 0.08000 0.05350 0.05640 0.03700 0.03760 0.1040 0.1130 0.1170 0.09960 0.1010 0.1010 0.1030 0.03450 0.06020 0.04800 0.003650 2.000 4.270 0.01150 0.03990 0.02020 0.02190 0.07770 0.05340 0.05620 0.03700 0.03760 0.09030 0.1080 0.1110 0.09820 0.09970 0.09790 0.09890 0.03240 0.03870 0.02990 0.002480 2.400 3.890 0.01150 0.03940 0.02020 0.02180 0.07240 0.05310 0.05580 0.03700 0.03760 0.06600 0.09740 0.09830 0.09400 0.09520 0.08980 0.09000 0.03190 0.01760 0.01450 0.002120 3.000 3.390 0.01150 0.03850 0.02020 0.02180 0.06370 0.05230 0.05480 0.03690 0.03750 0.03950 0.07730 0.07540 0.08380 0.08430 0.07620 0.07560 0.02540 0.01150 0.01150 0.001860 4.000 2.560 0.01140 0.03670 0.02010 0.02180 0.04850 0.04980 0.05170 0.03670 0.03730 0.02050 0.04380 0.03970 0.06030 0.05960 0.05450 0.05330 0.009650 0.01030 0.009370 0.0007410 5.000 1.890 0.01140 0.03450 0.02010 0.02170 0.03470 0.04570 0.04670 0.03620 0.03670 0.01780 0.02100 0.01800 0.03700 0.03590 0.03710 0.03600 0.003520 0.005890 0.004660 0.0002410 6.000 1.430 0.01130 0.03200 0.02000 0.02160 0.02350 0.04030 0.04030 0.03520 0.03570 0.01730 0.01050 0.009520 0.02010 0.01910 0.02460 0.02370 0.002790 0.002560 0.001880 0.0001710 7.000 1.130 0.01120 0.02940 0.01980 0.02130 0.01570 0.03400 0.03320 0.03360 0.03400 0.01460 0.007500 0.007660 0.01020 0.009560 0.01600 0.01530 0.002670 0.001190 0.0009740 0.0001660 8.000 0.9340 0.01110 0.02660 0.01960 0.02100 0.01080 0.02750 0.02610 0.03140 0.03170 0.01050 0.007150 0.007560 0.005380 0.005050 0.01040 0.009890 0.002130 0.0008680 0.0008380 0.0001340 10.00 0.6700 0.01100 0.02100 0.01900 0.02000 0.007200 0.01600 0.01400 0.02600 0.02600 0.004000 0.006400 0.006200 0.002900 0.002900 0.004300 0.004100 0.0008600 0.0008200 0.0007600 5.500E-05 15.00 0.3300 0.010000 0.009700 0.01600 0.01600 0.006200 0.003300 0.002900 0.01200 0.01200 0.001600 0.001500 0.001200 0.002300 0.002300 0.0005300 0.0004900 0.0002400 0.0002200 0.0001500 1.500E-05 20.00 0.1800 0.009200 0.003800 0.01200 0.01200 0.003200 0.001900 0.002200 0.004600 0.004400 0.001000 0.0004200 0.0004500 0.001100 0.001000 7.900E-05 7.300E-05 0.0001600 5.200E-05 5.000E-05 9.800E-06 30.00 0.06400 0.007100 0.001100 0.005400 0.004600 0.0003700 0.001400 0.001400 0.0006300 0.0005600 0.0001100 0.0003200 0.0003200 0.0001500 0.0001400 3.300E-06 2.900E-06 1.700E-05 4.000E-05 3.600E-05 1.100E-06 40.00 0.02900 0.005100 0.001000 0.002200 0.001700 0.0002100 0.0006400 0.0005200 1.000E-04 8.700E-05 4.900E-05 0.0001500 0.0001200 2.500E-05 2.100E-05 2.500E-07 2.100E-07 7.300E-06 1.900E-05 1.400E-05 4.400E-07 60.00 0.008800 0.002500 0.0005900 0.0003800 0.0002300 0.0001400 0.0001100 7.000E-05 5.300E-06 4.100E-06 3.400E-05 2.500E-05 1.700E-05 1.300E-06 9.600E-07 4.700E-09 3.700E-09 5.000E-06 3.100E-06 1.900E-06 3.000E-07 100.0 0.001600 0.0005700 0.0001200 2.600E-05 1.100E-05 2.700E-05 7.000E-06 3.000E-06 9.000E-08 5.300E-08 6.500E-06 1.600E-06 6.900E-07 2.100E-08 1.200E-08 2.500E-11 1.700E-11 9.600E-07 2.000E-07 7.900E-08 5.900E-08 #S 68 Er #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 6 2 2 4 2 #UBIND 5.749E+04 9752. 9265. 8358. 2211. 2010. 1816. 1457. 1413. 453.0 366.0 320.0 172.0 169.0 0.000 0.000 64.00 33.00 33.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 11.80 0.01130 0.04020 0.01980 0.02150 0.08930 0.05260 0.05560 0.03620 0.03690 0.1900 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.5170 0.3530 0.3830 1.970 0.05000 11.60 0.01130 0.04020 0.01980 0.02150 0.08930 0.05260 0.05560 0.03620 0.03690 0.1900 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.5160 0.3530 0.3830 1.900 0.1000 11.20 0.01130 0.04020 0.01980 0.02150 0.08930 0.05260 0.05560 0.03620 0.03690 0.1900 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.5110 0.3530 0.3830 1.690 0.1500 10.60 0.01130 0.04020 0.01980 0.02150 0.08930 0.05260 0.05560 0.03620 0.03690 0.1900 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.5030 0.3530 0.3830 1.400 0.2000 9.910 0.01130 0.04020 0.01980 0.02150 0.08920 0.05260 0.05560 0.03620 0.03690 0.1890 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.4920 0.3520 0.3820 1.080 0.3000 8.740 0.01130 0.04020 0.01980 0.02150 0.08900 0.05260 0.05560 0.03620 0.03690 0.1870 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.4620 0.3510 0.3800 0.5280 0.4000 8.000 0.01130 0.04020 0.01980 0.02150 0.08880 0.05260 0.05560 0.03620 0.03690 0.1850 0.1210 0.1280 0.09960 0.1010 0.1070 0.1100 0.4240 0.3470 0.3740 0.2150 0.5000 7.590 0.01130 0.04020 0.01980 0.02150 0.08850 0.05260 0.05560 0.03620 0.03690 0.1810 0.1210 0.1270 0.09960 0.1010 0.1070 0.1100 0.3790 0.3390 0.3630 0.08970 0.6000 7.330 0.01130 0.04010 0.01980 0.02150 0.08810 0.05260 0.05560 0.03620 0.03690 0.1780 0.1210 0.1270 0.09960 0.1010 0.1070 0.1100 0.3310 0.3270 0.3470 0.05590 0.7000 7.100 0.01130 0.04010 0.01980 0.02150 0.08770 0.05260 0.05560 0.03620 0.03690 0.1730 0.1200 0.1270 0.09960 0.1010 0.1070 0.1100 0.2830 0.3110 0.3250 0.05170 0.8000 6.850 0.01130 0.04000 0.01980 0.02150 0.08720 0.05260 0.05560 0.03620 0.03690 0.1690 0.1200 0.1270 0.09960 0.1010 0.1070 0.1100 0.2370 0.2910 0.2990 0.05110 1.000 6.290 0.01130 0.04000 0.01980 0.02150 0.08600 0.05260 0.05550 0.03620 0.03690 0.1580 0.1200 0.1260 0.09950 0.1010 0.1060 0.1090 0.1570 0.2420 0.2390 0.04090 1.200 5.730 0.01130 0.03980 0.01980 0.02150 0.08450 0.05260 0.05550 0.03620 0.03690 0.1450 0.1180 0.1240 0.09930 0.1010 0.1060 0.1080 0.09890 0.1880 0.1760 0.02570 1.400 5.250 0.01130 0.03970 0.01980 0.02150 0.08280 0.05260 0.05550 0.03620 0.03690 0.1320 0.1170 0.1220 0.09900 0.1010 0.1040 0.1060 0.06240 0.1380 0.1220 0.01400 1.600 4.860 0.01130 0.03950 0.01980 0.02150 0.08090 0.05250 0.05540 0.03620 0.03690 0.1180 0.1140 0.1190 0.09850 0.1000 0.1020 0.1040 0.04290 0.09540 0.08000 0.007140 1.800 4.560 0.01130 0.03940 0.01980 0.02150 0.07880 0.05240 0.05530 0.03620 0.03690 0.1040 0.1110 0.1150 0.09760 0.09930 0.09930 0.1010 0.03440 0.06330 0.05050 0.003820 2.000 4.320 0.01130 0.03920 0.01980 0.02150 0.07660 0.05230 0.05520 0.03620 0.03690 0.09110 0.1070 0.1100 0.09640 0.09800 0.09610 0.09720 0.03180 0.04110 0.03160 0.002490 2.400 3.940 0.01130 0.03870 0.01980 0.02150 0.07160 0.05200 0.05480 0.03620 0.03690 0.06730 0.09680 0.09800 0.09260 0.09380 0.08860 0.08890 0.03130 0.01840 0.01490 0.002040 3.000 3.440 0.01130 0.03790 0.01980 0.02150 0.06330 0.05130 0.05390 0.03620 0.03680 0.04080 0.07790 0.07610 0.08330 0.08380 0.07590 0.07540 0.02570 0.01130 0.01120 0.001830 4.000 2.620 0.01120 0.03620 0.01970 0.02140 0.04870 0.04900 0.05100 0.03600 0.03660 0.02070 0.04530 0.04120 0.06110 0.06040 0.05510 0.05410 0.01030 0.01020 0.009420 0.0007780 5.000 1.940 0.01120 0.03410 0.01970 0.02140 0.03520 0.04520 0.04630 0.03550 0.03610 0.01750 0.02230 0.01910 0.03840 0.03730 0.03810 0.03700 0.003670 0.006150 0.004910 0.0002510 6.000 1.470 0.01110 0.03170 0.01960 0.02120 0.02420 0.04010 0.04030 0.03460 0.03510 0.01710 0.01110 0.009870 0.02140 0.02030 0.02560 0.02470 0.002730 0.002770 0.002020 0.0001650 7.000 1.170 0.01100 0.02920 0.01940 0.02100 0.01630 0.03420 0.03340 0.03320 0.03360 0.01480 0.007540 0.007600 0.01110 0.01040 0.01690 0.01630 0.002650 0.001260 0.001000 0.0001610 8.000 0.9560 0.01090 0.02650 0.01920 0.02070 0.01120 0.02800 0.02660 0.03120 0.03150 0.01090 0.007020 0.007450 0.005830 0.005460 0.01110 0.01060 0.002180 0.0008600 0.0008190 0.0001350 10.00 0.6900 0.01090 0.02100 0.01900 0.02000 0.007200 0.01700 0.01500 0.02600 0.02600 0.004400 0.006500 0.006300 0.002900 0.002900 0.004700 0.004500 0.0009400 0.0008000 0.0007600 5.900E-05 15.00 0.3300 0.010000 0.010000 0.01600 0.01600 0.006200 0.003500 0.003000 0.01200 0.01200 0.001600 0.001700 0.001300 0.002300 0.002300 0.0006000 0.0005600 0.0002300 0.0002400 0.0001700 1.400E-05 20.00 0.1800 0.009100 0.004000 0.01200 0.01200 0.003300 0.001900 0.002200 0.004900 0.004600 0.001100 0.0004300 0.0004500 0.001100 0.001100 9.200E-05 8.500E-05 0.0001700 5.300E-05 5.000E-05 1.000E-05 30.00 0.06600 0.007000 0.001100 0.005500 0.004800 0.0004100 0.001500 0.001400 0.0006900 0.0006200 0.0001200 0.0003300 0.0003300 0.0001700 0.0001500 4.000E-06 3.500E-06 1.900E-05 4.000E-05 3.700E-05 1.200E-06 40.00 0.03000 0.005100 0.0009800 0.002300 0.001700 0.0002100 0.0006700 0.0005600 0.0001100 9.700E-05 4.900E-05 0.0001600 0.0001300 2.800E-05 2.400E-05 3.000E-07 2.600E-07 7.200E-06 1.900E-05 1.500E-05 4.300E-07 60.00 0.009200 0.002500 0.0006000 0.0004100 0.0002500 0.0001400 0.0001200 7.600E-05 6.100E-06 4.600E-06 3.500E-05 2.800E-05 1.800E-05 1.500E-06 1.100E-06 5.800E-09 4.600E-09 5.100E-06 3.400E-06 2.000E-06 3.100E-07 100.0 0.001700 0.0006000 0.0001200 2.900E-05 1.200E-05 2.900E-05 7.800E-06 3.300E-06 1.000E-07 6.100E-08 7.000E-06 1.800E-06 7.700E-07 2.400E-08 1.400E-08 3.200E-11 2.100E-11 1.000E-06 2.200E-07 8.700E-08 6.200E-08 #S 69 Tm #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 7 2 2 4 2 #UBIND 5.939E+04 1.012E+04 9617. 8648. 2305. 2088. 1883. 1513. 1466. 470.0 382.0 333.0 180.0 176.0 0.000 0.000 51.00 30.00 30.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 11.70 0.01110 0.03950 0.01940 0.02110 0.08760 0.05160 0.05450 0.03550 0.03620 0.1860 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.5090 0.3470 0.3780 1.950 0.05000 11.50 0.01110 0.03950 0.01940 0.02110 0.08760 0.05160 0.05450 0.03550 0.03620 0.1860 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.5070 0.3470 0.3780 1.880 0.1000 11.10 0.01110 0.03950 0.01940 0.02110 0.08750 0.05160 0.05450 0.03550 0.03620 0.1860 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.5030 0.3470 0.3780 1.680 0.1500 10.50 0.01110 0.03950 0.01940 0.02110 0.08750 0.05160 0.05450 0.03550 0.03620 0.1860 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.4950 0.3470 0.3770 1.390 0.2000 9.880 0.01110 0.03950 0.01940 0.02110 0.08740 0.05160 0.05450 0.03550 0.03620 0.1850 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.4850 0.3470 0.3770 1.080 0.3000 8.720 0.01110 0.03950 0.01940 0.02110 0.08730 0.05160 0.05450 0.03550 0.03620 0.1830 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.4560 0.3450 0.3750 0.5370 0.4000 7.980 0.01110 0.03940 0.01940 0.02110 0.08710 0.05160 0.05450 0.03550 0.03620 0.1810 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.4190 0.3410 0.3690 0.2220 0.5000 7.580 0.01110 0.03940 0.01940 0.02110 0.08680 0.05160 0.05450 0.03550 0.03620 0.1780 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.3770 0.3340 0.3590 0.09190 0.6000 7.320 0.01110 0.03940 0.01940 0.02110 0.08640 0.05160 0.05450 0.03550 0.03620 0.1750 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.3310 0.3230 0.3440 0.05540 0.7000 7.090 0.01110 0.03940 0.01940 0.02110 0.08600 0.05160 0.05450 0.03550 0.03620 0.1710 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.2840 0.3080 0.3230 0.05030 0.8000 6.850 0.01110 0.03930 0.01940 0.02110 0.08550 0.05150 0.05450 0.03550 0.03620 0.1660 0.1180 0.1250 0.09750 0.09940 0.1040 0.1070 0.2390 0.2890 0.2980 0.04990 1.000 6.310 0.01110 0.03920 0.01940 0.02110 0.08440 0.05150 0.05450 0.03550 0.03620 0.1550 0.1170 0.1240 0.09740 0.09930 0.1040 0.1070 0.1600 0.2430 0.2400 0.04080 1.200 5.770 0.01110 0.03910 0.01940 0.02110 0.08300 0.05150 0.05450 0.03550 0.03620 0.1440 0.1160 0.1220 0.09730 0.09920 0.1030 0.1060 0.1020 0.1900 0.1790 0.02620 1.400 5.300 0.01110 0.03900 0.01940 0.02110 0.08140 0.05150 0.05440 0.03550 0.03620 0.1310 0.1140 0.1200 0.09700 0.09890 0.1020 0.1040 0.06460 0.1410 0.1250 0.01460 1.600 4.910 0.01110 0.03880 0.01940 0.02110 0.07960 0.05140 0.05440 0.03550 0.03620 0.1180 0.1120 0.1170 0.09650 0.09830 0.09980 0.1020 0.04390 0.09880 0.08310 0.007530 1.800 4.600 0.01110 0.03870 0.01940 0.02110 0.07760 0.05140 0.05430 0.03550 0.03620 0.1050 0.1090 0.1140 0.09570 0.09750 0.09740 0.09880 0.03440 0.06640 0.05300 0.004010 2.000 4.360 0.01110 0.03850 0.01940 0.02110 0.07550 0.05130 0.05420 0.03550 0.03620 0.09170 0.1060 0.1090 0.09460 0.09630 0.09440 0.09550 0.03120 0.04350 0.03340 0.002530 2.400 3.980 0.01110 0.03800 0.01940 0.02110 0.07070 0.05100 0.05380 0.03550 0.03620 0.06850 0.09610 0.09760 0.09120 0.09250 0.08750 0.08790 0.03060 0.01940 0.01530 0.001960 3.000 3.490 0.01110 0.03730 0.01940 0.02110 0.06280 0.05030 0.05300 0.03550 0.03610 0.04200 0.07830 0.07670 0.08260 0.08320 0.07550 0.07520 0.02590 0.01100 0.01090 0.001800 4.000 2.690 0.01100 0.03560 0.01940 0.02110 0.04880 0.04820 0.05030 0.03530 0.03600 0.02110 0.04680 0.04260 0.06170 0.06110 0.05560 0.05470 0.01100 0.01010 0.009430 0.0008130 5.000 2.000 0.01100 0.03370 0.01930 0.02100 0.03570 0.04470 0.04600 0.03490 0.03550 0.01720 0.02360 0.02020 0.03970 0.03860 0.03900 0.03800 0.003870 0.006390 0.005140 0.0002640 6.000 1.520 0.01090 0.03140 0.01920 0.02090 0.02480 0.04000 0.04020 0.03410 0.03460 0.01690 0.01180 0.01030 0.02270 0.02160 0.02660 0.02570 0.002670 0.002980 0.002170 0.0001600 7.000 1.200 0.01080 0.02900 0.01910 0.02070 0.01680 0.03430 0.03370 0.03280 0.03320 0.01490 0.007640 0.007570 0.01200 0.01120 0.01780 0.01710 0.002610 0.001350 0.001040 0.0001560 8.000 0.9790 0.01070 0.02640 0.01890 0.02040 0.01160 0.02840 0.02700 0.03090 0.03120 0.01130 0.006900 0.007320 0.006320 0.005890 0.01180 0.01130 0.002230 0.0008610 0.0008030 0.0001350 10.00 0.7000 0.01070 0.02100 0.01800 0.02000 0.007200 0.01700 0.01600 0.02600 0.02600 0.004800 0.006500 0.006400 0.002900 0.002900 0.005200 0.004900 0.001000 0.0007900 0.0007500 6.300E-05 15.00 0.3400 0.009800 0.010000 0.01600 0.01600 0.006200 0.003700 0.003200 0.01300 0.01300 0.001600 0.001800 0.001400 0.002400 0.002300 0.0006800 0.0006400 0.0002300 0.0002600 0.0001800 1.400E-05 20.00 0.1900 0.009000 0.004200 0.01200 0.01200 0.003500 0.001900 0.002100 0.005200 0.004900 0.001100 0.0004400 0.0004500 0.001200 0.001100 0.0001100 9.900E-05 0.0001700 5.500E-05 5.000E-05 1.000E-05 30.00 0.06800 0.007000 0.001100 0.005700 0.005000 0.0004400 0.001500 0.001500 0.0007500 0.0006700 0.0001400 0.0003400 0.0003400 0.0001900 0.0001700 4.700E-06 4.200E-06 2.100E-05 4.000E-05 3.800E-05 1.300E-06 40.00 0.03100 0.005200 0.0009700 0.002400 0.001800 0.0002100 0.0007100 0.0005900 0.0001300 0.0001100 4.800E-05 0.0001700 0.0001400 3.200E-05 2.700E-05 3.700E-07 3.200E-07 7.100E-06 2.000E-05 1.600E-05 4.200E-07 60.00 0.009500 0.002600 0.0006200 0.0004400 0.0002700 0.0001500 0.0001300 8.300E-05 6.900E-06 5.200E-06 3.600E-05 3.100E-05 2.000E-05 1.700E-06 1.300E-06 7.100E-09 5.600E-09 5.200E-06 3.700E-06 2.200E-06 3.100E-07 100.0 0.001700 0.0006200 0.0001300 3.200E-05 1.300E-05 3.100E-05 8.700E-06 3.700E-06 1.200E-07 7.100E-08 7.500E-06 2.000E-06 8.600E-07 2.800E-08 1.700E-08 4.000E-11 2.600E-11 1.100E-06 2.400E-07 9.600E-08 6.500E-08 #S 70 Yb #N 22 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 2 #UBIND 6.133E+04 1.049E+04 9978. 8943. 2397. 2172. 1949. 1576. 1527. 487.0 399.0 346.0 189.0 185.0 0.000 0.000 53.00 23.00 23.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 0.000 11.60 0.01090 0.03880 0.01900 0.02080 0.08590 0.05050 0.05350 0.03480 0.03550 0.1830 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.5010 0.3420 0.3730 1.940 0.05000 11.50 0.01090 0.03880 0.01900 0.02080 0.08590 0.05050 0.05350 0.03480 0.03550 0.1830 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4990 0.3420 0.3730 1.870 0.1000 11.10 0.01090 0.03880 0.01900 0.02080 0.08580 0.05050 0.05350 0.03480 0.03550 0.1820 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4950 0.3420 0.3730 1.670 0.1500 10.50 0.01090 0.03880 0.01900 0.02080 0.08580 0.05050 0.05350 0.03480 0.03550 0.1820 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4880 0.3420 0.3720 1.390 0.2000 9.850 0.01090 0.03870 0.01900 0.02080 0.08570 0.05050 0.05350 0.03480 0.03550 0.1810 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4780 0.3410 0.3720 1.080 0.3000 8.710 0.01090 0.03870 0.01900 0.02080 0.08560 0.05050 0.05350 0.03480 0.03550 0.1800 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4510 0.3400 0.3700 0.5450 0.4000 7.970 0.01090 0.03870 0.01900 0.02080 0.08540 0.05050 0.05350 0.03480 0.03550 0.1780 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.4150 0.3360 0.3640 0.2280 0.5000 7.560 0.01090 0.03870 0.01900 0.02080 0.08510 0.05050 0.05350 0.03480 0.03550 0.1750 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.3740 0.3300 0.3550 0.09430 0.6000 7.310 0.01090 0.03870 0.01900 0.02080 0.08480 0.05050 0.05350 0.03480 0.03550 0.1720 0.1160 0.1230 0.09540 0.09740 0.1020 0.1050 0.3300 0.3200 0.3400 0.05510 0.7000 7.090 0.01090 0.03860 0.01900 0.02080 0.08440 0.05050 0.05350 0.03480 0.03550 0.1680 0.1150 0.1220 0.09540 0.09740 0.1020 0.1050 0.2850 0.3050 0.3210 0.04890 0.8000 6.860 0.01090 0.03860 0.01900 0.02080 0.08390 0.05050 0.05350 0.03480 0.03550 0.1630 0.1150 0.1220 0.09540 0.09740 0.1020 0.1040 0.2410 0.2870 0.2970 0.04860 1.000 6.340 0.01090 0.03850 0.01900 0.02080 0.08280 0.05050 0.05350 0.03480 0.03550 0.1530 0.1150 0.1210 0.09540 0.09730 0.1020 0.1040 0.1630 0.2430 0.2400 0.04060 1.200 5.810 0.01090 0.03840 0.01900 0.02080 0.08150 0.05050 0.05350 0.03480 0.03550 0.1420 0.1140 0.1200 0.09530 0.09720 0.1010 0.1030 0.1050 0.1920 0.1810 0.02660 1.400 5.340 0.01090 0.03830 0.01900 0.02080 0.08000 0.05050 0.05350 0.03480 0.03550 0.1300 0.1120 0.1180 0.09500 0.09690 0.09960 0.1020 0.06690 0.1440 0.1280 0.01510 1.600 4.950 0.01090 0.03810 0.01900 0.02080 0.07830 0.05040 0.05340 0.03480 0.03550 0.1170 0.1100 0.1160 0.09460 0.09640 0.09780 0.09950 0.04500 0.1020 0.08610 0.007930 1.800 4.650 0.01090 0.03800 0.01900 0.02080 0.07650 0.05040 0.05330 0.03480 0.03550 0.1050 0.1080 0.1120 0.09390 0.09570 0.09550 0.09690 0.03460 0.06950 0.05550 0.004210 2.000 4.400 0.01090 0.03780 0.01900 0.02080 0.07440 0.05030 0.05320 0.03480 0.03550 0.09230 0.1040 0.1080 0.09290 0.09460 0.09280 0.09390 0.03080 0.04590 0.03520 0.002580 2.400 4.030 0.01090 0.03740 0.01900 0.02080 0.06990 0.05000 0.05290 0.03480 0.03550 0.06960 0.09540 0.09710 0.08980 0.09110 0.08630 0.08680 0.02990 0.02050 0.01580 0.001880 3.000 3.540 0.01090 0.03670 0.01900 0.02080 0.06240 0.04940 0.05210 0.03480 0.03550 0.04320 0.07850 0.07720 0.08190 0.08260 0.07500 0.07480 0.02600 0.01090 0.01060 0.001760 4.000 2.750 0.01080 0.03510 0.01900 0.02070 0.04890 0.04750 0.04960 0.03470 0.03530 0.02150 0.04810 0.04400 0.06230 0.06170 0.05590 0.05520 0.01160 0.009990 0.009410 0.0008460 5.000 2.060 0.01080 0.03320 0.01890 0.02070 0.03610 0.04420 0.04560 0.03430 0.03490 0.01690 0.02490 0.02130 0.04100 0.03980 0.03970 0.03890 0.004090 0.006600 0.005350 0.0002780 6.000 1.570 0.01070 0.03110 0.01890 0.02060 0.02540 0.03980 0.04020 0.03350 0.03410 0.01670 0.01240 0.01070 0.02390 0.02280 0.02740 0.02670 0.002630 0.003200 0.002320 0.0001560 7.000 1.230 0.01060 0.02870 0.01870 0.02040 0.01740 0.03450 0.03390 0.03230 0.03280 0.01510 0.007780 0.007580 0.01290 0.01200 0.01860 0.01800 0.002570 0.001440 0.001080 0.0001500 8.000 1.000 0.01050 0.02630 0.01860 0.02010 0.01200 0.02870 0.02750 0.03060 0.03100 0.01170 0.006800 0.007200 0.006850 0.006370 0.01250 0.01200 0.002250 0.0008710 0.0007920 0.0001340 10.00 0.7100 0.010000 0.02100 0.01800 0.01900 0.007200 0.01800 0.01600 0.02600 0.02600 0.005100 0.006500 0.006500 0.003000 0.002900 0.005600 0.005300 0.001100 0.0007700 0.0007400 6.600E-05 15.00 0.3500 0.009700 0.01100 0.01600 0.01600 0.006200 0.003900 0.003300 0.01300 0.01300 0.001500 0.002000 0.001500 0.002400 0.002400 0.0007700 0.0007200 0.0002300 0.0002800 0.0002000 1.300E-05 20.00 0.1900 0.008900 0.004400 0.01200 0.01200 0.003700 0.001900 0.002100 0.005500 0.005200 0.001200 0.0004600 0.0004500 0.001300 0.001200 0.0001200 0.0001100 0.0001800 5.800E-05 5.000E-05 1.000E-05 30.00 0.07100 0.007000 0.001100 0.005800 0.005100 0.0004900 0.001500 0.001500 0.0008200 0.0007300 0.0001500 0.0003400 0.0003500 0.0002100 0.0001800 5.600E-06 5.000E-06 2.400E-05 4.000E-05 3.800E-05 1.400E-06 40.00 0.03200 0.005200 0.0009500 0.002500 0.001900 0.0002000 0.0007400 0.0006200 0.0001400 0.0001200 4.800E-05 0.0001800 0.0001500 3.600E-05 3.000E-05 4.400E-07 3.800E-07 7.000E-06 2.100E-05 1.700E-05 4.100E-07 60.00 0.009900 0.002600 0.0006300 0.0004800 0.0002900 0.0001500 0.0001400 9.000E-05 7.800E-06 5.900E-06 3.600E-05 3.300E-05 2.200E-05 1.900E-06 1.400E-06 8.700E-09 6.900E-09 5.300E-06 4.000E-06 2.400E-06 3.100E-07 100.0 0.001800 0.0006500 0.0001400 3.600E-05 1.400E-05 3.300E-05 9.700E-06 4.100E-06 1.400E-07 8.100E-08 8.000E-06 2.300E-06 9.600E-07 3.300E-08 1.900E-08 4.900E-11 3.200E-11 1.200E-06 2.700E-07 1.100E-07 6.800E-08 #S 71 Lu #N 23 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 1 2 #UBIND 6.331E+04 1.087E+04 1.035E+04 9244. 2491. 2264. 2024. 1640. 1589. 507.0 412.0 359.0 206.0 196.0 7.000 7.000 57.00 28.00 28.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 0.000 11.50 0.01070 0.03810 0.01870 0.02050 0.08420 0.04950 0.05260 0.03420 0.03490 0.1790 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4800 0.3270 0.3540 0.4890 1.770 0.05000 11.30 0.01070 0.03810 0.01870 0.02050 0.08420 0.04950 0.05260 0.03420 0.03490 0.1790 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4780 0.3270 0.3540 0.4890 1.720 0.1000 11.00 0.01070 0.03810 0.01870 0.02050 0.08420 0.04950 0.05260 0.03420 0.03490 0.1780 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4740 0.3270 0.3540 0.4890 1.560 0.1500 10.60 0.01070 0.03810 0.01870 0.02050 0.08410 0.04950 0.05260 0.03420 0.03490 0.1780 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4680 0.3270 0.3540 0.4890 1.330 0.2000 10.00 0.01070 0.03800 0.01870 0.02050 0.08410 0.04950 0.05260 0.03420 0.03490 0.1770 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4590 0.3270 0.3540 0.4880 1.070 0.3000 8.990 0.01070 0.03800 0.01870 0.02050 0.08400 0.04950 0.05260 0.03420 0.03490 0.1760 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4350 0.3260 0.3520 0.4820 0.5940 0.4000 8.240 0.01070 0.03800 0.01870 0.02050 0.08380 0.04950 0.05260 0.03420 0.03490 0.1740 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.4030 0.3230 0.3480 0.4650 0.2760 0.5000 7.780 0.01070 0.03800 0.01870 0.02050 0.08350 0.04950 0.05260 0.03420 0.03490 0.1710 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.3660 0.3170 0.3400 0.4330 0.1210 0.6000 7.470 0.01070 0.03800 0.01870 0.02050 0.08320 0.04950 0.05260 0.03420 0.03490 0.1680 0.1130 0.1200 0.09310 0.09510 0.09530 0.09670 0.3260 0.3090 0.3280 0.3890 0.06470 0.7000 7.210 0.01070 0.03790 0.01870 0.02050 0.08280 0.04950 0.05260 0.03420 0.03490 0.1650 0.1130 0.1200 0.09310 0.09500 0.09520 0.09670 0.2840 0.2970 0.3120 0.3400 0.05130 0.8000 6.960 0.01070 0.03790 0.01870 0.02050 0.08240 0.04950 0.05260 0.03420 0.03490 0.1600 0.1130 0.1200 0.09310 0.09500 0.09520 0.09670 0.2430 0.2810 0.2920 0.2910 0.05020 1.000 6.410 0.01070 0.03780 0.01870 0.02050 0.08140 0.04950 0.05260 0.03420 0.03490 0.1510 0.1120 0.1190 0.09300 0.09500 0.09500 0.09650 0.1690 0.2420 0.2420 0.2030 0.04560 1.200 5.850 0.01070 0.03770 0.01870 0.02050 0.08010 0.04950 0.05250 0.03420 0.03490 0.1410 0.1110 0.1180 0.09290 0.09490 0.09470 0.09610 0.1120 0.1960 0.1880 0.1360 0.03300 1.400 5.360 0.01070 0.03760 0.01870 0.02050 0.07870 0.04950 0.05250 0.03420 0.03490 0.1290 0.1100 0.1160 0.09270 0.09460 0.09400 0.09540 0.07240 0.1500 0.1370 0.08950 0.02020 1.600 4.950 0.01070 0.03750 0.01870 0.02050 0.07710 0.04940 0.05250 0.03420 0.03490 0.1170 0.1080 0.1140 0.09240 0.09420 0.09300 0.09420 0.04870 0.1100 0.09520 0.05780 0.01130 1.800 4.630 0.01070 0.03730 0.01870 0.02050 0.07530 0.04940 0.05240 0.03420 0.03490 0.1050 0.1060 0.1100 0.09180 0.09360 0.09150 0.09270 0.03650 0.07670 0.06320 0.03690 0.006090 2.000 4.380 0.01070 0.03720 0.01870 0.02050 0.07340 0.04930 0.05230 0.03420 0.03490 0.09280 0.1030 0.1060 0.09090 0.09260 0.08960 0.09060 0.03150 0.05190 0.04090 0.02330 0.003560 2.400 4.000 0.01070 0.03680 0.01870 0.02050 0.06910 0.04910 0.05200 0.03420 0.03490 0.07070 0.09450 0.09650 0.08820 0.08960 0.08460 0.08540 0.03000 0.02350 0.01830 0.009210 0.002230 3.000 3.540 0.01070 0.03610 0.01870 0.02040 0.06190 0.04850 0.05130 0.03420 0.03480 0.04450 0.07880 0.07770 0.08110 0.08190 0.07520 0.07550 0.02700 0.01150 0.01110 0.002820 0.002120 4.000 2.790 0.01060 0.03460 0.01860 0.02040 0.04900 0.04670 0.04900 0.03400 0.03470 0.02200 0.04950 0.04540 0.06290 0.06240 0.05780 0.05750 0.01300 0.01040 0.01010 0.001770 0.001110 5.000 2.110 0.01060 0.03280 0.01860 0.02040 0.03660 0.04370 0.04520 0.03370 0.03430 0.01670 0.02630 0.02250 0.04230 0.04120 0.04200 0.04150 0.004610 0.007230 0.006050 0.001610 0.0003750 6.000 1.610 0.01050 0.03080 0.01850 0.02020 0.02600 0.03960 0.04000 0.03300 0.03360 0.01650 0.01330 0.01130 0.02530 0.02410 0.02950 0.02900 0.002720 0.003660 0.002720 0.001130 0.0001900 7.000 1.260 0.01040 0.02850 0.01840 0.02010 0.01790 0.03450 0.03410 0.03190 0.03240 0.01520 0.008010 0.007650 0.01390 0.01300 0.02030 0.01980 0.002630 0.001660 0.001240 0.0006640 0.0001780 8.000 1.030 0.01040 0.02610 0.01820 0.01980 0.01240 0.02900 0.02780 0.03030 0.03070 0.01210 0.006750 0.007120 0.007480 0.006930 0.01380 0.01340 0.002380 0.0009470 0.0008470 0.0003560 0.0001640 10.00 0.7300 0.010000 0.02100 0.01800 0.01900 0.007300 0.01900 0.01700 0.02600 0.02600 0.005600 0.006500 0.006500 0.003100 0.003000 0.006300 0.006100 0.001200 0.0008000 0.0007900 0.0001200 8.600E-05 15.00 0.3600 0.009500 0.01100 0.01500 0.01600 0.006200 0.004200 0.003500 0.01400 0.01300 0.001500 0.002200 0.001700 0.002400 0.002400 0.0009000 0.0008500 0.0002300 0.0003200 0.0002300 7.900E-05 1.600E-05 20.00 0.1900 0.008800 0.004600 0.01200 0.01200 0.003800 0.001900 0.002100 0.005700 0.005400 0.001200 0.0004900 0.0004600 0.001300 0.001300 0.0001500 0.0001400 0.0001900 6.500E-05 5.500E-05 4.600E-05 1.300E-05 30.00 0.07300 0.007000 0.001100 0.006000 0.005300 0.0005300 0.001500 0.001500 0.0008900 0.0008000 0.0001700 0.0003400 0.0003500 0.0002300 0.0002000 6.900E-06 6.200E-06 2.800E-05 4.200E-05 4.100E-05 7.900E-06 1.900E-06 40.00 0.03300 0.005200 0.0009400 0.002600 0.002000 0.0002000 0.0007800 0.0006500 0.0001600 0.0001300 4.800E-05 0.0001900 0.0001600 4.000E-05 3.400E-05 5.600E-07 4.800E-07 7.300E-06 2.300E-05 1.900E-05 1.400E-06 4.900E-07 60.00 0.010000 0.002700 0.0006400 0.0005100 0.0003100 0.0001500 0.0001500 9.700E-05 8.900E-06 6.700E-06 3.700E-05 3.600E-05 2.400E-05 2.200E-06 1.600E-06 1.100E-08 8.900E-09 5.700E-06 4.500E-06 2.800E-06 7.700E-08 3.800E-07 100.0 0.001900 0.0006800 0.0001500 3.900E-05 1.600E-05 3.500E-05 1.100E-05 4.500E-06 1.600E-07 9.300E-08 8.500E-06 2.500E-06 1.100E-06 3.800E-08 2.200E-08 6.400E-11 4.200E-11 1.300E-06 3.200E-07 1.300E-07 1.300E-09 8.700E-08 #S 72 Hf #N 23 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 2 2 #UBIND 6.535E+04 1.127E+04 1.074E+04 9561. 2601. 2365. 2108. 1716. 1662. 538.0 437.0 380.0 224.0 214.0 19.00 17.00 65.00 38.00 31.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 0.000 11.40 0.01050 0.03740 0.01830 0.02010 0.08260 0.04860 0.05170 0.03360 0.03430 0.1750 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4610 0.3140 0.3380 0.4230 1.680 0.05000 11.30 0.01050 0.03740 0.01830 0.02010 0.08260 0.04860 0.05170 0.03360 0.03430 0.1750 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4590 0.3140 0.3380 0.4230 1.630 0.1000 11.00 0.01050 0.03740 0.01830 0.02010 0.08260 0.04860 0.05170 0.03360 0.03430 0.1740 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4560 0.3140 0.3380 0.4230 1.500 0.1500 10.60 0.01050 0.03740 0.01830 0.02010 0.08260 0.04860 0.05170 0.03360 0.03430 0.1740 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4500 0.3140 0.3380 0.4230 1.300 0.2000 10.10 0.01050 0.03740 0.01830 0.02010 0.08250 0.04860 0.05170 0.03360 0.03430 0.1740 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4420 0.3140 0.3380 0.4230 1.070 0.3000 9.140 0.01050 0.03740 0.01830 0.02010 0.08240 0.04860 0.05170 0.03360 0.03430 0.1720 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.4200 0.3130 0.3360 0.4200 0.6210 0.4000 8.410 0.01050 0.03730 0.01830 0.02010 0.08220 0.04860 0.05170 0.03360 0.03430 0.1700 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.3920 0.3110 0.3330 0.4120 0.3080 0.5000 7.940 0.01050 0.03730 0.01830 0.02010 0.08190 0.04860 0.05170 0.03360 0.03430 0.1680 0.1100 0.1180 0.09080 0.09270 0.08980 0.09070 0.3580 0.3060 0.3270 0.3960 0.1410 0.6000 7.620 0.01050 0.03730 0.01830 0.02010 0.08160 0.04860 0.05170 0.03360 0.03430 0.1650 0.1100 0.1170 0.09080 0.09270 0.08980 0.09070 0.3210 0.2990 0.3170 0.3710 0.07270 0.7000 7.350 0.01050 0.03730 0.01830 0.02010 0.08130 0.04860 0.05170 0.03360 0.03430 0.1610 0.1100 0.1170 0.09080 0.09270 0.08980 0.09070 0.2830 0.2880 0.3030 0.3390 0.05260 0.8000 7.090 0.01050 0.03720 0.01830 0.02010 0.08090 0.04860 0.05170 0.03360 0.03430 0.1580 0.1100 0.1170 0.09070 0.09270 0.08980 0.09070 0.2450 0.2750 0.2860 0.3020 0.04960 1.000 6.530 0.01050 0.03720 0.01830 0.02010 0.07990 0.04860 0.05160 0.03360 0.03430 0.1490 0.1100 0.1170 0.09070 0.09270 0.08970 0.09060 0.1740 0.2400 0.2420 0.2290 0.04720 1.200 5.960 0.01050 0.03710 0.01830 0.02010 0.07880 0.04850 0.05160 0.03360 0.03430 0.1390 0.1090 0.1150 0.09060 0.09260 0.08950 0.09040 0.1180 0.1980 0.1930 0.1650 0.03660 1.400 5.430 0.01050 0.03700 0.01830 0.02010 0.07740 0.04850 0.05160 0.03360 0.03430 0.1280 0.1080 0.1140 0.09050 0.09240 0.08910 0.09010 0.07760 0.1560 0.1450 0.1150 0.02400 1.600 4.990 0.01050 0.03680 0.01830 0.02010 0.07590 0.04850 0.05150 0.03360 0.03430 0.1160 0.1060 0.1120 0.09020 0.09200 0.08850 0.08940 0.05240 0.1160 0.1030 0.07790 0.01410 1.800 4.640 0.01050 0.03670 0.01830 0.02010 0.07420 0.04840 0.05150 0.03360 0.03430 0.1050 0.1040 0.1090 0.08970 0.09150 0.08750 0.08840 0.03850 0.08340 0.07040 0.05190 0.007850 2.000 4.360 0.01050 0.03650 0.01830 0.02010 0.07240 0.04840 0.05140 0.03360 0.03430 0.09320 0.1010 0.1050 0.08890 0.09070 0.08620 0.08700 0.03230 0.05780 0.04670 0.03400 0.004530 2.400 3.970 0.01050 0.03620 0.01830 0.02010 0.06830 0.04820 0.05110 0.03360 0.03420 0.07170 0.09350 0.09580 0.08650 0.08800 0.08240 0.08320 0.02990 0.02680 0.02100 0.01420 0.002470 3.000 3.530 0.01050 0.03550 0.01830 0.02010 0.06140 0.04760 0.05040 0.03350 0.03420 0.04580 0.07880 0.07810 0.08020 0.08110 0.07470 0.07520 0.02780 0.01210 0.01160 0.004240 0.002310 4.000 2.820 0.01040 0.03410 0.01830 0.02010 0.04900 0.04600 0.04830 0.03340 0.03410 0.02260 0.05080 0.04680 0.06340 0.06300 0.05910 0.05910 0.01440 0.01060 0.01060 0.002240 0.001340 5.000 2.160 0.01040 0.03240 0.01830 0.02000 0.03690 0.04320 0.04470 0.03310 0.03380 0.01660 0.02770 0.02370 0.04360 0.04250 0.04400 0.04370 0.005220 0.007840 0.006750 0.002100 0.0004700 6.000 1.650 0.01030 0.03040 0.01820 0.01990 0.02650 0.03930 0.03990 0.03250 0.03310 0.01620 0.01410 0.01190 0.02670 0.02550 0.03150 0.03110 0.002840 0.004160 0.003150 0.001540 0.0002170 7.000 1.300 0.01030 0.02830 0.01810 0.01980 0.01850 0.03460 0.03420 0.03150 0.03200 0.01520 0.008310 0.007770 0.01500 0.01410 0.02200 0.02160 0.002680 0.001920 0.001420 0.0009370 0.0001950 8.000 1.050 0.01020 0.02600 0.01790 0.01960 0.01290 0.02930 0.02820 0.03000 0.03040 0.01250 0.006720 0.007040 0.008170 0.007550 0.01520 0.01480 0.002490 0.001040 0.0009090 0.0005150 0.0001850 10.00 0.7400 0.010000 0.02100 0.01700 0.01900 0.007400 0.01900 0.01700 0.02600 0.02600 0.006000 0.006500 0.006600 0.003200 0.003100 0.007100 0.006800 0.001400 0.0008200 0.0008300 0.0001600 1.000E-04 15.00 0.3700 0.009400 0.01100 0.01500 0.01600 0.006100 0.004500 0.003700 0.01400 0.01400 0.001500 0.002400 0.001800 0.002500 0.002400 0.001100 0.001000 0.0002400 0.0003600 0.0002700 1.000E-04 1.700E-05 20.00 0.2000 0.008700 0.004800 0.01200 0.01200 0.004000 0.001900 0.002100 0.006000 0.005700 0.001200 0.0005200 0.0004700 0.001400 0.001300 0.0001800 0.0001700 0.0002000 7.300E-05 6.000E-05 6.300E-05 1.500E-05 30.00 0.07500 0.006900 0.001100 0.006200 0.005400 0.0005900 0.001500 0.001600 0.0009700 0.0008600 0.0001900 0.0003500 0.0003600 0.0002500 0.0002200 8.500E-06 7.700E-06 3.200E-05 4.400E-05 4.500E-05 1.100E-05 2.400E-06 40.00 0.03400 0.005200 0.0009200 0.002700 0.002100 0.0002000 0.0008100 0.0006900 0.0001700 0.0001500 4.900E-05 0.0002000 0.0001700 4.500E-05 3.800E-05 7.000E-07 6.000E-07 7.700E-06 2.500E-05 2.100E-05 2.100E-06 5.500E-07 60.00 0.01100 0.002700 0.0006500 0.0005500 0.0003300 0.0001600 0.0001600 0.0001100 1.000E-05 7.500E-06 3.800E-05 4.000E-05 2.600E-05 2.500E-06 1.900E-06 1.400E-08 1.100E-08 6.000E-06 5.200E-06 3.200E-06 1.100E-07 4.300E-07 100.0 0.002000 0.0007100 0.0001500 4.300E-05 1.700E-05 3.700E-05 1.200E-05 5.000E-06 1.800E-07 1.100E-07 9.100E-06 2.800E-06 1.200E-06 4.400E-08 2.600E-08 8.200E-11 5.400E-11 1.400E-06 3.700E-07 1.500E-07 2.000E-09 1.000E-07 #S 73 Ta #N 23 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 3 2 #UBIND 6.741E+04 1.168E+04 1.114E+04 9881. 2705. 2466. 2191. 1790. 1732. 563.0 462.0 402.0 239.0 227.0 25.00 23.00 68.00 42.00 34.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 0.000 11.30 0.01030 0.03670 0.01800 0.01980 0.08110 0.04760 0.05080 0.03300 0.03370 0.1710 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4430 0.3020 0.3240 0.3820 1.610 0.05000 11.20 0.01030 0.03670 0.01800 0.01980 0.08110 0.04760 0.05080 0.03300 0.03370 0.1710 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4420 0.3020 0.3240 0.3820 1.560 0.1000 10.90 0.01030 0.03670 0.01800 0.01980 0.08100 0.04760 0.05080 0.03300 0.03370 0.1710 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4390 0.3020 0.3240 0.3820 1.440 0.1500 10.60 0.01030 0.03670 0.01800 0.01980 0.08100 0.04760 0.05080 0.03300 0.03370 0.1700 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4340 0.3020 0.3240 0.3820 1.270 0.2000 10.10 0.01030 0.03670 0.01800 0.01980 0.08100 0.04760 0.05080 0.03300 0.03370 0.1700 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4270 0.3020 0.3240 0.3820 1.060 0.3000 9.240 0.01030 0.03670 0.01800 0.01980 0.08080 0.04760 0.05080 0.03300 0.03370 0.1680 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.4070 0.3010 0.3230 0.3810 0.6400 0.4000 8.540 0.01030 0.03670 0.01800 0.01980 0.08060 0.04760 0.05080 0.03300 0.03370 0.1670 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.3810 0.2990 0.3200 0.3760 0.3330 0.5000 8.070 0.01030 0.03670 0.01800 0.01980 0.08040 0.04760 0.05080 0.03300 0.03370 0.1640 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.3500 0.2950 0.3150 0.3670 0.1590 0.6000 7.740 0.01030 0.03660 0.01800 0.01980 0.08010 0.04760 0.05080 0.03300 0.03370 0.1620 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.3160 0.2890 0.3060 0.3500 0.08060 0.7000 7.480 0.01030 0.03660 0.01800 0.01980 0.07980 0.04760 0.05080 0.03300 0.03370 0.1580 0.1080 0.1150 0.08850 0.09050 0.08530 0.08580 0.2810 0.2800 0.2950 0.3280 0.05420 0.8000 7.220 0.01030 0.03660 0.01800 0.01980 0.07940 0.04760 0.05080 0.03300 0.03370 0.1550 0.1080 0.1150 0.08850 0.09050 0.08520 0.08580 0.2450 0.2680 0.2800 0.3010 0.04870 1.000 6.670 0.01030 0.03650 0.01800 0.01980 0.07850 0.04760 0.05070 0.03300 0.03370 0.1470 0.1070 0.1140 0.08850 0.09040 0.08520 0.08580 0.1780 0.2370 0.2410 0.2410 0.04720 1.200 6.080 0.01030 0.03640 0.01800 0.01980 0.07740 0.04760 0.05070 0.03300 0.03370 0.1370 0.1070 0.1130 0.08840 0.09040 0.08510 0.08570 0.1230 0.2000 0.1960 0.1820 0.03880 1.400 5.530 0.01030 0.03630 0.01800 0.01980 0.07610 0.04760 0.05070 0.03300 0.03370 0.1270 0.1050 0.1120 0.08820 0.09020 0.08480 0.08550 0.08270 0.1600 0.1510 0.1330 0.02690 1.600 5.060 0.01030 0.03620 0.01800 0.01980 0.07470 0.04760 0.05070 0.03300 0.03370 0.1160 0.1040 0.1100 0.08800 0.08990 0.08440 0.08510 0.05610 0.1220 0.1100 0.09410 0.01660 1.800 4.680 0.01030 0.03610 0.01800 0.01980 0.07310 0.04750 0.05060 0.03300 0.03370 0.1050 0.1020 0.1070 0.08760 0.08940 0.08370 0.08440 0.04080 0.08960 0.07730 0.06510 0.009610 2.000 4.370 0.01030 0.03590 0.01800 0.01980 0.07130 0.04750 0.05050 0.03300 0.03370 0.09350 0.09930 0.1040 0.08690 0.08870 0.08280 0.08350 0.03330 0.06350 0.05250 0.04410 0.005560 2.400 3.940 0.01030 0.03560 0.01800 0.01980 0.06750 0.04730 0.05030 0.03290 0.03360 0.07270 0.09250 0.09510 0.08490 0.08640 0.08000 0.08070 0.02980 0.03040 0.02390 0.01950 0.002710 3.000 3.510 0.01030 0.03490 0.01800 0.01980 0.06090 0.04680 0.04960 0.03290 0.03360 0.04710 0.07880 0.07840 0.07920 0.08020 0.07370 0.07430 0.02840 0.01300 0.01210 0.005870 0.002420 4.000 2.840 0.01030 0.03360 0.01800 0.01980 0.04900 0.04530 0.04760 0.03280 0.03350 0.02320 0.05200 0.04820 0.06370 0.06350 0.05990 0.06010 0.01580 0.01080 0.01110 0.002600 0.001540 5.000 2.200 0.01020 0.03200 0.01790 0.01970 0.03730 0.04270 0.04430 0.03260 0.03320 0.01650 0.02920 0.02500 0.04490 0.04380 0.04560 0.04550 0.005910 0.008410 0.007450 0.002490 0.0005700 6.000 1.700 0.01020 0.03010 0.01790 0.01960 0.02710 0.03910 0.03980 0.03200 0.03260 0.01600 0.01510 0.01260 0.02810 0.02690 0.03330 0.03300 0.002990 0.004680 0.003630 0.001910 0.0002450 7.000 1.330 0.01010 0.02800 0.01780 0.01950 0.01900 0.03450 0.03430 0.03110 0.03160 0.01520 0.008670 0.007940 0.01620 0.01510 0.02360 0.02330 0.002730 0.002200 0.001630 0.001210 0.0002080 8.000 1.080 0.010000 0.02590 0.01760 0.01930 0.01330 0.02950 0.02850 0.02970 0.03010 0.01280 0.006730 0.006980 0.008910 0.008220 0.01650 0.01620 0.002590 0.001150 0.0009810 0.0006800 0.0002010 10.00 0.7600 0.009800 0.02100 0.01700 0.01900 0.007500 0.02000 0.01800 0.02600 0.02600 0.006400 0.006400 0.006600 0.003400 0.003200 0.007900 0.007600 0.001500 0.0008400 0.0008700 0.0002100 0.0001200 15.00 0.3700 0.009300 0.01100 0.01500 0.01600 0.006100 0.004800 0.003900 0.01400 0.01400 0.001500 0.002500 0.002000 0.002500 0.002500 0.001200 0.001200 0.0002500 0.0004000 0.0003000 0.0001200 1.900E-05 20.00 0.2000 0.008600 0.005000 0.01200 0.01200 0.004100 0.001900 0.002000 0.006300 0.006000 0.001300 0.0005600 0.0004900 0.001500 0.001400 0.0002100 0.0002000 0.0002100 8.300E-05 6.600E-05 7.800E-05 1.600E-05 30.00 0.07700 0.006900 0.001100 0.006300 0.005500 0.0006500 0.001600 0.001600 0.001000 0.0009300 0.0002200 0.0003500 0.0003700 0.0002700 0.0002400 1.000E-05 9.300E-06 3.800E-05 4.600E-05 4.800E-05 1.500E-05 2.900E-06 40.00 0.03500 0.005200 0.0009000 0.002900 0.002200 0.0002000 0.0008500 0.0007200 0.0001900 0.0001600 4.900E-05 0.0002100 0.0001800 5.000E-05 4.200E-05 8.600E-07 7.500E-07 8.100E-06 2.800E-05 2.300E-05 2.700E-06 6.100E-07 60.00 0.01100 0.002800 0.0006600 0.0005900 0.0003600 0.0001600 0.0001800 0.0001100 1.100E-05 8.400E-06 3.900E-05 4.300E-05 2.800E-05 2.900E-06 2.100E-06 1.800E-08 1.400E-08 6.300E-06 5.800E-06 3.600E-06 1.600E-07 4.800E-07 100.0 0.002100 0.0007400 0.0001600 4.800E-05 1.900E-05 4.000E-05 1.300E-05 5.500E-06 2.100E-07 1.200E-07 9.700E-06 3.200E-06 1.300E-06 5.200E-08 3.000E-08 1.000E-10 6.900E-11 1.600E-06 4.300E-07 1.700E-07 2.800E-09 1.200E-07 #S 74 W #N 23 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 2 #UBIND 6.952E+04 1.210E+04 1.154E+04 1.020E+04 2817. 2572. 2278. 1869. 1807. 592.0 489.0 423.0 255.0 243.0 34.00 32.00 74.00 44.00 34.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 0.000 11.20 0.01010 0.03610 0.01770 0.01950 0.07960 0.04670 0.04990 0.03240 0.03310 0.1670 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.4270 0.2910 0.3110 0.3520 1.550 0.05000 11.10 0.01010 0.03610 0.01770 0.01950 0.07960 0.04670 0.04990 0.03240 0.03310 0.1670 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.4260 0.2910 0.3110 0.3520 1.510 0.1000 10.90 0.01010 0.03610 0.01770 0.01950 0.07950 0.04670 0.04990 0.03240 0.03310 0.1670 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.4230 0.2910 0.3110 0.3520 1.400 0.1500 10.50 0.01010 0.03610 0.01770 0.01950 0.07950 0.04670 0.04990 0.03240 0.03310 0.1660 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.4180 0.2910 0.3110 0.3520 1.240 0.2000 10.10 0.01010 0.03610 0.01770 0.01950 0.07950 0.04670 0.04990 0.03240 0.03310 0.1660 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.4120 0.2910 0.3110 0.3520 1.040 0.3000 9.320 0.01010 0.03600 0.01770 0.01950 0.07930 0.04670 0.04990 0.03240 0.03310 0.1650 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.3940 0.2900 0.3100 0.3510 0.6540 0.4000 8.640 0.01010 0.03600 0.01770 0.01950 0.07920 0.04670 0.04990 0.03240 0.03310 0.1630 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.3710 0.2890 0.3080 0.3490 0.3540 0.5000 8.170 0.01010 0.03600 0.01770 0.01950 0.07890 0.04670 0.04990 0.03240 0.03310 0.1610 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.3420 0.2850 0.3030 0.3420 0.1740 0.6000 7.850 0.01010 0.03600 0.01770 0.01950 0.07870 0.04670 0.04990 0.03240 0.03310 0.1580 0.1050 0.1130 0.08630 0.08830 0.08130 0.08170 0.3110 0.2800 0.2960 0.3310 0.08850 0.7000 7.590 0.01010 0.03600 0.01770 0.01950 0.07840 0.04670 0.04990 0.03240 0.03310 0.1550 0.1050 0.1120 0.08630 0.08830 0.08130 0.08170 0.2780 0.2720 0.2860 0.3150 0.05620 0.8000 7.340 0.01010 0.03590 0.01770 0.01950 0.07800 0.04670 0.04990 0.03240 0.03310 0.1520 0.1050 0.1120 0.08630 0.08830 0.08130 0.08170 0.2450 0.2620 0.2730 0.2950 0.04780 1.000 6.800 0.01010 0.03590 0.01770 0.01950 0.07710 0.04670 0.04990 0.03240 0.03310 0.1440 0.1050 0.1120 0.08630 0.08830 0.08130 0.08170 0.1820 0.2340 0.2390 0.2450 0.04650 1.200 6.220 0.01010 0.03580 0.01770 0.01950 0.07610 0.04670 0.04990 0.03240 0.03310 0.1350 0.1040 0.1110 0.08620 0.08820 0.08120 0.08160 0.1280 0.2000 0.1980 0.1940 0.04000 1.400 5.650 0.01010 0.03570 0.01770 0.01950 0.07490 0.04670 0.04980 0.03240 0.03310 0.1250 0.1030 0.1100 0.08610 0.08810 0.08110 0.08150 0.08750 0.1630 0.1560 0.1470 0.02910 1.600 5.150 0.01010 0.03560 0.01770 0.01950 0.07350 0.04670 0.04980 0.03240 0.03310 0.1150 0.1020 0.1080 0.08590 0.08780 0.08080 0.08120 0.05990 0.1270 0.1170 0.1080 0.01880 1.800 4.740 0.01010 0.03540 0.01760 0.01950 0.07200 0.04660 0.04970 0.03240 0.03310 0.1040 0.1000 0.1050 0.08550 0.08740 0.08030 0.08080 0.04320 0.09540 0.08370 0.07690 0.01130 2.000 4.400 0.01010 0.03530 0.01760 0.01950 0.07030 0.04660 0.04970 0.03240 0.03310 0.09370 0.09770 0.1020 0.08500 0.08680 0.07960 0.08010 0.03450 0.06910 0.05810 0.05370 0.006660 2.400 3.940 0.01010 0.03500 0.01760 0.01950 0.06670 0.04640 0.04940 0.03240 0.03310 0.07350 0.09140 0.09430 0.08320 0.08480 0.07750 0.07810 0.02970 0.03410 0.02710 0.02500 0.002990 3.000 3.490 0.01010 0.03440 0.01760 0.01950 0.06040 0.04600 0.04890 0.03230 0.03300 0.04830 0.07870 0.07860 0.07820 0.07920 0.07240 0.07300 0.02870 0.01400 0.01280 0.007770 0.002480 4.000 2.860 0.01010 0.03310 0.01760 0.01950 0.04890 0.04460 0.04700 0.03230 0.03290 0.02390 0.05310 0.04940 0.06390 0.06380 0.06030 0.06060 0.01710 0.01090 0.01140 0.002930 0.001720 5.000 2.240 0.010000 0.03160 0.01760 0.01940 0.03760 0.04220 0.04390 0.03200 0.03270 0.01650 0.03060 0.02630 0.04600 0.04500 0.04690 0.04700 0.006680 0.008940 0.008110 0.002800 0.0006770 6.000 1.740 0.009980 0.02980 0.01750 0.01940 0.02760 0.03880 0.03960 0.03150 0.03210 0.01580 0.01600 0.01330 0.02950 0.02820 0.03490 0.03470 0.003170 0.005220 0.004130 0.002250 0.0002750 7.000 1.360 0.009920 0.02780 0.01740 0.01920 0.01950 0.03450 0.03440 0.03070 0.03120 0.01520 0.009090 0.008150 0.01730 0.01620 0.02520 0.02490 0.002760 0.002530 0.001870 0.001470 0.0002170 8.000 1.100 0.009850 0.02570 0.01730 0.01900 0.01370 0.02970 0.02880 0.02940 0.02980 0.01300 0.006780 0.006940 0.009690 0.008930 0.01780 0.01750 0.002680 0.001280 0.001060 0.0008540 0.0002130 10.00 0.7700 0.009700 0.02100 0.01700 0.01800 0.007700 0.02000 0.01800 0.02600 0.02600 0.006800 0.006400 0.006700 0.003600 0.003400 0.008700 0.008400 0.001600 0.0008500 0.0009000 0.0002700 0.0001400 15.00 0.3800 0.009100 0.01200 0.01500 0.01600 0.006000 0.005100 0.004100 0.01500 0.01400 0.001500 0.002700 0.002100 0.002500 0.002500 0.001400 0.001300 0.0002600 0.0004400 0.0003500 0.0001400 2.100E-05 20.00 0.2100 0.008500 0.005300 0.01200 0.01200 0.004200 0.001900 0.002000 0.006600 0.006200 0.001300 0.0006000 0.0005100 0.001500 0.001500 0.0002500 0.0002300 0.0002200 9.500E-05 7.300E-05 9.300E-05 1.700E-05 30.00 0.07900 0.006800 0.001200 0.006400 0.005700 0.0007100 0.001600 0.001600 0.001100 0.001000 0.0002400 0.0003500 0.0003800 0.0003000 0.0002700 1.200E-05 1.100E-05 4.300E-05 4.700E-05 5.100E-05 1.900E-05 3.400E-06 40.00 0.03700 0.005200 0.0008900 0.003000 0.002300 0.0002000 0.0008800 0.0007500 0.0002100 0.0001800 5.100E-05 0.0002100 0.0001900 5.600E-05 4.700E-05 1.100E-06 9.200E-07 8.600E-06 3.000E-05 2.500E-05 3.500E-06 6.700E-07 60.00 0.01100 0.002800 0.0006700 0.0006300 0.0003800 0.0001600 0.0001900 0.0001200 1.300E-05 9.400E-06 4.000E-05 4.700E-05 3.000E-05 3.300E-06 2.400E-06 2.200E-08 1.800E-08 6.600E-06 6.500E-06 4.100E-06 2.000E-07 5.100E-07 100.0 0.002200 0.0007700 0.0001700 5.300E-05 2.000E-05 4.200E-05 1.500E-05 6.000E-06 2.400E-07 1.400E-07 1.000E-05 3.600E-06 1.500E-06 6.000E-08 3.400E-08 1.300E-10 8.700E-11 1.700E-06 4.900E-07 2.000E-07 3.700E-09 1.300E-07 #S 75 Re #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 1 2 #UBIND 7.168E+04 1.253E+04 1.196E+04 1.053E+04 2932. 2682. 2367. 1949. 1883. 625.0 518.0 445.0 274.0 260.0 43.00 40.00 83.00 46.00 35.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 11.10 0.009970 0.03540 0.01730 0.01920 0.07810 0.04580 0.04910 0.03180 0.03250 0.1630 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.4120 0.2790 0.3000 0.3270 0.3460 1.490 0.05000 11.00 0.009970 0.03540 0.01730 0.01920 0.07810 0.04580 0.04910 0.03180 0.03250 0.1630 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.4110 0.2790 0.3000 0.3270 0.3460 1.460 0.1000 10.80 0.009970 0.03540 0.01730 0.01920 0.07810 0.04580 0.04910 0.03180 0.03250 0.1630 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.4080 0.2790 0.3000 0.3270 0.3460 1.360 0.1500 10.50 0.009970 0.03540 0.01730 0.01920 0.07800 0.04580 0.04910 0.03180 0.03250 0.1630 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.4040 0.2790 0.3000 0.3270 0.3460 1.210 0.2000 10.20 0.009970 0.03540 0.01730 0.01920 0.07800 0.04580 0.04910 0.03180 0.03250 0.1620 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.3980 0.2790 0.3000 0.3270 0.3460 1.030 0.3000 9.390 0.009970 0.03540 0.01730 0.01920 0.07790 0.04580 0.04910 0.03180 0.03250 0.1610 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.3820 0.2790 0.2990 0.3270 0.3460 0.6660 0.4000 8.740 0.009970 0.03540 0.01730 0.01920 0.07770 0.04580 0.04910 0.03180 0.03250 0.1600 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.3600 0.2770 0.2970 0.3250 0.3430 0.3740 0.5000 8.270 0.009970 0.03540 0.01730 0.01920 0.07750 0.04580 0.04910 0.03180 0.03250 0.1580 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.3350 0.2740 0.2930 0.3210 0.3370 0.1910 0.6000 7.950 0.009970 0.03540 0.01730 0.01920 0.07730 0.04580 0.04910 0.03180 0.03250 0.1550 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.3060 0.2700 0.2870 0.3130 0.3270 0.09780 0.7000 7.690 0.009970 0.03530 0.01730 0.01920 0.07700 0.04580 0.04910 0.03180 0.03250 0.1520 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.2750 0.2630 0.2790 0.3020 0.3120 0.05930 0.8000 7.450 0.009970 0.03530 0.01730 0.01920 0.07660 0.04580 0.04910 0.03180 0.03250 0.1490 0.1030 0.1100 0.08420 0.08620 0.07770 0.07820 0.2440 0.2550 0.2670 0.2860 0.2920 0.04760 1.000 6.930 0.009970 0.03530 0.01730 0.01920 0.07580 0.04580 0.04900 0.03180 0.03250 0.1420 0.1030 0.1090 0.08420 0.08620 0.07760 0.07820 0.1840 0.2310 0.2370 0.2460 0.2450 0.04550 1.200 6.350 0.009960 0.03520 0.01730 0.01920 0.07480 0.04580 0.04900 0.03180 0.03250 0.1330 0.1020 0.1090 0.08410 0.08610 0.07760 0.07820 0.1320 0.2000 0.1990 0.2010 0.1950 0.04090 1.400 5.780 0.009960 0.03510 0.01730 0.01920 0.07370 0.04580 0.04900 0.03180 0.03250 0.1240 0.1010 0.1080 0.08400 0.08600 0.07750 0.07810 0.09210 0.1660 0.1600 0.1570 0.1490 0.03120 1.600 5.250 0.009960 0.03500 0.01730 0.01920 0.07240 0.04580 0.04900 0.03180 0.03250 0.1140 0.09990 0.1060 0.08380 0.08580 0.07730 0.07790 0.06370 0.1320 0.1220 0.1190 0.1100 0.02110 1.800 4.810 0.009960 0.03480 0.01730 0.01920 0.07090 0.04570 0.04890 0.03180 0.03250 0.1040 0.09820 0.1040 0.08350 0.08540 0.07700 0.07760 0.04580 0.1010 0.08950 0.08780 0.07950 0.01320 2.000 4.450 0.009950 0.03470 0.01730 0.01920 0.06940 0.04570 0.04880 0.03180 0.03250 0.09390 0.09600 0.1010 0.08310 0.08490 0.07650 0.07710 0.03590 0.07510 0.06340 0.06320 0.05610 0.007920 2.400 3.940 0.009940 0.03440 0.01730 0.01920 0.06580 0.04550 0.04860 0.03180 0.03250 0.07430 0.09030 0.09340 0.08150 0.08310 0.07500 0.07560 0.02970 0.03840 0.03040 0.03100 0.02660 0.003370 3.000 3.470 0.009930 0.03380 0.01730 0.01920 0.05990 0.04510 0.04810 0.03180 0.03250 0.04950 0.07850 0.07870 0.07710 0.07820 0.07090 0.07150 0.02890 0.01550 0.01360 0.01010 0.008380 0.002530 4.000 2.860 0.009900 0.03260 0.01730 0.01920 0.04890 0.04390 0.04640 0.03170 0.03240 0.02470 0.05410 0.05060 0.06400 0.06400 0.06050 0.06080 0.01850 0.01100 0.01160 0.003290 0.003080 0.001910 5.000 2.280 0.009860 0.03110 0.01730 0.01910 0.03790 0.04170 0.04350 0.03150 0.03220 0.01650 0.03190 0.02760 0.04700 0.04610 0.04810 0.04810 0.007540 0.009490 0.008720 0.003090 0.002940 0.0008000 6.000 1.770 0.009810 0.02940 0.01720 0.01910 0.02800 0.03850 0.03940 0.03100 0.03160 0.01550 0.01710 0.01410 0.03080 0.02960 0.03640 0.03620 0.003410 0.005840 0.004640 0.002590 0.002380 0.0003140 7.000 1.400 0.009750 0.02760 0.01710 0.01890 0.02010 0.03440 0.03440 0.03020 0.03080 0.01510 0.009560 0.008410 0.01850 0.01740 0.02670 0.02640 0.002800 0.002910 0.002130 0.001760 0.001560 0.0002280 8.000 1.130 0.009680 0.02550 0.01700 0.01880 0.01420 0.02990 0.02900 0.02910 0.02950 0.01320 0.006860 0.006930 0.01050 0.009690 0.01910 0.01880 0.002750 0.001460 0.001160 0.001050 0.0009050 0.0002240 10.00 0.7900 0.009500 0.02100 0.01700 0.01800 0.007900 0.02100 0.01900 0.02600 0.02600 0.007300 0.006300 0.006700 0.003800 0.003600 0.009500 0.009200 0.001800 0.0008800 0.0009300 0.0003300 0.0002900 0.0001500 15.00 0.3900 0.009000 0.01200 0.01500 0.01600 0.005900 0.005400 0.004300 0.01500 0.01500 0.001500 0.002900 0.002300 0.002500 0.002500 0.001600 0.001500 0.0002800 0.0004900 0.0003900 0.0001600 0.0001500 2.200E-05 20.00 0.2100 0.008300 0.005500 0.01200 0.01200 0.004300 0.001900 0.002000 0.006900 0.006500 0.001300 0.0006600 0.0005300 0.001600 0.001500 0.0002900 0.0002700 0.0002300 0.0001100 8.100E-05 0.0001100 9.900E-05 1.900E-05 30.00 0.08200 0.006800 0.001200 0.006600 0.005800 0.0007800 0.001600 0.001600 0.001200 0.001100 0.0002700 0.0003500 0.0003800 0.0003300 0.0002900 1.500E-05 1.300E-05 4.900E-05 4.900E-05 5.300E-05 2.300E-05 1.900E-05 4.000E-06 40.00 0.03800 0.005300 0.0008700 0.003100 0.002400 0.0002000 0.0009100 0.0007900 0.0002300 0.0001900 5.300E-05 0.0002200 0.0002000 6.300E-05 5.200E-05 1.300E-06 1.100E-06 9.200E-06 3.200E-05 2.800E-05 4.400E-06 3.400E-06 7.400E-07 60.00 0.01200 0.002900 0.0006800 0.0006800 0.0004100 0.0001600 0.0002000 0.0001300 1.400E-05 1.100E-05 4.000E-05 5.100E-05 3.300E-05 3.700E-06 2.800E-06 2.700E-08 2.200E-08 6.900E-06 7.400E-06 4.700E-06 2.600E-07 1.800E-07 5.500E-07 100.0 0.002400 0.0008000 0.0001800 5.800E-05 2.200E-05 4.400E-05 1.600E-05 6.600E-06 2.700E-07 1.600E-07 1.100E-05 4.000E-06 1.600E-06 6.900E-08 3.900E-08 1.700E-10 1.100E-10 1.900E-06 5.700E-07 2.300E-07 4.800E-09 2.600E-09 1.500E-07 #S 76 Os #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 2 2 #UBIND 7.387E+04 1.297E+04 1.238E+04 1.087E+04 3049. 2792. 2458. 2031. 1960. 655.0 547.0 469.0 290.0 273.0 54.00 51.00 84.00 58.00 46.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 11.00 0.009800 0.03480 0.01700 0.01890 0.07670 0.04500 0.04820 0.03120 0.03200 0.1600 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3970 0.2680 0.2900 0.3070 0.3240 1.440 0.05000 11.00 0.009800 0.03480 0.01700 0.01890 0.07670 0.04500 0.04820 0.03120 0.03200 0.1600 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3970 0.2680 0.2900 0.3070 0.3240 1.410 0.1000 10.80 0.009800 0.03480 0.01700 0.01890 0.07670 0.04500 0.04820 0.03120 0.03200 0.1600 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3940 0.2680 0.2900 0.3070 0.3240 1.320 0.1500 10.50 0.009800 0.03480 0.01700 0.01890 0.07660 0.04500 0.04820 0.03120 0.03200 0.1590 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3900 0.2680 0.2900 0.3070 0.3240 1.190 0.2000 10.20 0.009800 0.03480 0.01700 0.01890 0.07660 0.04500 0.04820 0.03120 0.03200 0.1590 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3850 0.2680 0.2900 0.3070 0.3240 1.020 0.3000 9.440 0.009790 0.03480 0.01700 0.01890 0.07650 0.04500 0.04820 0.03120 0.03200 0.1580 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3700 0.2680 0.2890 0.3070 0.3240 0.6760 0.4000 8.820 0.009790 0.03480 0.01700 0.01890 0.07630 0.04500 0.04820 0.03120 0.03200 0.1560 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3510 0.2670 0.2870 0.3060 0.3220 0.3920 0.5000 8.360 0.009790 0.03480 0.01700 0.01890 0.07610 0.04500 0.04820 0.03120 0.03200 0.1540 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3270 0.2640 0.2840 0.3030 0.3180 0.2070 0.6000 8.030 0.009790 0.03480 0.01700 0.01890 0.07590 0.04500 0.04820 0.03120 0.03200 0.1520 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.3010 0.2610 0.2790 0.2980 0.3110 0.1070 0.7000 7.780 0.009790 0.03470 0.01700 0.01890 0.07560 0.04500 0.04820 0.03120 0.03200 0.1500 0.1010 0.1080 0.08220 0.08420 0.07440 0.07510 0.2720 0.2550 0.2710 0.2890 0.2990 0.06290 0.8000 7.550 0.009790 0.03470 0.01700 0.01890 0.07530 0.04500 0.04820 0.03120 0.03200 0.1470 0.1010 0.1080 0.08220 0.08410 0.07440 0.07510 0.2430 0.2470 0.2610 0.2770 0.2840 0.04770 1.000 7.050 0.009790 0.03460 0.01700 0.01890 0.07450 0.04500 0.04820 0.03120 0.03200 0.1400 0.1000 0.1070 0.08220 0.08410 0.07440 0.07510 0.1860 0.2270 0.2340 0.2440 0.2450 0.04430 1.200 6.490 0.009790 0.03460 0.01700 0.01890 0.07360 0.04500 0.04820 0.03120 0.03200 0.1320 0.09980 0.1070 0.08210 0.08410 0.07440 0.07510 0.1360 0.1990 0.2000 0.2050 0.2010 0.04120 1.400 5.910 0.009790 0.03450 0.01700 0.01890 0.07250 0.04500 0.04820 0.03120 0.03200 0.1230 0.09900 0.1050 0.08210 0.08400 0.07430 0.07500 0.09640 0.1680 0.1630 0.1650 0.1580 0.03280 1.600 5.370 0.009780 0.03440 0.01700 0.01890 0.07130 0.04490 0.04820 0.03120 0.03200 0.1130 0.09790 0.1040 0.08190 0.08380 0.07420 0.07490 0.06750 0.1370 0.1270 0.1280 0.1200 0.02310 1.800 4.900 0.009780 0.03430 0.01700 0.01890 0.06990 0.04490 0.04810 0.03120 0.03200 0.1040 0.09640 0.1020 0.08160 0.08350 0.07400 0.07470 0.04850 0.1070 0.09480 0.09730 0.08940 0.01490 2.000 4.510 0.009780 0.03410 0.01700 0.01890 0.06840 0.04490 0.04810 0.03120 0.03200 0.09390 0.09440 0.09930 0.08120 0.08300 0.07370 0.07430 0.03740 0.08070 0.06850 0.07190 0.06470 0.009210 2.400 3.950 0.009770 0.03380 0.01700 0.01890 0.06500 0.04470 0.04790 0.03120 0.03200 0.07500 0.08920 0.09240 0.07990 0.08150 0.07250 0.07320 0.02970 0.04280 0.03380 0.03700 0.03220 0.003800 3.000 3.450 0.009760 0.03330 0.01700 0.01890 0.05930 0.04440 0.04740 0.03120 0.03190 0.05060 0.07820 0.07860 0.07600 0.07710 0.06930 0.06990 0.02890 0.01710 0.01450 0.01260 0.01060 0.002550 4.000 2.870 0.009730 0.03210 0.01700 0.01890 0.04880 0.04320 0.04580 0.03120 0.03190 0.02550 0.05500 0.05170 0.06400 0.06410 0.06030 0.06060 0.01970 0.01110 0.01170 0.003680 0.003410 0.002060 5.000 2.300 0.009690 0.03070 0.01700 0.01890 0.03810 0.04110 0.04300 0.03100 0.03160 0.01660 0.03320 0.02880 0.04790 0.04710 0.04900 0.04900 0.008460 0.009970 0.009280 0.003320 0.003190 0.0009290 6.000 1.810 0.009640 0.02910 0.01690 0.01880 0.02850 0.03810 0.03920 0.03050 0.03120 0.01530 0.01810 0.01490 0.03210 0.03090 0.03770 0.03750 0.003700 0.006450 0.005180 0.002900 0.002690 0.0003590 7.000 1.430 0.009580 0.02730 0.01680 0.01870 0.02060 0.03430 0.03440 0.02980 0.03040 0.01500 0.01010 0.008720 0.01970 0.01850 0.02810 0.02770 0.002850 0.003340 0.002420 0.002050 0.001840 0.0002380 8.000 1.160 0.009520 0.02540 0.01670 0.01850 0.01460 0.03000 0.02930 0.02880 0.02920 0.01340 0.006990 0.006930 0.01140 0.01050 0.02040 0.02000 0.002810 0.001660 0.001270 0.001260 0.001090 0.0002330 10.00 0.8100 0.009400 0.02100 0.01600 0.01800 0.008000 0.02100 0.01900 0.02600 0.02600 0.007700 0.006200 0.006600 0.004000 0.003800 0.010000 0.010000 0.001900 0.0009000 0.0009500 0.0004100 0.0003500 0.0001700 15.00 0.4000 0.008900 0.01200 0.01500 0.01600 0.005800 0.005800 0.004600 0.01500 0.01500 0.001500 0.003100 0.002500 0.002500 0.002500 0.001800 0.001700 0.0002900 0.0005500 0.0004300 0.0001700 0.0001700 2.400E-05 20.00 0.2200 0.008200 0.005700 0.01200 0.01200 0.004400 0.001900 0.002000 0.007200 0.006800 0.001300 0.0007200 0.0005600 0.001700 0.001600 0.0003400 0.0003100 0.0002400 0.0001300 9.000E-05 0.0001200 0.0001100 2.000E-05 30.00 0.08400 0.006800 0.001300 0.006700 0.005900 0.0008500 0.001600 0.001700 0.001300 0.001200 0.0002900 0.0003500 0.0003900 0.0003500 0.0003200 1.800E-05 1.600E-05 5.600E-05 5.100E-05 5.600E-05 2.700E-05 2.300E-05 4.700E-06 40.00 0.03900 0.005300 0.0008600 0.003200 0.002500 0.0002100 0.0009400 0.0008200 0.0002500 0.0002100 5.500E-05 0.0002300 0.0002100 7.000E-05 5.800E-05 1.500E-06 1.300E-06 9.900E-06 3.500E-05 3.000E-05 5.400E-06 4.200E-06 8.100E-07 60.00 0.01200 0.002900 0.0006900 0.0007200 0.0004300 0.0001600 0.0002200 0.0001400 1.600E-05 1.200E-05 4.100E-05 5.500E-05 3.600E-05 4.200E-06 3.100E-06 3.400E-08 2.700E-08 7.100E-06 8.300E-06 5.200E-06 3.300E-07 2.300E-07 5.800E-07 100.0 0.002500 0.0008300 0.0001900 6.300E-05 2.400E-05 4.700E-05 1.800E-05 7.200E-06 3.100E-07 1.800E-07 1.200E-05 4.400E-06 1.800E-06 8.000E-08 4.500E-08 2.100E-10 1.400E-10 2.100E-06 6.600E-07 2.600E-07 6.100E-09 3.300E-09 1.700E-07 #S 77 Ir #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 3 2 #UBIND 7.611E+04 1.342E+04 1.282E+04 1.122E+04 3174. 2909. 2551. 2116. 2041. 690.0 577.0 495.0 312.0 296.0 64.00 61.00 96.00 63.00 51.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 11.00 0.009620 0.03420 0.01670 0.01870 0.07530 0.04420 0.04750 0.03070 0.03140 0.1560 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3840 0.2580 0.2810 0.2910 0.3060 1.400 0.05000 10.90 0.009620 0.03420 0.01670 0.01870 0.07530 0.04420 0.04750 0.03070 0.03140 0.1560 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3830 0.2580 0.2810 0.2910 0.3060 1.370 0.1000 10.70 0.009620 0.03420 0.01670 0.01870 0.07530 0.04420 0.04750 0.03070 0.03140 0.1560 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3810 0.2580 0.2810 0.2910 0.3060 1.290 0.1500 10.50 0.009620 0.03420 0.01670 0.01870 0.07530 0.04420 0.04750 0.03070 0.03140 0.1560 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3780 0.2580 0.2800 0.2910 0.3060 1.160 0.2000 10.20 0.009620 0.03420 0.01670 0.01870 0.07520 0.04420 0.04750 0.03070 0.03140 0.1560 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3730 0.2580 0.2800 0.2910 0.3060 1.010 0.3000 9.490 0.009620 0.03420 0.01670 0.01870 0.07510 0.04420 0.04750 0.03070 0.03140 0.1550 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3600 0.2580 0.2800 0.2900 0.3060 0.6830 0.4000 8.880 0.009620 0.03420 0.01670 0.01870 0.07500 0.04420 0.04750 0.03070 0.03140 0.1530 0.09860 0.1060 0.08030 0.08220 0.07160 0.07230 0.3420 0.2570 0.2780 0.2900 0.3050 0.4070 0.5000 8.430 0.009620 0.03420 0.01670 0.01870 0.07480 0.04420 0.04750 0.03070 0.03140 0.1510 0.09850 0.1060 0.08030 0.08220 0.07160 0.07230 0.3200 0.2550 0.2760 0.2880 0.3020 0.2210 0.6000 8.110 0.009620 0.03420 0.01670 0.01870 0.07460 0.04420 0.04750 0.03070 0.03140 0.1490 0.09850 0.1060 0.08030 0.08220 0.07160 0.07230 0.2950 0.2520 0.2710 0.2830 0.2960 0.1160 0.7000 7.860 0.009620 0.03410 0.01670 0.01870 0.07430 0.04420 0.04750 0.03070 0.03140 0.1470 0.09850 0.1060 0.08030 0.08220 0.07160 0.07230 0.2690 0.2470 0.2640 0.2770 0.2870 0.06700 0.8000 7.640 0.009620 0.03410 0.01670 0.01870 0.07400 0.04420 0.04750 0.03070 0.03140 0.1440 0.09840 0.1050 0.08030 0.08220 0.07160 0.07230 0.2420 0.2410 0.2550 0.2670 0.2750 0.04830 1.000 7.170 0.009620 0.03410 0.01670 0.01870 0.07320 0.04420 0.04740 0.03070 0.03140 0.1370 0.09810 0.1050 0.08030 0.08220 0.07160 0.07230 0.1880 0.2220 0.2310 0.2400 0.2430 0.04290 1.200 6.620 0.009620 0.03400 0.01670 0.01870 0.07240 0.04420 0.04740 0.03070 0.03140 0.1300 0.09770 0.1040 0.08020 0.08210 0.07160 0.07230 0.1400 0.1980 0.2000 0.2070 0.2040 0.04090 1.400 6.040 0.009620 0.03390 0.01670 0.01870 0.07130 0.04410 0.04740 0.03070 0.03140 0.1210 0.09700 0.1030 0.08020 0.08210 0.07150 0.07220 0.1000 0.1700 0.1650 0.1700 0.1650 0.03390 1.600 5.490 0.009610 0.03380 0.01670 0.01870 0.07020 0.04410 0.04740 0.03070 0.03140 0.1120 0.09590 0.1020 0.08000 0.08190 0.07140 0.07220 0.07110 0.1400 0.1310 0.1360 0.1290 0.02470 1.800 5.000 0.009610 0.03370 0.01670 0.01870 0.06880 0.04410 0.04730 0.03070 0.03140 0.1030 0.09460 0.1000 0.07980 0.08170 0.07130 0.07200 0.05130 0.1110 0.09960 0.1050 0.09800 0.01660 2.000 4.590 0.009610 0.03360 0.01670 0.01870 0.06740 0.04400 0.04730 0.03070 0.03140 0.09380 0.09280 0.09780 0.07950 0.08130 0.07100 0.07180 0.03910 0.08580 0.07330 0.07980 0.07270 0.01050 2.400 3.980 0.009600 0.03330 0.01670 0.01870 0.06420 0.04390 0.04710 0.03070 0.03140 0.07560 0.08800 0.09150 0.07830 0.07990 0.07020 0.07090 0.02980 0.04720 0.03720 0.04300 0.03780 0.004310 3.000 3.440 0.009590 0.03280 0.01670 0.01870 0.05880 0.04360 0.04660 0.03070 0.03140 0.05170 0.07780 0.07850 0.07480 0.07600 0.06760 0.06820 0.02880 0.01900 0.01550 0.01540 0.01300 0.002570 4.000 2.860 0.009560 0.03170 0.01670 0.01860 0.04870 0.04250 0.04510 0.03060 0.03140 0.02630 0.05580 0.05270 0.06390 0.06410 0.06000 0.06030 0.02080 0.01120 0.01180 0.004130 0.003780 0.002180 5.000 2.330 0.009520 0.03030 0.01670 0.01860 0.03830 0.04060 0.04260 0.03050 0.03120 0.01670 0.03450 0.03010 0.04870 0.04790 0.04960 0.04960 0.009430 0.01040 0.009780 0.003500 0.003400 0.001060 6.000 1.850 0.009480 0.02880 0.01660 0.01850 0.02890 0.03780 0.03890 0.03010 0.03070 0.01510 0.01920 0.01580 0.03330 0.03210 0.03890 0.03870 0.004040 0.007050 0.005720 0.003170 0.002980 0.0004110 7.000 1.460 0.009420 0.02700 0.01650 0.01840 0.02100 0.03420 0.03440 0.02940 0.03000 0.01490 0.01070 0.009080 0.02080 0.01960 0.02940 0.02900 0.002900 0.003790 0.002740 0.002330 0.002110 0.0002490 8.000 1.180 0.009360 0.02520 0.01640 0.01830 0.01510 0.03010 0.02940 0.02850 0.02890 0.01360 0.007170 0.006970 0.01220 0.01130 0.02160 0.02120 0.002850 0.001900 0.001400 0.001470 0.001290 0.0002390 10.00 0.8200 0.009200 0.02100 0.01600 0.01800 0.008200 0.02100 0.02000 0.02600 0.02600 0.008100 0.006100 0.006600 0.004300 0.004000 0.01100 0.01100 0.002100 0.0009300 0.0009700 0.0004900 0.0004200 0.0001800 15.00 0.4100 0.008700 0.01200 0.01500 0.01600 0.005700 0.006100 0.004800 0.01500 0.01500 0.001500 0.003300 0.002700 0.002500 0.002500 0.002000 0.001900 0.0003100 0.0006000 0.0004700 0.0001900 0.0001800 2.700E-05 20.00 0.2300 0.008100 0.005900 0.01200 0.01200 0.004500 0.002000 0.002000 0.007400 0.007000 0.001300 0.0007900 0.0006000 0.001700 0.001700 0.0003900 0.0003600 0.0002500 0.0001400 1.000E-04 0.0001400 0.0001300 2.100E-05 30.00 0.08600 0.006700 0.001300 0.006800 0.006100 0.0009200 0.001600 0.001700 0.001400 0.001200 0.0003200 0.0003500 0.0003900 0.0003800 0.0003400 2.100E-05 1.900E-05 6.400E-05 5.200E-05 5.800E-05 3.200E-05 2.700E-05 5.300E-06 40.00 0.04000 0.005300 0.0008400 0.003300 0.002600 0.0002100 0.0009700 0.0008500 0.0002800 0.0002300 5.800E-05 0.0002400 0.0002200 7.700E-05 6.400E-05 1.900E-06 1.600E-06 1.100E-05 3.700E-05 3.300E-05 6.500E-06 5.100E-06 9.000E-07 60.00 0.01300 0.002900 0.0006900 0.0007700 0.0004600 0.0001600 0.0002300 0.0001500 1.800E-05 1.300E-05 4.100E-05 5.900E-05 3.800E-05 4.800E-06 3.500E-06 4.100E-08 3.300E-08 7.400E-06 9.300E-06 5.800E-06 4.000E-07 2.800E-07 6.100E-07 100.0 0.002600 0.0008600 0.0002000 6.900E-05 2.600E-05 4.900E-05 2.000E-05 7.900E-06 3.500E-07 2.000E-07 1.200E-05 4.900E-06 2.000E-06 9.200E-08 5.200E-08 2.600E-10 1.700E-10 2.200E-06 7.600E-07 3.000E-07 7.600E-09 4.100E-09 1.800E-07 #S 78 Pt #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 5 1 #UBIND 7.840E+04 1.388E+04 1.327E+04 1.156E+04 3298. 3027. 2646. 2202. 2121. 724.0 608.0 520.0 332.0 315.0 75.00 71.00 102.0 66.00 52.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 10.10 0.009460 0.03360 0.01640 0.01840 0.07400 0.04340 0.04670 0.03020 0.03090 0.1530 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3750 0.2500 0.2740 0.2870 0.3040 1.450 0.05000 10.00 0.009460 0.03360 0.01640 0.01840 0.07400 0.04340 0.04670 0.03020 0.03090 0.1530 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3740 0.2500 0.2740 0.2870 0.3040 1.410 0.1000 9.930 0.009460 0.03360 0.01640 0.01840 0.07390 0.04340 0.04670 0.03020 0.03090 0.1530 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3720 0.2500 0.2740 0.2870 0.3040 1.320 0.1500 9.790 0.009460 0.03360 0.01640 0.01840 0.07390 0.04340 0.04670 0.03020 0.03090 0.1530 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3690 0.2500 0.2740 0.2870 0.3040 1.190 0.2000 9.620 0.009460 0.03360 0.01640 0.01840 0.07390 0.04340 0.04670 0.03020 0.03090 0.1520 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3650 0.2500 0.2740 0.2870 0.3040 1.030 0.3000 9.240 0.009460 0.03360 0.01640 0.01840 0.07380 0.04340 0.04670 0.03020 0.03090 0.1510 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3520 0.2500 0.2730 0.2860 0.3030 0.6820 0.4000 8.900 0.009460 0.03360 0.01640 0.01840 0.07360 0.04340 0.04670 0.03020 0.03090 0.1500 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3350 0.2490 0.2720 0.2860 0.3020 0.3980 0.5000 8.630 0.009460 0.03360 0.01640 0.01840 0.07350 0.04340 0.04670 0.03020 0.03090 0.1480 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.3150 0.2470 0.2690 0.2830 0.2990 0.2110 0.6000 8.410 0.009460 0.03360 0.01640 0.01840 0.07330 0.04340 0.04670 0.03020 0.03090 0.1460 0.09640 0.1040 0.07840 0.08030 0.06900 0.06980 0.2910 0.2450 0.2650 0.2790 0.2930 0.1080 0.7000 8.200 0.009450 0.03360 0.01640 0.01840 0.07300 0.04340 0.04670 0.03020 0.03090 0.1440 0.09630 0.1030 0.07840 0.08030 0.06900 0.06980 0.2660 0.2400 0.2590 0.2730 0.2840 0.06120 0.8000 7.980 0.009450 0.03350 0.01640 0.01840 0.07270 0.04340 0.04670 0.03020 0.03090 0.1410 0.09620 0.1030 0.07840 0.08030 0.06900 0.06980 0.2400 0.2350 0.2510 0.2630 0.2720 0.04370 1.000 7.470 0.009450 0.03350 0.01640 0.01840 0.07200 0.04340 0.04670 0.03020 0.03090 0.1350 0.09600 0.1030 0.07840 0.08030 0.06900 0.06980 0.1890 0.2180 0.2280 0.2370 0.2390 0.03880 1.200 6.880 0.009450 0.03340 0.01640 0.01840 0.07120 0.04330 0.04670 0.03020 0.03090 0.1280 0.09560 0.1020 0.07840 0.08030 0.06900 0.06980 0.1420 0.1960 0.1990 0.2040 0.2020 0.03700 1.400 6.260 0.009450 0.03330 0.01640 0.01840 0.07020 0.04330 0.04660 0.03020 0.03090 0.1200 0.09500 0.1020 0.07830 0.08020 0.06900 0.06980 0.1030 0.1700 0.1660 0.1700 0.1640 0.03070 1.600 5.670 0.009450 0.03320 0.01640 0.01840 0.06910 0.04330 0.04660 0.03020 0.03090 0.1120 0.09400 0.1000 0.07820 0.08010 0.06890 0.06970 0.07390 0.1420 0.1330 0.1370 0.1290 0.02270 1.800 5.140 0.009440 0.03310 0.01640 0.01840 0.06780 0.04330 0.04660 0.03020 0.03090 0.1030 0.09280 0.09850 0.07800 0.07980 0.06880 0.06960 0.05340 0.1150 0.1030 0.1070 0.09950 0.01540 2.000 4.700 0.009440 0.03300 0.01640 0.01840 0.06650 0.04320 0.04650 0.03020 0.03090 0.09370 0.09120 0.09630 0.07770 0.07950 0.06860 0.06940 0.04050 0.08990 0.07690 0.08240 0.07490 0.009940 2.400 4.030 0.009430 0.03270 0.01640 0.01840 0.06340 0.04310 0.04640 0.03020 0.03090 0.07610 0.08670 0.09040 0.07670 0.07830 0.06790 0.06870 0.02980 0.05100 0.04000 0.04580 0.04020 0.004120 3.000 3.430 0.009420 0.03220 0.01640 0.01840 0.05820 0.04280 0.04590 0.03020 0.03090 0.05270 0.07730 0.07830 0.07360 0.07480 0.06590 0.06660 0.02840 0.02080 0.01640 0.01720 0.01450 0.002290 4.000 2.860 0.009390 0.03120 0.01640 0.01840 0.04850 0.04180 0.04450 0.03010 0.03090 0.02710 0.05650 0.05360 0.06370 0.06400 0.05940 0.05970 0.02160 0.01120 0.01170 0.004390 0.003920 0.002000 5.000 2.350 0.009360 0.02990 0.01640 0.01830 0.03850 0.04010 0.04210 0.03000 0.03070 0.01690 0.03580 0.03130 0.04940 0.04870 0.05000 0.05000 0.01030 0.01060 0.01010 0.003460 0.003330 0.001040 6.000 1.880 0.009320 0.02840 0.01630 0.01830 0.02930 0.03740 0.03870 0.02960 0.03030 0.01490 0.02030 0.01670 0.03450 0.03330 0.03990 0.03970 0.004390 0.007580 0.006170 0.003230 0.003020 0.0004130 7.000 1.500 0.009270 0.02680 0.01630 0.01820 0.02150 0.03410 0.03440 0.02900 0.02960 0.01480 0.01130 0.009480 0.02200 0.02080 0.03060 0.03020 0.002940 0.004240 0.003050 0.002460 0.002220 0.0002320 8.000 1.210 0.009210 0.02500 0.01620 0.01800 0.01550 0.03020 0.02960 0.02810 0.02860 0.01360 0.007380 0.007030 0.01310 0.01220 0.02280 0.02240 0.002850 0.002150 0.001530 0.001610 0.001400 0.0002150 10.00 0.8400 0.009100 0.02100 0.01600 0.01800 0.008500 0.02200 0.02000 0.02500 0.02600 0.008500 0.006100 0.006600 0.004700 0.004300 0.01200 0.01200 0.002200 0.0009600 0.0009700 0.0005500 0.0004600 0.0001700 15.00 0.4100 0.008600 0.01200 0.01400 0.01500 0.005600 0.006500 0.005100 0.01600 0.01500 0.001600 0.003500 0.002800 0.002500 0.002500 0.002300 0.002100 0.0003400 0.0006400 0.0005100 0.0001900 0.0001800 2.600E-05 20.00 0.2300 0.008000 0.006100 0.01200 0.01200 0.004600 0.002000 0.002000 0.007700 0.007300 0.001400 0.0008700 0.0006400 0.001800 0.001700 0.0004400 0.0004100 0.0002500 0.0001600 0.0001100 0.0001500 0.0001300 1.900E-05 30.00 0.08900 0.006700 0.001400 0.006900 0.006200 0.001000 0.001500 0.001700 0.001500 0.001300 0.0003600 0.0003400 0.0004000 0.0004200 0.0003700 2.500E-05 2.200E-05 7.100E-05 5.300E-05 5.900E-05 3.500E-05 3.000E-05 5.400E-06 40.00 0.04100 0.005300 0.0008300 0.003500 0.002700 0.0002200 0.001000 0.0008800 0.0003000 0.0002500 6.200E-05 0.0002500 0.0002300 8.500E-05 7.100E-05 2.200E-06 1.900E-06 1.200E-05 4.000E-05 3.500E-05 7.300E-06 5.700E-06 8.900E-07 60.00 0.01300 0.003000 0.0007000 0.0008200 0.0004900 0.0001700 0.0002500 0.0001600 2.000E-05 1.400E-05 4.100E-05 6.400E-05 4.100E-05 5.400E-06 4.000E-06 5.000E-08 3.900E-08 7.500E-06 1.000E-05 6.400E-06 4.600E-07 3.200E-07 5.600E-07 100.0 0.002700 0.0008900 0.0002100 7.500E-05 2.800E-05 5.100E-05 2.200E-05 8.600E-06 3.900E-07 2.200E-07 1.300E-05 5.400E-06 2.200E-06 1.100E-07 6.000E-08 3.100E-10 2.000E-10 2.400E-06 8.600E-07 3.300E-07 8.900E-09 4.700E-09 1.800E-07 #S 79 Au #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 1 #UBIND 8.072E+04 1.435E+04 1.373E+04 1.192E+04 3425. 3150. 2743. 2291. 2206. 759.0 644.0 546.0 352.0 334.0 88.00 84.00 108.0 72.00 54.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 10.00 0.009290 0.03310 0.01610 0.01810 0.07270 0.04260 0.04590 0.02970 0.03040 0.1500 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3630 0.2420 0.2660 0.2730 0.2880 1.410 0.05000 9.990 0.009290 0.03310 0.01610 0.01810 0.07270 0.04260 0.04590 0.02970 0.03040 0.1500 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3630 0.2420 0.2660 0.2730 0.2880 1.380 0.1000 9.900 0.009290 0.03310 0.01610 0.01810 0.07260 0.04260 0.04590 0.02970 0.03040 0.1500 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3610 0.2420 0.2660 0.2730 0.2880 1.300 0.1500 9.770 0.009290 0.03310 0.01610 0.01810 0.07260 0.04260 0.04590 0.02970 0.03040 0.1490 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3580 0.2420 0.2660 0.2730 0.2880 1.170 0.2000 9.610 0.009290 0.03310 0.01610 0.01810 0.07260 0.04260 0.04590 0.02970 0.03040 0.1490 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3540 0.2420 0.2660 0.2730 0.2880 1.020 0.3000 9.250 0.009290 0.03310 0.01610 0.01810 0.07250 0.04260 0.04590 0.02970 0.03040 0.1480 0.09430 0.1020 0.07670 0.07850 0.06660 0.06750 0.3420 0.2410 0.2650 0.2720 0.2880 0.6880 0.4000 8.920 0.009290 0.03300 0.01610 0.01810 0.07240 0.04260 0.04590 0.02970 0.03040 0.1470 0.09430 0.1010 0.07670 0.07850 0.06660 0.06750 0.3270 0.2410 0.2640 0.2720 0.2870 0.4100 0.5000 8.660 0.009290 0.03300 0.01610 0.01810 0.07220 0.04260 0.04590 0.02970 0.03040 0.1450 0.09430 0.1010 0.07670 0.07850 0.06660 0.06750 0.3080 0.2390 0.2620 0.2700 0.2850 0.2230 0.6000 8.450 0.009290 0.03300 0.01610 0.01810 0.07200 0.04260 0.04590 0.02970 0.03040 0.1430 0.09430 0.1010 0.07670 0.07850 0.06660 0.06750 0.2860 0.2370 0.2580 0.2670 0.2810 0.1160 0.7000 8.250 0.009290 0.03300 0.01610 0.01810 0.07170 0.04260 0.04590 0.02970 0.03040 0.1410 0.09420 0.1010 0.07670 0.07850 0.06660 0.06750 0.2630 0.2330 0.2530 0.2620 0.2730 0.06480 0.8000 8.050 0.009290 0.03300 0.01610 0.01810 0.07150 0.04260 0.04590 0.02970 0.03040 0.1390 0.09420 0.1010 0.07670 0.07850 0.06660 0.06750 0.2390 0.2280 0.2450 0.2540 0.2630 0.04430 1.000 7.570 0.009290 0.03290 0.01610 0.01810 0.07080 0.04260 0.04590 0.02970 0.03040 0.1330 0.09400 0.1010 0.07670 0.07850 0.06660 0.06750 0.1900 0.2140 0.2250 0.2320 0.2360 0.03740 1.200 7.000 0.009290 0.03290 0.01610 0.01810 0.07000 0.04260 0.04590 0.02970 0.03040 0.1260 0.09360 0.1000 0.07660 0.07840 0.06660 0.06750 0.1450 0.1940 0.1980 0.2040 0.2030 0.03630 1.400 6.400 0.009290 0.03280 0.01610 0.01810 0.06910 0.04260 0.04590 0.02970 0.03040 0.1190 0.09300 0.09960 0.07660 0.07840 0.06660 0.06750 0.1070 0.1700 0.1680 0.1730 0.1690 0.03110 1.600 5.800 0.009280 0.03270 0.01610 0.01810 0.06800 0.04250 0.04590 0.02970 0.03040 0.1110 0.09220 0.09850 0.07650 0.07830 0.06660 0.06740 0.07730 0.1440 0.1360 0.1420 0.1360 0.02370 1.800 5.270 0.009280 0.03260 0.01610 0.01810 0.06680 0.04250 0.04580 0.02970 0.03040 0.1020 0.09100 0.09690 0.07630 0.07810 0.06650 0.06740 0.05620 0.1180 0.1070 0.1140 0.1060 0.01660 2.000 4.800 0.009280 0.03250 0.01610 0.01810 0.06550 0.04250 0.04580 0.02970 0.03040 0.09350 0.08960 0.09480 0.07610 0.07780 0.06640 0.06720 0.04240 0.09420 0.08120 0.08900 0.08170 0.01100 2.400 4.080 0.009270 0.03220 0.01610 0.01810 0.06270 0.04240 0.04560 0.02970 0.03040 0.07650 0.08550 0.08940 0.07520 0.07680 0.06590 0.06670 0.03020 0.05520 0.04350 0.05140 0.04560 0.004620 3.000 3.440 0.009260 0.03170 0.01610 0.01810 0.05770 0.04210 0.04520 0.02970 0.03040 0.05370 0.07670 0.07810 0.07240 0.07370 0.06420 0.06490 0.02810 0.02300 0.01780 0.02030 0.01720 0.002310 4.000 2.850 0.009230 0.03080 0.01610 0.01810 0.04830 0.04120 0.04390 0.02960 0.03040 0.02800 0.05710 0.05450 0.06340 0.06380 0.05870 0.05900 0.02250 0.01130 0.01170 0.005010 0.004400 0.002060 5.000 2.360 0.009200 0.02950 0.01610 0.01810 0.03870 0.03950 0.04170 0.02950 0.03020 0.01710 0.03690 0.03250 0.05000 0.04940 0.05030 0.05030 0.01130 0.01080 0.01050 0.003600 0.003460 0.001150 6.000 1.910 0.009160 0.02810 0.01600 0.01800 0.02970 0.03710 0.03840 0.02920 0.02990 0.01470 0.02140 0.01760 0.03550 0.03440 0.04070 0.04050 0.004850 0.008120 0.006700 0.003440 0.003230 0.0004700 7.000 1.530 0.009110 0.02650 0.01600 0.01790 0.02200 0.03390 0.03440 0.02860 0.02920 0.01460 0.01200 0.009920 0.02310 0.02190 0.03170 0.03130 0.003030 0.004740 0.003420 0.002720 0.002460 0.0002450 8.000 1.230 0.009060 0.02480 0.01590 0.01780 0.01590 0.03020 0.02970 0.02780 0.02830 0.01370 0.007650 0.007130 0.01410 0.01310 0.02390 0.02350 0.002870 0.002440 0.001710 0.001830 0.001610 0.0002180 10.00 0.8600 0.008900 0.02100 0.01590 0.01700 0.008700 0.02200 0.02000 0.02500 0.02600 0.008900 0.006000 0.006500 0.005000 0.004600 0.01300 0.01300 0.002300 0.001000 0.0009900 0.0006500 0.0005500 0.0001800 15.00 0.4200 0.008500 0.01300 0.01400 0.01500 0.005600 0.006900 0.005300 0.01600 0.01600 0.001600 0.003700 0.003000 0.002500 0.002500 0.002500 0.002400 0.0003700 0.0006900 0.0005600 0.0002000 0.0001900 2.900E-05 20.00 0.2400 0.007900 0.006300 0.01200 0.01200 0.004600 0.002100 0.002000 0.008000 0.007600 0.001400 0.0009500 0.0006900 0.001900 0.001800 0.0005000 0.0004700 0.0002600 0.0001900 0.0001200 0.0001600 0.0001500 1.900E-05 30.00 0.09100 0.006600 0.001400 0.007000 0.006300 0.001100 0.001500 0.001700 0.001600 0.001400 0.0003900 0.0003400 0.0004000 0.0004500 0.0004000 2.900E-05 2.600E-05 7.900E-05 5.400E-05 6.100E-05 4.100E-05 3.400E-05 6.000E-06 40.00 0.04200 0.005300 0.0008200 0.003600 0.002800 0.0002300 0.001000 0.0009200 0.0003300 0.0002700 6.700E-05 0.0002600 0.0002400 9.400E-05 7.900E-05 2.600E-06 2.300E-06 1.300E-05 4.200E-05 3.700E-05 8.600E-06 6.700E-06 9.800E-07 60.00 0.01300 0.003000 0.0007000 0.0008700 0.0005200 0.0001700 0.0002700 0.0001700 2.200E-05 1.600E-05 4.100E-05 6.800E-05 4.500E-05 6.100E-06 4.500E-06 6.000E-08 4.700E-08 7.700E-06 1.100E-05 7.000E-06 5.500E-07 3.800E-07 5.800E-07 100.0 0.002800 0.0009200 0.0002200 8.200E-05 3.100E-05 5.400E-05 2.400E-05 9.400E-06 4.500E-07 2.500E-07 1.400E-05 6.000E-06 2.400E-06 1.200E-07 6.800E-08 3.800E-10 2.500E-10 2.600E-06 9.900E-07 3.800E-07 1.100E-08 5.700E-09 1.900E-07 #S 80 Hg #N 24 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 #UBIND 8.310E+04 1.484E+04 1.421E+04 1.228E+04 3562. 3279. 2847. 2385. 2295. 800.0 677.0 571.0 379.0 360.0 104.0 100.0 120.0 81.00 58.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 0.000 10.80 0.009130 0.03250 0.01580 0.01790 0.07140 0.04180 0.04520 0.02920 0.03000 0.1470 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3500 0.2330 0.2560 0.2520 0.2650 1.290 0.05000 10.80 0.009130 0.03250 0.01580 0.01790 0.07140 0.04180 0.04520 0.02920 0.03000 0.1470 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3490 0.2330 0.2560 0.2520 0.2650 1.270 0.1000 10.60 0.009130 0.03250 0.01580 0.01790 0.07140 0.04180 0.04520 0.02920 0.03000 0.1460 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3470 0.2330 0.2560 0.2520 0.2650 1.210 0.1500 10.40 0.009130 0.03250 0.01580 0.01790 0.07140 0.04180 0.04520 0.02920 0.03000 0.1460 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3450 0.2330 0.2560 0.2520 0.2650 1.100 0.2000 10.20 0.009130 0.03250 0.01580 0.01790 0.07130 0.04180 0.04520 0.02920 0.03000 0.1460 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3410 0.2330 0.2560 0.2520 0.2650 0.9760 0.3000 9.570 0.009130 0.03250 0.01580 0.01790 0.07120 0.04180 0.04520 0.02920 0.03000 0.1450 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3310 0.2320 0.2550 0.2520 0.2650 0.6960 0.4000 9.020 0.009130 0.03250 0.01580 0.01790 0.07110 0.04180 0.04520 0.02920 0.03000 0.1440 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.3170 0.2320 0.2540 0.2520 0.2640 0.4430 0.5000 8.600 0.009130 0.03250 0.01580 0.01790 0.07090 0.04180 0.04520 0.02920 0.03000 0.1420 0.09230 0.09950 0.07500 0.07680 0.06450 0.06530 0.2990 0.2310 0.2530 0.2510 0.2630 0.2580 0.6000 8.280 0.009130 0.03250 0.01580 0.01790 0.07080 0.04180 0.04520 0.02920 0.03000 0.1410 0.09230 0.09940 0.07500 0.07680 0.06450 0.06530 0.2800 0.2290 0.2490 0.2490 0.2610 0.1430 0.7000 8.050 0.009130 0.03240 0.01580 0.01790 0.07050 0.04180 0.04520 0.02920 0.03000 0.1390 0.09220 0.09940 0.07500 0.07680 0.06450 0.06530 0.2590 0.2260 0.2450 0.2460 0.2560 0.08120 0.8000 7.850 0.009130 0.03240 0.01580 0.01790 0.07030 0.04180 0.04520 0.02920 0.03000 0.1360 0.09220 0.09930 0.07500 0.07680 0.06450 0.06530 0.2360 0.2210 0.2390 0.2410 0.2500 0.05260 1.000 7.450 0.009130 0.03240 0.01580 0.01790 0.06960 0.04180 0.04520 0.02920 0.03000 0.1310 0.09200 0.09900 0.07500 0.07680 0.06450 0.06530 0.1910 0.2090 0.2210 0.2250 0.2310 0.03910 1.200 6.970 0.009130 0.03230 0.01580 0.01790 0.06890 0.04180 0.04520 0.02920 0.03000 0.1240 0.09160 0.09850 0.07490 0.07670 0.06450 0.06530 0.1480 0.1920 0.1970 0.2040 0.2050 0.03860 1.400 6.430 0.009130 0.03220 0.01580 0.01790 0.06800 0.04180 0.04520 0.02920 0.03000 0.1170 0.09110 0.09780 0.07490 0.07670 0.06450 0.06530 0.1110 0.1700 0.1690 0.1780 0.1760 0.03490 1.600 5.880 0.009120 0.03210 0.01580 0.01790 0.06700 0.04180 0.04520 0.02920 0.03000 0.1100 0.09040 0.09670 0.07480 0.07660 0.06440 0.06530 0.08130 0.1460 0.1400 0.1500 0.1460 0.02810 1.800 5.350 0.009120 0.03200 0.01580 0.01790 0.06590 0.04180 0.04510 0.02920 0.03000 0.1010 0.08930 0.09530 0.07470 0.07640 0.06440 0.06520 0.05960 0.1220 0.1120 0.1230 0.1170 0.02070 2.000 4.880 0.009120 0.03190 0.01580 0.01790 0.06460 0.04170 0.04510 0.02920 0.03000 0.09320 0.08800 0.09340 0.07450 0.07620 0.06430 0.06510 0.04490 0.09860 0.08620 0.09890 0.09250 0.01420 2.400 4.140 0.009110 0.03170 0.01580 0.01790 0.06190 0.04160 0.04490 0.02920 0.03000 0.07680 0.08430 0.08840 0.07370 0.07530 0.06390 0.06470 0.03090 0.05970 0.04770 0.05980 0.05400 0.006160 3.000 3.450 0.009100 0.03120 0.01580 0.01790 0.05710 0.04140 0.04460 0.02920 0.02990 0.05460 0.07610 0.07770 0.07120 0.07250 0.06260 0.06330 0.02800 0.02560 0.01960 0.02500 0.02150 0.002700 4.000 2.850 0.009080 0.03030 0.01580 0.01780 0.04820 0.04050 0.04340 0.02920 0.02990 0.02880 0.05760 0.05520 0.06300 0.06350 0.05790 0.05830 0.02340 0.01160 0.01190 0.006060 0.005300 0.002390 5.000 2.380 0.009040 0.02910 0.01580 0.01780 0.03880 0.03900 0.04120 0.02900 0.02970 0.01740 0.03800 0.03360 0.05040 0.05000 0.05030 0.05040 0.01250 0.01110 0.01090 0.003900 0.003790 0.001440 6.000 1.930 0.009000 0.02780 0.01580 0.01780 0.03000 0.03670 0.03820 0.02870 0.02940 0.01460 0.02250 0.01850 0.03650 0.03550 0.04140 0.04120 0.005410 0.008680 0.007310 0.003790 0.003620 0.0006090 7.000 1.560 0.008960 0.02630 0.01570 0.01770 0.02240 0.03370 0.03430 0.02820 0.02890 0.01440 0.01270 0.01040 0.02420 0.02300 0.03270 0.03230 0.003170 0.005280 0.003860 0.003110 0.002860 0.0002980 8.000 1.260 0.008910 0.02460 0.01560 0.01750 0.01640 0.03020 0.02990 0.02750 0.02800 0.01370 0.007960 0.007250 0.01500 0.01400 0.02500 0.02460 0.002910 0.002790 0.001920 0.002170 0.001930 0.0002490 10.00 0.8700 0.008800 0.02100 0.01500 0.01700 0.008900 0.02200 0.02100 0.02500 0.02500 0.009300 0.005900 0.006500 0.005400 0.005000 0.01400 0.01300 0.002500 0.001100 0.001000 0.0008000 0.0006800 0.0002200 15.00 0.4300 0.008400 0.01300 0.01400 0.01500 0.005500 0.007200 0.005600 0.01600 0.01600 0.001700 0.003800 0.003200 0.002500 0.002500 0.002800 0.002600 0.0004100 0.0007400 0.0006100 0.0002200 0.0002100 3.600E-05 20.00 0.2400 0.007800 0.006500 0.01200 0.01200 0.004700 0.002200 0.002100 0.008200 0.007800 0.001400 0.001000 0.0007400 0.001900 0.001900 0.0005700 0.0005300 0.0002600 0.0002100 0.0001400 0.0001800 0.0001700 2.200E-05 30.00 0.09300 0.006600 0.001500 0.007100 0.006400 0.001200 0.001500 0.001700 0.001700 0.001500 0.0004200 0.0003400 0.0004000 0.0004800 0.0004300 3.300E-05 3.000E-05 8.900E-05 5.600E-05 6.400E-05 4.900E-05 4.100E-05 7.600E-06 40.00 0.04400 0.005200 0.0008100 0.003700 0.002900 0.0002400 0.001100 0.0009500 0.0003500 0.0003000 7.200E-05 0.0002600 0.0002400 1.000E-04 8.700E-05 3.100E-06 2.700E-06 1.500E-05 4.400E-05 4.000E-05 1.000E-05 8.300E-06 1.200E-06 60.00 0.01400 0.003100 0.0007000 0.0009200 0.0005500 0.0001600 0.0002800 0.0001800 2.400E-05 1.800E-05 4.100E-05 7.300E-05 4.800E-05 6.900E-06 5.100E-06 7.200E-08 5.700E-08 7.900E-06 1.300E-05 7.900E-06 6.900E-07 4.800E-07 6.700E-07 100.0 0.002900 0.0009500 0.0002300 9.000E-05 3.300E-05 5.600E-05 2.600E-05 1.000E-05 5.000E-07 2.800E-07 1.400E-05 6.600E-06 2.600E-06 1.400E-07 7.800E-08 4.700E-10 3.000E-10 2.800E-06 1.100E-06 4.300E-07 1.400E-08 7.400E-09 2.400E-07 #S 81 Tl #N 25 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 1 #UBIND 8.553E+04 1.535E+04 1.470E+04 1.266E+04 3704. 3416. 2957. 2485. 2390. 846.0 722.0 609.0 407.0 386.0 123.0 119.0 137.0 100.0 76.00 15.00 12.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 0.000 11.10 0.008980 0.03200 0.01550 0.01760 0.07020 0.04110 0.04450 0.02870 0.02950 0.1440 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3370 0.2240 0.2460 0.2350 0.2440 1.150 0.9130 0.05000 11.00 0.008980 0.03200 0.01550 0.01760 0.07010 0.04110 0.04450 0.02870 0.02950 0.1440 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3370 0.2240 0.2460 0.2350 0.2440 1.130 0.9130 0.1000 10.90 0.008980 0.03200 0.01550 0.01760 0.07010 0.04110 0.04450 0.02870 0.02950 0.1430 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3350 0.2240 0.2460 0.2350 0.2440 1.080 0.9100 0.1500 10.80 0.008980 0.03200 0.01550 0.01760 0.07010 0.04110 0.04450 0.02870 0.02950 0.1430 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3330 0.2240 0.2460 0.2350 0.2440 1.010 0.8990 0.2000 10.60 0.008980 0.03200 0.01550 0.01760 0.07010 0.04110 0.04450 0.02870 0.02950 0.1430 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3290 0.2240 0.2460 0.2350 0.2440 0.9130 0.8740 0.3000 9.980 0.008980 0.03200 0.01550 0.01760 0.07000 0.04110 0.04450 0.02870 0.02950 0.1420 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3200 0.2230 0.2460 0.2350 0.2440 0.6930 0.7710 0.4000 9.360 0.008980 0.03190 0.01550 0.01760 0.06990 0.04110 0.04450 0.02870 0.02950 0.1410 0.09030 0.09760 0.07330 0.07510 0.06250 0.06330 0.3070 0.2230 0.2450 0.2350 0.2440 0.4770 0.6130 0.5000 8.800 0.008970 0.03190 0.01550 0.01760 0.06970 0.04110 0.04450 0.02870 0.02950 0.1400 0.09030 0.09750 0.07330 0.07510 0.06250 0.06330 0.2920 0.2220 0.2430 0.2340 0.2430 0.3040 0.4440 0.6000 8.340 0.008970 0.03190 0.01550 0.01760 0.06950 0.04110 0.04450 0.02870 0.02950 0.1380 0.09030 0.09750 0.07330 0.07510 0.06250 0.06330 0.2740 0.2200 0.2410 0.2330 0.2420 0.1820 0.2980 0.7000 8.000 0.008970 0.03190 0.01550 0.01760 0.06930 0.04110 0.04450 0.02870 0.02950 0.1360 0.09030 0.09750 0.07330 0.07510 0.06250 0.06330 0.2540 0.2180 0.2370 0.2310 0.2390 0.1080 0.1890 0.8000 7.730 0.008970 0.03190 0.01550 0.01760 0.06910 0.04110 0.04450 0.02870 0.02950 0.1340 0.09020 0.09740 0.07330 0.07510 0.06250 0.06330 0.2330 0.2140 0.2320 0.2280 0.2350 0.06860 0.1150 1.000 7.310 0.008970 0.03180 0.01550 0.01760 0.06850 0.04110 0.04450 0.02870 0.02950 0.1290 0.09010 0.09710 0.07330 0.07510 0.06250 0.06330 0.1910 0.2040 0.2160 0.2170 0.2230 0.04370 0.04010 1.200 6.890 0.008970 0.03180 0.01550 0.01760 0.06780 0.04110 0.04450 0.02870 0.02950 0.1230 0.08980 0.09670 0.07330 0.07510 0.06250 0.06330 0.1500 0.1880 0.1950 0.2000 0.2030 0.04220 0.01630 1.400 6.420 0.008970 0.03170 0.01550 0.01760 0.06690 0.04110 0.04450 0.02870 0.02950 0.1160 0.08930 0.09600 0.07330 0.07500 0.06250 0.06330 0.1140 0.1690 0.1700 0.1790 0.1800 0.04020 0.01060 1.600 5.920 0.008970 0.03160 0.01550 0.01760 0.06600 0.04100 0.04450 0.02870 0.02950 0.1080 0.08860 0.09500 0.07320 0.07500 0.06250 0.06330 0.08510 0.1480 0.1430 0.1550 0.1530 0.03430 0.010000 1.800 5.430 0.008960 0.03150 0.01550 0.01760 0.06490 0.04100 0.04440 0.02870 0.02950 0.1010 0.08760 0.09370 0.07310 0.07480 0.06240 0.06320 0.06290 0.1250 0.1160 0.1310 0.1270 0.02650 0.009900 2.000 4.960 0.008960 0.03140 0.01550 0.01760 0.06370 0.04100 0.04440 0.02870 0.02950 0.09290 0.08640 0.09200 0.07290 0.07460 0.06230 0.06310 0.04750 0.1030 0.09110 0.1070 0.1030 0.01890 0.009270 2.400 4.200 0.008960 0.03120 0.01550 0.01760 0.06110 0.04090 0.04430 0.02870 0.02950 0.07710 0.08300 0.08730 0.07220 0.07380 0.06200 0.06280 0.03180 0.06430 0.05210 0.06780 0.06290 0.008550 0.006730 3.000 3.470 0.008940 0.03080 0.01550 0.01760 0.05660 0.04070 0.04390 0.02870 0.02950 0.05540 0.07550 0.07730 0.07000 0.07140 0.06100 0.06170 0.02780 0.02850 0.02160 0.02990 0.02650 0.003370 0.003040 4.000 2.840 0.008920 0.02990 0.01550 0.01760 0.04790 0.03990 0.04280 0.02870 0.02940 0.02970 0.05800 0.05580 0.06260 0.06310 0.05700 0.05750 0.02420 0.01190 0.01200 0.007310 0.006450 0.002860 0.0007650 5.000 2.390 0.008890 0.02880 0.01550 0.01760 0.03890 0.03850 0.04080 0.02860 0.02930 0.01770 0.03910 0.03470 0.05080 0.05040 0.05030 0.05040 0.01360 0.01130 0.01130 0.004200 0.004130 0.001860 0.0006000 6.000 1.960 0.008850 0.02750 0.01550 0.01750 0.03030 0.03630 0.03790 0.02830 0.02900 0.01440 0.02360 0.01940 0.03740 0.03640 0.04200 0.04180 0.006030 0.009220 0.007930 0.004100 0.004010 0.0008200 0.0005310 7.000 1.580 0.008810 0.02600 0.01540 0.01740 0.02280 0.03350 0.03420 0.02790 0.02850 0.01420 0.01350 0.01090 0.02530 0.02410 0.03350 0.03320 0.003340 0.005850 0.004340 0.003490 0.003290 0.0003820 0.0003530 8.000 1.290 0.008760 0.02440 0.01540 0.01730 0.01680 0.03020 0.02990 0.02710 0.02770 0.01370 0.008320 0.007410 0.01590 0.01490 0.02600 0.02560 0.002940 0.003170 0.002180 0.002510 0.002280 0.0002970 0.0001940 10.00 0.8900 0.008600 0.02100 0.01500 0.01700 0.009200 0.02300 0.02100 0.02500 0.02500 0.009600 0.005900 0.006400 0.005900 0.005400 0.01500 0.01400 0.002600 0.001100 0.001100 0.0009600 0.0008300 0.0002700 6.400E-05 15.00 0.4400 0.008300 0.01300 0.01400 0.01500 0.005400 0.007600 0.005900 0.01600 0.01600 0.001800 0.004000 0.003400 0.002500 0.002500 0.003000 0.002900 0.0004500 0.0008000 0.0006600 0.0002400 0.0002400 4.800E-05 4.300E-05 20.00 0.2500 0.007700 0.006700 0.01200 0.01200 0.004700 0.002300 0.002100 0.008500 0.008100 0.001300 0.001100 0.0007900 0.002000 0.001900 0.0006400 0.0006000 0.0002700 0.0002400 0.0001600 0.0002100 0.0001900 2.700E-05 1.300E-05 30.00 0.09600 0.006500 0.001600 0.007200 0.006500 0.001300 0.001500 0.001700 0.001800 0.001600 0.0004600 0.0003300 0.0004000 0.0005200 0.0004700 3.900E-05 3.500E-05 9.900E-05 5.700E-05 6.600E-05 5.700E-05 5.000E-05 1.000E-05 3.000E-06 40.00 0.04500 0.005200 0.0008000 0.003800 0.003000 0.0002500 0.001100 0.0009800 0.0003800 0.0003200 7.900E-05 0.0002700 0.0002500 0.0001100 9.500E-05 3.600E-06 3.100E-06 1.600E-05 4.700E-05 4.300E-05 1.300E-05 1.000E-05 1.600E-06 2.500E-06 60.00 0.01400 0.003100 0.0007000 0.0009700 0.0005800 0.0001600 0.0003000 0.0001900 2.700E-05 2.000E-05 4.100E-05 7.800E-05 5.200E-05 7.700E-06 5.700E-06 8.600E-08 6.800E-08 8.100E-06 1.400E-05 8.800E-06 8.500E-07 6.000E-07 8.100E-07 7.400E-07 100.0 0.003100 0.0009800 0.0002400 9.700E-05 3.600E-05 5.900E-05 2.800E-05 1.100E-05 5.700E-07 3.200E-07 1.500E-05 7.300E-06 2.900E-06 1.600E-07 8.800E-08 5.600E-10 3.700E-10 3.000E-06 1.300E-06 4.900E-07 1.700E-08 9.400E-09 3.000E-07 6.800E-08 #S 82 Pb #N 25 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 #UBIND 8.800E+04 1.586E+04 1.520E+04 1.304E+04 3851. 3554. 3066. 2586. 2484. 894.0 764.0 645.0 434.0 412.0 141.0 136.0 148.0 105.0 86.00 20.00 18.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 0.000 11.30 0.008820 0.03140 0.01530 0.01740 0.06890 0.04030 0.04380 0.02830 0.02900 0.1410 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3250 0.2150 0.2370 0.2210 0.2270 1.040 0.8080 0.05000 11.20 0.008820 0.03140 0.01530 0.01740 0.06890 0.04030 0.04380 0.02830 0.02900 0.1410 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3250 0.2150 0.2370 0.2210 0.2270 1.030 0.8080 0.1000 11.20 0.008820 0.03140 0.01530 0.01740 0.06890 0.04030 0.04380 0.02830 0.02900 0.1400 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3230 0.2150 0.2370 0.2210 0.2270 0.9920 0.8060 0.1500 11.00 0.008820 0.03140 0.01530 0.01740 0.06890 0.04030 0.04380 0.02830 0.02900 0.1400 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3210 0.2150 0.2370 0.2210 0.2270 0.9340 0.8000 0.2000 10.80 0.008820 0.03140 0.01530 0.01740 0.06890 0.04030 0.04380 0.02830 0.02900 0.1400 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3180 0.2150 0.2370 0.2210 0.2270 0.8590 0.7850 0.3000 10.30 0.008820 0.03140 0.01530 0.01740 0.06880 0.04030 0.04380 0.02830 0.02900 0.1390 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.3100 0.2150 0.2360 0.2210 0.2270 0.6790 0.7190 0.4000 9.700 0.008820 0.03140 0.01530 0.01740 0.06870 0.04030 0.04380 0.02830 0.02900 0.1380 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.2980 0.2140 0.2360 0.2210 0.2270 0.4940 0.6080 0.5000 9.070 0.008820 0.03140 0.01530 0.01740 0.06850 0.04030 0.04380 0.02830 0.02900 0.1370 0.08840 0.09570 0.07180 0.07350 0.06060 0.06140 0.2840 0.2140 0.2350 0.2210 0.2270 0.3340 0.4740 0.6000 8.520 0.008820 0.03140 0.01530 0.01740 0.06840 0.04030 0.04380 0.02830 0.02900 0.1350 0.08840 0.09560 0.07180 0.07350 0.06060 0.06140 0.2670 0.2120 0.2320 0.2200 0.2260 0.2140 0.3450 0.7000 8.070 0.008820 0.03140 0.01530 0.01740 0.06820 0.04030 0.04380 0.02830 0.02900 0.1330 0.08840 0.09560 0.07180 0.07350 0.06060 0.06140 0.2490 0.2100 0.2290 0.2190 0.2250 0.1330 0.2370 0.8000 7.720 0.008820 0.03130 0.01530 0.01740 0.06790 0.04030 0.04380 0.02830 0.02900 0.1310 0.08830 0.09550 0.07180 0.07350 0.06060 0.06140 0.2300 0.2070 0.2250 0.2160 0.2220 0.08480 0.1560 1.000 7.210 0.008820 0.03130 0.01530 0.01740 0.06740 0.04030 0.04380 0.02830 0.02900 0.1260 0.08820 0.09530 0.07180 0.07350 0.06060 0.06140 0.1910 0.1980 0.2110 0.2080 0.2130 0.04850 0.06230 1.200 6.800 0.008820 0.03120 0.01530 0.01740 0.06670 0.04030 0.04380 0.02830 0.02900 0.1210 0.08790 0.09490 0.07170 0.07350 0.06060 0.06140 0.1520 0.1850 0.1930 0.1960 0.1990 0.04420 0.02540 1.400 6.380 0.008820 0.03120 0.01530 0.01740 0.06590 0.04030 0.04380 0.02830 0.02900 0.1140 0.08750 0.09430 0.07170 0.07350 0.06060 0.06140 0.1170 0.1680 0.1700 0.1780 0.1800 0.04340 0.01420 1.600 5.930 0.008810 0.03110 0.01530 0.01740 0.06500 0.04030 0.04380 0.02830 0.02900 0.1070 0.08690 0.09340 0.07170 0.07340 0.06060 0.06140 0.08860 0.1480 0.1450 0.1580 0.1580 0.03890 0.01190 1.800 5.470 0.008810 0.03100 0.01530 0.01740 0.06400 0.04030 0.04380 0.02830 0.02900 0.1000 0.08600 0.09210 0.07160 0.07330 0.06060 0.06140 0.06620 0.1270 0.1200 0.1360 0.1340 0.03150 0.01180 2.000 5.030 0.008810 0.03090 0.01530 0.01740 0.06280 0.04030 0.04370 0.02830 0.02900 0.09250 0.08490 0.09050 0.07140 0.07310 0.06050 0.06130 0.05010 0.1060 0.09560 0.1140 0.1110 0.02360 0.01150 2.400 4.270 0.008800 0.03070 0.01530 0.01740 0.06030 0.04020 0.04360 0.02830 0.02900 0.07730 0.08180 0.08620 0.07080 0.07240 0.06030 0.06110 0.03290 0.06880 0.05650 0.07490 0.07110 0.01130 0.009040 3.000 3.500 0.008790 0.03030 0.01530 0.01740 0.05600 0.04000 0.04330 0.02830 0.02900 0.05620 0.07480 0.07690 0.06890 0.07020 0.05950 0.06020 0.02760 0.03160 0.02390 0.03500 0.03180 0.004120 0.004450 4.000 2.840 0.008770 0.02940 0.01530 0.01740 0.04770 0.03920 0.04220 0.02820 0.02900 0.03060 0.05840 0.05640 0.06210 0.06270 0.05610 0.05660 0.02480 0.01240 0.01220 0.008750 0.007780 0.003250 0.001090 5.000 2.400 0.008740 0.02840 0.01520 0.01730 0.03900 0.03790 0.04030 0.02810 0.02890 0.01810 0.04010 0.03580 0.05110 0.05080 0.05010 0.05020 0.01480 0.01140 0.01160 0.004510 0.004460 0.002270 0.0007570 6.000 1.980 0.008710 0.02710 0.01520 0.01730 0.03060 0.03590 0.03760 0.02790 0.02860 0.01440 0.02460 0.02040 0.03830 0.03730 0.04240 0.04230 0.006730 0.009730 0.008540 0.004350 0.004340 0.001050 0.0006970 7.000 1.610 0.008660 0.02570 0.01520 0.01720 0.02330 0.03330 0.03410 0.02750 0.02810 0.01400 0.01430 0.01150 0.02630 0.02510 0.03430 0.03400 0.003550 0.006440 0.004850 0.003830 0.003690 0.0004760 0.0004890 8.000 1.310 0.008620 0.02420 0.01510 0.01710 0.01720 0.03010 0.03000 0.02680 0.02740 0.01360 0.008730 0.007600 0.01690 0.01580 0.02690 0.02650 0.002980 0.003600 0.002470 0.002850 0.002640 0.0003420 0.0002790 10.00 0.9100 0.008500 0.02100 0.01500 0.01700 0.009500 0.02300 0.02100 0.02500 0.02500 0.009900 0.005800 0.006400 0.006300 0.005800 0.01500 0.01500 0.002700 0.001200 0.001100 0.001100 0.001000 0.0003100 8.800E-05 15.00 0.4500 0.008100 0.01300 0.01400 0.01500 0.005300 0.008000 0.006200 0.01600 0.01600 0.001900 0.004200 0.003500 0.002400 0.002500 0.003300 0.003200 0.0005000 0.0008500 0.0007200 0.0002600 0.0002600 6.000E-05 5.700E-05 20.00 0.2500 0.007600 0.006900 0.01200 0.01200 0.004700 0.002400 0.002100 0.008800 0.008300 0.001300 0.001300 0.0008600 0.002000 0.002000 0.0007200 0.0006700 0.0002700 0.0002800 0.0001800 0.0002300 0.0002200 3.000E-05 1.900E-05 30.00 0.09800 0.006500 0.001600 0.007300 0.006700 0.001400 0.001500 0.001700 0.001900 0.001700 0.0005000 0.0003300 0.0004000 0.0005600 0.0005000 4.500E-05 4.000E-05 0.0001100 5.800E-05 6.900E-05 6.700E-05 5.900E-05 1.200E-05 3.900E-06 40.00 0.04600 0.005200 0.0007900 0.003900 0.003100 0.0002700 0.001100 0.001000 0.0004200 0.0003500 8.600E-05 0.0002700 0.0002600 0.0001200 1.000E-04 4.300E-06 3.700E-06 1.900E-05 5.000E-05 4.600E-05 1.500E-05 1.200E-05 2.100E-06 3.300E-06 60.00 0.01500 0.003100 0.0007000 0.001000 0.0006100 0.0001600 0.0003200 0.0002100 2.900E-05 2.200E-05 4.100E-05 8.300E-05 5.500E-05 8.600E-06 6.300E-06 1.000E-07 8.100E-08 8.300E-06 1.500E-05 9.700E-06 1.000E-06 7.400E-07 9.300E-07 1.000E-06 100.0 0.003200 0.001000 0.0002400 0.0001100 3.800E-05 6.100E-05 3.100E-05 1.200E-05 6.400E-07 3.600E-07 1.600E-05 8.000E-06 3.200E-06 1.800E-07 1.000E-07 6.800E-10 4.400E-10 3.200E-06 1.500E-06 5.500E-07 2.100E-08 1.200E-08 3.600E-07 9.800E-08 #S 83 Bi #N 26 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 1 #UBIND 9.053E+04 1.639E+04 1.571E+04 1.342E+04 3999. 3696. 3177. 2687. 2580. 939.0 805.0 679.0 464.0 441.0 162.0 157.0 160.0 117.0 93.00 27.00 25.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 0.000 11.50 0.008670 0.03090 0.01500 0.01710 0.06780 0.03960 0.04320 0.02780 0.02860 0.1380 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3140 0.2070 0.2280 0.2080 0.2140 0.9550 0.7130 0.8320 0.05000 11.40 0.008670 0.03090 0.01500 0.01710 0.06780 0.03960 0.04320 0.02780 0.02860 0.1380 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3130 0.2070 0.2280 0.2080 0.2140 0.9450 0.7130 0.8320 0.1000 11.40 0.008670 0.03090 0.01500 0.01710 0.06780 0.03960 0.04320 0.02780 0.02860 0.1380 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3120 0.2070 0.2280 0.2080 0.2140 0.9160 0.7120 0.8300 0.1500 11.30 0.008670 0.03090 0.01500 0.01710 0.06770 0.03960 0.04320 0.02780 0.02860 0.1370 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3100 0.2070 0.2280 0.2080 0.2140 0.8700 0.7090 0.8230 0.2000 11.10 0.008670 0.03090 0.01500 0.01710 0.06770 0.03960 0.04320 0.02780 0.02860 0.1370 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3070 0.2070 0.2280 0.2080 0.2140 0.8090 0.7000 0.8070 0.3000 10.60 0.008670 0.03090 0.01500 0.01710 0.06760 0.03960 0.04320 0.02780 0.02860 0.1360 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.3000 0.2070 0.2280 0.2080 0.2140 0.6610 0.6600 0.7330 0.4000 10.00 0.008670 0.03090 0.01500 0.01710 0.06750 0.03960 0.04320 0.02780 0.02860 0.1350 0.08660 0.09390 0.07030 0.07200 0.05890 0.05970 0.2890 0.2070 0.2270 0.2080 0.2140 0.5010 0.5860 0.6120 0.5000 9.350 0.008670 0.03090 0.01500 0.01710 0.06740 0.03960 0.04320 0.02780 0.02860 0.1340 0.08660 0.09380 0.07030 0.07200 0.05890 0.05970 0.2760 0.2060 0.2260 0.2080 0.2140 0.3560 0.4870 0.4680 0.6000 8.720 0.008670 0.03090 0.01500 0.01710 0.06720 0.03960 0.04320 0.02780 0.02860 0.1330 0.08660 0.09380 0.07030 0.07200 0.05890 0.05970 0.2610 0.2050 0.2240 0.2080 0.2140 0.2400 0.3810 0.3330 0.7000 8.180 0.008670 0.03080 0.01500 0.01710 0.06700 0.03960 0.04320 0.02780 0.02860 0.1310 0.08650 0.09380 0.07030 0.07200 0.05890 0.05970 0.2450 0.2030 0.2220 0.2070 0.2130 0.1560 0.2820 0.2230 0.8000 7.750 0.008670 0.03080 0.01500 0.01710 0.06680 0.03960 0.04320 0.02780 0.02860 0.1290 0.08650 0.09370 0.07030 0.07200 0.05890 0.05970 0.2270 0.2010 0.2180 0.2050 0.2110 0.1020 0.1990 0.1420 1.000 7.140 0.008670 0.03080 0.01500 0.01710 0.06630 0.03960 0.04320 0.02780 0.02860 0.1240 0.08640 0.09350 0.07030 0.07200 0.05890 0.05970 0.1900 0.1930 0.2060 0.2000 0.2050 0.05440 0.09000 0.05380 1.200 6.710 0.008670 0.03070 0.01500 0.01710 0.06560 0.03960 0.04320 0.02780 0.02860 0.1190 0.08610 0.09310 0.07020 0.07200 0.05890 0.05970 0.1530 0.1810 0.1900 0.1900 0.1940 0.04580 0.03890 0.02240 1.400 6.320 0.008660 0.03070 0.01500 0.01710 0.06490 0.03960 0.04310 0.02780 0.02860 0.1130 0.08570 0.09260 0.07020 0.07200 0.05890 0.05970 0.1200 0.1660 0.1700 0.1760 0.1780 0.04540 0.01970 0.01430 1.600 5.920 0.008660 0.03060 0.01500 0.01710 0.06400 0.03960 0.04310 0.02780 0.02860 0.1060 0.08520 0.09170 0.07020 0.07190 0.05890 0.05960 0.09190 0.1490 0.1470 0.1590 0.1590 0.04250 0.01450 0.01330 1.800 5.500 0.008660 0.03050 0.01500 0.01710 0.06300 0.03960 0.04310 0.02780 0.02860 0.09930 0.08440 0.09060 0.07010 0.07180 0.05890 0.05960 0.06950 0.1290 0.1230 0.1390 0.1390 0.03610 0.01380 0.01310 2.000 5.080 0.008660 0.03040 0.01500 0.01710 0.06200 0.03960 0.04310 0.02780 0.02860 0.09210 0.08340 0.08910 0.06990 0.07160 0.05880 0.05960 0.05280 0.1090 0.09970 0.1200 0.1170 0.02820 0.01370 0.01230 2.400 4.330 0.008650 0.03020 0.01500 0.01710 0.05960 0.03950 0.04290 0.02780 0.02860 0.07750 0.08050 0.08510 0.06940 0.07100 0.05860 0.05940 0.03410 0.07290 0.06090 0.08190 0.07830 0.01430 0.01160 0.008800 3.000 3.530 0.008640 0.02980 0.01500 0.01710 0.05540 0.03930 0.04260 0.02780 0.02860 0.05690 0.07410 0.07640 0.06770 0.06910 0.05800 0.05870 0.02750 0.03470 0.02640 0.04040 0.03700 0.005060 0.006260 0.003770 4.000 2.830 0.008620 0.02900 0.01500 0.01710 0.04750 0.03860 0.04170 0.02780 0.02860 0.03140 0.05860 0.05690 0.06150 0.06220 0.05510 0.05560 0.02530 0.01290 0.01250 0.01050 0.009300 0.003590 0.001540 0.0009810 5.000 2.400 0.008590 0.02800 0.01500 0.01710 0.03900 0.03740 0.03990 0.02770 0.02850 0.01850 0.04100 0.03680 0.05130 0.05110 0.04980 0.05000 0.01590 0.01150 0.01190 0.004870 0.004780 0.002690 0.0009370 0.0008390 6.000 2.000 0.008560 0.02680 0.01490 0.01700 0.03090 0.03550 0.03730 0.02750 0.02820 0.01430 0.02570 0.02130 0.03900 0.03810 0.04270 0.04260 0.007480 0.01020 0.009140 0.004600 0.004610 0.001320 0.0008890 0.0006960 7.000 1.640 0.008520 0.02550 0.01490 0.01700 0.02360 0.03310 0.03400 0.02710 0.02780 0.01380 0.01510 0.01210 0.02730 0.02610 0.03500 0.03480 0.003820 0.007020 0.005390 0.004180 0.004050 0.0005870 0.0006560 0.0004280 8.000 1.340 0.008470 0.02400 0.01480 0.01690 0.01770 0.03010 0.03010 0.02650 0.02710 0.01360 0.009180 0.007820 0.01780 0.01670 0.02770 0.02740 0.003020 0.004050 0.002790 0.003210 0.003000 0.0003890 0.0003890 0.0002210 10.00 0.9300 0.008400 0.02100 0.01480 0.01690 0.009800 0.02300 0.02200 0.02500 0.02500 0.010000 0.005800 0.006300 0.006800 0.006200 0.01600 0.01600 0.002800 0.001300 0.001200 0.001300 0.001200 0.0003600 0.0001200 8.200E-05 15.00 0.4600 0.008000 0.01300 0.01400 0.01500 0.005200 0.008400 0.006500 0.01700 0.01600 0.002000 0.004300 0.003700 0.002400 0.002500 0.003600 0.003500 0.0005600 0.0008900 0.0007800 0.0002800 0.0002800 7.500E-05 7.400E-05 5.500E-05 20.00 0.2600 0.007500 0.007100 0.01200 0.01200 0.004700 0.002500 0.002200 0.009000 0.008600 0.001300 0.001400 0.0009300 0.002100 0.002000 0.0008000 0.0007500 0.0002700 0.0003100 0.0002000 0.0002500 0.0002400 3.400E-05 2.600E-05 1.400E-05 30.00 0.1000 0.006400 0.001700 0.007400 0.006800 0.001500 0.001500 0.001700 0.002000 0.001800 0.0005300 0.0003300 0.0004000 0.0005900 0.0005300 5.100E-05 4.600E-05 0.0001200 6.000E-05 7.100E-05 7.700E-05 6.800E-05 1.500E-05 4.900E-06 4.900E-06 40.00 0.04700 0.005200 0.0007900 0.004000 0.003200 0.0002900 0.001100 0.001000 0.0004500 0.0003700 9.500E-05 0.0002800 0.0002700 0.0001400 0.0001100 5.000E-06 4.300E-06 2.100E-05 5.200E-05 4.900E-05 1.800E-05 1.500E-05 2.600E-06 4.300E-06 3.400E-06 60.00 0.01500 0.003100 0.0007000 0.001100 0.0006400 0.0001600 0.0003400 0.0002200 3.200E-05 2.400E-05 4.100E-05 8.900E-05 5.900E-05 9.600E-06 7.100E-06 1.200E-07 9.600E-08 8.400E-06 1.700E-05 1.100E-05 1.300E-06 9.000E-07 1.000E-06 1.400E-06 7.400E-07 100.0 0.003300 0.001000 0.0002500 0.0001100 4.100E-05 6.400E-05 3.400E-05 1.300E-05 7.100E-07 4.000E-07 1.700E-05 8.800E-06 3.500E-06 2.000E-07 1.100E-07 8.100E-10 5.300E-10 3.500E-06 1.700E-06 6.300E-07 2.600E-08 1.400E-08 4.300E-07 1.400E-07 4.300E-08 #S 84 Po #N 26 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 2 #UBIND 9.311E+04 1.694E+04 1.624E+04 1.381E+04 4149. 3854. 3302. 2798. 2683. 995.0 851.0 705.0 500.0 473.0 184.0 184.0 177.0 132.0 104.0 31.00 31.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 0.000 11.60 0.008520 0.03040 0.01470 0.01690 0.06660 0.03900 0.04250 0.02740 0.02820 0.1350 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.3030 0.2000 0.2200 0.1970 0.2030 0.8870 0.6450 0.7530 0.05000 11.60 0.008520 0.03040 0.01470 0.01690 0.06660 0.03900 0.04250 0.02740 0.02820 0.1350 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.3030 0.2000 0.2200 0.1970 0.2030 0.8780 0.6450 0.7530 0.1000 11.50 0.008520 0.03040 0.01470 0.01690 0.06660 0.03900 0.04250 0.02740 0.02820 0.1350 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.3020 0.2000 0.2200 0.1970 0.2030 0.8550 0.6450 0.7520 0.1500 11.50 0.008520 0.03040 0.01470 0.01690 0.06660 0.03900 0.04250 0.02740 0.02820 0.1350 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.3000 0.2000 0.2200 0.1970 0.2030 0.8170 0.6430 0.7470 0.2000 11.30 0.008520 0.03040 0.01470 0.01690 0.06660 0.03900 0.04250 0.02740 0.02820 0.1340 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.2970 0.2000 0.2200 0.1970 0.2030 0.7670 0.6370 0.7360 0.3000 10.90 0.008520 0.03040 0.01470 0.01690 0.06650 0.03900 0.04250 0.02740 0.02820 0.1340 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.2900 0.2000 0.2200 0.1970 0.2030 0.6410 0.6110 0.6870 0.4000 10.30 0.008520 0.03040 0.01470 0.01690 0.06640 0.03900 0.04250 0.02740 0.02820 0.1330 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.2810 0.1990 0.2190 0.1970 0.2030 0.5020 0.5590 0.5980 0.5000 9.640 0.008520 0.03040 0.01470 0.01690 0.06630 0.03900 0.04250 0.02740 0.02820 0.1320 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.2690 0.1990 0.2180 0.1970 0.2030 0.3710 0.4850 0.4830 0.6000 8.960 0.008520 0.03040 0.01470 0.01690 0.06610 0.03900 0.04250 0.02740 0.02820 0.1300 0.08480 0.09210 0.06880 0.07060 0.05730 0.05800 0.2550 0.1980 0.2170 0.1970 0.2030 0.2600 0.3980 0.3660 0.7000 8.360 0.008520 0.03030 0.01470 0.01690 0.06590 0.03900 0.04250 0.02740 0.02820 0.1290 0.08480 0.09200 0.06880 0.07060 0.05730 0.05800 0.2400 0.1960 0.2150 0.1960 0.2020 0.1760 0.3100 0.2610 0.8000 7.850 0.008520 0.03030 0.01470 0.01690 0.06570 0.03900 0.04250 0.02740 0.02820 0.1270 0.08470 0.09200 0.06880 0.07060 0.05730 0.05800 0.2240 0.1940 0.2110 0.1950 0.2010 0.1180 0.2320 0.1780 1.000 7.120 0.008520 0.03030 0.01470 0.01690 0.06520 0.03900 0.04250 0.02740 0.02820 0.1220 0.08460 0.09180 0.06880 0.07050 0.05730 0.05800 0.1890 0.1880 0.2010 0.1910 0.1960 0.06090 0.1160 0.07470 1.200 6.640 0.008520 0.03020 0.01470 0.01690 0.06460 0.03890 0.04250 0.02740 0.02820 0.1170 0.08440 0.09140 0.06880 0.07050 0.05730 0.05800 0.1550 0.1780 0.1870 0.1840 0.1880 0.04730 0.05380 0.03160 1.400 6.260 0.008520 0.03020 0.01470 0.01690 0.06390 0.03890 0.04250 0.02740 0.02820 0.1110 0.08400 0.09090 0.06880 0.07050 0.05730 0.05800 0.1230 0.1640 0.1690 0.1730 0.1750 0.04640 0.02660 0.01780 1.600 5.890 0.008510 0.03010 0.01470 0.01690 0.06300 0.03890 0.04250 0.02740 0.02820 0.1050 0.08350 0.09010 0.06870 0.07050 0.05730 0.05800 0.09500 0.1480 0.1470 0.1580 0.1590 0.04480 0.01730 0.01500 1.800 5.510 0.008510 0.03000 0.01470 0.01690 0.06210 0.03890 0.04250 0.02740 0.02820 0.09850 0.08280 0.08910 0.06860 0.07040 0.05720 0.05800 0.07260 0.1300 0.1250 0.1420 0.1410 0.03970 0.01530 0.01480 2.000 5.120 0.008510 0.02990 0.01470 0.01690 0.06110 0.03890 0.04240 0.02740 0.02820 0.09160 0.08190 0.08780 0.06850 0.07020 0.05720 0.05800 0.05550 0.1120 0.1030 0.1240 0.1220 0.03220 0.01520 0.01440 2.400 4.390 0.008510 0.02970 0.01470 0.01690 0.05880 0.03880 0.04230 0.02740 0.02820 0.07750 0.07920 0.08400 0.06800 0.06970 0.05710 0.05780 0.03550 0.07670 0.06510 0.08800 0.08460 0.01760 0.01380 0.01110 3.000 3.570 0.008500 0.02940 0.01470 0.01690 0.05480 0.03860 0.04200 0.02740 0.02820 0.05760 0.07330 0.07590 0.06650 0.06800 0.05650 0.05720 0.02750 0.03800 0.02910 0.04570 0.04220 0.006170 0.008150 0.005190 4.000 2.830 0.008480 0.02860 0.01470 0.01690 0.04720 0.03800 0.04110 0.02740 0.02810 0.03230 0.05870 0.05730 0.06100 0.06170 0.05410 0.05460 0.02570 0.01370 0.01280 0.01250 0.01100 0.003860 0.002090 0.001280 5.000 2.400 0.008450 0.02760 0.01470 0.01690 0.03910 0.03690 0.03950 0.02730 0.02800 0.01890 0.04180 0.03770 0.05140 0.05130 0.04940 0.04970 0.01700 0.01150 0.01220 0.005290 0.005120 0.003100 0.001100 0.001000 6.000 2.020 0.008420 0.02650 0.01470 0.01680 0.03110 0.03520 0.03700 0.02710 0.02780 0.01430 0.02670 0.02220 0.03970 0.03890 0.04290 0.04290 0.008290 0.01050 0.009710 0.004810 0.004840 0.001610 0.001060 0.0008720 7.000 1.660 0.008380 0.02520 0.01460 0.01670 0.02400 0.03280 0.03380 0.02670 0.02740 0.01360 0.01600 0.01270 0.02820 0.02710 0.03560 0.03540 0.004130 0.007580 0.005950 0.004490 0.004390 0.0007140 0.0008230 0.0005620 8.000 1.360 0.008340 0.02380 0.01460 0.01660 0.01810 0.03000 0.03010 0.02620 0.02680 0.01350 0.009660 0.008080 0.01870 0.01760 0.02850 0.02820 0.003080 0.004520 0.003140 0.003560 0.003350 0.0004370 0.0005090 0.0002990 10.00 0.9400 0.008200 0.02100 0.01400 0.01600 0.010000 0.02300 0.02200 0.02400 0.02500 0.010000 0.005800 0.006200 0.007300 0.006700 0.01700 0.01700 0.002900 0.001500 0.001200 0.001600 0.001400 0.0004000 0.0001600 1.000E-04 15.00 0.4700 0.007900 0.01300 0.01300 0.01500 0.005100 0.008800 0.006800 0.01700 0.01700 0.002100 0.004400 0.003900 0.002400 0.002500 0.003900 0.003800 0.0006300 0.0009400 0.0008400 0.0003000 0.0003000 9.100E-05 9.000E-05 6.900E-05 20.00 0.2600 0.007400 0.007300 0.01200 0.01200 0.004700 0.002700 0.002200 0.009200 0.008800 0.001300 0.001500 0.001000 0.002100 0.002100 0.0008900 0.0008300 0.0002800 0.0003500 0.0002300 0.0002700 0.0002600 3.700E-05 3.500E-05 1.900E-05 30.00 0.1000 0.006400 0.001800 0.007500 0.006900 0.001600 0.001400 0.001700 0.002100 0.001900 0.0005700 0.0003200 0.0004000 0.0006300 0.0005700 5.800E-05 5.300E-05 0.0001300 6.100E-05 7.300E-05 8.800E-05 7.800E-05 1.800E-05 5.800E-06 5.900E-06 40.00 0.04900 0.005200 0.0007900 0.004100 0.003300 0.0003100 0.001100 0.001100 0.0004800 0.0004000 1.000E-04 0.0002800 0.0002800 0.0001500 0.0001200 5.800E-06 5.000E-06 2.400E-05 5.400E-05 5.300E-05 2.100E-05 1.700E-05 3.200E-06 5.200E-06 4.300E-06 60.00 0.01600 0.003200 0.0006900 0.001100 0.0006700 0.0001600 0.0003500 0.0002300 3.500E-05 2.600E-05 4.100E-05 9.400E-05 6.300E-05 1.100E-05 7.900E-06 1.400E-07 1.100E-07 8.600E-06 1.800E-05 1.200E-05 1.500E-06 1.100E-06 1.100E-06 1.800E-06 9.700E-07 100.0 0.003500 0.001100 0.0002600 0.0001200 4.400E-05 6.700E-05 3.700E-05 1.400E-05 8.000E-07 4.400E-07 1.800E-05 9.700E-06 3.800E-06 2.300E-07 1.300E-07 9.700E-10 6.300E-10 3.700E-06 1.900E-06 7.100E-07 3.200E-08 1.700E-08 5.000E-07 1.800E-07 5.800E-08 #S 85 At #N 26 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 3 #UBIND 9.573E+04 1.749E+04 1.678E+04 1.421E+04 4317. 4008. 3426. 2909. 2787. 1042. 886.0 740.0 533.0 507.0 210.0 210.0 195.0 148.0 115.0 40.00 40.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 0.000 11.70 0.008380 0.02990 0.01450 0.01670 0.06550 0.03830 0.04190 0.02700 0.02780 0.1320 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2930 0.1930 0.2120 0.1880 0.1940 0.8300 0.5930 0.6920 0.05000 11.70 0.008380 0.02990 0.01450 0.01670 0.06550 0.03830 0.04190 0.02700 0.02780 0.1320 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2930 0.1930 0.2120 0.1880 0.1940 0.8230 0.5930 0.6920 0.1000 11.70 0.008380 0.02990 0.01450 0.01670 0.06550 0.03830 0.04190 0.02700 0.02780 0.1320 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2920 0.1930 0.2120 0.1880 0.1940 0.8030 0.5930 0.6920 0.1500 11.60 0.008380 0.02990 0.01450 0.01670 0.06550 0.03830 0.04190 0.02700 0.02780 0.1320 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2900 0.1930 0.2120 0.1880 0.1940 0.7710 0.5910 0.6890 0.2000 11.50 0.008380 0.02990 0.01450 0.01670 0.06550 0.03830 0.04190 0.02700 0.02780 0.1320 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2880 0.1930 0.2120 0.1880 0.1940 0.7290 0.5880 0.6810 0.3000 11.10 0.008380 0.02990 0.01450 0.01670 0.06540 0.03830 0.04190 0.02700 0.02780 0.1310 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2810 0.1930 0.2120 0.1880 0.1940 0.6220 0.5690 0.6460 0.4000 10.60 0.008380 0.02990 0.01450 0.01670 0.06530 0.03830 0.04190 0.02700 0.02780 0.1300 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2730 0.1930 0.2120 0.1880 0.1940 0.5000 0.5320 0.5780 0.5000 9.910 0.008380 0.02990 0.01450 0.01670 0.06520 0.03830 0.04190 0.02700 0.02780 0.1290 0.08310 0.09040 0.06740 0.06910 0.05580 0.05650 0.2620 0.1920 0.2110 0.1880 0.1940 0.3800 0.4740 0.4860 0.6000 9.220 0.008380 0.02990 0.01450 0.01670 0.06500 0.03830 0.04190 0.02700 0.02780 0.1280 0.08310 0.09030 0.06740 0.06910 0.05580 0.05650 0.2490 0.1910 0.2100 0.1870 0.1930 0.2760 0.4040 0.3850 0.7000 8.560 0.008380 0.02980 0.01450 0.01670 0.06480 0.03830 0.04190 0.02700 0.02780 0.1260 0.08310 0.09030 0.06740 0.06910 0.05580 0.05650 0.2350 0.1900 0.2080 0.1870 0.1930 0.1930 0.3280 0.2890 0.8000 8.000 0.008380 0.02980 0.01450 0.01670 0.06460 0.03830 0.04190 0.02700 0.02780 0.1240 0.08300 0.09030 0.06740 0.06910 0.05580 0.05650 0.2200 0.1880 0.2050 0.1860 0.1920 0.1320 0.2560 0.2070 1.000 7.150 0.008370 0.02980 0.01450 0.01670 0.06420 0.03830 0.04190 0.02700 0.02780 0.1200 0.08290 0.09010 0.06740 0.06910 0.05580 0.05650 0.1880 0.1830 0.1960 0.1830 0.1880 0.06800 0.1400 0.09540 1.200 6.600 0.008370 0.02970 0.01450 0.01670 0.06360 0.03830 0.04190 0.02700 0.02780 0.1150 0.08270 0.08980 0.06740 0.06910 0.05580 0.05650 0.1550 0.1740 0.1840 0.1780 0.1820 0.04900 0.06950 0.04210 1.400 6.200 0.008370 0.02970 0.01450 0.01670 0.06290 0.03830 0.04190 0.02700 0.02780 0.1100 0.08240 0.08930 0.06740 0.06910 0.05580 0.05650 0.1250 0.1620 0.1670 0.1690 0.1720 0.04680 0.03470 0.02200 1.600 5.850 0.008370 0.02960 0.01450 0.01670 0.06210 0.03830 0.04190 0.02700 0.02780 0.1040 0.08190 0.08860 0.06730 0.06910 0.05570 0.05650 0.09780 0.1470 0.1480 0.1570 0.1580 0.04610 0.02090 0.01660 1.800 5.500 0.008370 0.02950 0.01450 0.01670 0.06120 0.03820 0.04180 0.02700 0.02780 0.09770 0.08130 0.08760 0.06730 0.06900 0.05570 0.05650 0.07550 0.1310 0.1270 0.1420 0.1420 0.04230 0.01680 0.01600 2.000 5.140 0.008370 0.02950 0.01450 0.01670 0.06020 0.03820 0.04180 0.02700 0.02780 0.09110 0.08040 0.08640 0.06720 0.06880 0.05570 0.05640 0.05820 0.1140 0.1060 0.1260 0.1250 0.03570 0.01630 0.01580 2.400 4.440 0.008360 0.02930 0.01450 0.01670 0.05810 0.03820 0.04170 0.02700 0.02780 0.07760 0.07800 0.08290 0.06670 0.06840 0.05560 0.05630 0.03700 0.08020 0.06900 0.09300 0.08990 0.02090 0.01550 0.01320 3.000 3.610 0.008350 0.02890 0.01450 0.01670 0.05430 0.03800 0.04140 0.02700 0.02780 0.05820 0.07250 0.07530 0.06540 0.06680 0.05520 0.05590 0.02750 0.04120 0.03190 0.05090 0.04720 0.007480 0.01010 0.006710 4.000 2.830 0.008330 0.02820 0.01450 0.01670 0.04700 0.03740 0.04060 0.02700 0.02770 0.03310 0.05880 0.05770 0.06030 0.06110 0.05310 0.05370 0.02600 0.01450 0.01320 0.01470 0.01300 0.004090 0.002750 0.001620 5.000 2.410 0.008310 0.02720 0.01450 0.01660 0.03910 0.03640 0.03900 0.02690 0.02760 0.01940 0.04260 0.03860 0.05150 0.05150 0.04900 0.04920 0.01800 0.01160 0.01240 0.005770 0.005520 0.003470 0.001270 0.001150 6.000 2.030 0.008280 0.02620 0.01440 0.01660 0.03140 0.03470 0.03670 0.02670 0.02740 0.01430 0.02770 0.02320 0.04030 0.03960 0.04300 0.04300 0.009150 0.01090 0.01020 0.004980 0.005030 0.001910 0.001210 0.001040 7.000 1.680 0.008240 0.02490 0.01440 0.01650 0.02440 0.03260 0.03370 0.02640 0.02710 0.01340 0.01680 0.01330 0.02910 0.02800 0.03610 0.03590 0.004500 0.008120 0.006530 0.004760 0.004680 0.0008600 0.0009880 0.0007000 8.000 1.390 0.008200 0.02360 0.01430 0.01640 0.01850 0.02990 0.03010 0.02590 0.02650 0.01330 0.01020 0.008370 0.01960 0.01850 0.02920 0.02890 0.003160 0.005020 0.003530 0.003890 0.003690 0.0004910 0.0006390 0.0003850 10.00 0.9600 0.008100 0.02100 0.01400 0.01600 0.010000 0.02400 0.02200 0.02400 0.02500 0.01100 0.005800 0.006200 0.007800 0.007100 0.01800 0.01700 0.002900 0.001600 0.001300 0.001800 0.001600 0.0004300 0.0002000 0.0001300 15.00 0.4700 0.007800 0.01300 0.01300 0.01500 0.005100 0.009100 0.007100 0.01700 0.01700 0.002200 0.004500 0.004000 0.002400 0.002500 0.004300 0.004100 0.0007100 0.0009800 0.0008900 0.0003200 0.0003200 0.0001100 0.0001100 8.400E-05 20.00 0.2700 0.007400 0.007500 0.01200 0.01200 0.004700 0.002800 0.002300 0.009500 0.009100 0.001300 0.001600 0.001100 0.002100 0.002100 0.0009900 0.0009200 0.0002800 0.0003900 0.0002500 0.0002900 0.0002800 4.000E-05 4.300E-05 2.400E-05 30.00 0.1100 0.006300 0.001900 0.007600 0.006900 0.001700 0.001400 0.001700 0.002300 0.002000 0.0006100 0.0003200 0.0004000 0.0006700 0.0006100 6.700E-05 6.000E-05 0.0001400 6.300E-05 7.500E-05 9.900E-05 8.800E-05 2.100E-05 6.700E-06 6.900E-06 40.00 0.05000 0.005200 0.0007900 0.004300 0.003400 0.0003300 0.001100 0.001100 0.0005200 0.0004300 0.0001200 0.0002900 0.0002900 0.0001600 0.0001300 6.600E-06 5.700E-06 2.700E-05 5.600E-05 5.600E-05 2.400E-05 2.000E-05 3.900E-06 6.000E-06 5.200E-06 60.00 0.01600 0.003200 0.0006900 0.001200 0.0007100 0.0001600 0.0003700 0.0002500 3.900E-05 2.800E-05 4.000E-05 1.000E-04 6.700E-05 1.200E-05 8.700E-06 1.700E-07 1.300E-07 8.700E-06 2.000E-05 1.300E-05 1.800E-06 1.300E-06 1.200E-06 2.200E-06 1.200E-06 100.0 0.003600 0.001100 0.0002700 0.0001300 4.800E-05 6.900E-05 4.000E-05 1.500E-05 8.900E-07 4.900E-07 1.800E-05 1.100E-05 4.100E-06 2.600E-07 1.500E-07 1.200E-09 7.400E-10 4.000E-06 2.100E-06 8.000E-07 3.900E-08 2.100E-08 5.600E-07 2.300E-07 7.400E-08 #S 86 Rn #N 26 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 4 #UBIND 9.840E+04 1.805E+04 1.738E+04 1.462E+04 4482. 4159. 3538. 3022. 2892. 1097. 929.0 768.0 567.0 541.0 238.0 238.0 214.0 164.0 127.0 48.00 48.00 26.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 0.000 11.90 0.008230 0.02940 0.01420 0.01650 0.06440 0.03760 0.04130 0.02660 0.02740 0.1300 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2840 0.1870 0.2050 0.1790 0.1850 0.7820 0.5510 0.6440 0.05000 11.80 0.008230 0.02940 0.01420 0.01650 0.06440 0.03760 0.04130 0.02660 0.02740 0.1300 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2830 0.1870 0.2050 0.1790 0.1850 0.7760 0.5510 0.6440 0.1000 11.80 0.008230 0.02940 0.01420 0.01650 0.06440 0.03760 0.04130 0.02660 0.02740 0.1290 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2820 0.1870 0.2050 0.1790 0.1850 0.7590 0.5500 0.6430 0.1500 11.70 0.008230 0.02940 0.01420 0.01650 0.06440 0.03760 0.04130 0.02660 0.02740 0.1290 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2810 0.1870 0.2050 0.1790 0.1850 0.7320 0.5490 0.6410 0.2000 11.60 0.008230 0.02940 0.01420 0.01650 0.06440 0.03760 0.04130 0.02660 0.02740 0.1290 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2790 0.1870 0.2050 0.1790 0.1850 0.6960 0.5470 0.6360 0.3000 11.30 0.008230 0.02940 0.01420 0.01650 0.06430 0.03760 0.04130 0.02660 0.02740 0.1290 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2730 0.1870 0.2050 0.1790 0.1850 0.6020 0.5340 0.6090 0.4000 10.80 0.008230 0.02940 0.01420 0.01650 0.06420 0.03760 0.04130 0.02660 0.02740 0.1280 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2650 0.1860 0.2050 0.1790 0.1850 0.4940 0.5050 0.5570 0.5000 10.20 0.008230 0.02940 0.01420 0.01650 0.06410 0.03760 0.04130 0.02660 0.02740 0.1270 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2550 0.1860 0.2040 0.1790 0.1850 0.3860 0.4610 0.4830 0.6000 9.470 0.008230 0.02940 0.01420 0.01650 0.06400 0.03760 0.04130 0.02660 0.02740 0.1250 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2430 0.1850 0.2030 0.1790 0.1850 0.2880 0.4030 0.3960 0.7000 8.790 0.008230 0.02940 0.01420 0.01650 0.06380 0.03760 0.04130 0.02660 0.02740 0.1240 0.08140 0.08870 0.06610 0.06780 0.05430 0.05500 0.2300 0.1840 0.2010 0.1790 0.1850 0.2070 0.3380 0.3080 0.8000 8.170 0.008230 0.02930 0.01420 0.01650 0.06360 0.03760 0.04130 0.02660 0.02740 0.1220 0.08140 0.08860 0.06610 0.06780 0.05430 0.05500 0.2160 0.1830 0.1990 0.1780 0.1840 0.1460 0.2730 0.2300 1.000 7.210 0.008230 0.02930 0.01420 0.01650 0.06310 0.03760 0.04130 0.02660 0.02740 0.1180 0.08120 0.08850 0.06610 0.06780 0.05430 0.05500 0.1860 0.1780 0.1910 0.1760 0.1810 0.07550 0.1600 0.1150 1.200 6.580 0.008230 0.02930 0.01420 0.01650 0.06260 0.03760 0.04130 0.02660 0.02740 0.1140 0.08110 0.08820 0.06600 0.06780 0.05430 0.05500 0.1560 0.1700 0.1800 0.1720 0.1760 0.05120 0.08520 0.05350 1.400 6.160 0.008230 0.02920 0.01420 0.01650 0.06190 0.03760 0.04130 0.02660 0.02740 0.1090 0.08080 0.08770 0.06600 0.06780 0.05430 0.05500 0.1260 0.1590 0.1650 0.1640 0.1680 0.04690 0.04390 0.02710 1.600 5.810 0.008230 0.02910 0.01420 0.01650 0.06120 0.03760 0.04120 0.02660 0.02740 0.1030 0.08030 0.08710 0.06600 0.06770 0.05430 0.05500 0.1000 0.1460 0.1480 0.1550 0.1560 0.04660 0.02520 0.01850 1.800 5.490 0.008230 0.02910 0.01420 0.01650 0.06030 0.03760 0.04120 0.02660 0.02740 0.09680 0.07980 0.08620 0.06590 0.06760 0.05430 0.05500 0.07830 0.1310 0.1290 0.1420 0.1430 0.04410 0.01860 0.01690 2.000 5.150 0.008220 0.02900 0.01420 0.01650 0.05940 0.03760 0.04120 0.02660 0.02740 0.09050 0.07900 0.08510 0.06580 0.06750 0.05430 0.05500 0.06080 0.1150 0.1090 0.1280 0.1270 0.03860 0.01720 0.01680 2.400 4.490 0.008220 0.02880 0.01420 0.01650 0.05730 0.03750 0.04110 0.02660 0.02740 0.07750 0.07680 0.08190 0.06550 0.06710 0.05420 0.05490 0.03860 0.08330 0.07270 0.09720 0.09430 0.02410 0.01670 0.01480 3.000 3.650 0.008210 0.02850 0.01420 0.01640 0.05370 0.03740 0.04090 0.02660 0.02740 0.05880 0.07170 0.07470 0.06430 0.06570 0.05390 0.05450 0.02760 0.04440 0.03480 0.05580 0.05200 0.008990 0.01190 0.008280 4.000 2.840 0.008190 0.02780 0.01420 0.01640 0.04670 0.03680 0.04000 0.02650 0.02730 0.03390 0.05880 0.05800 0.05970 0.06060 0.05210 0.05270 0.02610 0.01550 0.01370 0.01700 0.01510 0.004280 0.003530 0.002030 5.000 2.410 0.008170 0.02690 0.01420 0.01640 0.03910 0.03590 0.03860 0.02650 0.02730 0.01990 0.04330 0.03950 0.05150 0.05150 0.04840 0.04880 0.01900 0.01160 0.01250 0.006350 0.005980 0.003810 0.001440 0.001270 6.000 2.050 0.008140 0.02580 0.01420 0.01640 0.03160 0.03430 0.03640 0.02630 0.02710 0.01440 0.02870 0.02410 0.04090 0.04020 0.04300 0.04300 0.010000 0.01110 0.01070 0.005140 0.005190 0.002230 0.001350 0.001190 7.000 1.710 0.008100 0.02470 0.01410 0.01630 0.02470 0.03230 0.03350 0.02600 0.02670 0.01330 0.01770 0.01400 0.03000 0.02890 0.03650 0.03640 0.004920 0.008640 0.007110 0.004990 0.004940 0.001030 0.001150 0.0008400 8.000 1.410 0.008060 0.02340 0.01410 0.01620 0.01890 0.02970 0.03010 0.02550 0.02620 0.01320 0.01080 0.008690 0.02050 0.01930 0.02990 0.02960 0.003270 0.005520 0.003950 0.004210 0.004010 0.0005530 0.0007750 0.0004780 10.00 0.9800 0.008000 0.02100 0.01400 0.01600 0.01100 0.02400 0.02200 0.02400 0.02400 0.01100 0.005800 0.006200 0.008400 0.007600 0.01900 0.01800 0.003000 0.001800 0.001400 0.002100 0.001800 0.0004600 0.0002500 0.0001500 15.00 0.4800 0.007700 0.01300 0.01300 0.01500 0.005000 0.009500 0.007400 0.01700 0.01700 0.002400 0.004600 0.004200 0.002400 0.002500 0.004600 0.004400 0.0007900 0.001000 0.0009500 0.0003400 0.0003400 0.0001300 0.0001200 9.800E-05 20.00 0.2700 0.007300 0.007700 0.01200 0.01200 0.004700 0.003000 0.002400 0.009700 0.009300 0.001300 0.001700 0.001200 0.002200 0.002100 0.001100 0.001000 0.0002900 0.0004300 0.0002800 0.0003100 0.0003000 4.300E-05 5.300E-05 3.000E-05 30.00 0.1100 0.006300 0.002000 0.007600 0.007000 0.001700 0.001400 0.001700 0.002400 0.002100 0.0006500 0.0003200 0.0004000 0.0007200 0.0006500 7.600E-05 6.800E-05 0.0001600 6.500E-05 7.700E-05 0.0001100 9.900E-05 2.400E-05 7.600E-06 7.800E-06 40.00 0.05100 0.005200 0.0008000 0.004400 0.003400 0.0003600 0.001200 0.001100 0.0005600 0.0004600 0.0001300 0.0002900 0.0003000 0.0001700 0.0001500 7.700E-06 6.600E-06 3.100E-05 5.800E-05 5.900E-05 2.700E-05 2.300E-05 4.700E-06 6.800E-06 6.000E-06 60.00 0.01700 0.003200 0.0006800 0.001300 0.0007400 0.0001500 0.0003900 0.0002600 4.300E-05 3.100E-05 4.000E-05 0.0001100 7.200E-05 1.300E-05 9.700E-06 2.000E-07 1.500E-07 8.800E-06 2.200E-05 1.400E-05 2.100E-06 1.500E-06 1.300E-06 2.600E-06 1.500E-06 100.0 0.003700 0.001100 0.0002800 0.0001500 5.100E-05 7.200E-05 4.300E-05 1.700E-05 9.900E-07 5.500E-07 1.900E-05 1.200E-05 4.500E-06 3.000E-07 1.600E-07 1.400E-09 8.800E-10 4.200E-06 2.400E-06 9.000E-07 4.600E-08 2.500E-08 6.300E-07 2.800E-07 9.100E-08 #S 87 Fr #N 27 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 4 1 #UBIND 1.011E+05 1.864E+04 1.791E+04 1.503E+04 4652. 4327. 3663. 3136. 3000. 1153. 980.0 810.0 603.0 577.0 268.0 268.0 234.0 182.0 140.0 58.00 58.00 34.00 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 0.000 13.80 0.008090 0.02890 0.01400 0.01620 0.06340 0.03700 0.04070 0.02620 0.02700 0.1270 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2750 0.1810 0.1990 0.1720 0.1770 0.7140 0.4960 0.5710 2.650 0.05000 13.60 0.008090 0.02890 0.01400 0.01620 0.06340 0.03700 0.04070 0.02620 0.02700 0.1270 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2740 0.1810 0.1990 0.1720 0.1770 0.7090 0.4960 0.5710 2.470 0.1000 13.10 0.008090 0.02890 0.01400 0.01620 0.06330 0.03700 0.04070 0.02620 0.02700 0.1270 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2730 0.1810 0.1990 0.1720 0.1770 0.6960 0.4960 0.5700 2.010 0.1500 12.50 0.008090 0.02890 0.01400 0.01620 0.06330 0.03700 0.04070 0.02620 0.02700 0.1270 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2720 0.1810 0.1990 0.1720 0.1770 0.6750 0.4960 0.5690 1.440 0.2000 11.90 0.008090 0.02890 0.01400 0.01620 0.06330 0.03700 0.04070 0.02620 0.02700 0.1270 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2700 0.1810 0.1990 0.1720 0.1770 0.6460 0.4940 0.5660 0.9080 0.3000 11.00 0.008090 0.02890 0.01400 0.01620 0.06320 0.03700 0.04070 0.02620 0.02700 0.1260 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2650 0.1800 0.1980 0.1720 0.1770 0.5720 0.4860 0.5510 0.2740 0.4000 10.40 0.008090 0.02890 0.01400 0.01620 0.06320 0.03700 0.04070 0.02620 0.02700 0.1250 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2580 0.1800 0.1980 0.1720 0.1770 0.4830 0.4680 0.5180 0.09520 0.5000 9.950 0.008090 0.02890 0.01400 0.01620 0.06310 0.03700 0.04070 0.02620 0.02700 0.1240 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2480 0.1800 0.1980 0.1720 0.1770 0.3900 0.4370 0.4680 0.07540 0.6000 9.410 0.008090 0.02890 0.01400 0.01620 0.06290 0.03700 0.04070 0.02620 0.02700 0.1230 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2380 0.1790 0.1970 0.1710 0.1770 0.3020 0.3950 0.4040 0.07320 0.7000 8.820 0.008090 0.02890 0.01400 0.01620 0.06280 0.03700 0.04070 0.02620 0.02700 0.1220 0.07980 0.08710 0.06480 0.06650 0.05300 0.05370 0.2260 0.1780 0.1950 0.1710 0.1770 0.2260 0.3450 0.3330 0.06170 0.8000 8.250 0.008090 0.02890 0.01400 0.01620 0.06260 0.03700 0.04070 0.02620 0.02700 0.1200 0.07970 0.08700 0.06480 0.06650 0.05300 0.05370 0.2130 0.1770 0.1930 0.1710 0.1760 0.1650 0.2910 0.2640 0.04580 1.000 7.280 0.008090 0.02880 0.01400 0.01620 0.06210 0.03700 0.04070 0.02620 0.02700 0.1160 0.07960 0.08690 0.06470 0.06650 0.05300 0.05370 0.1850 0.1730 0.1870 0.1690 0.1740 0.08830 0.1870 0.1480 0.02000 1.200 6.600 0.008090 0.02880 0.01400 0.01620 0.06160 0.03700 0.04070 0.02620 0.02700 0.1120 0.07950 0.08660 0.06470 0.06650 0.05300 0.05370 0.1560 0.1660 0.1770 0.1660 0.1700 0.05670 0.1080 0.07480 0.007940 1.400 6.130 0.008090 0.02870 0.01400 0.01620 0.06100 0.03700 0.04070 0.02620 0.02700 0.1070 0.07920 0.08620 0.06470 0.06640 0.05300 0.05370 0.1280 0.1570 0.1630 0.1600 0.1630 0.04880 0.05870 0.03800 0.004030 1.600 5.780 0.008090 0.02870 0.01400 0.01620 0.06030 0.03700 0.04070 0.02620 0.02700 0.1020 0.07880 0.08560 0.06470 0.06640 0.05300 0.05370 0.1030 0.1450 0.1480 0.1520 0.1540 0.04830 0.03310 0.02350 0.003260 1.800 5.460 0.008090 0.02860 0.01400 0.01620 0.05950 0.03700 0.04060 0.02620 0.02700 0.09590 0.07830 0.08480 0.06460 0.06640 0.05300 0.05370 0.08090 0.1310 0.1300 0.1410 0.1420 0.04700 0.02240 0.01950 0.003220 2.000 5.150 0.008080 0.02850 0.01400 0.01620 0.05860 0.03690 0.04060 0.02620 0.02700 0.08990 0.07760 0.08370 0.06460 0.06630 0.05290 0.05360 0.06330 0.1160 0.1110 0.1290 0.1280 0.04270 0.01920 0.01910 0.003090 2.400 4.530 0.008080 0.02840 0.01400 0.01620 0.05660 0.03690 0.04050 0.02620 0.02700 0.07740 0.07550 0.08080 0.06420 0.06590 0.05290 0.05360 0.04020 0.08600 0.07610 0.1010 0.09820 0.02860 0.01870 0.01780 0.002230 3.000 3.690 0.008070 0.02800 0.01400 0.01620 0.05310 0.03670 0.04030 0.02620 0.02700 0.05920 0.07080 0.07410 0.06320 0.06470 0.05260 0.05330 0.02770 0.04750 0.03760 0.06030 0.05670 0.01130 0.01440 0.01090 0.0008930 4.000 2.850 0.008050 0.02740 0.01400 0.01620 0.04640 0.03630 0.03950 0.02620 0.02700 0.03480 0.05880 0.05820 0.05900 0.05990 0.05110 0.05170 0.02620 0.01660 0.01430 0.01960 0.01740 0.004680 0.004730 0.002800 0.0002980 5.000 2.410 0.008030 0.02650 0.01400 0.01620 0.03900 0.03530 0.03820 0.02610 0.02690 0.02040 0.04390 0.04030 0.05140 0.05150 0.04790 0.04830 0.01990 0.01160 0.01260 0.007030 0.006540 0.004300 0.001740 0.001530 0.0002750 6.000 2.060 0.008000 0.02550 0.01390 0.01620 0.03170 0.03390 0.03610 0.02590 0.02670 0.01440 0.02960 0.02500 0.04130 0.04070 0.04290 0.04300 0.01100 0.01130 0.01120 0.005290 0.005350 0.002690 0.001550 0.001460 0.0001760 7.000 1.720 0.007970 0.02440 0.01390 0.01610 0.02500 0.03200 0.03330 0.02570 0.02640 0.01310 0.01850 0.01460 0.03080 0.02970 0.03680 0.03670 0.005400 0.009120 0.007670 0.005190 0.005180 0.001270 0.001380 0.001080 8.370E-05 8.000 1.430 0.007930 0.02320 0.01390 0.01600 0.01920 0.02960 0.03000 0.02520 0.02590 0.01300 0.01130 0.009040 0.02140 0.02020 0.03050 0.03020 0.003400 0.006040 0.004380 0.004500 0.004330 0.0006550 0.0009740 0.0006390 4.200E-05 10.00 1.000 0.007800 0.02100 0.01390 0.01600 0.01100 0.02400 0.02300 0.02400 0.02400 0.01100 0.005900 0.006100 0.009000 0.008200 0.01900 0.01900 0.003000 0.002000 0.001500 0.002300 0.002100 0.0005100 0.0003200 0.0002000 3.200E-05 15.00 0.4900 0.007500 0.01400 0.01300 0.01400 0.004900 0.009900 0.007700 0.01700 0.01700 0.002600 0.004700 0.004300 0.002400 0.002500 0.005000 0.004700 0.0008800 0.001000 0.001000 0.0003700 0.0003600 0.0001600 0.0001400 0.0001200 1.000E-05 20.00 0.2800 0.007200 0.007900 0.01100 0.01200 0.004700 0.003200 0.002400 0.009900 0.009500 0.001300 0.001900 0.001300 0.002200 0.002200 0.001200 0.001100 0.0002900 0.0004800 0.0003200 0.0003200 0.0003200 4.800E-05 6.700E-05 4.000E-05 3.000E-06 30.00 0.1100 0.006200 0.002100 0.007700 0.007100 0.001800 0.001400 0.001700 0.002500 0.002200 0.0006800 0.0003200 0.0004000 0.0007600 0.0006900 8.500E-05 7.700E-05 0.0001700 6.700E-05 7.900E-05 0.0001200 0.0001100 2.800E-05 9.100E-06 9.500E-06 1.700E-06 40.00 0.05300 0.005100 0.0008000 0.004500 0.003500 0.0003900 0.001200 0.001100 0.0006000 0.0005000 0.0001400 0.0002900 0.0003100 0.0001900 0.0001600 8.800E-06 7.600E-06 3.500E-05 6.000E-05 6.300E-05 3.100E-05 2.600E-05 5.800E-06 8.100E-06 7.500E-06 3.600E-07 60.00 0.01700 0.003200 0.0006800 0.001300 0.0007800 0.0001500 0.0004100 0.0002700 4.700E-05 3.400E-05 3.900E-05 0.0001100 7.600E-05 1.500E-05 1.100E-05 2.300E-07 1.800E-07 8.800E-06 2.400E-05 1.600E-05 2.400E-06 1.700E-06 1.400E-06 3.200E-06 1.900E-06 9.000E-08 100.0 0.003900 0.001200 0.0002900 0.0001600 5.500E-05 7.400E-05 4.700E-05 1.800E-05 1.100E-06 6.100E-07 2.000E-05 1.300E-05 4.900E-06 3.400E-07 1.800E-07 1.600E-09 1.000E-09 4.500E-06 2.700E-06 1.000E-06 5.500E-08 3.000E-08 7.400E-07 3.600E-07 1.200E-07 4.600E-08 #S 88 Ra #N 27 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 4 2 #UBIND 1.039E+05 1.924E+04 1.848E+04 1.544E+04 4822. 4490. 3792. 3248. 3105. 1208. 1058. 879.0 636.0 603.0 299.0 299.0 254.0 200.0 153.0 68.00 68.00 44.00 19.00 19.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 0.000 14.90 0.007960 0.02850 0.01370 0.01600 0.06230 0.03640 0.04010 0.02580 0.02660 0.1250 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2660 0.1750 0.1920 0.1650 0.1700 0.6570 0.4560 0.5200 2.190 0.05000 14.70 0.007960 0.02850 0.01370 0.01600 0.06230 0.03640 0.04010 0.02580 0.02660 0.1250 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2660 0.1750 0.1920 0.1650 0.1700 0.6530 0.4560 0.5200 2.080 0.1000 14.10 0.007960 0.02850 0.01370 0.01600 0.06230 0.03640 0.04010 0.02580 0.02660 0.1240 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2650 0.1750 0.1920 0.1650 0.1700 0.6430 0.4560 0.5200 1.790 0.1500 13.30 0.007960 0.02850 0.01370 0.01600 0.06230 0.03640 0.04010 0.02580 0.02660 0.1240 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2640 0.1750 0.1920 0.1650 0.1700 0.6260 0.4550 0.5190 1.400 0.2000 12.40 0.007960 0.02850 0.01370 0.01600 0.06230 0.03640 0.04010 0.02580 0.02660 0.1240 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2620 0.1750 0.1920 0.1650 0.1700 0.6030 0.4540 0.5170 1.000 0.3000 11.00 0.007960 0.02850 0.01370 0.01600 0.06220 0.03640 0.04010 0.02580 0.02660 0.1240 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2570 0.1750 0.1920 0.1650 0.1700 0.5420 0.4490 0.5070 0.4070 0.4000 10.20 0.007960 0.02840 0.01370 0.01600 0.06210 0.03640 0.04010 0.02580 0.02660 0.1230 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2500 0.1750 0.1920 0.1650 0.1700 0.4680 0.4360 0.4840 0.1510 0.5000 9.750 0.007960 0.02840 0.01370 0.01600 0.06200 0.03640 0.04010 0.02580 0.02660 0.1220 0.07820 0.08560 0.06350 0.06520 0.05170 0.05240 0.2420 0.1740 0.1920 0.1650 0.1700 0.3890 0.4140 0.4480 0.08730 0.6000 9.290 0.007960 0.02840 0.01370 0.01600 0.06190 0.03640 0.04010 0.02580 0.02660 0.1210 0.07820 0.08550 0.06350 0.06520 0.05170 0.05240 0.2320 0.1740 0.1910 0.1640 0.1700 0.3110 0.3820 0.3990 0.08200 0.7000 8.810 0.007960 0.02840 0.01370 0.01600 0.06170 0.03640 0.04010 0.02580 0.02660 0.1200 0.07820 0.08550 0.06350 0.06520 0.05170 0.05240 0.2210 0.1730 0.1900 0.1640 0.1690 0.2410 0.3430 0.3410 0.07900 0.8000 8.300 0.007960 0.02840 0.01370 0.01600 0.06160 0.03640 0.04010 0.02580 0.02660 0.1180 0.07820 0.08550 0.06350 0.06520 0.05170 0.05240 0.2090 0.1720 0.1880 0.1640 0.1690 0.1810 0.2980 0.2820 0.06800 1.000 7.350 0.007950 0.02840 0.01370 0.01600 0.06110 0.03640 0.04010 0.02580 0.02660 0.1150 0.07810 0.08530 0.06350 0.06520 0.05170 0.05240 0.1830 0.1680 0.1820 0.1630 0.1680 0.1010 0.2060 0.1720 0.03790 1.200 6.620 0.007950 0.02830 0.01370 0.01600 0.06060 0.03640 0.04010 0.02580 0.02660 0.1100 0.07790 0.08510 0.06350 0.06520 0.05170 0.05240 0.1550 0.1620 0.1730 0.1600 0.1640 0.06300 0.1280 0.09460 0.01690 1.400 6.110 0.007950 0.02830 0.01370 0.01600 0.06000 0.03640 0.04010 0.02580 0.02660 0.1060 0.07770 0.08470 0.06350 0.06520 0.05170 0.05240 0.1290 0.1540 0.1610 0.1560 0.1590 0.05100 0.07370 0.04990 0.007730 1.600 5.740 0.007950 0.02820 0.01370 0.01600 0.05940 0.03630 0.04010 0.02580 0.02660 0.1010 0.07730 0.08420 0.06340 0.06520 0.05170 0.05240 0.1050 0.1430 0.1470 0.1490 0.1510 0.04940 0.04220 0.02930 0.005020 1.800 5.430 0.007950 0.02810 0.01370 0.01600 0.05860 0.03630 0.04010 0.02580 0.02660 0.09500 0.07680 0.08340 0.06340 0.06510 0.05170 0.05240 0.08330 0.1310 0.1300 0.1400 0.1410 0.04890 0.02700 0.02220 0.004640 2.000 5.140 0.007950 0.02810 0.01370 0.01600 0.05780 0.03630 0.04000 0.02580 0.02660 0.08930 0.07620 0.08250 0.06330 0.06500 0.05170 0.05240 0.06580 0.1170 0.1130 0.1290 0.1290 0.04590 0.02140 0.02080 0.004600 2.400 4.550 0.007940 0.02790 0.01370 0.01600 0.05590 0.03630 0.03990 0.02580 0.02660 0.07730 0.07430 0.07970 0.06310 0.06470 0.05160 0.05230 0.04190 0.08840 0.07910 0.1030 0.1010 0.03300 0.02000 0.02010 0.003680 3.000 3.730 0.007930 0.02760 0.01370 0.01600 0.05250 0.03610 0.03970 0.02580 0.02660 0.05970 0.07000 0.07350 0.06210 0.06360 0.05140 0.05210 0.02800 0.05060 0.04040 0.06460 0.06120 0.01400 0.01680 0.01350 0.001620 4.000 2.860 0.007920 0.02700 0.01370 0.01600 0.04610 0.03570 0.03900 0.02580 0.02660 0.03550 0.05870 0.05840 0.05830 0.05930 0.05020 0.05070 0.02610 0.01790 0.01500 0.02230 0.01990 0.005110 0.006100 0.003680 0.0004640 5.000 2.410 0.007900 0.02620 0.01370 0.01600 0.03900 0.03480 0.03770 0.02570 0.02650 0.02090 0.04450 0.04100 0.05130 0.05150 0.04730 0.04770 0.02070 0.01170 0.01260 0.007820 0.007210 0.004760 0.002090 0.001770 0.0004250 6.000 2.060 0.007870 0.02520 0.01370 0.01600 0.03190 0.03350 0.03580 0.02560 0.02630 0.01460 0.03050 0.02590 0.04180 0.04120 0.04280 0.04290 0.01190 0.01140 0.01150 0.005440 0.005490 0.003180 0.001730 0.001700 0.0002930 7.000 1.740 0.007840 0.02410 0.01370 0.01590 0.02530 0.03170 0.03320 0.02530 0.02610 0.01300 0.01940 0.01530 0.03150 0.03050 0.03710 0.03700 0.005930 0.009550 0.008220 0.005350 0.005380 0.001570 0.001600 0.001320 0.0001450 8.000 1.450 0.007800 0.02300 0.01360 0.01580 0.01960 0.02940 0.03000 0.02490 0.02560 0.01290 0.01200 0.009420 0.02220 0.02100 0.03100 0.03080 0.003570 0.006550 0.004840 0.004780 0.004630 0.0007790 0.001180 0.0008100 7.040E-05 10.00 1.000 0.007700 0.02000 0.01300 0.01580 0.01100 0.02400 0.02300 0.02400 0.02400 0.01100 0.006000 0.006100 0.009600 0.008700 0.02000 0.02000 0.003100 0.002300 0.001600 0.002600 0.002300 0.0005500 0.0004100 0.0002500 4.800E-05 15.00 0.5000 0.007400 0.01400 0.01300 0.01400 0.004900 0.010000 0.008000 0.01700 0.01700 0.002800 0.004800 0.004400 0.002400 0.002500 0.005300 0.005100 0.0009800 0.001100 0.001100 0.0003900 0.0003800 0.0001900 0.0001600 0.0001500 1.700E-05 20.00 0.2900 0.007100 0.008100 0.01100 0.01200 0.004700 0.003300 0.002500 0.010000 0.009700 0.001300 0.002000 0.001400 0.002200 0.002200 0.001300 0.001200 0.0003000 0.0005300 0.0003500 0.0003400 0.0003400 5.300E-05 8.200E-05 5.000E-05 4.600E-06 30.00 0.1100 0.006100 0.002200 0.007700 0.007200 0.001900 0.001300 0.001700 0.002600 0.002300 0.0007200 0.0003200 0.0004000 0.0008000 0.0007300 9.600E-05 8.600E-05 0.0001800 7.000E-05 8.100E-05 0.0001400 0.0001200 3.300E-05 1.100E-05 1.100E-05 2.800E-06 40.00 0.05400 0.005100 0.0008200 0.004600 0.003600 0.0004300 0.001200 0.001200 0.0006400 0.0005300 0.0001600 0.0002900 0.0003100 0.0002000 0.0001700 1.000E-05 8.600E-06 3.900E-05 6.100E-05 6.600E-05 3.500E-05 2.900E-05 7.100E-06 9.200E-06 9.000E-06 6.200E-07 60.00 0.01800 0.003300 0.0006700 0.001400 0.0008200 0.0001500 0.0004300 0.0002900 5.100E-05 3.700E-05 3.900E-05 0.0001200 8.100E-05 1.600E-05 1.200E-05 2.700E-07 2.100E-07 8.900E-06 2.500E-05 1.700E-05 2.800E-06 2.000E-06 1.600E-06 3.800E-06 2.400E-06 1.400E-07 100.0 0.004000 0.001200 0.0003000 0.0001700 5.800E-05 7.600E-05 5.100E-05 1.900E-05 1.200E-06 6.800E-07 2.100E-05 1.400E-05 5.300E-06 3.800E-07 2.100E-07 1.900E-09 1.200E-09 4.800E-06 3.000E-06 1.100E-06 6.500E-08 3.500E-08 8.400E-07 4.500E-07 1.500E-07 7.300E-08 #S 89 Ac #N 28 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 4 1 2 #UBIND 1.068E+05 1.984E+04 1.908E+04 1.587E+04 5002. 4656. 3909. 3370. 3219. 1269. 1080. 890.0 675.0 639.0 319.0 319.0 272.0 215.0 167.0 80.00 80.00 0.000 0.000 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 0.000 14.70 0.007820 0.02800 0.01350 0.01580 0.06130 0.03580 0.03950 0.02540 0.02620 0.1220 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2580 0.1690 0.1870 0.1580 0.1630 0.6170 0.4290 0.4840 0.6080 1.990 0.05000 14.50 0.007820 0.02800 0.01350 0.01580 0.06130 0.03580 0.03950 0.02540 0.02620 0.1220 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2580 0.1690 0.1870 0.1580 0.1630 0.6140 0.4290 0.4840 0.6080 1.910 0.1000 14.00 0.007820 0.02800 0.01350 0.01580 0.06130 0.03580 0.03950 0.02540 0.02620 0.1220 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2570 0.1690 0.1870 0.1580 0.1630 0.6060 0.4290 0.4840 0.6080 1.680 0.1500 13.40 0.007820 0.02800 0.01350 0.01580 0.06130 0.03580 0.03950 0.02540 0.02620 0.1220 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2560 0.1690 0.1870 0.1580 0.1630 0.5910 0.4290 0.4840 0.6070 1.360 0.2000 12.60 0.007820 0.02800 0.01350 0.01580 0.06130 0.03580 0.03950 0.02540 0.02620 0.1220 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2540 0.1690 0.1870 0.1580 0.1630 0.5720 0.4280 0.4820 0.6060 1.020 0.3000 11.40 0.007820 0.02800 0.01350 0.01580 0.06120 0.03580 0.03950 0.02540 0.02620 0.1210 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2500 0.1690 0.1860 0.1580 0.1630 0.5200 0.4240 0.4750 0.5920 0.4730 0.4000 10.50 0.007820 0.02800 0.01350 0.01580 0.06110 0.03580 0.03950 0.02540 0.02620 0.1210 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2440 0.1690 0.1860 0.1580 0.1630 0.4550 0.4140 0.4580 0.5550 0.1900 0.5000 9.980 0.007820 0.02800 0.01350 0.01580 0.06100 0.03580 0.03950 0.02540 0.02620 0.1200 0.07670 0.08410 0.06230 0.06400 0.05050 0.05120 0.2360 0.1690 0.1860 0.1580 0.1630 0.3840 0.3960 0.4300 0.4930 0.09730 0.6000 9.500 0.007820 0.02800 0.01350 0.01580 0.06090 0.03580 0.03950 0.02540 0.02620 0.1190 0.07670 0.08400 0.06230 0.06400 0.05050 0.05120 0.2270 0.1690 0.1850 0.1580 0.1630 0.3140 0.3700 0.3900 0.4160 0.08210 0.7000 9.000 0.007820 0.02800 0.01350 0.01580 0.06080 0.03580 0.03950 0.02540 0.02620 0.1170 0.07670 0.08400 0.06230 0.06400 0.05050 0.05120 0.2170 0.1680 0.1840 0.1580 0.1630 0.2490 0.3370 0.3430 0.3360 0.08130 0.8000 8.470 0.007820 0.02790 0.01350 0.01580 0.06060 0.03580 0.03950 0.02540 0.02620 0.1160 0.07660 0.08400 0.06230 0.06400 0.05050 0.05120 0.2050 0.1670 0.1820 0.1580 0.1630 0.1920 0.2990 0.2910 0.2610 0.07520 1.000 7.480 0.007820 0.02790 0.01350 0.01580 0.06020 0.03580 0.03950 0.02540 0.02620 0.1130 0.07660 0.08390 0.06230 0.06400 0.05050 0.05120 0.1810 0.1640 0.1770 0.1570 0.1610 0.1110 0.2160 0.1900 0.1460 0.04860 1.200 6.690 0.007820 0.02790 0.01350 0.01580 0.05970 0.03570 0.03950 0.02540 0.02620 0.1090 0.07640 0.08360 0.06230 0.06400 0.05050 0.05120 0.1550 0.1580 0.1700 0.1550 0.1590 0.06860 0.1420 0.1110 0.07580 0.02430 1.400 6.120 0.007820 0.02780 0.01350 0.01580 0.05910 0.03570 0.03950 0.02540 0.02620 0.1040 0.07620 0.08330 0.06230 0.06400 0.05050 0.05120 0.1300 0.1510 0.1590 0.1510 0.1550 0.05280 0.08570 0.06120 0.03740 0.01130 1.600 5.720 0.007820 0.02780 0.01350 0.01580 0.05850 0.03570 0.03950 0.02540 0.02620 0.09940 0.07590 0.08280 0.06220 0.06390 0.05050 0.05120 0.1060 0.1420 0.1460 0.1450 0.1480 0.04970 0.05040 0.03540 0.01840 0.006440 1.800 5.400 0.007810 0.02770 0.01350 0.01580 0.05780 0.03570 0.03950 0.02540 0.02620 0.09410 0.07540 0.08210 0.06220 0.06390 0.05050 0.05120 0.08550 0.1300 0.1310 0.1380 0.1400 0.04950 0.03160 0.02490 0.009940 0.005330 2.000 5.120 0.007810 0.02760 0.01350 0.01580 0.05700 0.03570 0.03950 0.02540 0.02620 0.08860 0.07480 0.08120 0.06210 0.06380 0.05050 0.05110 0.06810 0.1180 0.1140 0.1280 0.1290 0.04760 0.02340 0.02200 0.006720 0.005270 2.400 4.570 0.007810 0.02750 0.01350 0.01580 0.05510 0.03570 0.03940 0.02540 0.02620 0.07710 0.07310 0.07860 0.06190 0.06350 0.05040 0.05110 0.04370 0.09050 0.08190 0.1050 0.1040 0.03650 0.02060 0.02150 0.005650 0.004600 3.000 3.770 0.007800 0.02720 0.01350 0.01580 0.05200 0.03550 0.03920 0.02540 0.02620 0.06010 0.06910 0.07280 0.06110 0.06260 0.05020 0.05090 0.02840 0.05350 0.04330 0.06850 0.06530 0.01670 0.01830 0.01580 0.005140 0.002260 4.000 2.870 0.007780 0.02660 0.01350 0.01580 0.04580 0.03510 0.03850 0.02540 0.02620 0.03630 0.05850 0.05850 0.05760 0.05870 0.04920 0.04980 0.02590 0.01930 0.01570 0.02510 0.02260 0.005520 0.007440 0.004660 0.002360 0.0005890 5.000 2.410 0.007760 0.02580 0.01350 0.01580 0.03890 0.03430 0.03730 0.02530 0.02610 0.02150 0.04500 0.04170 0.05110 0.05140 0.04670 0.04710 0.02140 0.01180 0.01270 0.008760 0.007990 0.005110 0.002450 0.001990 0.0007500 0.0005140 6.000 2.070 0.007740 0.02490 0.01350 0.01580 0.03210 0.03310 0.03550 0.02520 0.02600 0.01470 0.03140 0.02670 0.04210 0.04160 0.04260 0.04280 0.01290 0.01150 0.01180 0.005620 0.005650 0.003630 0.001850 0.001890 0.0003540 0.0003810 7.000 1.760 0.007710 0.02390 0.01340 0.01570 0.02560 0.03140 0.03300 0.02500 0.02570 0.01280 0.02030 0.01600 0.03220 0.03120 0.03730 0.03720 0.006520 0.009930 0.008760 0.005490 0.005550 0.001870 0.001760 0.001540 0.0003230 0.0001980 8.000 1.470 0.007670 0.02270 0.01340 0.01560 0.02000 0.02930 0.03000 0.02460 0.02530 0.01270 0.01260 0.009820 0.02310 0.02190 0.03150 0.03130 0.003770 0.007060 0.005310 0.005030 0.004910 0.0009130 0.001350 0.0009790 0.0003070 9.480E-05 10.00 1.000 0.007600 0.02000 0.01300 0.01500 0.01200 0.02400 0.02300 0.02300 0.02400 0.01100 0.006100 0.006100 0.010000 0.009300 0.02100 0.02000 0.003100 0.002500 0.001700 0.002900 0.002600 0.0005900 0.0005000 0.0003000 0.0001800 5.800E-05 15.00 0.5100 0.007300 0.01400 0.01300 0.01400 0.004900 0.01100 0.008300 0.01700 0.01700 0.003000 0.004800 0.004600 0.002400 0.002500 0.005700 0.005400 0.001100 0.001100 0.001100 0.0004200 0.0004100 0.0002300 0.0001800 0.0001700 2.600E-05 2.300E-05 20.00 0.2900 0.007000 0.008200 0.01100 0.01200 0.004600 0.003500 0.002600 0.010000 0.009900 0.001200 0.002100 0.001500 0.002200 0.002200 0.001400 0.001300 0.0003000 0.0005700 0.0003900 0.0003600 0.0003600 5.800E-05 9.600E-05 6.100E-05 2.100E-05 5.700E-06 30.00 0.1200 0.006100 0.002300 0.007800 0.007300 0.002000 0.001300 0.001700 0.002800 0.002500 0.0007500 0.0003300 0.0004000 0.0008400 0.0007700 0.0001100 9.700E-05 0.0001900 7.300E-05 8.300E-05 0.0001500 0.0001400 3.700E-05 1.200E-05 1.200E-05 9.200E-06 3.600E-06 40.00 0.05500 0.005100 0.0008300 0.004700 0.003700 0.0004600 0.001200 0.001200 0.0006800 0.0005700 0.0001700 0.0002900 0.0003200 0.0002200 0.0001800 1.100E-05 9.800E-06 4.400E-05 6.300E-05 6.900E-05 4.000E-05 3.300E-05 8.500E-06 1.000E-05 1.000E-05 2.500E-06 8.400E-07 60.00 0.01800 0.003300 0.0006600 0.001500 0.0008500 0.0001500 0.0004500 0.0003000 5.600E-05 4.000E-05 3.800E-05 0.0001200 8.500E-05 1.800E-05 1.300E-05 3.100E-07 2.400E-07 8.900E-06 2.700E-05 1.900E-05 3.200E-06 2.300E-06 1.700E-06 4.400E-06 2.800E-06 2.000E-07 1.700E-07 100.0 0.004200 0.001200 0.0003100 0.0001800 6.200E-05 7.900E-05 5.500E-05 2.100E-05 1.400E-06 7.500E-07 2.100E-05 1.500E-05 5.800E-06 4.200E-07 2.300E-07 2.200E-09 1.400E-09 5.000E-06 3.300E-06 1.300E-06 7.600E-08 4.100E-08 9.400E-07 5.300E-07 1.900E-07 4.700E-09 9.300E-08 #S 90 Th #N 28 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 4 2 2 #UBIND 1.097E+05 2.047E+04 1.969E+04 1.630E+04 5182. 4831. 4046. 3491. 3332. 1330. 1168. 968.0 714.0 677.0 344.0 335.0 290.0 229.0 182.0 95.00 88.00 60.00 49.00 43.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 0.000 14.50 0.007690 0.02750 0.01330 0.01560 0.06030 0.03520 0.03900 0.02500 0.02590 0.1200 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2510 0.1640 0.1810 0.1520 0.1570 0.5850 0.4080 0.4560 0.5350 1.860 0.05000 14.40 0.007690 0.02750 0.01330 0.01560 0.06030 0.03520 0.03900 0.02500 0.02590 0.1200 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2500 0.1640 0.1810 0.1520 0.1570 0.5820 0.4080 0.4560 0.5350 1.790 0.1000 14.00 0.007690 0.02750 0.01330 0.01560 0.06030 0.03520 0.03900 0.02500 0.02590 0.1200 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2500 0.1640 0.1810 0.1520 0.1570 0.5750 0.4080 0.4560 0.5350 1.600 0.1500 13.40 0.007690 0.02750 0.01330 0.01560 0.06030 0.03520 0.03900 0.02500 0.02590 0.1200 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2490 0.1640 0.1810 0.1520 0.1570 0.5620 0.4070 0.4560 0.5350 1.330 0.2000 12.80 0.007690 0.02750 0.01330 0.01560 0.06030 0.03520 0.03900 0.02500 0.02590 0.1190 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2470 0.1640 0.1810 0.1520 0.1570 0.5450 0.4070 0.4550 0.5340 1.030 0.3000 11.60 0.007690 0.02750 0.01330 0.01560 0.06020 0.03520 0.03900 0.02500 0.02590 0.1190 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2430 0.1640 0.1810 0.1520 0.1570 0.5000 0.4030 0.4490 0.5280 0.5170 0.4000 10.80 0.007690 0.02750 0.01330 0.01560 0.06010 0.03520 0.03900 0.02500 0.02590 0.1180 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2370 0.1640 0.1810 0.1520 0.1570 0.4430 0.3950 0.4360 0.5080 0.2220 0.5000 10.20 0.007690 0.02750 0.01330 0.01560 0.06000 0.03520 0.03900 0.02500 0.02590 0.1180 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2300 0.1640 0.1810 0.1520 0.1570 0.3790 0.3810 0.4130 0.4710 0.1080 0.6000 9.700 0.007690 0.02750 0.01330 0.01560 0.05990 0.03520 0.03900 0.02500 0.02590 0.1170 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2220 0.1640 0.1800 0.1520 0.1570 0.3150 0.3590 0.3810 0.4180 0.08160 0.7000 9.210 0.007690 0.02750 0.01330 0.01560 0.05980 0.03520 0.03900 0.02500 0.02590 0.1150 0.07520 0.08260 0.06110 0.06280 0.04930 0.05000 0.2120 0.1630 0.1790 0.1520 0.1570 0.2540 0.3310 0.3400 0.3560 0.07990 0.8000 8.690 0.007690 0.02750 0.01330 0.01560 0.05960 0.03520 0.03900 0.02500 0.02590 0.1140 0.07520 0.08250 0.06110 0.06280 0.04930 0.05000 0.2020 0.1620 0.1780 0.1520 0.1570 0.2000 0.2970 0.2950 0.2930 0.07710 1.000 7.660 0.007690 0.02750 0.01330 0.01560 0.05920 0.03520 0.03900 0.02500 0.02590 0.1110 0.07510 0.08240 0.06110 0.06280 0.04930 0.05000 0.1790 0.1590 0.1730 0.1510 0.1560 0.1190 0.2230 0.2020 0.1820 0.05570 1.200 6.790 0.007690 0.02740 0.01330 0.01560 0.05880 0.03520 0.03900 0.02500 0.02590 0.1070 0.07500 0.08220 0.06110 0.06280 0.04930 0.05000 0.1540 0.1550 0.1660 0.1500 0.1540 0.07400 0.1520 0.1250 0.1040 0.03080 1.400 6.160 0.007680 0.02740 0.01330 0.01560 0.05820 0.03510 0.03900 0.02500 0.02590 0.1030 0.07480 0.08190 0.06110 0.06280 0.04930 0.05000 0.1300 0.1480 0.1570 0.1470 0.1500 0.05480 0.09640 0.07190 0.05570 0.01500 1.600 5.710 0.007680 0.02730 0.01330 0.01560 0.05760 0.03510 0.03900 0.02500 0.02590 0.09820 0.07450 0.08140 0.06110 0.06280 0.04930 0.05000 0.1080 0.1400 0.1450 0.1420 0.1450 0.04980 0.05840 0.04180 0.02890 0.008020 1.800 5.380 0.007680 0.02730 0.01330 0.01560 0.05690 0.03510 0.03890 0.02500 0.02590 0.09320 0.07410 0.08080 0.06100 0.06270 0.04930 0.05000 0.08750 0.1290 0.1310 0.1350 0.1380 0.04940 0.03640 0.02790 0.01550 0.005910 2.000 5.100 0.007680 0.02720 0.01330 0.01560 0.05620 0.03510 0.03890 0.02500 0.02590 0.08790 0.07350 0.08000 0.06100 0.06270 0.04930 0.05000 0.07020 0.1180 0.1150 0.1270 0.1280 0.04840 0.02570 0.02320 0.009530 0.005620 2.400 4.580 0.007680 0.02700 0.01330 0.01560 0.05440 0.03510 0.03880 0.02500 0.02590 0.07690 0.07190 0.07760 0.06080 0.06240 0.04930 0.05000 0.04540 0.09220 0.08440 0.1070 0.1060 0.03930 0.02090 0.02230 0.006810 0.005220 3.000 3.810 0.007670 0.02680 0.01330 0.01560 0.05140 0.03500 0.03870 0.02500 0.02590 0.06040 0.06820 0.07210 0.06000 0.06150 0.04910 0.04980 0.02880 0.05630 0.04600 0.07200 0.06900 0.01950 0.01940 0.01770 0.006460 0.002880 4.000 2.890 0.007650 0.02620 0.01330 0.01560 0.04540 0.03460 0.03800 0.02500 0.02580 0.03700 0.05830 0.05850 0.05690 0.05800 0.04820 0.04880 0.02570 0.02080 0.01660 0.02800 0.02530 0.005980 0.008790 0.005730 0.003350 0.0007160 5.000 2.410 0.007630 0.02540 0.01320 0.01560 0.03890 0.03380 0.03690 0.02500 0.02580 0.02200 0.04540 0.04230 0.05090 0.05120 0.04600 0.04650 0.02200 0.01190 0.01270 0.009840 0.008880 0.005370 0.002860 0.002210 0.001120 0.0005780 6.000 2.080 0.007610 0.02460 0.01320 0.01560 0.03220 0.03270 0.03510 0.02490 0.02570 0.01490 0.03220 0.02760 0.04240 0.04200 0.04230 0.04250 0.01380 0.01150 0.01210 0.005840 0.005810 0.004060 0.001940 0.002040 0.0004800 0.0004580 7.000 1.770 0.007580 0.02360 0.01320 0.01550 0.02590 0.03110 0.03280 0.02470 0.02540 0.01270 0.02110 0.01670 0.03290 0.03190 0.03740 0.03740 0.007140 0.01030 0.009260 0.005600 0.005690 0.002190 0.001880 0.001730 0.0004120 0.0002510 8.000 1.490 0.007550 0.02250 0.01320 0.01540 0.02030 0.02910 0.02990 0.02430 0.02500 0.01250 0.01330 0.01030 0.02380 0.02270 0.03190 0.03170 0.004010 0.007550 0.005800 0.005250 0.005170 0.001070 0.001510 0.001150 0.0004000 0.0001200 10.00 1.100 0.007500 0.02000 0.01300 0.01500 0.01200 0.02400 0.02300 0.02300 0.02400 0.01100 0.006300 0.006100 0.01100 0.009800 0.02100 0.02100 0.003100 0.002800 0.001900 0.003100 0.002900 0.0006200 0.0005900 0.0003600 0.0002500 6.600E-05 15.00 0.5200 0.007200 0.01400 0.01200 0.01400 0.004900 0.01100 0.008600 0.01700 0.01700 0.003200 0.004900 0.004700 0.002400 0.002500 0.006100 0.005800 0.001200 0.001100 0.001100 0.0004600 0.0004400 0.0002600 0.0001900 0.0001900 3.600E-05 2.800E-05 20.00 0.3000 0.006900 0.008400 0.01100 0.01200 0.004600 0.003700 0.002700 0.01100 0.010000 0.001200 0.002300 0.001600 0.002200 0.002300 0.001600 0.001500 0.0003100 0.0006200 0.0004300 0.0003700 0.0003700 6.300E-05 0.0001100 7.200E-05 2.800E-05 6.700E-06 30.00 0.1200 0.006000 0.002400 0.007800 0.007400 0.002100 0.001300 0.001700 0.002900 0.002600 0.0007900 0.0003300 0.0003900 0.0008900 0.0008100 0.0001200 0.0001100 0.0002000 7.800E-05 8.500E-05 0.0001600 0.0001500 4.100E-05 1.300E-05 1.400E-05 1.300E-05 4.400E-06 40.00 0.05700 0.005100 0.0008500 0.004700 0.003800 0.0005000 0.001200 0.001200 0.0007300 0.0006000 0.0001900 0.0002900 0.0003300 0.0002400 0.0002000 1.300E-05 1.100E-05 5.000E-05 6.400E-05 7.200E-05 4.500E-05 3.700E-05 1.000E-05 1.100E-05 1.200E-05 3.500E-06 1.100E-06 60.00 0.01900 0.003300 0.0006500 0.001500 0.0008900 0.0001400 0.0004700 0.0003200 6.100E-05 4.400E-05 3.800E-05 0.0001300 9.000E-05 2.000E-05 1.400E-05 3.600E-07 2.800E-07 9.000E-06 2.900E-05 2.000E-05 3.700E-06 2.700E-06 1.800E-06 5.000E-06 3.300E-06 2.900E-07 1.900E-07 100.0 0.004300 0.001300 0.0003200 0.0002000 6.700E-05 8.100E-05 5.900E-05 2.200E-05 1.500E-06 8.300E-07 2.200E-05 1.600E-05 6.200E-06 4.800E-07 2.600E-07 2.600E-09 1.700E-09 5.300E-06 3.700E-06 1.400E-06 8.900E-08 4.800E-08 1.000E-06 6.200E-07 2.200E-07 6.900E-09 1.100E-07 #S 91 Pa #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 2 2 2 4 1 2 #UBIND 1.126E+05 2.111E+04 2.031E+04 1.673E+04 5367. 5001. 4174. 3611. 3442. 1387. 1224. 1007. 743.0 708.0 371.0 360.0 310.0 223.0 223.0 94.00 94.00 0.000 0.000 0.000 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 14.40 0.007560 0.02710 0.01300 0.01540 0.05930 0.03460 0.03840 0.02470 0.02550 0.1180 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2450 0.1610 0.1770 0.1490 0.1530 0.2070 0.5800 0.4010 0.4590 0.5580 1.930 0.05000 14.30 0.007560 0.02710 0.01300 0.01540 0.05930 0.03460 0.03840 0.02470 0.02550 0.1180 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2440 0.1610 0.1770 0.1490 0.1530 0.2070 0.5780 0.4010 0.4590 0.5580 1.860 0.1000 13.80 0.007560 0.02710 0.01300 0.01540 0.05930 0.03460 0.03840 0.02470 0.02550 0.1180 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2440 0.1610 0.1770 0.1490 0.1530 0.2070 0.5700 0.4010 0.4590 0.5580 1.650 0.1500 13.20 0.007560 0.02710 0.01300 0.01540 0.05930 0.03460 0.03840 0.02470 0.02550 0.1170 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2430 0.1610 0.1770 0.1490 0.1530 0.2070 0.5580 0.4010 0.4590 0.5580 1.360 0.2000 12.50 0.007560 0.02710 0.01300 0.01540 0.05930 0.03460 0.03840 0.02470 0.02550 0.1170 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2410 0.1610 0.1770 0.1490 0.1530 0.2070 0.5420 0.4000 0.4580 0.5570 1.040 0.3000 11.30 0.007560 0.02710 0.01300 0.01540 0.05920 0.03460 0.03840 0.02470 0.02550 0.1170 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2380 0.1610 0.1770 0.1490 0.1530 0.2070 0.4970 0.3970 0.4520 0.5480 0.5010 0.4000 10.50 0.007560 0.02710 0.01300 0.01540 0.05920 0.03460 0.03840 0.02470 0.02550 0.1160 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2320 0.1600 0.1770 0.1490 0.1530 0.2070 0.4420 0.3900 0.4380 0.5230 0.2070 0.5000 10.00 0.007560 0.02710 0.01300 0.01540 0.05910 0.03460 0.03840 0.02470 0.02550 0.1150 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2250 0.1600 0.1770 0.1490 0.1530 0.2060 0.3800 0.3760 0.4150 0.4770 0.09860 0.6000 9.570 0.007560 0.02710 0.01300 0.01540 0.05900 0.03460 0.03840 0.02470 0.02550 0.1140 0.07380 0.08120 0.06000 0.06170 0.04820 0.04890 0.2180 0.1600 0.1760 0.1490 0.1530 0.2060 0.3170 0.3560 0.3820 0.4170 0.07560 0.7000 9.130 0.007560 0.02710 0.01300 0.01540 0.05880 0.03460 0.03840 0.02470 0.02550 0.1130 0.07370 0.08120 0.06000 0.06170 0.04820 0.04890 0.2090 0.1590 0.1750 0.1490 0.1530 0.2040 0.2570 0.3290 0.3410 0.3490 0.07440 0.8000 8.670 0.007560 0.02700 0.01300 0.01540 0.05870 0.03460 0.03840 0.02470 0.02550 0.1120 0.07370 0.08110 0.06000 0.06170 0.04820 0.04890 0.1990 0.1590 0.1740 0.1490 0.1530 0.2020 0.2030 0.2970 0.2950 0.2830 0.07120 1.000 7.740 0.007560 0.02700 0.01300 0.01540 0.05830 0.03460 0.03840 0.02470 0.02550 0.1090 0.07370 0.08100 0.06000 0.06170 0.04820 0.04890 0.1770 0.1560 0.1700 0.1480 0.1520 0.1940 0.1220 0.2260 0.2020 0.1710 0.05030 1.200 6.950 0.007550 0.02700 0.01300 0.01540 0.05790 0.03460 0.03840 0.02470 0.02550 0.1060 0.07360 0.08080 0.06000 0.06170 0.04820 0.04890 0.1540 0.1520 0.1640 0.1470 0.1500 0.1820 0.07480 0.1560 0.1250 0.09610 0.02740 1.400 6.350 0.007550 0.02690 0.01300 0.01540 0.05740 0.03460 0.03840 0.02470 0.02550 0.1020 0.07340 0.08060 0.06000 0.06170 0.04820 0.04890 0.1310 0.1460 0.1550 0.1440 0.1470 0.1670 0.05420 0.1000 0.07180 0.05110 0.01330 1.600 5.900 0.007550 0.02690 0.01300 0.01540 0.05680 0.03460 0.03840 0.02470 0.02550 0.09710 0.07310 0.08010 0.06000 0.06170 0.04820 0.04890 0.1090 0.1380 0.1430 0.1400 0.1430 0.1500 0.04830 0.06130 0.04140 0.02640 0.006980 1.800 5.560 0.007550 0.02680 0.01300 0.01540 0.05610 0.03450 0.03840 0.02470 0.02550 0.09230 0.07270 0.07950 0.05990 0.06160 0.04820 0.04890 0.08900 0.1280 0.1300 0.1340 0.1360 0.1330 0.04770 0.03810 0.02710 0.01410 0.005050 2.000 5.260 0.007550 0.02680 0.01300 0.01540 0.05540 0.03450 0.03840 0.02470 0.02550 0.08720 0.07220 0.07870 0.05990 0.06160 0.04820 0.04890 0.07200 0.1170 0.1160 0.1260 0.1270 0.1160 0.04700 0.02630 0.02200 0.008600 0.004750 2.400 4.700 0.007550 0.02660 0.01300 0.01540 0.05370 0.03450 0.03830 0.02470 0.02550 0.07660 0.07080 0.07650 0.05970 0.06130 0.04820 0.04890 0.04690 0.09330 0.08620 0.1070 0.1060 0.08520 0.03920 0.02020 0.02090 0.005940 0.004470 3.000 3.900 0.007540 0.02640 0.01300 0.01540 0.05080 0.03440 0.03810 0.02470 0.02550 0.06070 0.06730 0.07140 0.05900 0.06050 0.04810 0.04870 0.02920 0.05840 0.04820 0.07370 0.07120 0.05000 0.02040 0.01920 0.01700 0.005670 0.002570 4.000 2.920 0.007520 0.02580 0.01300 0.01540 0.04510 0.03400 0.03760 0.02470 0.02550 0.03770 0.05810 0.05850 0.05620 0.05730 0.04730 0.04790 0.02540 0.02210 0.01740 0.02990 0.02740 0.01810 0.006060 0.009390 0.005870 0.003120 0.0006440 5.000 2.410 0.007500 0.02510 0.01300 0.01540 0.03880 0.03340 0.03650 0.02460 0.02540 0.02260 0.04580 0.04290 0.05060 0.05100 0.04540 0.04580 0.02240 0.01210 0.01260 0.01060 0.009650 0.005990 0.005230 0.003100 0.002160 0.001090 0.0004890 6.000 2.080 0.007480 0.02430 0.01300 0.01540 0.03230 0.03230 0.03480 0.02450 0.02530 0.01510 0.03300 0.02840 0.04260 0.04230 0.04200 0.04230 0.01460 0.01140 0.01220 0.005920 0.005890 0.002280 0.004160 0.001920 0.001920 0.0004500 0.0004060 7.000 1.780 0.007450 0.02330 0.01300 0.01530 0.02610 0.03080 0.03260 0.02430 0.02510 0.01270 0.02200 0.01740 0.03340 0.03250 0.03740 0.03750 0.007770 0.01040 0.009650 0.005550 0.005700 0.001420 0.002360 0.001880 0.001690 0.0003650 0.0002350 8.000 1.510 0.007420 0.02230 0.01290 0.01530 0.02060 0.02890 0.02980 0.02400 0.02470 0.01230 0.01400 0.01070 0.02460 0.02340 0.03220 0.03210 0.004280 0.007950 0.006220 0.005300 0.005290 0.001320 0.001160 0.001570 0.001160 0.0003580 0.0001140 10.00 1.100 0.007300 0.02000 0.01290 0.01500 0.01200 0.02400 0.02300 0.02300 0.02300 0.01100 0.006500 0.006100 0.01100 0.010000 0.02200 0.02200 0.003100 0.003100 0.002100 0.003300 0.003100 0.001200 0.0006100 0.0006600 0.0003700 0.0002400 5.700E-05 15.00 0.5300 0.007100 0.01400 0.01200 0.01400 0.004800 0.01100 0.008900 0.01700 0.01700 0.003400 0.004900 0.004800 0.002400 0.002500 0.006400 0.006200 0.001300 0.001100 0.001200 0.0004900 0.0004600 0.0004900 0.0002800 0.0001900 0.0001800 3.400E-05 2.700E-05 20.00 0.3000 0.006800 0.008500 0.01100 0.01200 0.004500 0.004000 0.002800 0.01100 0.010000 0.001200 0.002400 0.001700 0.002200 0.002300 0.001700 0.001600 0.0003200 0.0006600 0.0004600 0.0003700 0.0003800 0.0001400 6.500E-05 0.0001200 7.400E-05 2.500E-05 6.000E-06 30.00 0.1200 0.006000 0.002500 0.007800 0.007400 0.002200 0.001300 0.001600 0.003000 0.002700 0.0008200 0.0003400 0.0003900 0.0009300 0.0008500 0.0001400 0.0001200 0.0002100 8.200E-05 8.600E-05 0.0001700 0.0001600 1.100E-05 4.200E-05 1.400E-05 1.300E-05 1.200E-05 3.900E-06 40.00 0.05800 0.005000 0.0008700 0.004800 0.003900 0.0005500 0.001200 0.001200 0.0007700 0.0006400 0.0002100 0.0002900 0.0003300 0.0002600 0.0002100 1.500E-05 1.300E-05 5.600E-05 6.400E-05 7.400E-05 4.900E-05 4.100E-05 1.200E-06 1.100E-05 1.100E-05 1.100E-05 3.400E-06 1.000E-06 60.00 0.01900 0.003300 0.0006400 0.001600 0.0009300 0.0001400 0.0004900 0.0003300 6.600E-05 4.800E-05 3.700E-05 0.0001400 9.500E-05 2.200E-05 1.600E-05 4.200E-07 3.200E-07 9.000E-06 3.100E-05 2.200E-05 4.200E-06 3.000E-06 3.500E-08 1.800E-06 5.200E-06 3.300E-06 2.900E-07 1.600E-07 100.0 0.004500 0.001300 0.0003300 0.0002100 7.100E-05 8.300E-05 6.400E-05 2.400E-05 1.700E-06 9.100E-07 2.300E-05 1.800E-05 6.700E-06 5.300E-07 2.900E-07 3.000E-09 1.900E-09 5.500E-06 4.000E-06 1.500E-06 1.000E-07 5.500E-08 2.600E-10 1.100E-06 6.800E-07 2.300E-07 7.000E-09 9.900E-08 #S 92 U #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 3 2 2 4 1 2 #UBIND 1.156E+05 2.176E+04 2.095E+04 1.717E+04 5548. 5181. 4304. 3728. 3552. 1442. 1273. 1045. 780.0 738.0 392.0 381.0 324.0 260.0 195.0 105.0 96.00 0.000 71.00 43.00 33.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 14.30 0.007430 0.02670 0.01280 0.01520 0.05840 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2380 0.1570 0.1730 0.1450 0.1490 0.1930 0.5650 0.3900 0.4500 0.5430 1.910 0.05000 14.20 0.007430 0.02670 0.01280 0.01520 0.05840 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2380 0.1570 0.1730 0.1450 0.1490 0.1930 0.5630 0.3900 0.4500 0.5430 1.840 0.1000 13.70 0.007430 0.02670 0.01280 0.01520 0.05840 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2380 0.1570 0.1730 0.1450 0.1490 0.1930 0.5560 0.3900 0.4500 0.5430 1.640 0.1500 13.20 0.007430 0.02670 0.01280 0.01520 0.05840 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2370 0.1570 0.1730 0.1450 0.1490 0.1930 0.5450 0.3900 0.4500 0.5430 1.350 0.2000 12.50 0.007430 0.02670 0.01280 0.01520 0.05830 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2360 0.1570 0.1730 0.1450 0.1490 0.1930 0.5290 0.3890 0.4490 0.5420 1.040 0.3000 11.30 0.007430 0.02670 0.01280 0.01520 0.05830 0.03400 0.03790 0.02430 0.02520 0.1150 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2320 0.1570 0.1730 0.1450 0.1490 0.1930 0.4880 0.3860 0.4430 0.5350 0.5130 0.4000 10.50 0.007430 0.02670 0.01280 0.01520 0.05820 0.03400 0.03790 0.02430 0.02520 0.1140 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2270 0.1560 0.1730 0.1450 0.1490 0.1930 0.4360 0.3800 0.4310 0.5120 0.2140 0.5000 9.990 0.007430 0.02660 0.01280 0.01520 0.05810 0.03400 0.03790 0.02430 0.02520 0.1130 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2210 0.1560 0.1720 0.1450 0.1490 0.1930 0.3770 0.3680 0.4090 0.4710 0.09980 0.6000 9.580 0.007430 0.02660 0.01280 0.01520 0.05800 0.03400 0.03790 0.02430 0.02520 0.1120 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2130 0.1560 0.1720 0.1450 0.1490 0.1920 0.3170 0.3490 0.3790 0.4150 0.07330 0.7000 9.170 0.007430 0.02660 0.01280 0.01520 0.05790 0.03400 0.03790 0.02430 0.02520 0.1110 0.07240 0.07980 0.05890 0.06060 0.04720 0.04790 0.2050 0.1550 0.1710 0.1450 0.1490 0.1920 0.2600 0.3250 0.3400 0.3510 0.07130 0.8000 8.730 0.007430 0.02660 0.01280 0.01520 0.05780 0.03400 0.03790 0.02430 0.02520 0.1100 0.07230 0.07980 0.05890 0.06060 0.04720 0.04790 0.1950 0.1550 0.1700 0.1450 0.1490 0.1900 0.2070 0.2960 0.2970 0.2880 0.06910 1.000 7.840 0.007430 0.02660 0.01280 0.01520 0.05740 0.03400 0.03790 0.02430 0.02520 0.1070 0.07230 0.07970 0.05890 0.06060 0.04720 0.04790 0.1750 0.1520 0.1660 0.1450 0.1480 0.1850 0.1260 0.2290 0.2070 0.1790 0.05060 1.200 7.060 0.007430 0.02650 0.01280 0.01520 0.05700 0.03400 0.03790 0.02430 0.02520 0.1040 0.07220 0.07950 0.05890 0.06060 0.04720 0.04790 0.1530 0.1490 0.1610 0.1430 0.1470 0.1760 0.07780 0.1620 0.1300 0.1030 0.02850 1.400 6.450 0.007420 0.02650 0.01280 0.01520 0.05650 0.03400 0.03790 0.02430 0.02520 0.1000 0.07200 0.07920 0.05890 0.06060 0.04720 0.04790 0.1310 0.1430 0.1520 0.1410 0.1440 0.1640 0.05510 0.1060 0.07630 0.05630 0.01410 1.600 6.000 0.007420 0.02650 0.01280 0.01520 0.05590 0.03400 0.03790 0.02430 0.02520 0.09590 0.07180 0.07880 0.05890 0.06060 0.04720 0.04790 0.1100 0.1360 0.1420 0.1370 0.1400 0.1500 0.04770 0.06640 0.04420 0.02980 0.007300 1.800 5.640 0.007420 0.02640 0.01280 0.01520 0.05530 0.03400 0.03790 0.02430 0.02520 0.09130 0.07140 0.07830 0.05890 0.06050 0.04720 0.04790 0.09060 0.1270 0.1300 0.1320 0.1340 0.1350 0.04670 0.04140 0.02820 0.01600 0.004970 2.000 5.340 0.007420 0.02630 0.01280 0.01520 0.05460 0.03400 0.03790 0.02430 0.02520 0.08650 0.07100 0.07760 0.05880 0.06050 0.04720 0.04790 0.07370 0.1170 0.1160 0.1250 0.1260 0.1200 0.04630 0.02790 0.02200 0.009490 0.004520 2.400 4.780 0.007420 0.02620 0.01280 0.01520 0.05300 0.03390 0.03780 0.02430 0.02520 0.07640 0.06960 0.07550 0.05860 0.06030 0.04720 0.04780 0.04850 0.09440 0.08790 0.1070 0.1070 0.09070 0.04000 0.02010 0.02040 0.005950 0.004340 3.000 3.970 0.007410 0.02600 0.01280 0.01520 0.05030 0.03380 0.03760 0.02430 0.02520 0.06090 0.06640 0.07070 0.05800 0.05960 0.04700 0.04770 0.02980 0.06050 0.05040 0.07570 0.07350 0.05560 0.02200 0.01920 0.01730 0.005680 0.002670 4.000 2.950 0.007400 0.02540 0.01280 0.01520 0.04480 0.03350 0.03710 0.02430 0.02520 0.03840 0.05780 0.05850 0.05550 0.05660 0.04640 0.04700 0.02510 0.02360 0.01840 0.03220 0.02970 0.02140 0.006400 0.01030 0.006430 0.003390 0.0006790 5.000 2.420 0.007380 0.02480 0.01280 0.01520 0.03860 0.03290 0.03610 0.02430 0.02510 0.02320 0.04610 0.04340 0.05030 0.05080 0.04470 0.04520 0.02270 0.01230 0.01260 0.01170 0.01060 0.007440 0.005220 0.003480 0.002250 0.001250 0.0004710 6.000 2.080 0.007360 0.02400 0.01280 0.01520 0.03240 0.03190 0.03450 0.02420 0.02500 0.01540 0.03370 0.02910 0.04280 0.04250 0.04170 0.04200 0.01540 0.01130 0.01230 0.006120 0.006040 0.002790 0.004360 0.001970 0.001910 0.0004940 0.0004100 7.000 1.790 0.007330 0.02310 0.01280 0.01510 0.02640 0.03050 0.03240 0.02400 0.02480 0.01260 0.02280 0.01810 0.03400 0.03310 0.03740 0.03750 0.008430 0.01060 0.010000 0.005560 0.005720 0.001600 0.002590 0.001900 0.001730 0.0003730 0.0002500 8.000 1.520 0.007300 0.02210 0.01270 0.01510 0.02090 0.02870 0.02970 0.02370 0.02440 0.01220 0.01460 0.01120 0.02530 0.02420 0.03250 0.03240 0.004590 0.008340 0.006670 0.005390 0.005420 0.001430 0.001300 0.001650 0.001240 0.0003670 0.0001240 10.00 1.100 0.007200 0.02000 0.01270 0.01500 0.01300 0.02400 0.02300 0.02300 0.02300 0.01100 0.006700 0.006200 0.01200 0.01100 0.02200 0.02200 0.003100 0.003400 0.002200 0.003500 0.003300 0.001400 0.0006200 0.0007400 0.0004100 0.0002600 5.600E-05 15.00 0.5400 0.007000 0.01400 0.01200 0.01400 0.004800 0.01200 0.009200 0.01700 0.01700 0.003600 0.004900 0.004900 0.002500 0.002500 0.006800 0.006500 0.001400 0.001100 0.001200 0.0005300 0.0004900 0.0005700 0.0003100 0.0001900 0.0001900 3.800E-05 2.800E-05 20.00 0.3100 0.006700 0.008700 0.01100 0.01200 0.004500 0.004200 0.002900 0.01100 0.01100 0.001200 0.002600 0.001800 0.002200 0.002300 0.001800 0.001700 0.0003400 0.0007100 0.0005000 0.0003800 0.0003900 0.0001700 6.900E-05 0.0001300 8.000E-05 2.600E-05 6.200E-06 30.00 0.1200 0.005900 0.002600 0.007900 0.007500 0.002300 0.001300 0.001600 0.003100 0.002800 0.0008500 0.0003500 0.0003900 0.0009800 0.0008900 0.0001500 0.0001300 0.0002200 8.800E-05 8.700E-05 0.0001900 0.0001700 1.400E-05 4.500E-05 1.500E-05 1.300E-05 1.300E-05 4.000E-06 40.00 0.06000 0.005000 0.0008900 0.004900 0.004000 0.0005900 0.001200 0.001200 0.0008200 0.0006800 0.0002300 0.0002900 0.0003400 0.0002700 0.0002300 1.700E-05 1.400E-05 6.200E-05 6.400E-05 7.700E-05 5.400E-05 4.500E-05 1.600E-06 1.200E-05 1.100E-05 1.200E-05 3.800E-06 1.100E-06 60.00 0.02000 0.003300 0.0006300 0.001700 0.0009700 0.0001400 0.0005100 0.0003500 7.200E-05 5.100E-05 3.700E-05 0.0001400 1.000E-04 2.400E-05 1.700E-05 4.800E-07 3.700E-07 9.100E-06 3.300E-05 2.300E-05 4.700E-06 3.400E-06 4.500E-08 1.800E-06 5.600E-06 3.500E-06 3.300E-07 1.600E-07 100.0 0.004600 0.001300 0.0003300 0.0002300 7.600E-05 8.500E-05 6.900E-05 2.600E-05 1.900E-06 1.000E-06 2.300E-05 1.900E-05 7.300E-06 6.000E-07 3.200E-07 3.500E-09 2.200E-09 5.700E-06 4.400E-06 1.700E-06 1.200E-07 6.300E-08 3.400E-10 1.100E-06 7.600E-07 2.600E-07 8.100E-09 1.000E-07 #S 93 Np #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 4 2 2 4 1 2 #UBIND 1.187E+05 2.242E+04 2.160E+04 1.761E+04 5722. 5366. 4435. 3850. 3664. 1501. 1328. 1087. 817.0 773.0 415.0 404.0 338.0 283.0 206.0 109.0 101.0 0.000 0.000 0.000 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 14.20 0.007300 0.02620 0.01260 0.01500 0.05750 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2330 0.1530 0.1690 0.1420 0.1450 0.1820 0.5520 0.3800 0.4420 0.5320 1.890 0.05000 14.10 0.007300 0.02620 0.01260 0.01500 0.05750 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2330 0.1530 0.1690 0.1420 0.1450 0.1820 0.5500 0.3800 0.4420 0.5320 1.820 0.1000 13.70 0.007300 0.02620 0.01260 0.01500 0.05740 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2320 0.1530 0.1690 0.1420 0.1450 0.1820 0.5430 0.3800 0.4420 0.5320 1.630 0.1500 13.10 0.007300 0.02620 0.01260 0.01500 0.05740 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2310 0.1530 0.1690 0.1420 0.1450 0.1820 0.5330 0.3790 0.4420 0.5320 1.350 0.2000 12.40 0.007300 0.02620 0.01260 0.01500 0.05740 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2300 0.1530 0.1690 0.1420 0.1450 0.1820 0.5180 0.3790 0.4410 0.5310 1.050 0.3000 11.30 0.007300 0.02620 0.01260 0.01500 0.05740 0.03340 0.03740 0.02400 0.02480 0.1130 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2270 0.1530 0.1690 0.1420 0.1450 0.1820 0.4800 0.3760 0.4360 0.5240 0.5240 0.4000 10.50 0.007300 0.02620 0.01260 0.01500 0.05730 0.03340 0.03740 0.02400 0.02480 0.1120 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2220 0.1530 0.1690 0.1420 0.1450 0.1820 0.4300 0.3700 0.4240 0.5030 0.2210 0.5000 9.980 0.007300 0.02620 0.01260 0.01500 0.05720 0.03340 0.03740 0.02400 0.02480 0.1110 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2160 0.1530 0.1690 0.1420 0.1450 0.1820 0.3750 0.3600 0.4040 0.4650 0.1010 0.6000 9.590 0.007300 0.02620 0.01260 0.01500 0.05710 0.03340 0.03740 0.02400 0.02480 0.1100 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2090 0.1520 0.1680 0.1420 0.1450 0.1820 0.3180 0.3430 0.3750 0.4120 0.07140 0.7000 9.200 0.007300 0.02620 0.01260 0.01500 0.05700 0.03340 0.03740 0.02400 0.02480 0.1090 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.2010 0.1520 0.1670 0.1420 0.1450 0.1810 0.2620 0.3210 0.3390 0.3520 0.06860 0.8000 8.780 0.007300 0.02620 0.01260 0.01500 0.05690 0.03340 0.03740 0.02400 0.02480 0.1080 0.07100 0.07850 0.05790 0.05960 0.04620 0.04690 0.1920 0.1510 0.1660 0.1420 0.1450 0.1800 0.2110 0.2940 0.2980 0.2910 0.06710 1.000 7.920 0.007300 0.02620 0.01260 0.01500 0.05650 0.03340 0.03740 0.02400 0.02480 0.1060 0.07090 0.07840 0.05790 0.05960 0.04620 0.04690 0.1730 0.1490 0.1630 0.1410 0.1450 0.1760 0.1310 0.2310 0.2110 0.1850 0.05070 1.200 7.160 0.007300 0.02610 0.01260 0.01500 0.05610 0.03340 0.03740 0.02400 0.02480 0.1020 0.07080 0.07820 0.05790 0.05950 0.04620 0.04690 0.1520 0.1460 0.1580 0.1400 0.1430 0.1700 0.08080 0.1670 0.1350 0.1090 0.02960 1.400 6.550 0.007300 0.02610 0.01260 0.01500 0.05570 0.03340 0.03740 0.02400 0.02480 0.09880 0.07070 0.07800 0.05790 0.05950 0.04620 0.04690 0.1310 0.1410 0.1500 0.1380 0.1410 0.1600 0.05610 0.1120 0.08040 0.06080 0.01500 1.600 6.090 0.007300 0.02600 0.01260 0.01500 0.05510 0.03340 0.03740 0.02400 0.02480 0.09480 0.07050 0.07760 0.05780 0.05950 0.04620 0.04690 0.1110 0.1340 0.1410 0.1350 0.1370 0.1490 0.04730 0.07130 0.04680 0.03290 0.007660 1.800 5.720 0.007300 0.02600 0.01260 0.01500 0.05450 0.03340 0.03740 0.02400 0.02480 0.09040 0.07010 0.07710 0.05780 0.05950 0.04620 0.04690 0.09190 0.1260 0.1290 0.1300 0.1320 0.1360 0.04560 0.04480 0.02940 0.01780 0.004960 2.000 5.420 0.007290 0.02590 0.01260 0.01500 0.05390 0.03340 0.03740 0.02400 0.02480 0.08580 0.06970 0.07640 0.05780 0.05940 0.04620 0.04690 0.07540 0.1170 0.1170 0.1240 0.1250 0.1220 0.04550 0.02980 0.02220 0.01040 0.004330 2.400 4.860 0.007290 0.02580 0.01260 0.01500 0.05230 0.03340 0.03730 0.02400 0.02480 0.07600 0.06850 0.07450 0.05760 0.05930 0.04620 0.04690 0.05000 0.09520 0.08930 0.1070 0.1070 0.09480 0.04040 0.02000 0.01980 0.005960 0.004190 3.000 4.040 0.007280 0.02560 0.01260 0.01500 0.04970 0.03330 0.03710 0.02400 0.02480 0.06110 0.06550 0.07000 0.05710 0.05860 0.04610 0.04680 0.03040 0.06250 0.05260 0.07750 0.07560 0.06040 0.02350 0.01910 0.01750 0.005610 0.002750 4.000 2.990 0.007270 0.02500 0.01260 0.01500 0.04440 0.03300 0.03660 0.02400 0.02480 0.03910 0.05750 0.05840 0.05480 0.05590 0.04550 0.04620 0.02480 0.02520 0.01930 0.03440 0.03190 0.02460 0.006800 0.01110 0.006950 0.003590 0.0007200 5.000 2.420 0.007250 0.02440 0.01260 0.01500 0.03850 0.03240 0.03570 0.02400 0.02480 0.02380 0.04640 0.04390 0.05000 0.05050 0.04400 0.04450 0.02290 0.01260 0.01260 0.01280 0.01160 0.008940 0.005170 0.003890 0.002370 0.001400 0.0004530 6.000 2.080 0.007230 0.02360 0.01260 0.01500 0.03240 0.03150 0.03420 0.02390 0.02470 0.01570 0.03430 0.02990 0.04290 0.04270 0.04130 0.04160 0.01610 0.01120 0.01230 0.006360 0.006230 0.003360 0.004520 0.002020 0.001880 0.0005410 0.0004110 7.000 1.800 0.007210 0.02280 0.01250 0.01490 0.02660 0.03010 0.03210 0.02370 0.02450 0.01260 0.02360 0.01880 0.03440 0.03370 0.03740 0.03750 0.009110 0.01070 0.01030 0.005550 0.005730 0.001790 0.002820 0.001910 0.001760 0.0003780 0.0002640 8.000 1.540 0.007180 0.02180 0.01250 0.01490 0.02120 0.02840 0.02960 0.02340 0.02420 0.01200 0.01540 0.01170 0.02600 0.02490 0.03270 0.03260 0.004940 0.008690 0.007100 0.005450 0.005520 0.001510 0.001450 0.001720 0.001310 0.0003710 0.0001350 10.00 1.100 0.007100 0.02000 0.01200 0.01490 0.01300 0.02400 0.02400 0.02200 0.02300 0.01100 0.006900 0.006200 0.01300 0.01200 0.02300 0.02300 0.003100 0.003800 0.002400 0.003800 0.003500 0.001500 0.0006300 0.0008200 0.0004500 0.0002800 5.500E-05 15.00 0.5500 0.006900 0.01400 0.01200 0.01400 0.004900 0.01200 0.009400 0.01700 0.01700 0.003900 0.004900 0.004900 0.002500 0.002500 0.007200 0.006900 0.001500 0.001100 0.001200 0.0005700 0.0005200 0.0006500 0.0003400 0.0001900 0.0001900 4.200E-05 3.000E-05 20.00 0.3100 0.006600 0.008800 0.01100 0.01200 0.004400 0.004400 0.003000 0.01100 0.01100 0.001300 0.002700 0.001900 0.002200 0.002300 0.002000 0.001900 0.0003500 0.0007500 0.0005400 0.0003800 0.0004000 0.0002000 7.400E-05 0.0001400 8.600E-05 2.600E-05 6.400E-06 30.00 0.1300 0.005800 0.002800 0.007900 0.007600 0.002400 0.001200 0.001600 0.003300 0.002900 0.0008800 0.0003700 0.0003900 0.001000 0.0009300 0.0001700 0.0001500 0.0002300 9.400E-05 8.900E-05 0.0002000 0.0001800 1.700E-05 4.700E-05 1.700E-05 1.300E-05 1.400E-05 4.100E-06 40.00 0.06100 0.005000 0.0009200 0.005000 0.004100 0.0006400 0.001200 0.001300 0.0008700 0.0007200 0.0002500 0.0002800 0.0003400 0.0002900 0.0002500 1.900E-05 1.600E-05 6.800E-05 6.400E-05 7.900E-05 5.800E-05 4.900E-05 1.900E-06 1.400E-05 1.100E-05 1.200E-05 4.100E-06 1.200E-06 60.00 0.02000 0.003300 0.0006200 0.001700 0.001000 0.0001400 0.0005300 0.0003600 7.800E-05 5.600E-05 3.700E-05 0.0001500 0.0001100 2.600E-05 1.900E-05 5.500E-07 4.300E-07 9.200E-06 3.400E-05 2.500E-05 5.200E-06 3.800E-06 5.600E-08 1.800E-06 6.000E-06 3.800E-06 3.700E-07 1.600E-07 100.0 0.004800 0.001300 0.0003400 0.0002400 8.100E-05 8.700E-05 7.400E-05 2.700E-05 2.100E-06 1.100E-06 2.400E-05 2.100E-05 7.900E-06 6.700E-07 3.600E-07 4.100E-09 2.600E-09 6.000E-06 4.900E-06 1.800E-06 1.300E-07 7.100E-08 4.300E-10 1.200E-06 8.500E-07 2.800E-07 9.300E-09 1.000E-07 #S 94 Pu #N 28 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 2 2 4 2 #UBIND 1.218E+05 2.310E+04 2.227E+04 1.806E+04 5933. 5546. 4562. 3973. 3778. 1558. 1377. 1120. 849.0 801.0 422.0 422.0 352.0 279.0 212.0 116.0 105.0 0.000 0.000 0.000 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 0.000 14.20 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2280 0.1500 0.1660 0.1390 0.1420 0.1830 0.5500 0.3760 0.4490 1.990 0.05000 14.00 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2270 0.1500 0.1660 0.1390 0.1420 0.1830 0.5480 0.3760 0.4490 1.910 0.1000 13.60 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2270 0.1500 0.1660 0.1390 0.1420 0.1830 0.5420 0.3760 0.4490 1.690 0.1500 12.90 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2260 0.1500 0.1660 0.1390 0.1420 0.1830 0.5310 0.3750 0.4490 1.390 0.2000 12.20 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2250 0.1500 0.1660 0.1390 0.1420 0.1830 0.5170 0.3750 0.4480 1.050 0.3000 11.00 0.007180 0.02580 0.01240 0.01490 0.05650 0.03290 0.03690 0.02370 0.02450 0.1110 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2220 0.1500 0.1660 0.1390 0.1420 0.1830 0.4790 0.3730 0.4420 0.4990 0.4000 10.20 0.007180 0.02580 0.01240 0.01490 0.05640 0.03290 0.03690 0.02370 0.02450 0.1100 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2170 0.1490 0.1660 0.1390 0.1420 0.1830 0.4300 0.3670 0.4300 0.1980 0.5000 9.770 0.007180 0.02580 0.01240 0.01490 0.05630 0.03290 0.03690 0.02370 0.02450 0.1090 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2120 0.1490 0.1650 0.1390 0.1420 0.1830 0.3760 0.3570 0.4090 0.08890 0.6000 9.440 0.007180 0.02580 0.01240 0.01490 0.05620 0.03290 0.03690 0.02370 0.02450 0.1090 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.2050 0.1490 0.1650 0.1390 0.1420 0.1820 0.3190 0.3410 0.3780 0.06470 0.7000 9.100 0.007180 0.02580 0.01240 0.01490 0.05610 0.03290 0.03690 0.02370 0.02450 0.1080 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.1980 0.1490 0.1640 0.1390 0.1420 0.1810 0.2640 0.3200 0.3400 0.06310 0.8000 8.740 0.007180 0.02580 0.01240 0.01490 0.05600 0.03290 0.03690 0.02370 0.02450 0.1070 0.06970 0.07720 0.05690 0.05850 0.04520 0.04600 0.1900 0.1480 0.1630 0.1390 0.1420 0.1800 0.2130 0.2940 0.2970 0.06100 1.000 7.980 0.007180 0.02570 0.01240 0.01490 0.05570 0.03290 0.03690 0.02370 0.02450 0.1040 0.06960 0.07710 0.05690 0.05850 0.04520 0.04600 0.1710 0.1460 0.1600 0.1390 0.1420 0.1750 0.1320 0.2330 0.2090 0.04440 1.200 7.280 0.007170 0.02570 0.01240 0.01490 0.05530 0.03290 0.03690 0.02370 0.02450 0.1010 0.06950 0.07700 0.05690 0.05850 0.04520 0.04600 0.1510 0.1430 0.1550 0.1380 0.1410 0.1680 0.08140 0.1690 0.1330 0.02510 1.400 6.710 0.007170 0.02570 0.01240 0.01490 0.05480 0.03290 0.03690 0.02370 0.02450 0.09750 0.06940 0.07670 0.05690 0.05850 0.04520 0.04600 0.1310 0.1380 0.1480 0.1360 0.1380 0.1580 0.05570 0.1150 0.07880 0.01250 1.600 6.260 0.007170 0.02560 0.01240 0.01490 0.05430 0.03290 0.03690 0.02370 0.02450 0.09360 0.06920 0.07640 0.05680 0.05850 0.04520 0.04590 0.1110 0.1320 0.1390 0.1330 0.1350 0.1460 0.04600 0.07350 0.04550 0.006350 1.800 5.900 0.007170 0.02560 0.01240 0.01490 0.05370 0.03290 0.03690 0.02370 0.02450 0.08940 0.06890 0.07590 0.05680 0.05850 0.04520 0.04590 0.09310 0.1250 0.1290 0.1280 0.1300 0.1330 0.04400 0.04630 0.02820 0.004090 2.000 5.580 0.007170 0.02550 0.01240 0.01490 0.05310 0.03290 0.03690 0.02370 0.02450 0.08500 0.06850 0.07530 0.05680 0.05840 0.04520 0.04590 0.07680 0.1160 0.1170 0.1220 0.1240 0.1200 0.04390 0.03050 0.02090 0.003550 2.400 4.990 0.007170 0.02540 0.01240 0.01480 0.05170 0.03280 0.03680 0.02370 0.02450 0.07570 0.06740 0.07350 0.05660 0.05830 0.04520 0.04590 0.05140 0.09570 0.09050 0.1070 0.1070 0.09390 0.03970 0.01960 0.01840 0.003440 3.000 4.140 0.007160 0.02520 0.01240 0.01480 0.04910 0.03270 0.03670 0.02370 0.02450 0.06130 0.06470 0.06930 0.05620 0.05770 0.04510 0.04580 0.03090 0.06420 0.05440 0.07850 0.07700 0.06070 0.02400 0.01850 0.01640 0.002330 4.000 3.040 0.007150 0.02470 0.01240 0.01480 0.04410 0.03250 0.03620 0.02370 0.02450 0.03970 0.05710 0.05830 0.05400 0.05530 0.04470 0.04530 0.02440 0.02660 0.02030 0.03620 0.03380 0.02570 0.007000 0.01140 0.006880 0.0006310 5.000 2.440 0.007130 0.02410 0.01230 0.01480 0.03840 0.03190 0.03530 0.02360 0.02450 0.02440 0.04660 0.04440 0.04960 0.05020 0.04330 0.04390 0.02300 0.01290 0.01260 0.01370 0.01250 0.009710 0.004980 0.004180 0.002320 0.0003700 6.000 2.090 0.007110 0.02330 0.01230 0.01480 0.03250 0.03110 0.03390 0.02350 0.02440 0.01600 0.03500 0.03060 0.04290 0.04280 0.04090 0.04120 0.01680 0.01110 0.01220 0.006580 0.006400 0.003680 0.004500 0.002030 0.001740 0.0003440 7.000 1.810 0.007090 0.02250 0.01230 0.01480 0.02680 0.02980 0.03190 0.02340 0.02420 0.01260 0.02440 0.01950 0.03490 0.03410 0.03730 0.03740 0.009790 0.01070 0.01060 0.005500 0.005700 0.001850 0.002950 0.001860 0.001660 0.0002320 8.000 1.550 0.007060 0.02160 0.01230 0.01470 0.02150 0.02820 0.02950 0.02310 0.02390 0.01180 0.01610 0.01220 0.02660 0.02550 0.03290 0.03280 0.005320 0.008970 0.007480 0.005430 0.005560 0.001470 0.001560 0.001730 0.001270 0.0001230 10.00 1.100 0.007000 0.02000 0.01200 0.01470 0.01300 0.02400 0.02400 0.02200 0.02300 0.01100 0.007200 0.006300 0.01300 0.01200 0.02400 0.02300 0.003100 0.004100 0.002600 0.003900 0.003700 0.001400 0.0006300 0.0008800 0.0004600 4.700E-05 15.00 0.5600 0.006800 0.01400 0.01200 0.01400 0.004900 0.01200 0.009700 0.01700 0.01700 0.004100 0.004800 0.005000 0.002600 0.002500 0.007600 0.007300 0.001600 0.001100 0.001200 0.0006100 0.0005600 0.0006700 0.0003600 0.0001900 0.0001800 2.700E-05 20.00 0.3200 0.006500 0.009000 0.01100 0.01200 0.004300 0.004600 0.003200 0.01100 0.01100 0.001300 0.002800 0.002000 0.002200 0.002300 0.002200 0.002000 0.0003700 0.0007900 0.0005800 0.0003800 0.0004000 0.0002100 7.700E-05 0.0001400 8.600E-05 5.700E-06 30.00 0.1300 0.005800 0.002900 0.007900 0.007600 0.002500 0.001200 0.001600 0.003400 0.003000 0.0009000 0.0003800 0.0003900 0.001100 0.0009800 0.0001900 0.0001700 0.0002400 1.000E-04 9.000E-05 0.0002100 0.0001900 1.900E-05 4.800E-05 1.800E-05 1.300E-05 3.500E-06 40.00 0.06300 0.004900 0.0009600 0.005100 0.004200 0.0006900 0.001100 0.001300 0.0009300 0.0007700 0.0002700 0.0002800 0.0003500 0.0003100 0.0002600 2.100E-05 1.800E-05 7.500E-05 6.400E-05 8.000E-05 6.300E-05 5.300E-05 2.100E-06 1.500E-05 1.100E-05 1.100E-05 1.100E-06 60.00 0.02100 0.003400 0.0006100 0.001800 0.001100 0.0001400 0.0005500 0.0003800 8.400E-05 6.000E-05 3.700E-05 0.0001500 0.0001100 2.900E-05 2.100E-05 6.300E-07 4.900E-07 9.300E-06 3.600E-05 2.600E-05 5.800E-06 4.200E-06 6.400E-08 1.800E-06 6.200E-06 3.700E-06 1.300E-07 100.0 0.005000 0.001400 0.0003500 0.0002600 8.600E-05 8.900E-05 7.900E-05 2.900E-05 2.300E-06 1.200E-06 2.500E-05 2.200E-05 8.500E-06 7.400E-07 4.000E-07 4.700E-09 3.000E-09 6.200E-06 5.300E-06 2.000E-06 1.500E-07 8.000E-08 5.000E-10 1.200E-06 9.100E-07 2.800E-07 8.800E-08 #S 95 Am #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 1 2 2 4 2 #UBIND 1.250E+05 2.377E+04 2.294E+04 1.850E+04 6120. 5710. 4667. 4092. 3887. 1617. 1412. 1136. 883.0 832.0 464.0 449.0 351.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 14.10 0.007050 0.02540 0.01210 0.01470 0.05570 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2220 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.5380 0.3670 0.4410 1.970 0.05000 13.90 0.007050 0.02540 0.01210 0.01470 0.05570 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2220 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.5360 0.3670 0.4410 1.890 0.1000 13.50 0.007050 0.02540 0.01210 0.01470 0.05560 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2220 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.5300 0.3670 0.4410 1.680 0.1500 12.90 0.007050 0.02540 0.01210 0.01470 0.05560 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2210 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.5200 0.3670 0.4410 1.380 0.2000 12.20 0.007050 0.02540 0.01210 0.01470 0.05560 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2200 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.5070 0.3660 0.4400 1.060 0.3000 11.00 0.007050 0.02540 0.01210 0.01470 0.05560 0.03240 0.03640 0.02330 0.02420 0.1090 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2170 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.4710 0.3640 0.4350 0.5100 0.4000 10.20 0.007050 0.02540 0.01210 0.01470 0.05550 0.03240 0.03640 0.02330 0.02420 0.1080 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2130 0.1460 0.1620 0.1360 0.1390 0.1740 0.1870 0.4250 0.3590 0.4230 0.2060 0.5000 9.760 0.007050 0.02540 0.01210 0.01470 0.05540 0.03240 0.03640 0.02330 0.02420 0.1070 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2080 0.1460 0.1620 0.1360 0.1390 0.1740 0.1860 0.3730 0.3500 0.4030 0.09070 0.6000 9.440 0.007050 0.02540 0.01210 0.01470 0.05540 0.03240 0.03640 0.02330 0.02420 0.1070 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.2010 0.1460 0.1620 0.1360 0.1390 0.1730 0.1860 0.3190 0.3360 0.3750 0.06320 0.7000 9.120 0.007050 0.02540 0.01210 0.01470 0.05520 0.03240 0.03640 0.02330 0.02420 0.1060 0.06840 0.07600 0.05590 0.05760 0.04430 0.04500 0.1940 0.1450 0.1610 0.1360 0.1390 0.1730 0.1850 0.2660 0.3160 0.3390 0.06090 0.8000 8.770 0.007050 0.02540 0.01210 0.01470 0.05510 0.03240 0.03640 0.02330 0.02420 0.1050 0.06840 0.07590 0.05590 0.05760 0.04430 0.04500 0.1870 0.1450 0.1600 0.1360 0.1390 0.1720 0.1830 0.2160 0.2920 0.2980 0.05940 1.000 8.040 0.007050 0.02530 0.01210 0.01470 0.05480 0.03230 0.03640 0.02330 0.02420 0.1020 0.06840 0.07590 0.05590 0.05760 0.04430 0.04500 0.1690 0.1430 0.1570 0.1350 0.1390 0.1680 0.1770 0.1360 0.2340 0.2130 0.04460 1.200 7.360 0.007050 0.02530 0.01210 0.01470 0.05450 0.03230 0.03640 0.02330 0.02420 0.09940 0.06830 0.07570 0.05590 0.05760 0.04430 0.04500 0.1500 0.1400 0.1530 0.1340 0.1380 0.1630 0.1680 0.08460 0.1740 0.1380 0.02600 1.400 6.790 0.007050 0.02530 0.01210 0.01470 0.05400 0.03230 0.03640 0.02330 0.02420 0.09610 0.06810 0.07550 0.05590 0.05760 0.04430 0.04500 0.1310 0.1360 0.1460 0.1330 0.1360 0.1540 0.1570 0.05710 0.1200 0.08290 0.01330 1.600 6.340 0.007050 0.02520 0.01210 0.01470 0.05350 0.03230 0.03640 0.02330 0.02420 0.09250 0.06800 0.07520 0.05590 0.05750 0.04430 0.04500 0.1120 0.1300 0.1380 0.1300 0.1330 0.1440 0.1440 0.04600 0.07810 0.04840 0.006720 1.800 5.970 0.007050 0.02520 0.01210 0.01470 0.05300 0.03230 0.03640 0.02330 0.02420 0.08850 0.06770 0.07480 0.05580 0.05750 0.04430 0.04500 0.09420 0.1230 0.1280 0.1260 0.1280 0.1330 0.1310 0.04320 0.04970 0.02970 0.004140 2.000 5.650 0.007050 0.02510 0.01210 0.01470 0.05240 0.03230 0.03640 0.02330 0.02420 0.08430 0.06730 0.07420 0.05580 0.05750 0.04430 0.04500 0.07820 0.1150 0.1170 0.1210 0.1220 0.1210 0.1170 0.04300 0.03260 0.02130 0.003430 2.400 5.070 0.007040 0.02500 0.01210 0.01470 0.05100 0.03230 0.03630 0.02330 0.02420 0.07530 0.06630 0.07250 0.05570 0.05730 0.04430 0.04500 0.05290 0.09620 0.09160 0.1070 0.1070 0.09680 0.09170 0.03980 0.01970 0.01800 0.003320 3.000 4.220 0.007040 0.02480 0.01210 0.01470 0.04860 0.03220 0.03620 0.02330 0.02420 0.06140 0.06380 0.06850 0.05530 0.05680 0.04430 0.04500 0.03160 0.06600 0.05630 0.08000 0.07850 0.06460 0.05960 0.02530 0.01820 0.01650 0.002370 4.000 3.090 0.007030 0.02430 0.01210 0.01470 0.04370 0.03200 0.03570 0.02330 0.02420 0.04030 0.05670 0.05820 0.05330 0.05460 0.04380 0.04450 0.02410 0.02830 0.02130 0.03840 0.03590 0.02870 0.02570 0.007530 0.01200 0.007380 0.0006750 5.000 2.450 0.007010 0.02370 0.01210 0.01460 0.03820 0.03150 0.03490 0.02330 0.02420 0.02500 0.04670 0.04470 0.04920 0.04990 0.04270 0.04330 0.02300 0.01340 0.01260 0.01500 0.01360 0.01130 0.009910 0.004940 0.004630 0.002470 0.0003590 6.000 2.090 0.006990 0.02300 0.01210 0.01460 0.03250 0.03060 0.03350 0.02320 0.02410 0.01630 0.03550 0.03130 0.04300 0.04290 0.04050 0.04080 0.01740 0.01100 0.01220 0.006940 0.006650 0.004370 0.003790 0.004590 0.002110 0.001730 0.0003400 7.000 1.820 0.006970 0.02220 0.01210 0.01460 0.02690 0.02950 0.03170 0.02310 0.02390 0.01260 0.02510 0.02020 0.03520 0.03460 0.03710 0.03730 0.01050 0.01080 0.01080 0.005520 0.005690 0.002100 0.001880 0.003160 0.001850 0.001670 0.0002420 8.000 1.560 0.006940 0.02140 0.01210 0.01450 0.02180 0.02800 0.02940 0.02290 0.02360 0.01170 0.01680 0.01270 0.02720 0.02620 0.03300 0.03300 0.005740 0.009250 0.007870 0.005460 0.005600 0.001560 0.001460 0.001720 0.001760 0.001330 0.0001330 10.00 1.100 0.006900 0.01900 0.01200 0.01400 0.01400 0.02400 0.02400 0.02200 0.02300 0.01100 0.007500 0.006400 0.01400 0.01300 0.02400 0.02400 0.003100 0.004400 0.002900 0.004100 0.003900 0.001500 0.001400 0.0006500 0.0009600 0.0005100 4.700E-05 15.00 0.5700 0.006700 0.01400 0.01200 0.01300 0.004900 0.01300 0.010000 0.01700 0.01700 0.004400 0.004800 0.005100 0.002600 0.002600 0.008000 0.007700 0.001800 0.001100 0.001300 0.0006600 0.0006000 0.0007500 0.0006800 0.0003800 0.0001900 0.0001800 2.800E-05 20.00 0.3300 0.006400 0.009100 0.01100 0.01200 0.004300 0.004900 0.003300 0.01100 0.01100 0.001300 0.002900 0.002100 0.002200 0.002300 0.002300 0.002200 0.0004000 0.0008300 0.0006200 0.0003900 0.0004100 0.0002400 0.0002100 8.300E-05 0.0001500 9.200E-05 6.100E-06 30.00 0.1300 0.005700 0.003000 0.007900 0.007700 0.002600 0.001200 0.001600 0.003500 0.003200 0.0009300 0.0004100 0.0003900 0.001100 0.001000 0.0002100 0.0001800 0.0002500 0.0001100 9.200E-05 0.0002200 0.0002100 2.200E-05 1.900E-05 4.900E-05 2.000E-05 1.300E-05 3.500E-06 40.00 0.06400 0.004900 0.0009900 0.005200 0.004200 0.0007500 0.001100 0.001300 0.0009800 0.0008100 0.0002900 0.0002800 0.0003500 0.0003300 0.0002800 2.400E-05 2.000E-05 8.300E-05 6.400E-05 8.200E-05 6.900E-05 5.800E-05 2.600E-06 2.100E-06 1.700E-05 1.100E-05 1.200E-05 1.200E-06 60.00 0.02100 0.003400 0.0006000 0.001900 0.001100 0.0001300 0.0005700 0.0004000 9.100E-05 6.500E-05 3.700E-05 0.0001600 0.0001200 3.100E-05 2.300E-05 7.200E-07 5.500E-07 9.400E-06 3.800E-05 2.800E-05 6.400E-06 4.700E-06 7.800E-08 5.600E-08 1.800E-06 6.500E-06 4.000E-06 1.300E-07 100.0 0.005100 0.001400 0.0003600 0.0002800 9.100E-05 9.100E-05 8.500E-05 3.100E-05 2.500E-06 1.300E-06 2.500E-05 2.400E-05 9.100E-06 8.300E-07 4.400E-07 5.500E-09 3.400E-09 6.400E-06 5.800E-06 2.200E-06 1.700E-07 9.100E-08 6.100E-10 3.600E-10 1.200E-06 1.000E-06 3.100E-07 8.900E-08 #S 96 Cm #N 30 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 1 2 2 4 1 2 #UBIND 1.282E+05 2.446E+04 2.378E+04 1.893E+04 6288. 5895. 4797. 4227. 3971. 1643. 1440. 1154. 883.0 832.0 464.0 449.0 382.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 Shell_28 0.000 13.90 0.006930 0.02500 0.01190 0.01450 0.05480 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2170 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.5160 0.3530 0.4210 0.5160 1.830 0.05000 13.80 0.006930 0.02500 0.01190 0.01450 0.05480 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2170 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.5150 0.3530 0.4210 0.5160 1.760 0.1000 13.40 0.006930 0.02500 0.01190 0.01450 0.05480 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2160 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.5090 0.3530 0.4210 0.5160 1.590 0.1500 12.90 0.006930 0.02500 0.01190 0.01450 0.05480 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2160 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.5010 0.3530 0.4210 0.5160 1.340 0.2000 12.30 0.006930 0.02500 0.01190 0.01450 0.05480 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2150 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.4890 0.3530 0.4200 0.5150 1.060 0.3000 11.20 0.006930 0.02500 0.01190 0.01450 0.05470 0.03180 0.03590 0.02300 0.02390 0.1070 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2120 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.4560 0.3510 0.4160 0.5090 0.5550 0.4000 10.40 0.006930 0.02500 0.01190 0.01450 0.05470 0.03180 0.03590 0.02300 0.02390 0.1060 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2080 0.1420 0.1590 0.1320 0.1360 0.1590 0.1660 0.4150 0.3470 0.4070 0.4900 0.2460 0.5000 9.950 0.006930 0.02500 0.01190 0.01450 0.05460 0.03180 0.03590 0.02300 0.02390 0.1060 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.2030 0.1420 0.1580 0.1320 0.1360 0.1590 0.1660 0.3670 0.3390 0.3900 0.4550 0.1090 0.6000 9.580 0.006930 0.02500 0.01190 0.01450 0.05450 0.03180 0.03590 0.02300 0.02390 0.1050 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.1970 0.1420 0.1580 0.1320 0.1360 0.1590 0.1660 0.3170 0.3260 0.3660 0.4060 0.06840 0.7000 9.240 0.006930 0.02500 0.01190 0.01450 0.05440 0.03180 0.03590 0.02300 0.02390 0.1040 0.06720 0.07480 0.05490 0.05660 0.04350 0.04420 0.1910 0.1420 0.1570 0.1320 0.1360 0.1590 0.1660 0.2670 0.3090 0.3350 0.3500 0.06200 0.8000 8.870 0.006930 0.02500 0.01190 0.01450 0.05430 0.03180 0.03590 0.02300 0.02390 0.1030 0.06720 0.07470 0.05490 0.05660 0.04350 0.04420 0.1830 0.1410 0.1570 0.1320 0.1350 0.1590 0.1650 0.2210 0.2880 0.2990 0.2940 0.06160 1.000 8.110 0.006930 0.02490 0.01190 0.01450 0.05400 0.03180 0.03590 0.02300 0.02390 0.1010 0.06710 0.07470 0.05490 0.05660 0.04350 0.04420 0.1670 0.1400 0.1540 0.1320 0.1350 0.1570 0.1630 0.1430 0.2350 0.2200 0.1930 0.05070 1.200 7.390 0.006930 0.02490 0.01190 0.01450 0.05360 0.03180 0.03590 0.02300 0.02390 0.09800 0.06700 0.07460 0.05490 0.05660 0.04350 0.04420 0.1490 0.1370 0.1500 0.1310 0.1340 0.1540 0.1580 0.08990 0.1790 0.1480 0.1180 0.03230 1.400 6.800 0.006930 0.02490 0.01190 0.01450 0.05320 0.03180 0.03590 0.02300 0.02390 0.09480 0.06690 0.07440 0.05490 0.05660 0.04350 0.04420 0.1310 0.1330 0.1440 0.1290 0.1330 0.1480 0.1510 0.06030 0.1270 0.09210 0.06940 0.01760 1.600 6.330 0.006930 0.02480 0.01190 0.01450 0.05280 0.03180 0.03590 0.02300 0.02390 0.09130 0.06680 0.07410 0.05490 0.05660 0.04350 0.04420 0.1120 0.1280 0.1360 0.1270 0.1300 0.1410 0.1430 0.04710 0.08510 0.05500 0.03940 0.009090 1.800 5.960 0.006930 0.02480 0.01190 0.01450 0.05220 0.03180 0.03590 0.02300 0.02390 0.08750 0.06650 0.07370 0.05490 0.05660 0.04350 0.04420 0.09530 0.1220 0.1270 0.1240 0.1260 0.1330 0.1330 0.04320 0.05520 0.03380 0.02200 0.005270 2.000 5.640 0.006930 0.02470 0.01190 0.01450 0.05170 0.03180 0.03590 0.02300 0.02390 0.08350 0.06620 0.07310 0.05490 0.05650 0.04350 0.04420 0.07960 0.1140 0.1160 0.1190 0.1210 0.1230 0.1220 0.04270 0.03630 0.02340 0.01270 0.004000 2.400 5.080 0.006920 0.02460 0.01190 0.01450 0.05030 0.03180 0.03580 0.02300 0.02390 0.07490 0.06520 0.07150 0.05480 0.05640 0.04350 0.04420 0.05440 0.09660 0.09260 0.1060 0.1070 0.1020 0.09930 0.04060 0.02060 0.01840 0.005890 0.003750 3.000 4.270 0.006920 0.02440 0.01190 0.01450 0.04800 0.03170 0.03570 0.02300 0.02390 0.06140 0.06290 0.06780 0.05440 0.05590 0.04340 0.04410 0.03250 0.06760 0.05820 0.08150 0.08010 0.07080 0.06780 0.02730 0.01830 0.01730 0.004940 0.002900 4.000 3.140 0.006910 0.02400 0.01190 0.01450 0.04330 0.03150 0.03530 0.02300 0.02390 0.04080 0.05630 0.05800 0.05260 0.05390 0.04310 0.04370 0.02380 0.03000 0.02250 0.04080 0.03820 0.03340 0.03110 0.008420 0.01300 0.008400 0.003760 0.0008810 5.000 2.470 0.006890 0.02340 0.01190 0.01450 0.03810 0.03100 0.03450 0.02300 0.02380 0.02560 0.04680 0.04510 0.04880 0.04960 0.04200 0.04260 0.02300 0.01400 0.01280 0.01650 0.01480 0.01380 0.01250 0.005030 0.005300 0.002820 0.001730 0.0004160 6.000 2.100 0.006870 0.02270 0.01190 0.01450 0.03250 0.03020 0.03320 0.02290 0.02380 0.01660 0.03610 0.03190 0.04290 0.04300 0.04000 0.04040 0.01800 0.01090 0.01220 0.007410 0.007000 0.005430 0.004890 0.004770 0.002290 0.001820 0.0006680 0.0003980 7.000 1.820 0.006850 0.02200 0.01190 0.01440 0.02710 0.02920 0.03150 0.02280 0.02360 0.01270 0.02580 0.02090 0.03560 0.03500 0.03700 0.03720 0.01120 0.01070 0.01100 0.005590 0.005720 0.002510 0.002320 0.003450 0.001890 0.001770 0.0003760 0.0002980 8.000 1.580 0.006830 0.02110 0.01190 0.01440 0.02210 0.02770 0.02920 0.02260 0.02330 0.01160 0.01750 0.01320 0.02780 0.02680 0.03310 0.03310 0.006210 0.009490 0.008260 0.005500 0.005660 0.001750 0.001690 0.001950 0.001830 0.001460 0.0003460 0.0001700 10.00 1.200 0.006800 0.01900 0.01190 0.01400 0.01400 0.02400 0.02400 0.02200 0.02200 0.01100 0.007800 0.006500 0.01500 0.01300 0.02400 0.02400 0.003100 0.004800 0.003100 0.004300 0.004100 0.001600 0.001600 0.0006900 0.001100 0.0005800 0.0002900 5.700E-05 15.00 0.5800 0.006600 0.01400 0.01100 0.01300 0.005000 0.01300 0.010000 0.01700 0.01700 0.004700 0.004800 0.005100 0.002700 0.002600 0.008400 0.008100 0.001900 0.001100 0.001300 0.0007300 0.0006500 0.0008700 0.0008200 0.0004200 0.0002000 0.0001900 5.100E-05 3.500E-05 20.00 0.3300 0.006300 0.009200 0.01100 0.01200 0.004200 0.005100 0.003400 0.01200 0.01100 0.001300 0.003100 0.002200 0.002200 0.002300 0.002500 0.002300 0.0004300 0.0008700 0.0006700 0.0003900 0.0004100 0.0002900 0.0002600 9.300E-05 0.0001600 1.000E-04 2.500E-05 7.700E-06 30.00 0.1300 0.005600 0.003100 0.007900 0.007700 0.002700 0.001200 0.001600 0.003700 0.003300 0.0009500 0.0004300 0.0003900 0.001100 0.001100 0.0002300 0.0002000 0.0002600 0.0001200 9.500E-05 0.0002300 0.0002200 2.700E-05 2.400E-05 5.200E-05 2.300E-05 1.400E-05 1.500E-05 4.200E-06 40.00 0.06600 0.004900 0.001000 0.005200 0.004300 0.0008100 0.001100 0.001300 0.001000 0.0008600 0.0003200 0.0002700 0.0003500 0.0003600 0.0003000 2.700E-05 2.300E-05 9.100E-05 6.400E-05 8.400E-05 7.500E-05 6.300E-05 3.200E-06 2.700E-06 1.900E-05 1.100E-05 1.200E-05 5.000E-06 1.500E-06 60.00 0.02200 0.003400 0.0005900 0.001900 0.001100 0.0001300 0.0005800 0.0004100 9.800E-05 7.000E-05 3.700E-05 0.0001600 0.0001200 3.400E-05 2.500E-05 8.200E-07 6.300E-07 9.700E-06 4.000E-05 3.000E-05 7.200E-06 5.200E-06 1.000E-07 7.400E-08 1.900E-06 7.000E-06 4.400E-06 4.800E-07 1.600E-07 100.0 0.005300 0.001400 0.0003600 0.0003000 9.600E-05 9.200E-05 9.100E-05 3.300E-05 2.700E-06 1.500E-06 2.600E-05 2.600E-05 9.800E-06 9.200E-07 4.900E-07 6.300E-09 3.900E-09 6.600E-06 6.300E-06 2.400E-06 1.900E-07 1.000E-07 7.900E-10 4.900E-10 1.300E-06 1.100E-06 3.500E-07 1.300E-08 1.100E-07 #S 97 Bk #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 3 2 2 4 2 #UBIND 1.316E+05 2.527E+04 2.438E+04 1.945E+04 6556. 6147. 4977. 4366. 4132. 1755. 1554. 1235. 883.0 832.0 464.0 449.0 398.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.90 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2130 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.5150 0.3510 0.4270 1.930 0.05000 13.70 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2120 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.5130 0.3510 0.4270 1.860 0.1000 13.30 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2120 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.5080 0.3510 0.4270 1.660 0.1500 12.70 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2110 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.4990 0.3510 0.4270 1.380 0.2000 12.10 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2100 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.4880 0.3510 0.4260 1.070 0.3000 10.90 0.006810 0.02460 0.01170 0.01430 0.05390 0.03130 0.03550 0.02270 0.02360 0.1050 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2080 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.4560 0.3490 0.4220 0.5300 0.4000 10.20 0.006810 0.02460 0.01170 0.01430 0.05380 0.03130 0.03550 0.02270 0.02360 0.1040 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2040 0.1390 0.1560 0.1300 0.1330 0.1600 0.1690 0.4150 0.3450 0.4120 0.2200 0.5000 9.730 0.006810 0.02460 0.01170 0.01430 0.05370 0.03130 0.03550 0.02270 0.02360 0.1040 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.2000 0.1390 0.1560 0.1290 0.1330 0.1600 0.1690 0.3680 0.3370 0.3940 0.09480 0.6000 9.420 0.006810 0.02460 0.01170 0.01430 0.05370 0.03130 0.03550 0.02270 0.02360 0.1030 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.1940 0.1390 0.1550 0.1290 0.1330 0.1590 0.1690 0.3180 0.3250 0.3690 0.06090 0.7000 9.130 0.006810 0.02460 0.01170 0.01430 0.05360 0.03130 0.03550 0.02270 0.02360 0.1020 0.06600 0.07360 0.05400 0.05570 0.04270 0.04340 0.1880 0.1390 0.1550 0.1290 0.1330 0.1590 0.1680 0.2690 0.3090 0.3360 0.05680 0.8000 8.820 0.006810 0.02460 0.01170 0.01430 0.05340 0.03130 0.03550 0.02270 0.02360 0.1010 0.06590 0.07360 0.05400 0.05570 0.04270 0.04340 0.1810 0.1380 0.1540 0.1290 0.1330 0.1590 0.1670 0.2220 0.2880 0.2990 0.05610 1.000 8.140 0.006810 0.02450 0.01170 0.01430 0.05320 0.03130 0.03550 0.02270 0.02360 0.09910 0.06590 0.07350 0.05400 0.05570 0.04270 0.04340 0.1650 0.1370 0.1520 0.1290 0.1330 0.1570 0.1640 0.1440 0.2360 0.2190 0.04450 1.200 7.490 0.006810 0.02450 0.01170 0.01430 0.05280 0.03130 0.03550 0.02270 0.02360 0.09650 0.06580 0.07340 0.05400 0.05570 0.04270 0.04340 0.1480 0.1340 0.1480 0.1280 0.1320 0.1530 0.1580 0.09080 0.1800 0.1460 0.02750 1.400 6.940 0.006810 0.02450 0.01170 0.01430 0.05240 0.03130 0.03550 0.02270 0.02360 0.09350 0.06570 0.07320 0.05400 0.05570 0.04270 0.04340 0.1300 0.1310 0.1420 0.1270 0.1310 0.1470 0.1500 0.06030 0.1290 0.09050 0.01470 1.600 6.480 0.006810 0.02440 0.01170 0.01430 0.05200 0.03130 0.03550 0.02270 0.02360 0.09020 0.06560 0.07300 0.05400 0.05570 0.04270 0.04340 0.1130 0.1260 0.1350 0.1250 0.1280 0.1400 0.1410 0.04640 0.08680 0.05390 0.007510 1.800 6.120 0.006810 0.02440 0.01170 0.01430 0.05150 0.03130 0.03540 0.02270 0.02360 0.08660 0.06540 0.07260 0.05400 0.05570 0.04270 0.04340 0.09610 0.1210 0.1260 0.1220 0.1250 0.1310 0.1310 0.04180 0.05660 0.03280 0.004340 2.000 5.800 0.006810 0.02430 0.01170 0.01430 0.05090 0.03130 0.03540 0.02270 0.02360 0.08270 0.06510 0.07210 0.05400 0.05560 0.04270 0.04340 0.08080 0.1130 0.1160 0.1180 0.1200 0.1210 0.1190 0.04120 0.03700 0.02240 0.003280 2.400 5.220 0.006800 0.02420 0.01170 0.01430 0.04970 0.03130 0.03540 0.02270 0.02360 0.07440 0.06420 0.07060 0.05390 0.05550 0.04260 0.04330 0.05570 0.09680 0.09330 0.1060 0.1070 0.1000 0.09700 0.03960 0.02030 0.01720 0.003060 3.000 4.380 0.006800 0.02400 0.01170 0.01430 0.04740 0.03120 0.03530 0.02270 0.02360 0.06150 0.06200 0.06710 0.05350 0.05510 0.04260 0.04330 0.03320 0.06900 0.05970 0.08230 0.08090 0.07060 0.06660 0.02750 0.01750 0.01630 0.002410 4.000 3.200 0.006790 0.02360 0.01170 0.01430 0.04300 0.03100 0.03490 0.02270 0.02360 0.04140 0.05580 0.05780 0.05190 0.05320 0.04230 0.04300 0.02350 0.03160 0.02360 0.04250 0.03970 0.03430 0.03130 0.008770 0.01300 0.008270 0.0007700 5.000 2.500 0.006770 0.02310 0.01170 0.01430 0.03790 0.03050 0.03410 0.02270 0.02360 0.02620 0.04690 0.04540 0.04840 0.04920 0.04140 0.04200 0.02290 0.01450 0.01290 0.01770 0.01580 0.01460 0.01310 0.004870 0.005570 0.002820 0.0003430 6.000 2.110 0.006760 0.02240 0.01170 0.01430 0.03250 0.02980 0.03290 0.02260 0.02350 0.01700 0.03650 0.03250 0.04290 0.04300 0.03960 0.04000 0.01850 0.01090 0.01200 0.007840 0.007280 0.005900 0.005200 0.004680 0.002340 0.001710 0.0003270 7.000 1.830 0.006740 0.02170 0.01170 0.01420 0.02720 0.02880 0.03120 0.02250 0.02330 0.01280 0.02650 0.02160 0.03590 0.03530 0.03680 0.03700 0.01180 0.01070 0.01110 0.005610 0.005690 0.002680 0.002400 0.003530 0.001820 0.001670 0.0002550 8.000 1.590 0.006710 0.02090 0.01170 0.01420 0.02230 0.02750 0.02910 0.02230 0.02310 0.01150 0.01820 0.01370 0.02830 0.02730 0.03310 0.03310 0.006690 0.009670 0.008570 0.005470 0.005620 0.001760 0.001650 0.002070 0.001780 0.001420 0.0001520 10.00 1.200 0.006700 0.01900 0.01170 0.01400 0.01400 0.02400 0.02400 0.02200 0.02200 0.01100 0.008200 0.006600 0.01500 0.01400 0.02500 0.02400 0.003200 0.005200 0.003400 0.004500 0.004300 0.001600 0.001500 0.0007000 0.001100 0.0006000 5.000E-05 15.00 0.5900 0.006500 0.01400 0.01100 0.01300 0.005000 0.01300 0.01100 0.01700 0.01700 0.004900 0.004700 0.005200 0.002800 0.002700 0.008800 0.008400 0.002000 0.001100 0.001300 0.0007900 0.0006900 0.0009000 0.0008300 0.0004300 0.0001900 0.0001800 3.000E-05 20.00 0.3400 0.006200 0.009400 0.010000 0.01200 0.004100 0.005400 0.003600 0.01200 0.01100 0.001400 0.003200 0.002300 0.002200 0.002300 0.002700 0.002500 0.0004600 0.0009000 0.0007100 0.0003900 0.0004100 0.0003000 0.0002700 1.000E-04 0.0001600 1.000E-04 6.900E-06 30.00 0.1400 0.005600 0.003200 0.007900 0.007800 0.002700 0.001200 0.001500 0.003800 0.003400 0.0009600 0.0004600 0.0004000 0.001200 0.001100 0.0002500 0.0002200 0.0002600 0.0001300 9.800E-05 0.0002500 0.0002300 3.000E-05 2.600E-05 5.200E-05 2.400E-05 1.400E-05 3.600E-06 40.00 0.06700 0.004800 0.001100 0.005300 0.004400 0.0008600 0.001100 0.001300 0.001100 0.0009000 0.0003400 0.0002700 0.0003600 0.0003800 0.0003200 3.000E-05 2.500E-05 9.900E-05 6.300E-05 8.500E-05 8.000E-05 6.800E-05 3.600E-06 2.900E-06 2.000E-05 1.100E-05 1.200E-05 1.400E-06 60.00 0.02200 0.003400 0.0005800 0.002000 0.001200 0.0001300 0.0006000 0.0004300 0.0001100 7.500E-05 3.800E-05 0.0001700 0.0001300 3.700E-05 2.700E-05 9.300E-07 7.100E-07 1.000E-05 4.100E-05 3.100E-05 7.900E-06 5.700E-06 1.100E-07 8.200E-08 2.000E-06 7.100E-06 4.400E-06 1.300E-07 100.0 0.005400 0.001500 0.0003700 0.0003200 1.000E-04 9.400E-05 9.700E-05 3.600E-05 3.000E-06 1.600E-06 2.600E-05 2.800E-05 1.100E-05 1.000E-06 5.400E-07 7.200E-09 4.500E-09 6.800E-06 6.900E-06 2.600E-06 2.200E-07 1.100E-07 9.100E-10 5.400E-10 1.300E-06 1.200E-06 3.600E-07 9.100E-08 #S 98 Cf #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 4 2 2 4 2 #UBIND 1.360E+05 2.611E+04 2.529E+04 1.993E+04 6754. 6359. 5109. 4497. 4253. 1791. 1616. 1279. 883.0 832.0 464.0 449.0 419.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.80 0.006700 0.02420 0.01150 0.01410 0.05310 0.03080 0.03500 0.02240 0.02330 0.1040 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2080 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.5050 0.3440 0.4210 1.910 0.05000 13.70 0.006700 0.02420 0.01150 0.01410 0.05310 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2080 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.5030 0.3440 0.4210 1.840 0.1000 13.30 0.006700 0.02420 0.01150 0.01410 0.05310 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2070 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.4980 0.3440 0.4210 1.650 0.1500 12.70 0.006700 0.02420 0.01150 0.01410 0.05310 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2070 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.4900 0.3440 0.4210 1.370 0.2000 12.10 0.006700 0.02420 0.01150 0.01410 0.05310 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2060 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.4790 0.3440 0.4200 1.070 0.3000 10.90 0.006700 0.02420 0.01150 0.01410 0.05300 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2030 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.4490 0.3420 0.4160 0.5400 0.4000 10.20 0.006700 0.02420 0.01150 0.01410 0.05300 0.03080 0.03500 0.02240 0.02330 0.1030 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.2000 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.4100 0.3380 0.4060 0.2280 0.5000 9.720 0.006700 0.02420 0.01150 0.01410 0.05290 0.03080 0.03500 0.02240 0.02330 0.1020 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.1960 0.1360 0.1530 0.1270 0.1310 0.1540 0.1620 0.3650 0.3310 0.3900 0.09720 0.6000 9.420 0.006700 0.02420 0.01150 0.01410 0.05280 0.03080 0.03500 0.02240 0.02330 0.1010 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.1910 0.1360 0.1520 0.1270 0.1310 0.1540 0.1620 0.3170 0.3200 0.3660 0.06010 0.7000 9.140 0.006700 0.02420 0.01150 0.01410 0.05270 0.03080 0.03500 0.02240 0.02330 0.1010 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.1850 0.1360 0.1520 0.1270 0.1310 0.1530 0.1620 0.2700 0.3050 0.3350 0.05490 0.8000 8.840 0.006690 0.02420 0.01150 0.01410 0.05260 0.03080 0.03500 0.02240 0.02330 0.09970 0.06480 0.07250 0.05310 0.05480 0.04190 0.04260 0.1780 0.1350 0.1510 0.1260 0.1310 0.1530 0.1610 0.2250 0.2850 0.2990 0.05450 1.000 8.190 0.006690 0.02410 0.01150 0.01410 0.05240 0.03080 0.03500 0.02240 0.02330 0.09760 0.06470 0.07240 0.05310 0.05480 0.04190 0.04260 0.1630 0.1340 0.1490 0.1260 0.1300 0.1510 0.1580 0.1480 0.2370 0.2210 0.04430 1.200 7.550 0.006690 0.02410 0.01150 0.01410 0.05200 0.03080 0.03500 0.02240 0.02330 0.09510 0.06470 0.07230 0.05310 0.05480 0.04190 0.04260 0.1470 0.1320 0.1460 0.1260 0.1300 0.1480 0.1540 0.09390 0.1830 0.1500 0.02810 1.400 7.000 0.006690 0.02410 0.01150 0.01410 0.05170 0.03080 0.03500 0.02240 0.02330 0.09230 0.06460 0.07210 0.05310 0.05480 0.04190 0.04260 0.1300 0.1290 0.1400 0.1240 0.1280 0.1430 0.1470 0.06210 0.1330 0.09400 0.01540 1.600 6.550 0.006690 0.02400 0.01150 0.01410 0.05120 0.03080 0.03500 0.02240 0.02330 0.08910 0.06440 0.07190 0.05310 0.05480 0.04190 0.04260 0.1130 0.1240 0.1340 0.1230 0.1260 0.1370 0.1390 0.04680 0.09100 0.05650 0.007940 1.800 6.180 0.006690 0.02400 0.01150 0.01410 0.05080 0.03080 0.03500 0.02240 0.02330 0.08560 0.06420 0.07150 0.05310 0.05480 0.04190 0.04260 0.09680 0.1190 0.1250 0.1200 0.1230 0.1290 0.1300 0.04140 0.06000 0.03440 0.004490 2.000 5.860 0.006690 0.02400 0.01150 0.01410 0.05020 0.03080 0.03500 0.02240 0.02330 0.08190 0.06390 0.07100 0.05310 0.05480 0.04190 0.04260 0.08190 0.1120 0.1160 0.1160 0.1180 0.1200 0.1190 0.04040 0.03940 0.02310 0.003240 2.400 5.290 0.006690 0.02390 0.01150 0.01410 0.04900 0.03080 0.03490 0.02240 0.02330 0.07400 0.06310 0.06970 0.05300 0.05470 0.04190 0.04260 0.05710 0.09680 0.09400 0.1050 0.1060 0.1010 0.09860 0.03920 0.02080 0.01690 0.002930 3.000 4.450 0.006680 0.02370 0.01150 0.01410 0.04690 0.03070 0.03480 0.02240 0.02330 0.06150 0.06110 0.06640 0.05270 0.05430 0.04180 0.04250 0.03410 0.07030 0.06130 0.08310 0.08180 0.07290 0.06940 0.02840 0.01700 0.01620 0.002410 4.000 3.260 0.006670 0.02330 0.01150 0.01410 0.04260 0.03050 0.03440 0.02240 0.02330 0.04190 0.05540 0.05750 0.05120 0.05250 0.04160 0.04220 0.02330 0.03320 0.02470 0.04440 0.04150 0.03680 0.03390 0.009460 0.01340 0.008670 0.0008190 5.000 2.530 0.006660 0.02270 0.01150 0.01410 0.03770 0.03010 0.03370 0.02240 0.02330 0.02680 0.04690 0.04560 0.04800 0.04880 0.04080 0.04130 0.02270 0.01520 0.01300 0.01910 0.01700 0.01630 0.01460 0.004870 0.006050 0.003020 0.0003390 6.000 2.110 0.006640 0.02210 0.01150 0.01410 0.03250 0.02940 0.03260 0.02230 0.02320 0.01740 0.03700 0.03310 0.04280 0.04300 0.03910 0.03960 0.01890 0.01080 0.01190 0.008370 0.007660 0.006750 0.005970 0.004680 0.002500 0.001710 0.0003180 7.000 1.830 0.006620 0.02140 0.01150 0.01410 0.02740 0.02850 0.03100 0.02220 0.02310 0.01290 0.02720 0.02220 0.03610 0.03560 0.03650 0.03680 0.01250 0.01060 0.01120 0.005680 0.005700 0.003020 0.002710 0.003690 0.001810 0.001650 0.0002590 8.000 1.600 0.006600 0.02070 0.01150 0.01400 0.02250 0.02720 0.02900 0.02200 0.02280 0.01140 0.01890 0.01430 0.02880 0.02780 0.03310 0.03320 0.007200 0.009810 0.008870 0.005460 0.005600 0.001870 0.001750 0.002250 0.001770 0.001440 0.0001610 10.00 1.200 0.006500 0.01900 0.01100 0.01400 0.01400 0.02400 0.02400 0.02100 0.02200 0.01100 0.008600 0.006800 0.01600 0.01500 0.02500 0.02500 0.003200 0.005500 0.003600 0.004600 0.004400 0.001600 0.001600 0.0007400 0.001200 0.0006400 5.200E-05 15.00 0.6000 0.006400 0.01400 0.01100 0.01300 0.005100 0.01300 0.01100 0.01700 0.01700 0.005200 0.004700 0.005200 0.002900 0.002800 0.009200 0.008800 0.002100 0.001100 0.001300 0.0008600 0.0007500 0.0009700 0.0009000 0.0004600 0.0001900 0.0001800 3.100E-05 20.00 0.3400 0.006100 0.009500 0.010000 0.01200 0.004100 0.005600 0.003700 0.01200 0.01200 0.001400 0.003300 0.002500 0.002200 0.002300 0.002800 0.002600 0.0005000 0.0009300 0.0007500 0.0004000 0.0004100 0.0003400 0.0003000 0.0001100 0.0001600 0.0001100 7.400E-06 30.00 0.1400 0.005500 0.003400 0.007900 0.007800 0.002800 0.001200 0.001500 0.004000 0.003500 0.0009800 0.0005000 0.0004000 0.001200 0.001100 0.0002700 0.0002400 0.0002700 0.0001500 1.000E-04 0.0002600 0.0002400 3.400E-05 2.900E-05 5.300E-05 2.700E-05 1.400E-05 3.600E-06 40.00 0.06800 0.004800 0.001100 0.005400 0.004500 0.0009300 0.001100 0.001300 0.001200 0.0009500 0.0003700 0.0002600 0.0003600 0.0004000 0.0003400 3.300E-05 2.800E-05 0.0001100 6.300E-05 8.600E-05 8.700E-05 7.300E-05 4.200E-06 3.400E-06 2.200E-05 1.100E-05 1.200E-05 1.400E-06 60.00 0.02300 0.003400 0.0005700 0.002100 0.001200 0.0001300 0.0006200 0.0004500 0.0001100 8.100E-05 3.900E-05 0.0001700 0.0001300 4.000E-05 2.900E-05 1.100E-06 8.100E-07 1.000E-05 4.300E-05 3.300E-05 8.800E-06 6.300E-06 1.300E-07 9.800E-08 2.100E-06 7.400E-06 4.600E-06 1.400E-07 100.0 0.005600 0.001500 0.0003800 0.0003400 0.0001100 9.500E-05 1.000E-04 3.800E-05 3.300E-06 1.700E-06 2.600E-05 3.000E-05 1.100E-05 1.100E-06 6.000E-07 8.300E-09 5.100E-09 6.900E-06 7.500E-06 2.800E-06 2.400E-07 1.300E-07 1.100E-09 6.500E-10 1.400E-06 1.300E-06 3.900E-07 9.100E-08 #S 99 Es #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 5 2 2 4 2 #UBIND 1.395E+05 2.690E+04 2.602E+04 2.041E+04 6977. 6574. 5252. 4630. 4374. 1868. 1680. 1321. 883.0 832.0 464.0 449.0 435.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.70 0.006580 0.02380 0.01130 0.01400 0.05230 0.03030 0.03460 0.02210 0.02300 0.1020 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.2040 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4950 0.3370 0.4150 1.890 0.05000 13.60 0.006580 0.02380 0.01130 0.01400 0.05230 0.03030 0.03460 0.02210 0.02300 0.1020 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.2030 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4930 0.3370 0.4150 1.820 0.1000 13.20 0.006580 0.02380 0.01130 0.01400 0.05230 0.03030 0.03460 0.02210 0.02300 0.1020 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.2030 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4880 0.3370 0.4150 1.640 0.1500 12.60 0.006580 0.02380 0.01130 0.01400 0.05220 0.03030 0.03460 0.02210 0.02300 0.1020 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.2020 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4810 0.3370 0.4150 1.370 0.2000 12.00 0.006580 0.02380 0.01130 0.01400 0.05220 0.03030 0.03460 0.02210 0.02300 0.1010 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.2020 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4700 0.3370 0.4140 1.070 0.3000 10.90 0.006580 0.02380 0.01130 0.01400 0.05220 0.03030 0.03460 0.02210 0.02300 0.1010 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1990 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4420 0.3360 0.4100 0.5490 0.4000 10.10 0.006580 0.02380 0.01130 0.01400 0.05220 0.03030 0.03460 0.02210 0.02300 0.1010 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1960 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.4050 0.3320 0.4020 0.2350 0.5000 9.700 0.006580 0.02380 0.01130 0.01400 0.05210 0.03030 0.03460 0.02210 0.02300 0.1000 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1920 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.3620 0.3260 0.3860 0.09990 0.6000 9.400 0.006580 0.02380 0.01130 0.01400 0.05200 0.03030 0.03460 0.02210 0.02300 0.09960 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1870 0.1330 0.1500 0.1240 0.1280 0.1490 0.1560 0.3170 0.3150 0.3630 0.05960 0.7000 9.140 0.006580 0.02380 0.01130 0.01400 0.05190 0.03030 0.03460 0.02210 0.02300 0.09890 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1820 0.1330 0.1490 0.1240 0.1280 0.1480 0.1560 0.2710 0.3010 0.3330 0.05320 0.8000 8.850 0.006580 0.02380 0.01130 0.01400 0.05180 0.03030 0.03460 0.02210 0.02300 0.09810 0.06360 0.07140 0.05230 0.05400 0.04110 0.04180 0.1750 0.1320 0.1490 0.1240 0.1280 0.1480 0.1550 0.2270 0.2830 0.2990 0.05290 1.000 8.220 0.006580 0.02380 0.01130 0.01400 0.05160 0.03030 0.03460 0.02210 0.02300 0.09610 0.06360 0.07130 0.05230 0.05400 0.04110 0.04180 0.1610 0.1310 0.1470 0.1240 0.1280 0.1470 0.1530 0.1510 0.2370 0.2230 0.04400 1.200 7.610 0.006580 0.02370 0.01130 0.01400 0.05130 0.03030 0.03460 0.02210 0.02300 0.09370 0.06350 0.07120 0.05230 0.05400 0.04110 0.04180 0.1460 0.1290 0.1430 0.1230 0.1270 0.1440 0.1490 0.09700 0.1860 0.1530 0.02870 1.400 7.060 0.006580 0.02370 0.01130 0.01400 0.05090 0.03030 0.03460 0.02210 0.02300 0.09100 0.06340 0.07100 0.05230 0.05400 0.04110 0.04180 0.1300 0.1260 0.1390 0.1220 0.1260 0.1400 0.1440 0.06400 0.1370 0.09740 0.01600 1.600 6.620 0.006580 0.02370 0.01130 0.01400 0.05050 0.03030 0.03460 0.02210 0.02300 0.08800 0.06330 0.07080 0.05230 0.05400 0.04110 0.04180 0.1130 0.1220 0.1320 0.1200 0.1240 0.1340 0.1370 0.04740 0.09490 0.05920 0.008380 1.800 6.250 0.006580 0.02360 0.01130 0.01400 0.05000 0.03030 0.03450 0.02210 0.02300 0.08460 0.06310 0.07050 0.05230 0.05390 0.04110 0.04180 0.09750 0.1170 0.1240 0.1180 0.1210 0.1270 0.1280 0.04100 0.06350 0.03600 0.004670 2.000 5.930 0.006570 0.02360 0.01130 0.01400 0.04950 0.03030 0.03450 0.02210 0.02300 0.08110 0.06290 0.07000 0.05220 0.05390 0.04110 0.04180 0.08290 0.1110 0.1150 0.1140 0.1170 0.1190 0.1190 0.03960 0.04190 0.02380 0.003230 2.400 5.360 0.006570 0.02350 0.01130 0.01400 0.04840 0.03030 0.03450 0.02210 0.02300 0.07350 0.06210 0.06880 0.05220 0.05380 0.04110 0.04180 0.05840 0.09680 0.09450 0.1040 0.1060 0.1020 0.09970 0.03880 0.02140 0.01660 0.002810 3.000 4.530 0.006570 0.02330 0.01130 0.01400 0.04630 0.03020 0.03440 0.02210 0.02300 0.06140 0.06020 0.06570 0.05190 0.05350 0.04110 0.04180 0.03490 0.07150 0.06270 0.08380 0.08260 0.07490 0.07170 0.02920 0.01670 0.01590 0.002400 4.000 3.320 0.006560 0.02290 0.01130 0.01400 0.04220 0.03000 0.03400 0.02210 0.02300 0.04230 0.05490 0.05730 0.05050 0.05190 0.04090 0.04150 0.02310 0.03480 0.02590 0.04620 0.04320 0.03910 0.03630 0.01020 0.01360 0.009020 0.0008700 5.000 2.560 0.006540 0.02240 0.01130 0.01400 0.03750 0.02960 0.03330 0.02210 0.02300 0.02730 0.04690 0.04590 0.04750 0.04840 0.04010 0.04070 0.02250 0.01600 0.01320 0.02040 0.01820 0.01790 0.01620 0.004890 0.006530 0.003220 0.0003390 6.000 2.120 0.006530 0.02180 0.01130 0.01390 0.03250 0.02900 0.03230 0.02200 0.02290 0.01780 0.03730 0.03360 0.04270 0.04290 0.03870 0.03910 0.01920 0.01090 0.01180 0.008940 0.008090 0.007630 0.006790 0.004660 0.002680 0.001720 0.0003080 7.000 1.840 0.006510 0.02120 0.01130 0.01390 0.02750 0.02810 0.03080 0.02190 0.02280 0.01300 0.02780 0.02290 0.03630 0.03590 0.03630 0.03650 0.01310 0.01050 0.01130 0.005770 0.005730 0.003400 0.003050 0.003830 0.001800 0.001630 0.0002620 8.000 1.600 0.006490 0.02040 0.01130 0.01390 0.02270 0.02700 0.02880 0.02180 0.02260 0.01130 0.01960 0.01480 0.02920 0.02830 0.03310 0.03320 0.007730 0.009910 0.009150 0.005440 0.005580 0.002000 0.001870 0.002430 0.001760 0.001460 0.0001700 10.00 1.200 0.006400 0.01900 0.01100 0.01390 0.01500 0.02400 0.02400 0.02100 0.02200 0.01100 0.009000 0.006900 0.01600 0.01500 0.02500 0.02500 0.003300 0.005900 0.003900 0.004700 0.004600 0.001700 0.001600 0.0007900 0.001200 0.0006900 5.400E-05 15.00 0.6100 0.006300 0.01400 0.01100 0.01300 0.005200 0.01400 0.01100 0.01700 0.01700 0.005400 0.004600 0.005200 0.003000 0.002800 0.009600 0.009200 0.002200 0.001100 0.001300 0.0009400 0.0008100 0.001000 0.0009700 0.0004800 0.0002000 0.0001800 3.200E-05 20.00 0.3500 0.006000 0.009600 0.010000 0.01200 0.004000 0.005900 0.003900 0.01200 0.01200 0.001500 0.003400 0.002600 0.002200 0.002300 0.003000 0.002800 0.0005500 0.0009600 0.0007900 0.0004000 0.0004200 0.0003700 0.0003400 0.0001200 0.0001700 0.0001100 8.000E-06 30.00 0.1400 0.005400 0.003500 0.007900 0.007900 0.002800 0.001200 0.001500 0.004100 0.003700 0.0009900 0.0005300 0.0004100 0.001300 0.001200 0.0003000 0.0002700 0.0002700 0.0001600 1.000E-04 0.0002700 0.0002500 3.900E-05 3.400E-05 5.400E-05 3.000E-05 1.500E-05 3.500E-06 40.00 0.07000 0.004700 0.001200 0.005400 0.004500 0.0009900 0.001100 0.001300 0.001200 0.001000 0.0004000 0.0002600 0.0003600 0.0004200 0.0003600 3.700E-05 3.100E-05 0.0001200 6.300E-05 8.700E-05 9.300E-05 7.800E-05 4.900E-06 4.000E-06 2.300E-05 1.100E-05 1.200E-05 1.500E-06 60.00 0.02300 0.003400 0.0005600 0.002200 0.001300 0.0001300 0.0006300 0.0004600 0.0001200 8.700E-05 4.000E-05 0.0001800 0.0001400 4.400E-05 3.100E-05 1.200E-06 9.100E-07 1.100E-05 4.400E-05 3.500E-05 9.600E-06 6.900E-06 1.600E-07 1.200E-07 2.200E-06 7.600E-06 4.900E-06 1.400E-07 100.0 0.005800 0.001500 0.0003800 0.0003600 0.0001100 9.600E-05 0.0001100 4.000E-05 3.600E-06 1.900E-06 2.700E-05 3.200E-05 1.200E-05 1.200E-06 6.600E-07 9.500E-09 5.800E-09 7.100E-06 8.100E-06 3.000E-06 2.700E-07 1.400E-07 1.300E-09 7.700E-10 1.400E-06 1.400E-06 4.200E-07 9.100E-08 #S 100 Fm #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 6 2 2 4 2 #UBIND 1.431E+05 2.770E+04 2.681E+04 2.090E+04 7205. 6793. 5397. 4766. 4498. 1937. 1747. 1366. 883.0 832.0 464.0 449.0 454.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.60 0.006460 0.02340 0.01110 0.01380 0.05150 0.02980 0.03410 0.02180 0.02270 0.09990 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1990 0.1300 0.1480 0.1210 0.1260 0.1440 0.1510 0.4850 0.3310 0.4100 1.870 0.05000 13.50 0.006460 0.02340 0.01110 0.01380 0.05150 0.02980 0.03410 0.02180 0.02270 0.09990 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1990 0.1300 0.1480 0.1210 0.1260 0.1440 0.1510 0.4840 0.3310 0.4100 1.810 0.1000 13.10 0.006460 0.02340 0.01110 0.01380 0.05140 0.02980 0.03410 0.02180 0.02270 0.09990 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1990 0.1300 0.1480 0.1210 0.1260 0.1440 0.1510 0.4790 0.3310 0.4100 1.630 0.1500 12.60 0.006460 0.02340 0.01110 0.01380 0.05140 0.02980 0.03410 0.02180 0.02270 0.09980 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1980 0.1300 0.1480 0.1210 0.1260 0.1440 0.1510 0.4720 0.3310 0.4100 1.370 0.2000 12.00 0.006460 0.02340 0.01110 0.01380 0.05140 0.02980 0.03410 0.02180 0.02270 0.09970 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1970 0.1300 0.1480 0.1210 0.1260 0.1440 0.1510 0.4620 0.3310 0.4090 1.070 0.3000 10.90 0.006460 0.02340 0.01110 0.01380 0.05140 0.02980 0.03410 0.02180 0.02270 0.09950 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1950 0.1300 0.1470 0.1210 0.1260 0.1440 0.1510 0.4350 0.3290 0.4050 0.5580 0.4000 10.10 0.006460 0.02340 0.01110 0.01380 0.05130 0.02980 0.03410 0.02180 0.02270 0.09910 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1920 0.1300 0.1470 0.1210 0.1260 0.1440 0.1510 0.4000 0.3260 0.3970 0.2420 0.5000 9.690 0.006460 0.02340 0.01110 0.01380 0.05130 0.02980 0.03410 0.02180 0.02270 0.09860 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1880 0.1300 0.1470 0.1210 0.1260 0.1440 0.1510 0.3590 0.3200 0.3820 0.1030 0.6000 9.390 0.006460 0.02340 0.01110 0.01380 0.05120 0.02980 0.03410 0.02180 0.02270 0.09800 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1840 0.1300 0.1470 0.1210 0.1260 0.1440 0.1510 0.3160 0.3110 0.3600 0.05930 0.7000 9.130 0.006460 0.02340 0.01110 0.01380 0.05110 0.02980 0.03410 0.02180 0.02270 0.09730 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1780 0.1300 0.1470 0.1210 0.1260 0.1440 0.1510 0.2710 0.2970 0.3320 0.05170 0.8000 8.860 0.006460 0.02340 0.01110 0.01380 0.05100 0.02980 0.03410 0.02180 0.02270 0.09650 0.06250 0.07030 0.05150 0.05320 0.04040 0.04110 0.1730 0.1290 0.1460 0.1210 0.1260 0.1440 0.1500 0.2290 0.2800 0.2990 0.05130 1.000 8.260 0.006460 0.02340 0.01110 0.01380 0.05080 0.02980 0.03410 0.02180 0.02270 0.09460 0.06250 0.07020 0.05150 0.05320 0.04040 0.04110 0.1590 0.1280 0.1440 0.1210 0.1250 0.1420 0.1490 0.1540 0.2370 0.2250 0.04370 1.200 7.650 0.006460 0.02340 0.01110 0.01380 0.05050 0.02980 0.03410 0.02180 0.02270 0.09230 0.06240 0.07020 0.05150 0.05310 0.04040 0.04110 0.1450 0.1270 0.1410 0.1210 0.1250 0.1400 0.1450 0.1000 0.1880 0.1560 0.02920 1.400 7.120 0.006460 0.02330 0.01110 0.01380 0.05020 0.02980 0.03410 0.02180 0.02270 0.08970 0.06230 0.07000 0.05150 0.05310 0.04040 0.04110 0.1290 0.1240 0.1370 0.1200 0.1240 0.1370 0.1410 0.06600 0.1400 0.1010 0.01670 1.600 6.680 0.006460 0.02330 0.01110 0.01380 0.04980 0.02980 0.03410 0.02180 0.02270 0.08680 0.06220 0.06980 0.05140 0.05310 0.04040 0.04110 0.1130 0.1200 0.1310 0.1180 0.1220 0.1320 0.1340 0.04820 0.09880 0.06170 0.008840 1.800 6.310 0.006460 0.02330 0.01110 0.01380 0.04930 0.02980 0.03410 0.02180 0.02270 0.08370 0.06200 0.06950 0.05140 0.05310 0.04040 0.04110 0.09810 0.1160 0.1230 0.1160 0.1190 0.1260 0.1270 0.04080 0.06690 0.03770 0.004880 2.000 5.990 0.006460 0.02320 0.01110 0.01380 0.04880 0.02980 0.03410 0.02180 0.02270 0.08030 0.06180 0.06910 0.05140 0.05310 0.04040 0.04110 0.08380 0.1100 0.1150 0.1130 0.1150 0.1180 0.1180 0.03880 0.04450 0.02460 0.003250 2.400 5.430 0.006460 0.02310 0.01110 0.01380 0.04770 0.02980 0.03410 0.02180 0.02270 0.07300 0.06110 0.06790 0.05130 0.05300 0.04040 0.04110 0.05960 0.09660 0.09500 0.1040 0.1050 0.1020 0.1000 0.03830 0.02220 0.01640 0.002700 3.000 4.610 0.006450 0.02290 0.01110 0.01380 0.04580 0.02970 0.03400 0.02180 0.02270 0.06140 0.05940 0.06500 0.05110 0.05270 0.04040 0.04100 0.03580 0.07250 0.06410 0.08430 0.08320 0.07650 0.07360 0.02990 0.01630 0.01570 0.002380 4.000 3.390 0.006440 0.02260 0.01110 0.01380 0.04180 0.02950 0.03360 0.02180 0.02270 0.04270 0.05440 0.05700 0.04990 0.05120 0.04020 0.04080 0.02300 0.03640 0.02700 0.04790 0.04490 0.04130 0.03860 0.01100 0.01390 0.009340 0.0009210 5.000 2.590 0.006430 0.02210 0.01110 0.01380 0.03730 0.02920 0.03300 0.02180 0.02270 0.02790 0.04680 0.04600 0.04710 0.04800 0.03950 0.04010 0.02230 0.01680 0.01350 0.02180 0.01940 0.01960 0.01780 0.004950 0.007010 0.003430 0.0003420 6.000 2.140 0.006420 0.02160 0.01110 0.01380 0.03240 0.02860 0.03200 0.02180 0.02260 0.01820 0.03770 0.03410 0.04250 0.04280 0.03820 0.03870 0.01950 0.01090 0.01170 0.009570 0.008560 0.008560 0.007640 0.004620 0.002890 0.001730 0.0002980 7.000 1.850 0.006400 0.02090 0.01110 0.01380 0.02760 0.02780 0.03050 0.02170 0.02250 0.01320 0.02840 0.02350 0.03650 0.03610 0.03600 0.03630 0.01370 0.01040 0.01130 0.005890 0.005780 0.003820 0.003420 0.003940 0.001810 0.001610 0.0002630 8.000 1.610 0.006380 0.02020 0.01110 0.01370 0.02290 0.02670 0.02870 0.02150 0.02230 0.01120 0.02020 0.01540 0.02970 0.02880 0.03300 0.03310 0.008270 0.009970 0.009400 0.005410 0.005550 0.002150 0.002000 0.002600 0.001740 0.001480 0.0001770 10.00 1.200 0.006300 0.01900 0.01100 0.01370 0.01500 0.02400 0.02400 0.02100 0.02200 0.01100 0.009400 0.007100 0.01700 0.01600 0.02600 0.02500 0.003400 0.006200 0.004200 0.004800 0.004700 0.001700 0.001600 0.0008500 0.001300 0.0007300 5.700E-05 15.00 0.6200 0.006200 0.01400 0.01100 0.01300 0.005300 0.01400 0.01100 0.01700 0.01700 0.005700 0.004500 0.005200 0.003100 0.002900 0.010000 0.009600 0.002300 0.001100 0.001300 0.001000 0.0008800 0.001100 0.001000 0.0005000 0.0002100 0.0001800 3.200E-05 20.00 0.3600 0.005900 0.009700 0.010000 0.01200 0.003900 0.006200 0.004000 0.01200 0.01200 0.001600 0.003500 0.002700 0.002200 0.002300 0.003200 0.003000 0.0006000 0.0009800 0.0008200 0.0004000 0.0004200 0.0004100 0.0003700 0.0001300 0.0001700 0.0001200 8.600E-06 30.00 0.1500 0.005400 0.003600 0.007900 0.007900 0.002900 0.001300 0.001500 0.004200 0.003800 0.0009900 0.0005800 0.0004100 0.001300 0.001200 0.0003200 0.0002900 0.0002700 0.0001800 0.0001100 0.0002800 0.0002600 4.400E-05 3.800E-05 5.500E-05 3.300E-05 1.500E-05 3.500E-06 40.00 0.07200 0.004700 0.001200 0.005500 0.004600 0.001100 0.001100 0.001300 0.001300 0.001100 0.0004200 0.0002600 0.0003600 0.0004500 0.0003800 4.100E-05 3.500E-05 0.0001200 6.300E-05 8.800E-05 1.000E-04 8.400E-05 5.600E-06 4.600E-06 2.500E-05 1.100E-05 1.200E-05 1.600E-06 60.00 0.02400 0.003400 0.0005400 0.002200 0.001300 0.0001400 0.0006500 0.0004800 0.0001300 9.300E-05 4.200E-05 0.0001800 0.0001500 4.700E-05 3.400E-05 1.300E-06 1.000E-06 1.200E-05 4.600E-05 3.700E-05 1.100E-05 7.500E-06 1.900E-07 1.400E-07 2.300E-06 7.900E-06 5.100E-06 1.500E-07 100.0 0.006000 0.001500 0.0003900 0.0003800 0.0001200 9.700E-05 0.0001200 4.300E-05 4.000E-06 2.100E-06 2.700E-05 3.400E-05 1.300E-05 1.400E-06 7.300E-07 1.100E-08 6.700E-09 7.200E-06 8.700E-06 3.300E-06 3.100E-07 1.600E-07 1.500E-09 9.100E-10 1.400E-06 1.500E-06 4.500E-07 9.100E-08 #S 101 Md #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 7 2 2 4 2 #UBIND 1.468E+05 2.853E+04 2.761E+04 2.139E+04 7441. 7019. 5546. 4903. 4622. 2010. 1814. 1410. 883.0 832.0 464.0 449.0 472.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.60 0.006350 0.02310 0.01090 0.01370 0.05070 0.02930 0.03370 0.02160 0.02250 0.09820 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1950 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4760 0.3250 0.4050 1.850 0.05000 13.40 0.006350 0.02310 0.01090 0.01370 0.05070 0.02930 0.03370 0.02160 0.02250 0.09820 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1950 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4740 0.3250 0.4050 1.790 0.1000 13.10 0.006350 0.02310 0.01090 0.01370 0.05070 0.02930 0.03370 0.02160 0.02250 0.09820 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1950 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4700 0.3250 0.4050 1.610 0.1500 12.50 0.006350 0.02310 0.01090 0.01370 0.05060 0.02930 0.03370 0.02160 0.02250 0.09810 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1940 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4640 0.3250 0.4050 1.360 0.2000 12.00 0.006350 0.02310 0.01090 0.01370 0.05060 0.02930 0.03370 0.02160 0.02250 0.09800 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1930 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4540 0.3240 0.4040 1.080 0.3000 10.90 0.006350 0.02300 0.01090 0.01370 0.05060 0.02930 0.03370 0.02160 0.02250 0.09780 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1910 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.4280 0.3230 0.4010 0.5670 0.4000 10.10 0.006350 0.02300 0.01090 0.01370 0.05060 0.02930 0.03370 0.02160 0.02250 0.09740 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1890 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.3950 0.3200 0.3930 0.2490 0.5000 9.670 0.006350 0.02300 0.01090 0.01370 0.05050 0.02930 0.03370 0.02160 0.02250 0.09690 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1850 0.1270 0.1450 0.1190 0.1230 0.1400 0.1460 0.3560 0.3150 0.3780 0.1060 0.6000 9.380 0.006350 0.02300 0.01090 0.01370 0.05040 0.02930 0.03370 0.02160 0.02250 0.09640 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1810 0.1270 0.1440 0.1190 0.1230 0.1400 0.1460 0.3140 0.3060 0.3570 0.05930 0.7000 9.130 0.006350 0.02300 0.01090 0.01370 0.05030 0.02930 0.03370 0.02160 0.02250 0.09570 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1760 0.1270 0.1440 0.1190 0.1230 0.1400 0.1460 0.2720 0.2940 0.3300 0.05020 0.8000 8.870 0.006350 0.02300 0.01090 0.01370 0.05030 0.02930 0.03370 0.02160 0.02250 0.09490 0.06140 0.06930 0.05060 0.05240 0.03970 0.04040 0.1700 0.1270 0.1440 0.1190 0.1230 0.1390 0.1460 0.2300 0.2780 0.2980 0.04980 1.000 8.280 0.006350 0.02300 0.01090 0.01370 0.05000 0.02930 0.03370 0.02160 0.02250 0.09310 0.06140 0.06920 0.05060 0.05240 0.03970 0.04040 0.1570 0.1260 0.1420 0.1190 0.1230 0.1390 0.1440 0.1570 0.2370 0.2270 0.04330 1.200 7.700 0.006350 0.02300 0.01090 0.01370 0.04970 0.02930 0.03370 0.02160 0.02250 0.09100 0.06130 0.06910 0.05060 0.05230 0.03970 0.04040 0.1430 0.1240 0.1390 0.1180 0.1230 0.1370 0.1420 0.1030 0.1900 0.1590 0.02960 1.400 7.180 0.006350 0.02300 0.01090 0.01370 0.04940 0.02930 0.03370 0.02160 0.02250 0.08850 0.06130 0.06900 0.05060 0.05230 0.03970 0.04040 0.1280 0.1220 0.1350 0.1170 0.1220 0.1340 0.1370 0.06810 0.1430 0.1040 0.01730 1.600 6.740 0.006350 0.02290 0.01090 0.01370 0.04910 0.02930 0.03370 0.02160 0.02250 0.08580 0.06110 0.06880 0.05060 0.05230 0.03970 0.04040 0.1130 0.1190 0.1290 0.1160 0.1200 0.1290 0.1320 0.04910 0.1020 0.06420 0.009320 1.800 6.370 0.006350 0.02290 0.01090 0.01370 0.04860 0.02930 0.03370 0.02160 0.02250 0.08270 0.06100 0.06850 0.05060 0.05230 0.03970 0.04040 0.09860 0.1140 0.1220 0.1140 0.1170 0.1240 0.1250 0.04070 0.07030 0.03940 0.005120 2.000 6.050 0.006350 0.02280 0.01090 0.01370 0.04820 0.02930 0.03370 0.02160 0.02250 0.07950 0.06080 0.06810 0.05060 0.05230 0.03970 0.04040 0.08470 0.1090 0.1140 0.1110 0.1140 0.1170 0.1180 0.03820 0.04720 0.02550 0.003300 2.400 5.490 0.006340 0.02280 0.01090 0.01370 0.04710 0.02930 0.03360 0.02160 0.02250 0.07250 0.06010 0.06700 0.05050 0.05220 0.03970 0.04040 0.06080 0.09630 0.09530 0.1030 0.1040 0.1020 0.1010 0.03770 0.02320 0.01620 0.002590 3.000 4.680 0.006340 0.02260 0.01090 0.01370 0.04520 0.02920 0.03350 0.02160 0.02250 0.06130 0.05850 0.06420 0.05030 0.05190 0.03970 0.04030 0.03670 0.07340 0.06530 0.08460 0.08370 0.07780 0.07530 0.03050 0.01600 0.01540 0.002350 4.000 3.460 0.006330 0.02220 0.01090 0.01370 0.04150 0.02910 0.03320 0.02150 0.02250 0.04310 0.05380 0.05670 0.04920 0.05060 0.03950 0.04010 0.02290 0.03800 0.02820 0.04950 0.04640 0.04330 0.04070 0.01180 0.01400 0.009630 0.0009710 5.000 2.630 0.006320 0.02180 0.01090 0.01360 0.03710 0.02880 0.03260 0.02150 0.02240 0.02850 0.04670 0.04620 0.04660 0.04760 0.03890 0.03950 0.02200 0.01770 0.01380 0.02320 0.02060 0.02120 0.01940 0.005040 0.007480 0.003650 0.0003480 6.000 2.150 0.006300 0.02130 0.01090 0.01360 0.03240 0.02820 0.03160 0.02150 0.02240 0.01860 0.03800 0.03460 0.04240 0.04270 0.03770 0.03820 0.01970 0.01110 0.01160 0.01020 0.009070 0.009520 0.008530 0.004570 0.003130 0.001750 0.0002890 7.000 1.850 0.006290 0.02060 0.01090 0.01360 0.02770 0.02740 0.03030 0.02140 0.02230 0.01330 0.02900 0.02410 0.03660 0.03630 0.03570 0.03600 0.01430 0.01030 0.01130 0.006050 0.005860 0.004280 0.003820 0.004030 0.001830 0.001580 0.0002630 8.000 1.620 0.006270 0.02000 0.01090 0.01360 0.02310 0.02640 0.02850 0.02120 0.02210 0.01120 0.02090 0.01590 0.03000 0.02920 0.03290 0.03310 0.008830 0.009990 0.009620 0.005390 0.005510 0.002330 0.002150 0.002770 0.001720 0.001480 0.0001850 10.00 1.200 0.006200 0.01800 0.01090 0.01300 0.01500 0.02400 0.02400 0.02100 0.02100 0.01100 0.009900 0.007300 0.01800 0.01600 0.02600 0.02600 0.003500 0.006600 0.004500 0.004900 0.004800 0.001700 0.001700 0.0009200 0.001400 0.0007800 6.100E-05 15.00 0.6300 0.006100 0.01400 0.01090 0.01300 0.005400 0.01400 0.01200 0.01700 0.01700 0.005900 0.004500 0.005200 0.003300 0.003000 0.010000 0.010000 0.002400 0.001200 0.001300 0.001100 0.0009500 0.001200 0.001100 0.0005100 0.0002100 0.0001700 3.200E-05 20.00 0.3600 0.005900 0.009800 0.009900 0.01100 0.003900 0.006400 0.004200 0.01200 0.01200 0.001600 0.003600 0.002800 0.002100 0.002300 0.003400 0.003200 0.0006500 0.001000 0.0008600 0.0004100 0.0004200 0.0004500 0.0004000 0.0001500 0.0001700 0.0001200 9.300E-06 30.00 0.1500 0.005300 0.003800 0.007900 0.007900 0.003000 0.001300 0.001500 0.004400 0.003900 0.001000 0.0006200 0.0004200 0.001300 0.001300 0.0003500 0.0003200 0.0002800 0.0002000 0.0001100 0.0002900 0.0002700 5.000E-05 4.300E-05 5.500E-05 3.600E-05 1.600E-05 3.400E-06 40.00 0.07300 0.004700 0.001300 0.005500 0.004700 0.001100 0.001000 0.001300 0.001300 0.001100 0.0004500 0.0002500 0.0003600 0.0004700 0.0004000 4.500E-05 3.800E-05 0.0001300 6.300E-05 8.900E-05 0.0001100 8.900E-05 6.400E-06 5.200E-06 2.700E-05 1.100E-05 1.200E-05 1.700E-06 60.00 0.02400 0.003400 0.0005300 0.002300 0.001400 0.0001400 0.0006600 0.0005000 0.0001400 1.000E-04 4.400E-05 0.0001900 0.0001500 5.100E-05 3.700E-05 1.500E-06 1.200E-06 1.200E-05 4.700E-05 3.800E-05 1.200E-05 8.200E-06 2.200E-07 1.600E-07 2.500E-06 8.100E-06 5.300E-06 1.600E-07 100.0 0.006100 0.001600 0.0003900 0.0004100 0.0001300 9.700E-05 0.0001300 4.600E-05 4.300E-06 2.300E-06 2.700E-05 3.600E-05 1.400E-05 1.500E-06 8.000E-07 1.200E-08 7.600E-09 7.300E-06 9.400E-06 3.500E-06 3.400E-07 1.800E-07 1.800E-09 1.100E-09 1.500E-06 1.600E-06 4.800E-07 9.100E-08 #S 102 No #N 29 #UOCCUP 2 2 2 4 2 2 4 4 6 2 2 4 4 6 6 8 2 2 4 4 6 6 8 2 2 4 2 #UBIND 1.505E+05 2.938E+04 2.844E+04 2.188E+04 7675. 7245. 5688. 5037. 4741. 2078. 1876. 1448. 883.0 832.0 464.0 449.0 484.0 290.0 216.0 119.0 109.0 0.000 0.000 50.00 40.00 32.00 0.000 #L pz total Shell_1 Shell_2 Shell_3 Shell_4 Shell_5 Shell_6 Shell_7 Shell_8 Shell_9 Shell_10 Shell_11 Shell_12 Shell_13 Shell_14 Shell_15 Shell_16 Shell_17 Shell_18 Shell_19 Shell_20 Shell_21 Shell_22 Shell_23 Shell_24 Shell_25 Shell_26 Shell_27 0.000 13.50 0.006240 0.02270 0.01070 0.01350 0.04990 0.02890 0.03330 0.02130 0.02220 0.09660 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1910 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4670 0.3190 0.4000 1.840 0.05000 13.40 0.006240 0.02270 0.01070 0.01350 0.04990 0.02890 0.03330 0.02130 0.02220 0.09650 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1910 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4660 0.3190 0.4000 1.770 0.1000 13.00 0.006240 0.02270 0.01070 0.01350 0.04990 0.02890 0.03330 0.02130 0.02220 0.09650 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1910 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4620 0.3190 0.4000 1.600 0.1500 12.50 0.006240 0.02270 0.01070 0.01350 0.04990 0.02890 0.03330 0.02130 0.02220 0.09640 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1900 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4550 0.3190 0.4000 1.360 0.2000 11.90 0.006240 0.02270 0.01070 0.01350 0.04990 0.02890 0.03330 0.02130 0.02220 0.09640 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1900 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4460 0.3180 0.3990 1.080 0.3000 10.80 0.006240 0.02270 0.01070 0.01350 0.04980 0.02890 0.03330 0.02130 0.02220 0.09610 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1880 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.4220 0.3170 0.3960 0.5750 0.4000 10.10 0.006240 0.02270 0.01070 0.01350 0.04980 0.02890 0.03330 0.02130 0.02220 0.09580 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1850 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.3900 0.3150 0.3880 0.2570 0.5000 9.660 0.006240 0.02270 0.01070 0.01350 0.04970 0.02890 0.03330 0.02130 0.02220 0.09530 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1820 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.3530 0.3090 0.3750 0.1090 0.6000 9.370 0.006240 0.02270 0.01070 0.01350 0.04970 0.02890 0.03330 0.02130 0.02220 0.09480 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1770 0.1250 0.1420 0.1160 0.1210 0.1360 0.1420 0.3130 0.3010 0.3550 0.05950 0.7000 9.120 0.006240 0.02270 0.01070 0.01350 0.04960 0.02890 0.03330 0.02130 0.02220 0.09410 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1730 0.1240 0.1420 0.1160 0.1210 0.1360 0.1420 0.2720 0.2900 0.3290 0.04900 0.8000 8.870 0.006240 0.02270 0.01070 0.01350 0.04950 0.02890 0.03330 0.02130 0.02220 0.09340 0.06030 0.06830 0.04990 0.05160 0.03900 0.03970 0.1670 0.1240 0.1410 0.1160 0.1210 0.1360 0.1420 0.2320 0.2750 0.2980 0.04840 1.000 8.310 0.006240 0.02260 0.01070 0.01350 0.04930 0.02890 0.03330 0.02130 0.02220 0.09170 0.06030 0.06820 0.04990 0.05160 0.03900 0.03970 0.1550 0.1230 0.1400 0.1160 0.1210 0.1350 0.1400 0.1600 0.2370 0.2290 0.04290 1.200 7.740 0.006240 0.02260 0.01070 0.01350 0.04900 0.02890 0.03330 0.02130 0.02220 0.08970 0.06030 0.06810 0.04990 0.05160 0.03900 0.03970 0.1420 0.1220 0.1370 0.1160 0.1200 0.1330 0.1380 0.1060 0.1920 0.1620 0.03000 1.400 7.230 0.006240 0.02260 0.01070 0.01350 0.04870 0.02890 0.03330 0.02130 0.02220 0.08730 0.06020 0.06800 0.04990 0.05160 0.03900 0.03970 0.1280 0.1200 0.1330 0.1150 0.1190 0.1310 0.1340 0.07030 0.1460 0.1070 0.01790 1.600 6.790 0.006240 0.02260 0.01070 0.01350 0.04830 0.02880 0.03330 0.02130 0.02220 0.08470 0.06010 0.06780 0.04990 0.05160 0.03900 0.03970 0.1130 0.1170 0.1280 0.1140 0.1180 0.1270 0.1300 0.05010 0.1060 0.06670 0.009810 1.800 6.430 0.006240 0.02250 0.01070 0.01350 0.04790 0.02880 0.03330 0.02130 0.02220 0.08180 0.05990 0.06750 0.04980 0.05160 0.03900 0.03970 0.09900 0.1130 0.1210 0.1120 0.1160 0.1220 0.1240 0.04070 0.07370 0.04110 0.005380 2.000 6.110 0.006230 0.02250 0.01070 0.01350 0.04750 0.02880 0.03330 0.02130 0.02220 0.07870 0.05970 0.06720 0.04980 0.05150 0.03900 0.03970 0.08550 0.1080 0.1140 0.1100 0.1130 0.1160 0.1170 0.03760 0.05000 0.02640 0.003370 2.400 5.560 0.006230 0.02240 0.01070 0.01350 0.04650 0.02880 0.03320 0.02130 0.02220 0.07200 0.05920 0.06610 0.04980 0.05150 0.03900 0.03970 0.06190 0.09590 0.09560 0.1020 0.1040 0.1020 0.1010 0.03700 0.02430 0.01610 0.002500 3.000 4.760 0.006230 0.02220 0.01070 0.01350 0.04470 0.02880 0.03310 0.02130 0.02220 0.06120 0.05770 0.06350 0.04960 0.05120 0.03900 0.03960 0.03770 0.07410 0.06650 0.08490 0.08420 0.07890 0.07670 0.03090 0.01570 0.01510 0.002310 4.000 3.520 0.006220 0.02190 0.01070 0.01350 0.04110 0.02860 0.03280 0.02130 0.02220 0.04350 0.05330 0.05640 0.04850 0.05000 0.03890 0.03950 0.02280 0.03950 0.02940 0.05100 0.04790 0.04520 0.04260 0.01260 0.01410 0.009870 0.001020 5.000 2.670 0.006210 0.02150 0.01070 0.01350 0.03690 0.02830 0.03230 0.02130 0.02220 0.02900 0.04660 0.04630 0.04620 0.04720 0.03840 0.03890 0.02170 0.01860 0.01420 0.02460 0.02190 0.02280 0.02090 0.005180 0.007940 0.003870 0.0003570 6.000 2.170 0.006190 0.02100 0.01070 0.01350 0.03230 0.02780 0.03130 0.02120 0.02210 0.01910 0.03820 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-7.2463800e-02 -6.3763800e-01 -3.6205600e-01 -2.1380000e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.5137700e-02 1.7155600e-02 5.5171700e-03 9.0000000e+01 5.1820000e+00 2.0470000e+01 1.0964900e+02 2.3200000e+02 1.1700000e+01 1.4773000e+01 1.7048300e+01 1.8548100e+01 1.3433600e+01 8.2784300e+00 1.7089000e-01 -1.9119200e+00 -2.5856900e+00 -2.6128100e+00 1.3480500e+00 4.7905600e-01 1.6556100e+00 0.0000000e+00 0.0000000e+00 -7.9057400e-03 -8.1328200e-01 -3.6765700e-01 -2.2970200e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.5566400e-02 1.7462100e-02 6.9251600e-03 9.2000000e+01 5.5490000e+00 2.1756000e+01 1.1560300e+02 2.3807000e+02 1.9050000e+01 1.4903600e+01 1.7035300e+01 1.7525800e+01 1.3795100e+01 8.3301000e+00 1.0827700e-01 -2.1214800e+00 -2.5690300e+00 -2.0723700e+00 1.2398300e+00 4.7831400e-01 1.7415800e+00 0.0000000e+00 0.0000000e+00 -7.2393200e-02 -8.0154500e-01 -3.6725000e-01 -2.5410400e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.5359600e-02 1.7412900e-02 8.9505600e-03 9.4000000e+01 5.9140000e+00 2.3095000e+01 1.2176000e+02 2.3910000e+02 1.9700000e+01 1.4853500e+01 1.7295300e+01 1.7551900e+01 1.8278700e+01 8.3817400e+00 3.8879100e-02 -1.8773300e+00 -2.6216400e+00 -2.0216200e+00 -1.1737100e+00 4.7708500e-01 1.8222900e+00 0.0000000e+00 0.0000000e+00 -8.2294000e-02 -3.6834400e-01 -3.6655600e-01 -2.7600900e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.9873800e-02 1.7342200e-02 1.0739200e-02 xrstools-0.15.0+git20210910+c147919d/XRStools/tools_sequencer_esynth.py000066400000000000000000000510431412732462000250700ustar00rootroot00000000000000import numpy as np import h5py import glob import json import os import h5py import math BATCH_PARALLELISM = 1 MAXNPROCS=0 try: import mpi4py _has_mpi4py_ = True print(" INFO: mpi4py loaded") except: _has_mpi4py_ = False print(" WARNING: mpi4py not available") import os def process_input(s, go=0, exploit_slurm_mpi = 0, stop_omp = False): open("input_tmp_%d.par"%go, "w").write(s) background_activator = "" if (go % BATCH_PARALLELISM ): background_activator = "&" prefix="" if stop_omp: prefix = prefix +"export OMP_NUM_THREADS=1 ;" if _has_mpi4py_ : if exploit_slurm_mpi>0 and MAXNPROCS: exploit_slurm_mpi = -MAXNPROCS if ( exploit_slurm_mpi==0 ): os.system(prefix +"mpirun -n 1 XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) elif ( exploit_slurm_mpi>0 ): os.system(prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: os.system(prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) else: os.system(prefix +"XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) def select_rois( datadir = None, roi_scan_num=None, roi_target_path = None, filter_path = None): if np.isscalar(roi_scan_num): scans = [roi_scan_num] else: scans = list(roi_scan_num) input_string = """ create_rois: expdata : {expdata} scans : {scans} roiaddress : {roi_target_path} filter_path : {filter_path} """.format( expdata = os.path.join( datadir, "hydra"), scans = scans, roi_target_path = roi_target_path, filter_path = filter_path ) process_input( input_string, exploit_slurm_mpi = 0 ) def get_reference( roi_path = None, datadir = None, reference_scan_list = None, monitor_column = None, extracted_reference_target_file = None, isolate_spot_by = None ): signal_path = extracted_reference_target_file + ":/" input_string = """ loadscan_2Dimages : expdata : {expdata} roiaddress : {roi_path} monitorcolumn : {monitor_column} scan_list : {reference_scan_list} signaladdress : "{extracted_reference_target_file}:/references" isolateSpot : {isolate_spot_by} save_also_roi : True sumto1D : 0 energycolumn : 'stx' """ s=input_string.format( expdata = os.path.join( datadir, "hydra"), reference_scan_list = reference_scan_list, monitor_column = monitor_column, roi_path = roi_path, isolate_spot_by = isolate_spot_by, signal_path = signal_path, extracted_reference_target_file = extracted_reference_target_file ) process_input( s , exploit_slurm_mpi = 0) def extract_sample_givenrois( roi_path = None, datadir = None, Start = None, End = None, Thickness = None , monitor_column = None, signals_target_file = None, ): for iE, start in enumerate(range(Start,End, Thickness)): end = start+Thickness signal_path = signals_target_file + ":/E{iE}".format(iE=iE) input_string = """ loadscan_2Dimages : expdata : {expdata} roiaddress : {roi_path} scan_interval : [{start}, {end}] energycolumn : sty signaladdress : {signal_path} monitorcolumn : {monitor_column} sumto1D : 0 """.format( expdata = os.path.join( datadir, "hydra"), roi_path = roi_path, start = start, end = end, monitor_column = monitor_column, signal_path = signal_path ) process_input(input_string, exploit_slurm_mpi = 0) def synthetise_response(scan_address=None, target_address=None, response_fit_options = None ): input_string = """ superR_fit_responses : foil_scan_address : "{scan_address}" nref : 7 # the number of subdivision per pixel dimension used to # represent the optical response function at higher resolution niter_optical : {niter_optical} # the number of iterations used in the optimisation of the optical # response beta_optical : {beta_optical} # The L1 norm factor in the regularisation # term for the optical functions pixel_dim : 1 # The pixel response function is represented with a # pixel_dim**2 array niter_pixel : 10 # The number of iterations in the pixel response optimisation # phase. A negative number stands for ISTA, positive for FISTA beta_pixel : 0.0 # L1 factor for the pixel response regularisation ## The used trajectories are always written whith the calculated response ## They can be reloaded and used as initialization(and freezed with do_refine_trajectory : 0 ) ## Uncomment the following line if you want to reload a set of trajectories ## without this options trajectories are initialised from the spots drifts ## # reload_trajectories_file : "response.h5" filter_rois : 0 ###### ## The method first find an estimation of the foil scan trajectory on each roi ## then, based on this, obtain a fit of the optical response function ## assuming a flat pixel response. Finally the pixel response is optimised ## ## There is a final phase where a global optimisation ## is done in niter_global steps. ## ## Each step is composed of optical response fit, followed by a pixel response fit. ## If do_refine_trajectory is different from zero, the trajectory is reoptimised at each step ## niter_global : {niter_global} ## if do_refine_trajectory=1 the start and end point of the trajectory are free ## if =2 then the start and end point are forced to a trajectory which is obtained ## from a reference scan : the foil scan may be short, then one can use the scan of ## an object to get another one : key *trajectory_reference_scan_address* ## do_refine_trajectory : 1 ## optional: only if do_refine_trajectory = 2 trajectory_reference_scansequence_address : "demo_newrois.h5:/ROI_FOIL/images/scans/" trajectory_threshold : 0.1 ## if the pixel response function is forced to be symmetrical simmetrizza : 1 ## where the found responses are written target_file : {target_address} # target_file : "fitted_responses.h5" """ s=input_string.format( scan_address=scan_address , target_address=target_address, niter_optical = response_fit_options[ "niter_optical"], beta_optical=response_fit_options["beta_optical"], niter_global=response_fit_options["niter_global"] ) process_input( s , exploit_slurm_mpi = 1, stop_omp = True) def resynthetise_scan( old_scan_address= None, response_file = None , target_address = None, original_roi_path = None, resynth_z_square = None ): input_string = """ superR_recreate_rois : ### we have calculated the responses in responsefilename ### and we want to enlarge the scan by a margin of 3 times ### the original scan on the right and on the left ### ( so for a total of a 7 expansion factor ) responsefilename : {response_file} nex : 0 ## the old scan covered by the old rois old_scan_address : {old_scan_address} ## where new rois and bnew scan are written target_filename : {target_address} filter_rois : 0 original_roi_path : {original_roi_path} resynth_z_square : {resynth_z_square} """ s=input_string.format( response_file = response_file , target_address = target_address, old_scan_address=old_scan_address, original_roi_path = original_roi_path +"/rois_definition", resynth_z_square = resynth_z_square) process_input( s , exploit_slurm_mpi = 0, stop_omp = True) def get_scalars( iE = None, signals_file = None, reference_file = None, target_file = None, selected_rois = None ): inputstring = """ superR_scal_deltaXimages_Esynt : sample_address : {signals_file}:/E{iE}/scans delta_address : {reference_file}:/rois_and_reference/scans/ScansSum load_factors_from : nbin : 1 target_address : {target_file}:/E{iE}/scal_prods roi_keys : {selected_rois} """ . format( iE = iE, signals_file = signals_file , reference_file = reference_file , target_file = target_file, selected_rois = list( selected_rois ) ) process_input( inputstring, exploit_slurm_mpi = 0) def InterpInfo_Esynt_components(peaks_shifts , signals_file=None, custom_ene_list = None, custom_components = None, selected_rois = None ): f = h5py.File(signals_file,"r") volumes_list = list(f.keys()) ene_list = [] for v in volumes_list: dg_Scans = [ t for t in f[v]["scans"].keys() if t[:4]=="Scan"] ene_list.append( f[v]["scans"][dg_Scans[0]] ["motorDict"]["energy"][()] ) components = h5py.File( custom_components ,"r")["components"] [()] info_dict = {} for i_interval in range(len(components)): info_dict[str(i_interval)] = {} info_dict[str(i_interval)]["E"] = custom_ene_list[ i_interval ] info_dict[str(i_interval)]["coefficients"]={} for i_n in range(len(energy_exp_grid)): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ]={} for roi_num, de in enumerate( peaks_shifts ): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ][ str(roi_num) ] = 0 for ic in range(len(components)): for i_interval in range(len(custom_ene_list)-1): cE1 = custom_ene_list[ i_interval ] cE2 = custom_ene_list[ i_interval+1 ] for i_ene, t_ene in enumerate( energy_exp_grid) : for roi_num, de in enumerate( peaks_shifts ): if selected_rois and roi_num not in selected_rois: continue if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2+cE1) info_dict[str(ic)]["coefficients" ][ str(i_ene) ][ str(roi_num) ] += alpha * components[ic][ i_interval ] info_dict[str(ic)]["coefficients" ][ str(i_ene) ][ str(roi_num) ] += (1-alpha)*components[ic][ i_interval+1 ] return info_dict def InterpInfo_Esynt( peaks_shifts , signals_file = None, custom_ene_list = None, selected_rois = None ): f = h5py.File(signals_file,"r") volumes_list = list(f.keys()) ene_list = [] for v in volumes_list: dg_Scans = [ t for t in f[v]["scans"].keys() if t[:4]=="Scan"] ene_list.append( f[v]["scans"][dg_Scans[0]] ["motorDict"]["energy"][()] ) print ( " Here custom ene list " , custom_ene_list) print (" Here all the analyser energies ", np.sort(np.add.outer( ene_list , peaks_shifts[selected_rois]).ravel()) ) order = list(np.argsort( ene_list )) ene_list = [ ene_list[ii] for ii in order] volumes_list = [ volumes_list[ii] for ii in order] info_dict = {} for i_intervallo in range(len(custom_ene_list)): info_dict[str(i_intervallo)] = {} info_dict[str(i_intervallo)]["E"] = custom_ene_list[ i_intervallo ] info_dict[str(i_intervallo)]["coefficients"]={} for t_vn, t_ene in list(zip( volumes_list, ene_list )): info_dict[str(i_intervallo)]["coefficients" ][ t_vn ]={} for i_intervallo in range(len( custom_ene_list)-1): cE1 = custom_ene_list[ i_intervallo ] cE2 = custom_ene_list[ i_intervallo+1 ] for t_vn, t_ene in list(zip(volumes_list, ene_list ))[0:]: for roi_num, de in enumerate( peaks_shifts ): if selected_rois and roi_num not in selected_rois: continue if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2-cE1) info_dict[str(i_intervallo)]["coefficients" ][ str(t_vn) ][ str(roi_num) ] = alpha info_dict[str(i_intervallo+1)]["coefficients"][ str(t_vn) ][ str(roi_num) ] = 1-alpha return info_dict def tools_sequencer( peaks_shifts = None, datadir = None, filter_path = None, roi_scan_num = None, roi_target_path = None, first_scan_num = None , Ydim = None , Zdim = None , Edim = None , monitor_column = None, signals_target_file = None, steps_to_do = None, ##################################################### ## can be left to None, will be set to the used target roi_path = None, signals_file = None, reference_clip = None, isolate_spot_by = None, reference_scan_list = None, extracted_reference_target_file = None , response_target_file = None, response_fit_options = None, resynthetised_reference_and_roi_target_file = None, selected_rois = None, scalar_products_target_file = None, energy_custom_grid = None, custom_components_file = None , interpolation_infos_file = None, resynth_z_square = None ) : if roi_path is None: roi_path = roi_target_path if signals_file is None: signals_file = signals_target_file if(steps_to_do["do_step_make_roi"]): # ROI selection and reference scan select_rois(datadir = datadir , roi_scan_num = roi_scan_num , roi_target_path = roi_target_path, filter_path = filter_path ) if(steps_to_do["do_step_sample_extraction"]): extract_sample_givenrois( roi_path = roi_path, datadir = datadir, Start = first_scan_num , End = (first_scan_num + Zdim * Edim ) , Thickness = Zdim, monitor_column = monitor_column, signals_target_file = signals_target_file ) if(steps_to_do["do_step_extract_reference_scan"]): # of course we need the REFERENCE SCAN get_reference( datadir = datadir, roi_path = roi_path, monitor_column = monitor_column, extracted_reference_target_file = extracted_reference_target_file, isolate_spot_by = isolate_spot_by, reference_scan_list = reference_scan_list ) if reference_clip is not None: clip1, clip2= reference_clip ftarget = h5py.File( extracted_reference_target_file ,"r+") for roi_scann in reference_scan_list: target_group = ftarget["references/scans/Scan%03d"% roi_scann ] for k in target_group.keys(): if k != "motorDict": print(" SHRINKING scan for ROI %s in file roi_%d.h5 " %( k, roi_scann )) for dsn in ["matrix", "monitor", "xscale"]: mat = target_group[k][dsn][()] del target_group[k][dsn] target_group[k][dsn] = mat[clip1:clip2] ftarget.close() ftarget = h5py.File( extracted_reference_target_file ,"r+") ftarget["references/scans/ScansSum"] = ftarget["references/scans/Scan%03d"% reference_scan_list[0] ] for other in reference_scan_list[1:]: source_group = ftarget["references/scans/Scan%03d"% other ] target_group = ftarget["references/scans/ScansSum" ] for k in target_group.keys(): if k != "motorDict": print(" ADDING data for ROI %s from file roi_%d.h5 " %( k, other )) mat = source_group[k]["matrix"][()] target_group[k]["matrix"][:] += mat ftarget.close() if(steps_to_do["do_step_fit_reference_response"]): synthetise_response( scan_address= extracted_reference_target_file +":references/scans/ScansSum" , target_address = response_target_file +":/FIT", response_fit_options = response_fit_options ) if(steps_to_do["do_step_resynthetise_reference"]): resynthetise_scan( old_scan_address= extracted_reference_target_file +":references/scans/ScansSum" , response_file = response_target_file +":/FIT", target_address = resynthetised_reference_and_roi_target_file + ":/rois_and_reference", original_roi_path = roi_path, resynth_z_square = resynth_z_square ) if(steps_to_do["do_step_scalars"]): os.system("rm %s"%scalar_products_target_file) for iE in range(Edim) : get_scalars( iE = iE, signals_file = signals_file, reference_file = resynthetised_reference_and_roi_target_file , target_file = scalar_products_target_file, selected_rois = selected_rois ) if(steps_to_do["do_step_interpolation_coefficients"]): if custom_components_file is None: info_dict = InterpInfo_Esynt( peaks_shifts , signals_file = signals_file, custom_ene_list = energy_custom_grid, selected_rois = selected_rois ) else: info_dict = InterpInfo_Esynt_components( peaks_shifts, signals_file = signals_file, custom_ene_list = energy_custom_grid, custom_components = custom_components_file, selected_rois = selected_rois ) json.dump(info_dict,open( interpolation_infos_file,"w"), indent=4) if( steps_to_do["do_step_finalise_for_fit"] ): get_volume_Esynt( scalar_products_file = scalar_products_target_file, interpolation_infos_file = interpolation_infos_file ) def get_volume_Esynt( scalar_products_file = None, interpolation_infos_file = None ): inputstring = """ superR_getVolume_Esynt : scalprods_address : {scalar_products_file}:/ dict_interp : {interpolation_infos_file} output_prefix : DATASFORCC/test0_ """.format( scalar_products_file = scalar_products_file, interpolation_infos_file = interpolation_infos_file ) os.system("mkdir DATASFORCC") process_input( inputstring, exploit_slurm_mpi = 0) xrstools-0.15.0+git20210910+c147919d/XRStools/tools_sequencer_esynth_galaxies.py000066400000000000000000000540141412732462000267460ustar00rootroot00000000000000import numpy as np import h5py import glob import json import sys BATCH_PARALLELISM = 1 import os def synthetise_response(scan_address=None, target_address=None, response_fit_options = None ): input_string = """ superR_fit_responses : foil_scan_address : "{scan_address}" nref : 7 # the number of subdivision per pixel dimension used to # represent the optical response function at higher resolution niter_optical : {niter_optical} # the number of iterations used in the optimisation of the optical # response beta_optical : {beta_optical} # The L1 norm factor in the regularisation # term for the optical functions pixel_dim : 1 # The pixel response function is represented with a # pixel_dim**2 array niter_pixel : 10 # The number of iterations in the pixel response optimisation # phase. A negative number stands for ISTA, positive for FISTA beta_pixel : 0.0 # L1 factor for the pixel response regularisation ## The used trajectories are always written whith the calculated response ## They can be reloaded and used as initialization(and freezed with do_refine_trajectory : 0 ) ## Uncomment the following line if you want to reload a set of trajectories ## without this options trajectories are initialised from the spots drifts ## # reload_trajectories_file : "response.h5" filter_rois : 0 ###### ## The method first find an estimation of the foil scan trajectory on each roi ## then, based on this, obtain a fit of the optical response function ## assuming a flat pixel response. Finally the pixel response is optimised ## ## There is a final phase where a global optimisation ## is done in niter_global steps. ## ## Each step is composed of optical response fit, followed by a pixel response fit. ## If do_refine_trajectory is different from zero, the trajectory is reoptimised at each step ## niter_global : {niter_global} ## if do_refine_trajectory=1 the start and end point of the trajectory are free ## if =2 then the start and end point are forced to a trajectory which is obtained ## from a reference scan : the foil scan may be short, then one can use the scan of ## an object to get another one : key *trajectory_reference_scan_address* ## do_refine_trajectory : 1 ## optional: only if do_refine_trajectory = 2 trajectory_reference_scansequence_address : "demo_newrois.h5:/ROI_FOIL/images/scans/" trajectory_threshold : 0.1 ## if the pixel response function is forced to be symmetrical simmetrizza : 1 ## where the found responses are written target_file : {target_address} # target_file : "fitted_responses.h5" """ s=input_string.format( scan_address=scan_address , target_address=target_address, niter_optical = response_fit_options[ "niter_optical"], beta_optical=response_fit_options["beta_optical"], niter_global=response_fit_options["niter_global"] ) process_input( s , exploit_slurm_mpi = 1, stop_omp = True) def resynthetise_scan( old_scan_address= None, response_file = None , target_address = None, original_roi_path = None, resynth_z_square = None ): input_string = """ superR_recreate_rois : ### we have calculated the responses in responsefilename ### and we want to enlarge the scan by a margin of 3 times ### the original scan on the right and on the left ### ( so for a total of a 7 expansion factor ) responsefilename : {response_file} nex : 0 ## the old scan covered by the old rois old_scan_address : {old_scan_address} ## where new rois and bnew scan are written target_filename : {target_address} filter_rois : 0 original_roi_path : {original_roi_path} resynth_z_square : {resynth_z_square} """ s=input_string.format( response_file = response_file , target_address = target_address, old_scan_address=old_scan_address, original_roi_path = original_roi_path +"/rois_definition", resynth_z_square = resynth_z_square) process_input( s , exploit_slurm_mpi = 0, stop_omp = True) def process_input(s, go=0, exploit_slurm_mpi = 0, stop_omp = False): open("input_tmp_%d.par"%go, "w").write(s) background_activator = "" if (go % BATCH_PARALLELISM ): background_activator = "&" prefix="" if stop_omp: prefix = prefix +"export OMP_NUM_THREADS=1 ;" if ( exploit_slurm_mpi==0 ): comando = (prefix +"mpirun -n 1 XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) elif ( exploit_slurm_mpi>0 ): comando = (prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: comando = (prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) res = os.system( comando ) assert (res==0) , " something went wrong running command : " + comando def select_rois( data_path_template=None, filter_path=None, roi_target_path=None, scans_to_use=None ): inputstring = """ create_rois_galaxies : expdata : {data_path_template} filter_path : {filter_path} roiaddress : {roi_target_path} # the target destination for rois scans : {scans_to_use} """ .format(data_path_template = data_path_template, filter_path = filter_path, roi_target_path = roi_target_path, scans_to_use = scans_to_use ) process_input( inputstring , exploit_slurm_mpi = 0 ) def extract_sample_givenrois( roi_path = None, data_path_template = None, monitor_path_template = None, scan_interval = None, Ydim = None, Zdim = None, Edim = None, signals_target_file = None ): inputstring = """ loadscan_2Dimages_galaxies : roiaddress : {roi_path} expdata : {data_path_template} monitor_address : {monitor_path_template} scan_interval : {scan_interval} Ydim : {Ydim} Zdim : {Zdim} Edim : {Edim} signalfile : {signals_target_file} """.format( roi_path = roi_path, data_path_template = data_path_template, monitor_path_template = monitor_path_template, scan_interval = scan_interval, Ydim = Ydim, Zdim = Zdim, Edim = Edim, signals_target_file = signals_target_file) process_input( inputstring, exploit_slurm_mpi = 0) def InterpInfo_Esynt_components(peaks_shifts , energy_exp_grid = None, custom_ene_list = None, custom_components = None ): components = h5py.File( custom_components ,"r")["components"] [()] info_dict = {} for i_interval in range(len(components)): info_dict[str(i_interval)] = {} info_dict[str(i_interval)]["E"] = custom_ene_list[ i_interval ] info_dict[str(i_interval)]["coefficients"]={} for i_n in range(len(energy_exp_grid)): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ]={} for roi_num, de in enumerate( peaks_shifts ): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ][ str(roi_num) ] = 0 for ic in range(len(components)): for i_interval in range(len(custom_ene_list)-1): cE1 = custom_ene_list[ i_interval ] cE2 = custom_ene_list[ i_interval+1 ] for i_ene, t_ene in enumerate( energy_exp_grid) : for roi_num, de in enumerate( peaks_shifts ): if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2+cE1) info_dict[str(ic)]["coefficients" ][ str(i_ene) ][ str(roi_num) ] += alpha * components[ic][ i_interval ] info_dict[str(ic)]["coefficients" ][ str(i_ene) ][ str(roi_num) ] += (1-alpha)*components[ic][ i_interval+1 ] return info_dict def InterpInfo_Esynt( peaks_shifts , energy_exp_grid = None, custom_ene_list = None): print(energy_exp_grid) print(peaks_shifts) info_dict = {"energy_exp_grid":list(energy_exp_grid), "de_list": list(peaks_shifts)} N_custom = len(custom_ene_list) N_data = len( energy_exp_grid ) for i_interval in range(len(custom_ene_list)): info_dict[str(i_interval)] = {} info_dict[str(i_interval)]["E"] = custom_ene_list[ i_interval ] info_dict[str(i_interval)]["coefficients"]={} for i_n in range(len(energy_exp_grid)): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ]={} for roi_num, de in enumerate( peaks_shifts ): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ][ str(roi_num) ] = 0 for i_interval in range( N_custom -1): cE1 = custom_ene_list[ i_interval ] cE2 = custom_ene_list[ i_interval+1 ] for i_ene, t_ene in enumerate( energy_exp_grid) : for roi_num, de in enumerate( peaks_shifts ): if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2-cE1) info_dict[str(i_interval)]["coefficients" ][ str(i_ene) ][ str(roi_num) ] = alpha info_dict[str(i_interval+1)]["coefficients"][ str(i_ene) ][ str(roi_num) ] = 1-alpha return info_dict def __init__(self, peaks_shifts, interp_file, source, custom_ene_list = None): volum_list = list(interp_file[source].keys()) scan_num_list = np.array([ int( t.split("_") [1]) for t in volum_list]) ene_list = np.array([ interp_file[source][vn]["scans"]["Scan%03d"%sn ]["motorDict"]["energy"][()] for vn,sn in zip(volum_list, scan_num_list ) ]) print ( " ecco la scannumlist " , scan_num_list) print (" ecco ene_list", ene_list) self.volum_list = volum_list self.scan_num_list = scan_num_list self.ene_list = ene_list order = np.argsort( self.ene_list ) self.ene_list = self.ene_list [order] if custom_ene_list is None: self.custom_ene_list = self.ene_list else: self.custom_ene_list = custom_ene_list self.scan_num_list = self.scan_num_list [order] self.volum_list = [ self.volum_list [ii] for ii in order ] self.interp_file=interp_file self.source= source self.peaks_shifts=peaks_shifts # info_dict={} # for i in range(NC): # dizio = {} # info_dict[str(i)] = {"coefficients":dizio} # c = model.components_[i] # np = len(c) # for j in range(np): # dizio[str(j)] = float(c[j]) # json.dump(info_dict,open( interpolation_infos_file,"w"), indent=4) def interpola_Esynt(self, roi_sel=roi_sel ): print ( " ECCO I DATI ") print ( self.ene_list ) print ( self.peaks_shifts ) info_dict = {} for i_intervallo in range(len(self.custom_ene_list)): info_dict[str(i_intervallo)] = {} info_dict[str(i_intervallo)]["E"] = self.custom_ene_list[ i_intervallo ] info_dict[str(i_intervallo)]["coefficients"]={} for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list )): info_dict[str(i_intervallo)]["coefficients" ][ t_vn ]={} for i_intervallo in range(len(self.custom_ene_list)-1): cE1 = self.custom_ene_list[ i_intervallo ] cE2 = self.custom_ene_list[ i_intervallo+1 ] for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list ))[0:]: for roi_num, de in enumerate( self.peaks_shifts ): if roi_num not in roi_sel: continue if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2-cE1) info_dict[str(i_intervallo)]["coefficients" ][ str(t_vn) ][ str(roi_num) ] = alpha info_dict[str(i_intervallo+1)]["coefficients"][ str(t_vn) ][ str(roi_num) ] = 1-alpha return info_dict def get_reference( roi_path = None, data_path_template = None, monitor_path_template = None , reference_scan_list = None, extracted_reference_target_file = None, isolate_spot_by = None ): signal_path = extracted_reference_target_file + ":/" input_string = """ loadscan_2Dimages_galaxies_foilscan : roiaddress : {roi_path} expdata : {data_path_template} signalfile : "{extracted_reference_target_file}" isolateSpot : {isolate_spot_by} scan_list : {reference_scan_list} """ s=input_string.format( data_path_template = data_path_template, reference_scan_list = reference_scan_list, roi_path = roi_path, isolate_spot_by = isolate_spot_by, signal_path = signal_path, extracted_reference_target_file = extracted_reference_target_file ) process_input( s , exploit_slurm_mpi = 0) def get_scalars( iE = None, signals_file = None, reference_file = None, target_file = None ): inputstring = """ superR_scal_deltaXimages_Esynt : sample_address : {signals_file}:/E{iE} delta_address : {reference_file}:/rois_and_reference/Scan0 load_factors_from : nbin : 1 target_address : {target_file}:/{iE}/scal_prods """ . format( iE = iE, signals_file = signals_file , reference_file = reference_file , target_file = target_file, ) process_input( inputstring, exploit_slurm_mpi = 0) def get_volume_Esynt( scalarprods_file = None, interpolation_file = None): os.system("mkdir DATASFORCC") inputstring = """ superR_getVolume_Esynt : scalprods_address : {scalarprods_file}:/ dict_interp : {interpolation_file} output_prefix : DATASFORCC/test0_ """.format( scalarprods_file = scalarprods_file , interpolation_file = interpolation_file ) process_input( inputstring, exploit_slurm_mpi = 0) def myOrder(tok): if("volume" not in tok): tokens = tok.split("_") print( tokens) return int(tokens[1])*10000+ int(tokens[2]) else: return 0 def reshuffle( volumefile = "volumes.h5", nick = None ): h5file_root = h5py.File( volumefile ,"r+" ) h5file = h5file_root[nick] scankeys = list( h5file.keys()) scankeys.sort(key=myOrder) print( scankeys) volumes = [] for k in scankeys: if k[:1]!="_": continue print( k) if "volume" in h5file[k]: volumes.append( h5file[k]["volume"] ) # volume = np.concatenate(volumes,axis=0) volume = np.array(volumes) if "concatenated_volume" in h5file: del h5file["concatenated_volume"] h5file["concatenated_volume"]=volume h5file_root.close() ## THE FOLLOWING PART IS THE RELEVANT ONE def tools_sequencer( peaks_shifts = None, filter_path = None, roi_scan_num = None, roi_target_path = None, data_path_template = None, reference_data_path_template = None, steps_to_do = None, scan_interval = None, Ydim = None, Zdim = None, Edim = None, monitor_path_template = None, signals_target_file = None, reference_scan_list = None, reference_clip = None, extracted_reference_target_file = None , isolate_spot_by = None, response_target_file = None, response_fit_options = None, resynthetised_reference_and_roi_target_file = None , resynth_z_square = None, selected_rois = None, scalar_products_target_file = None, energy_custom_grid = None , custom_components_file = None, interpolation_infos_file = None, energy_exp_grid = None ) : if(steps_to_do["do_step_make_roi"]): # ROI selection and reference scan select_rois( data_path_template = reference_data_path_template, filter_path = filter_path, roi_target_path = roi_target_path, scans_to_use = roi_scan_num ) roi_path = roi_target_path if("do_step_make_reference" in steps_to_do and steps_to_do["do_step_make_reference"]): get_reference( roi_path = roi_path , reference_target_file = resynthetised_reference_and_roi_target_file ) reference_file = resynthetised_reference_and_roi_target_file if(steps_to_do["do_step_sample_extraction"]): # SAMPLE extraction extract_sample_givenrois( roi_path = roi_path , data_path_template = data_path_template , monitor_path_template = monitor_path_template , scan_interval = scan_interval , Ydim = Ydim , Zdim = Zdim , Edim = Edim , signals_target_file = signals_target_file ) signals_file = signals_target_file if(steps_to_do["do_step_extract_reference_scan"]): # of course we need the REFERENCE SCAN get_reference( roi_path = roi_path, data_path_template = reference_data_path_template, monitor_path_template = monitor_path_template , extracted_reference_target_file = extracted_reference_target_file, isolate_spot_by = isolate_spot_by, reference_scan_list = reference_scan_list ) if reference_clip is not None: clip1, clip2= reference_clip print(extracted_reference_target_file ) ftarget = h5py.File( extracted_reference_target_file ,"r+") target_group = ftarget["Scan0" ] for k in target_group.keys(): if k != "motorDict": for dsn in ["matrix"]: mat = target_group[k][dsn][()] del target_group[k][dsn] target_group[k][dsn] = mat[clip1:clip2] ftarget.close() if(steps_to_do["do_step_fit_reference_response"]): synthetise_response( scan_address= extracted_reference_target_file +":Scan0" , target_address = response_target_file +":/FIT", response_fit_options = response_fit_options ) if(steps_to_do["do_step_resynthetise_reference"]): resynthetise_scan( old_scan_address= extracted_reference_target_file +":/Scan0" , response_file = response_target_file +":/FIT", target_address = resynthetised_reference_and_roi_target_file + ":/rois_and_reference", original_roi_path = roi_path, resynth_z_square = resynth_z_square ) if(steps_to_do["do_step_scalar"]): os.system("rm %s"%scalar_products_target_file) for iE in range(Edim) : get_scalars( iE = iE, signals_file = signals_file, reference_file = resynthetised_reference_and_roi_target_file, target_file = scalar_products_target_file ) scalarprods_file = scalar_products_target_file interpolation_infos_file = "interpolation_infos.json" if(steps_to_do["do_step_interpolation_coefficients"]): # INTERPOLATION ESYNTH if custom_components_file is None: info_dict = InterpInfo_Esynt( peaks_shifts , energy_exp_grid = energy_exp_grid, custom_ene_list = energy_custom_grid ) else: info_dict = InterpInfo_Esynt_components( peaks_shifts, energy_exp_grid = energy_exp_grid, custom_ene_list = energy_custom_grid, custom_components = custom_components_file ) json.dump(info_dict,open( interpolation_infos_file,"w"), indent=4) # ### ESYNTH if(steps_to_do["do_step_finalise_for_fit"]): get_volume_Esynt( scalarprods_file = scalarprods_file, interpolation_file = interpolation_infos_file) xrstools-0.15.0+git20210910+c147919d/XRStools/tools_sequencer_esynth_galaxies.py~000066400000000000000000000536241412732462000271520ustar00rootroot00000000000000def synthetise_response(scan_address=None, target_address=None, response_fit_options = None ): input_string = """ superR_fit_responses : foil_scan_address : "{scan_address}" nref : 7 # the number of subdivision per pixel dimension used to # represent the optical response function at higher resolution niter_optical : {niter_optical} # the number of iterations used in the optimisation of the optical # response beta_optical : {beta_optical} # The L1 norm factor in the regularisation # term for the optical functions pixel_dim : 1 # The pixel response function is represented with a # pixel_dim**2 array niter_pixel : 10 # The number of iterations in the pixel response optimisation # phase. A negative number stands for ISTA, positive for FISTA beta_pixel : 0.0 # L1 factor for the pixel response regularisation ## The used trajectories are always written whith the calculated response ## They can be reloaded and used as initialization(and freezed with do_refine_trajectory : 0 ) ## Uncomment the following line if you want to reload a set of trajectories ## without this options trajectories are initialised from the spots drifts ## # reload_trajectories_file : "response.h5" filter_rois : 0 ###### ## The method first find an estimation of the foil scan trajectory on each roi ## then, based on this, obtain a fit of the optical response function ## assuming a flat pixel response. Finally the pixel response is optimised ## ## There is a final phase where a global optimisation ## is done in niter_global steps. ## ## Each step is composed of optical response fit, followed by a pixel response fit. ## If do_refine_trajectory is different from zero, the trajectory is reoptimised at each step ## niter_global : {niter_global} ## if do_refine_trajectory=1 the start and end point of the trajectory are free ## if =2 then the start and end point are forced to a trajectory which is obtained ## from a reference scan : the foil scan may be short, then one can use the scan of ## an object to get another one : key *trajectory_reference_scan_address* ## do_refine_trajectory : 1 ## optional: only if do_refine_trajectory = 2 trajectory_reference_scansequence_address : "demo_newrois.h5:/ROI_FOIL/images/scans/" trajectory_threshold : 0.1 ## if the pixel response function is forced to be symmetrical simmetrizza : 1 ## where the found responses are written target_file : {target_address} # target_file : "fitted_responses.h5" """ s=input_string.format( scan_address=scan_address , target_address=target_address, niter_optical = response_fit_options[ "niter_optical"], beta_optical=response_fit_options["beta_optical"], niter_global=response_fit_options["niter_global"] ) process_input( s , exploit_slurm_mpi = 1, stop_omp = True) def resynthetise_scan( old_scan_address= None, response_file = None , target_address = None, original_roi_path = None, resynth_z_square = None ): input_string = """ superR_recreate_rois : ### we have calculated the responses in responsefilename ### and we want to enlarge the scan by a margin of 3 times ### the original scan on the right and on the left ### ( so for a total of a 7 expansion factor ) responsefilename : {response_file} nex : 0 ## the old scan covered by the old rois old_scan_address : {old_scan_address} ## where new rois and bnew scan are written target_filename : {target_address} filter_rois : 0 original_roi_path : {original_roi_path} resynth_z_square : {resynth_z_square} """ s=input_string.format( response_file = response_file , target_address = target_address, old_scan_address=old_scan_address, original_roi_path = original_roi_path +"/rois_definition", resynth_z_square = resynth_z_square) process_input( s , exploit_slurm_mpi = 0, stop_omp = True) def process_input(s, go=0, exploit_slurm_mpi = 0, stop_omp = False): open("input_tmp_%d.par"%go, "w").write(s) background_activator = "" if (go % BATCH_PARALLELISM ): background_activator = "&" prefix="" if stop_omp: prefix = prefix +"export OMP_NUM_THREADS=1 ;" if ( exploit_slurm_mpi==0 ): comando = (prefix +"mpirun -n 1 XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) elif ( exploit_slurm_mpi>0 ): comando = (prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: comando = (prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) res = os.system( comando ) assert (res==0) , " something went wrong running command : " + comando def select_rois( data_path_template=None, filter_path=None, roi_target_path=None, scans_to_use=None ): inputstring = """ create_rois_galaxies : expdata : {data_path_template} filter_path : {filter_path} roiaddress : {roi_target_path} # the target destination for rois scans : {scans_to_use} """ .format(data_path_template = data_path_template, filter_path = filter_path, roi_target_path = roi_target_path, scans_to_use = scans_to_use ) process_input( inputstring , exploit_slurm_mpi = 0 ) def extract_sample_givenrois( roi_path = None, data_path_template = None, monitor_path_template = None, scan_interval = None, Ydim = None, Zdim = None, Edim = None, signals_target_file = None ): inputstring = """ loadscan_2Dimages_galaxies : roiaddress : {roi_path} expdata : {data_path_template} monitor_address : {monitor_path_template} scan_interval : {scan_interval} Ydim : {Ydim} Zdim : {Zdim} Edim : {Edim} signalfile : {signals_target_file} """.format( roi_path = roi_path, data_path_template = data_path_template, monitor_path_template = monitor_path_template, scan_interval = scan_interval, Ydim = Ydim, Zdim = Zdim, Edim = Edim, signals_target_file = signals_target_file) process_input( inputstring, exploit_slurm_mpi = 0) def InterpInfo_Esynt_components(peaks_shifts , energy_exp_grid = None, custom_ene_list = None, custom_components = None ): components = h5py.File( custom_components ,"r")["components"] [()] info_dict = {} for i_interval in range(len(components)): info_dict[str(i_interval)] = {} info_dict[str(i_interval)]["E"] = custom_ene_list[ i_interval ] info_dict[str(i_interval)]["coefficients"]={} for i_n in range(len(energy_exp_grid)): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ]={} for roi_num, de in enumerate( peaks_shifts ): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ][ str(roi_num) ] = 0 for ic in range(len(components)): for i_interval in range(len(custom_ene_list)-1): cE1 = custom_ene_list[ i_interval ] cE2 = custom_ene_list[ i_interval+1 ] for i_ene, t_ene in enumerate( energy_exp_grid) : for roi_num, de in enumerate( peaks_shifts ): if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2+cE1) info_dict[str(ic)]["coefficients" ][ str(i_ene) ][ str(roi_num) ] += alpha * components[ic][ i_interval ] info_dict[str(ic)]["coefficients" ][ str(i_ene) ][ str(roi_num) ] += (1-alpha)*components[ic][ i_interval+1 ] return info_dict def InterpInfo_Esynt( peaks_shifts , energy_exp_grid = None, custom_ene_list = None): print(energy_exp_grid) print(peaks_shifts) info_dict = {"energy_exp_grid":list(energy_exp_grid), "de_list": list(peaks_shifts)} N_custom = len(custom_ene_list) N_data = len( energy_exp_grid ) for i_interval in range(len(custom_ene_list)): info_dict[str(i_interval)] = {} info_dict[str(i_interval)]["E"] = custom_ene_list[ i_interval ] info_dict[str(i_interval)]["coefficients"]={} for i_n in range(len(energy_exp_grid)): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ]={} for roi_num, de in enumerate( peaks_shifts ): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ][ str(roi_num) ] = 0 for i_interval in range( N_custom -1): cE1 = custom_ene_list[ i_interval ] cE2 = custom_ene_list[ i_interval+1 ] for i_ene, t_ene in enumerate( energy_exp_grid) : for roi_num, de in enumerate( peaks_shifts ): if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2-cE1) info_dict[str(i_interval)]["coefficients" ][ str(i_ene) ][ str(roi_num) ] = alpha info_dict[str(i_interval+1)]["coefficients"][ str(i_ene) ][ str(roi_num) ] = 1-alpha return info_dict def __init__(self, peaks_shifts, interp_file, source, custom_ene_list = None): volum_list = list(interp_file[source].keys()) scan_num_list = np.array([ int( t.split("_") [1]) for t in volum_list]) ene_list = np.array([ interp_file[source][vn]["scans"]["Scan%03d"%sn ]["motorDict"]["energy"][()] for vn,sn in zip(volum_list, scan_num_list ) ]) print ( " ecco la scannumlist " , scan_num_list) print (" ecco ene_list", ene_list) self.volum_list = volum_list self.scan_num_list = scan_num_list self.ene_list = ene_list order = np.argsort( self.ene_list ) self.ene_list = self.ene_list [order] if custom_ene_list is None: self.custom_ene_list = self.ene_list else: self.custom_ene_list = custom_ene_list self.scan_num_list = self.scan_num_list [order] self.volum_list = [ self.volum_list [ii] for ii in order ] self.interp_file=interp_file self.source= source self.peaks_shifts=peaks_shifts # info_dict={} # for i in range(NC): # dizio = {} # info_dict[str(i)] = {"coefficients":dizio} # c = model.components_[i] # np = len(c) # for j in range(np): # dizio[str(j)] = float(c[j]) # json.dump(info_dict,open( interpolation_infos_file,"w"), indent=4) def interpola_Esynt(self, roi_sel=roi_sel ): print ( " ECCO I DATI ") print ( self.ene_list ) print ( self.peaks_shifts ) info_dict = {} for i_intervallo in range(len(self.custom_ene_list)): info_dict[str(i_intervallo)] = {} info_dict[str(i_intervallo)]["E"] = self.custom_ene_list[ i_intervallo ] info_dict[str(i_intervallo)]["coefficients"]={} for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list )): info_dict[str(i_intervallo)]["coefficients" ][ t_vn ]={} for i_intervallo in range(len(self.custom_ene_list)-1): cE1 = self.custom_ene_list[ i_intervallo ] cE2 = self.custom_ene_list[ i_intervallo+1 ] for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list ))[0:]: for roi_num, de in enumerate( self.peaks_shifts ): if roi_num not in roi_sel: continue if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2-cE1) info_dict[str(i_intervallo)]["coefficients" ][ str(t_vn) ][ str(roi_num) ] = alpha info_dict[str(i_intervallo+1)]["coefficients"][ str(t_vn) ][ str(roi_num) ] = 1-alpha return info_dict def get_reference( roi_path = None, data_path_template = None, monitor_path_template = None , reference_scan_list = None, extracted_reference_target_file = None, isolate_spot_by = None ): signal_path = extracted_reference_target_file + ":/" input_string = """ loadscan_2Dimages_galaxies_foilscan : roiaddress : {roi_path} expdata : {data_path_template} signalfile : "{extracted_reference_target_file}" isolateSpot : {isolate_spot_by} scan_list : {reference_scan_list} """ s=input_string.format( data_path_template = data_path_template, reference_scan_list = reference_scan_list, roi_path = roi_path, isolate_spot_by = isolate_spot_by, signal_path = signal_path, extracted_reference_target_file = extracted_reference_target_file ) process_input( s , exploit_slurm_mpi = 0) def get_scalars( iE = None, signals_file = None, reference_file = None, target_file = None ): inputstring = """ superR_scal_deltaXimages_Esynt : sample_address : {signals_file}:/E{iE} delta_address : {reference_file}:/rois_and_reference/Scan0 load_factors_from : nbin : 1 target_address : {target_file}:/{iE}/scal_prods """ . format( iE = iE, signals_file = signals_file , reference_file = reference_file , target_file = target_file, ) process_input( inputstring, exploit_slurm_mpi = 0) def get_volume_Esynt( scalarprods_file = None, interpolation_file = None): os.system("mkdir DATASFORCC") inputstring = """ superR_getVolume_Esynt : scalprods_address : {scalarprods_file}:/ dict_interp : {interpolation_file} output_prefix : DATASFORCC/test0_ """.format( scalarprods_file = scalarprods_file , interpolation_file = interpolation_file ) process_input( inputstring, exploit_slurm_mpi = 0) def myOrder(tok): if("volume" not in tok): tokens = tok.split("_") print( tokens) return int(tokens[1])*10000+ int(tokens[2]) else: return 0 def reshuffle( volumefile = "volumes.h5", nick = None ): h5file_root = h5py.File( volumefile ,"r+" ) h5file = h5file_root[nick] scankeys = list( h5file.keys()) scankeys.sort(key=myOrder) print( scankeys) volumes = [] for k in scankeys: if k[:1]!="_": continue print( k) if "volume" in h5file[k]: volumes.append( h5file[k]["volume"] ) # volume = np.concatenate(volumes,axis=0) volume = np.array(volumes) if "concatenated_volume" in h5file: del h5file["concatenated_volume"] h5file["concatenated_volume"]=volume h5file_root.close() ## THE FOLLOWING PART IS THE RELEVANT ONE def tools_sequencer( peaks_shifts = None, filter_path = None, roi_scan_num = None, roi_target_path = None, data_path_template = None, reference_data_path_template = None, steps_to_do = None, scan_interval = None, Ydim = None, Zdim = None, Edim = None, monitor_path_template = None, signals_target_file = None, reference_scan_list = None, reference_clip = None, extracted_reference_target_file = None , isolate_spot_by = None, response_target_file = None, response_fit_options = None, resynthetised_reference_and_roi_target_file = None , resynth_z_square = None, selected_rois = None, scalar_products_target_file = None, energy_custom_grid = None , custom_components_file = None, interpolation_infos_file = None, energy_exp_grid = None ) : if(steps_to_do["do_step_make_roi"]): # ROI selection and reference scan select_rois( data_path_template = reference_data_path_template, filter_path = filter_path, roi_target_path = roi_target_path, scans_to_use = roi_scan_num ) roi_path = roi_target_path if("do_step_make_reference" in steps_to_do and steps_to_do["do_step_make_reference"]): get_reference( roi_path = roi_path , reference_target_file = resynthetised_reference_and_roi_target_file ) reference_file = resynthetised_reference_and_roi_target_file if(steps_to_do["do_step_sample_extraction"]): # SAMPLE extraction extract_sample_givenrois( roi_path = roi_path , data_path_template = data_path_template , monitor_path_template = monitor_path_template , scan_interval = scan_interval , Ydim = Ydim , Zdim = Zdim , Edim = Edim , signals_target_file = signals_target_file ) signals_file = signals_target_file if(steps_to_do["do_step_extract_reference_scan"]): # of course we need the REFERENCE SCAN get_reference( roi_path = roi_path, data_path_template = reference_data_path_template, monitor_path_template = monitor_path_template , extracted_reference_target_file = extracted_reference_target_file, isolate_spot_by = isolate_spot_by, reference_scan_list = reference_scan_list ) if reference_clip is not None: clip1, clip2= reference_clip print(extracted_reference_target_file ) ftarget = h5py.File( extracted_reference_target_file ,"r+") target_group = ftarget["Scan0" ] for k in target_group.keys(): if k != "motorDict": for dsn in ["matrix"]: mat = target_group[k][dsn][()] del target_group[k][dsn] target_group[k][dsn] = mat[clip1:clip2] ftarget.close() if(steps_to_do["do_step_fit_reference_response"]): synthetise_response( scan_address= extracted_reference_target_file +":Scan0" , target_address = +":/FIT", response_fit_options = response_fit_options ) if(steps_to_do["do_step_resynthetise_reference"]): resynthetise_scan( old_scan_address= extracted_reference_target_file +":/Scan0" , response_file = response_target_file +":/FIT", target_address = resynthetised_reference_and_roi_target_file + ":/rois_and_reference", original_roi_path = roi_path, resynth_z_square = resynth_z_square ) if(steps_to_do["do_step_scalar"]): os.system("rm %s"%scalar_products_target_file) for iE in range(Edim) : get_scalars( iE = iE, signals_file = signals_file, reference_file = resynthetised_reference_and_roi_target_file, target_file = scalar_products_target_file ) scalarprods_file = scalar_products_target_file interpolation_infos_file = "interpolation_infos.json" if(steps_to_do["do_step_interpolation_coefficients"]): # INTERPOLATION ESYNTH if custom_components_file is None: info_dict = InterpInfo_Esynt( peaks_shifts , energy_exp_grid = energy_exp_grid, custom_ene_list = energy_custom_grid ) else: info_dict = InterpInfo_Esynt_components( peaks_shifts, energy_exp_grid = energy_exp_grid, custom_ene_list = energy_custom_grid, custom_components = custom_components_file ) json.dump(info_dict,open( interpolation_infos_file,"w"), indent=4) # ### ESYNTH if(steps_to_do["do_step_finalise_for_fit"]): get_volume_Esynt( scalarprods_file = scalarprods_file, interpolation_file = interpolation_infos_file) xrstools-0.15.0+git20210910+c147919d/XRStools/tools_sequencer_interp.py000066400000000000000000000573031412732462000250640ustar00rootroot00000000000000import numpy as np import h5py import glob import json import os import h5py import math BATCH_PARALLELISM = 1 MAXNPROCS=0 import os try: import mpi4py _has_mpi4py_ = True print(" INFO: mpi4py loaded") except: _has_mpi4py_ = False print(" WARNING: mpi4py not available") def process_input(s, go=0, exploit_slurm_mpi = 0, stop_omp = False): open("input_tmp_%d.par"%go, "w").write(s) background_activator = "" if (go % BATCH_PARALLELISM ): background_activator = "&" prefix="" if stop_omp: prefix = prefix +"export OMP_NUM_THREADS=1 ;" if _has_mpi4py_ : if exploit_slurm_mpi>0 and MAXNPROCS: exploit_slurm_mpi = -MAXNPROCS if ( exploit_slurm_mpi==0 ): os.system(prefix +"mpirun -n 1 XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) elif ( exploit_slurm_mpi>0 ): os.system(prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: os.system(prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) else: os.system(prefix +"XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) def select_rois( datadir = None, roi_scan_num=None, roi_target_path = None, filter_path = None): if np.isscalar(roi_scan_num): scans = [roi_scan_num] else: scans = list(roi_scan_num) input_string = """ create_rois: expdata : {expdata} scans : {scans} roiaddress : {roi_target_path} filter_path : {filter_path} """.format( expdata = os.path.join( datadir, "hydra"), scans = scans, roi_target_path = roi_target_path, filter_path = filter_path ) process_input( input_string, exploit_slurm_mpi = 0 ) def get_reference( roi_path = None, datadir = None, reference_scan_list = None, monitor_column = None, extracted_reference_target_file = None, isolate_spot_by = None ): signal_path = extracted_reference_target_file + ":/" input_string = """ loadscan_2Dimages : expdata : {expdata} roiaddress : {roi_path} monitorcolumn : {monitor_column} scan_list : {reference_scan_list} signaladdress : "{extracted_reference_target_file}:/references" isolateSpot : {isolate_spot_by} save_also_roi : True sumto1D : 0 energycolumn : 'stx' """ s=input_string.format( expdata = os.path.join( datadir, "hydra"), reference_scan_list = reference_scan_list, monitor_column = monitor_column, roi_path = roi_path, isolate_spot_by = isolate_spot_by, signal_path = signal_path, extracted_reference_target_file = extracted_reference_target_file ) process_input( s , exploit_slurm_mpi = 0) def extract_sample_givenrois( roi_path = None, datadir = None, Start = None, End = None, Thickness = None , monitor_column = None, signals_target_file = None, ): for start in range(Start,End, Thickness): end = start+Thickness signal_path = signals_target_file + ":/_{start}_{end}".format(start=start, end=end) input_string = """ loadscan_2Dimages : expdata : {expdata} roiaddress : {roi_path} scan_interval : [{start}, {end}] energycolumn : sty signaladdress : {signal_path} monitorcolumn : {monitor_column} sumto1D : 0 """.format( expdata = os.path.join( datadir, "hydra"), roi_path = roi_path, start = start, end = end, monitor_column = monitor_column, signal_path = signal_path ) process_input(input_string, exploit_slurm_mpi = 1) def interpolate( peaks_shifts, interp_file_str, interp_file_target_str): interp_file = h5py.File( interp_file_str ,"r+") interp_file_target = h5py.File( interp_file_target_str ,"r+") volum_list = list(interp_file.keys()) scan_num_list = np.array([ int( t.split("_") [1]) for t in volum_list]) ene_list = np.array([ interp_file[vn]["scans"]["Scan%03d"%sn ]["motorDict"]["energy"].value for vn,sn in zip(volum_list, scan_num_list ) ]) print ( " ecco la scannumlist " , scan_num_list) print (" ecco ene_list", ene_list) order = np.argsort( ene_list ) ene_list = ene_list [order] scan_num_list = scan_num_list [order] volum_list = [ volum_list [ii] for ii in order ] # raise for t_vn, t_sn, t_ene in list(zip(volum_list, scan_num_list, ene_list ))[0:]: rois_coeffs={} for roi_num, de in enumerate( peaks_shifts ): print ( roi_num, "===== " , t_ene+de , ene_list .min() , t_ene+de , ene_list .max() ) if np.isnan(t_ene+de ): continue if t_ene+de < ene_list .min() or t_ene+de > ene_list .max(): continue print ( " CONTINUO ", t_ene+de, ene_list .min() ,ene_list .max() ) i0 = np.searchsorted( ene_list , t_ene+de )-1 assert(i0>=0) i1=i0+1 print (i0, i1, len(ene_list)) print (ene_list) assert(i1 mask.h5 ") filter_path = "mask.h5:/FILTER_MASK/filter" roi_scan_num = [245,246,247] reference_scan_list = [245, 246, 247] monitor_column = "izero/0.000001" first_scan_num = 651 Ydim = 25 Zdim = 10 Edim = 7 peaks_shifts = h5py.File("../peaks_positions_for_analysers.h5","r")["peaks_positions"][()] assert( len(peaks_shifts) == 72) Enominal = np.median(peaks_shifts) peaks_shifts-= Enominal datadir = "/data/id20/inhouse/data/run3_20/run3_es949" # If reference_clip is not None, then a smaller part of the reference scan is considered # This may be usefule to obtain smaller volumes containing the interesting part # The used reference scan will the correspond to the positions from reference_clip[0] to reference_clip[1]-1 included ########### # reference_clip = None reference_clip = [ 90, 180 ] ## in the reference scan for each position there is a spot with a maximum. We set zero the background which is further than ## such radius from the maximum isolate_spot_by = 6 #### For the fit of the response function based on reference scans response_fit_options = dict( [ ["niter_optical" , 100], ["beta_optical" , 0.1], ["niter_global" , 3 ] ]) selected_rois = list(range(0,24)) + list( range(36,60) ) scal_prod_use_optional_solution = True volume_retrieval_beta = 6.0e-20 volume_retrieval_niter = 100 steps_to_do = { "do_step_make_roi": False, "do_step_sample_extraction": False, "do_step_interpolation": False, "do_step_extract_reference_scan": False, "do_step_fit_reference_response": False, "do_step_resynthetise_reference": False, "do_step_scalars" : True, "do_step_volume_retrieval" : False, } os.system("mkdir results") scalar_products_and_volume_target_file = "results/scalar_products_and_volume.h5" roi_target_path = "results/myrois.h5:/ROIS" signals_target_file = "results/signals.h5" interpolated_signals_target_file = "results/interpolated_signals.h5" extracted_reference_target_file = "results/reference.h5" response_target_file = "results/response.h5" ############# correggi typo resynthetised_reference_and_roi_target_file = "results/resyntetised_roi_and_scan.h5" tools_sequencer( peaks_shifts = peaks_shifts , datadir = datadir , filter_path = filter_path , roi_scan_num = roi_scan_num , roi_target_path = roi_target_path , steps_to_do = steps_to_do, first_scan_num = first_scan_num, Ydim = Ydim , Zdim = Zdim , Edim = Edim , monitor_column = monitor_column, signals_target_file = signals_target_file, interpolated_signals_target_file = interpolated_signals_target_file, reference_scan_list = reference_scan_list, reference_clip = reference_clip, extracted_reference_target_file = extracted_reference_target_file , isolate_spot_by = isolate_spot_by, response_target_file = response_target_file, response_fit_options = response_fit_options, resynthetised_reference_and_roi_target_file = resynthetised_reference_and_roi_target_file, selected_rois = selected_rois, scal_prod_use_optional_solution = scal_prod_use_optional_solution , scalar_products_and_volume_target_file = scalar_products_and_volume_target_file , volume_retrieval_beta = volume_retrieval_beta , volume_retrieval_niter = volume_retrieval_niter ) def process_input(s, go=0, exploit_slurm_mpi = 0, stop_omp = False): open("input_tmp_%d.par"%go, "w").write(s) background_activator = "" if (go % BATCH_PARALLELISM ): background_activator = "&" prefix="" if stop_omp: prefix = prefix +"export OMP_NUM_THREADS=1 ;" if ( exploit_slurm_mpi==0 ): os.system(prefix +"mpirun -n 1 XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) elif ( exploit_slurm_mpi>0 ): os.system(prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: os.system(prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) def select_rois( datadir = None, roi_scan_num=None, roi_target_path = None, filter_path = None): if np.isscalar(roi_scan_num): scans = [roi_scan_num] else: scans = list(roi_scan_num) input_string = """ create_rois: expdata : {expdata} scans : {scans} roiaddress : {roi_target_path} filter_path : {filter_path} """.format( expdata = os.path.join( datadir, "hydra"), scans = scans, roi_target_path = roi_target_path, filter_path = filter_path ) process_input( input_string, exploit_slurm_mpi = 0 ) def get_reference( roi_path = None, datadir = None, reference_scan_list = None, monitor_column = None, extracted_reference_target_file = None, isolate_spot_by = None ): signal_path = extracted_reference_target_file + ":/" input_string = """ loadscan_2Dimages : expdata : {expdata} roiaddress : {roi_path} monitorcolumn : {monitor_column} scan_list : {reference_scan_list} signaladdress : "{extracted_reference_target_file}:/references" isolateSpot : {isolate_spot_by} save_also_roi : True sumto1D : 0 energycolumn : 'stx' """ s=input_string.format( expdata = os.path.join( datadir, "hydra"), reference_scan_list = reference_scan_list, monitor_column = monitor_column, roi_path = roi_path, isolate_spot_by = isolate_spot_by, signal_path = signal_path, extracted_reference_target_file = extracted_reference_target_file ) process_input( s , exploit_slurm_mpi = 0) def extract_sample_givenrois( roi_path = None, datadir = None, Start = None, End = None, Thickness = None , monitor_column = None, signals_target_file = None, ): for start in range(Start,End, Thickness): end = start+Thickness signal_path = signals_target_file + ":/_{start}_{end}".format(start=start, end=end) input_string = """ loadscan_2Dimages : expdata : {expdata} roiaddress : {roi_path} scan_interval : [{start}, {end}] energycolumn : sty signaladdress : {signal_path} monitorcolumn : {monitor_column} sumto1D : 0 """.format( expdata = os.path.join( datadir, "hydra"), roi_path = roi_path, start = start, end = end, monitor_column = monitor_column, signal_path = signal_path ) process_input(input_string, exploit_slurm_mpi = 1) def interpolate( peaks_shifts, interp_file_str, interp_file_target_str): interp_file = h5py.File( interp_file_str ,"r+") interp_file_target = h5py.File( interp_file_target_str ,"r+") volum_list = list(interp_file.keys()) scan_num_list = np.array([ int( t.split("_") [1]) for t in volum_list]) ene_list = np.array([ interp_file[vn]["scans"]["Scan%03d"%sn ]["motorDict"]["energy"].value for vn,sn in zip(volum_list, scan_num_list ) ]) print ( " ecco la scannumlist " , scan_num_list) print (" ecco ene_list", ene_list) order = np.argsort( ene_list ) ene_list = ene_list [order] scan_num_list = scan_num_list [order] volum_list = [ volum_list [ii] for ii in order ] # raise for t_vn, t_sn, t_ene in list(zip(volum_list, scan_num_list, ene_list ))[0:]: rois_coeffs={} for roi_num, de in enumerate( peaks_shifts ): print ( roi_num, "===== " , t_ene+de , ene_list .min() , t_ene+de , ene_list .max() ) if t_ene+de < ene_list .min() or t_ene+de > ene_list .max(): continue print ( " CONTINUO ", t_ene+de, ene_list .min() ,ene_list .max() ) i0 = np.searchsorted( ene_list , t_ene+de )-1 assert(i0>=0) i1=i0+1 print (i0, i1, len(ene_list)) print (ene_list) assert(i10 ): comando = (prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: comando = (prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) res = os.system( comando ) assert (res==0) , " something went wrong running command : " + comando def select_rois( data_path_template=None, filter_path=None, roi_target_path=None, scans_to_use=None ): inputstring = """ create_rois_galaxies : expdata : {data_path_template} filter_path : {filter_path} roiaddress : {roi_target_path} # the target destination for rois scans : {scans_to_use} """ .format(data_path_template = data_path_template, filter_path = filter_path, roi_target_path = roi_target_path, scans_to_use = scans_to_use ) process_input( inputstring , exploit_slurm_mpi = 0 ) def extract_sample_givenrois( roi_path = None, data_path_template = None, monitor_path_template = None, scan_interval = None, Ydim = None, Zdim = None, Edim = None, signals_target_file = None ): inputstring = """ loadscan_2Dimages_galaxies : roiaddress : {roi_path} expdata : {data_path_template} monitor_address : {monitor_path_template} scan_interval : {scan_interval} Ydim : {Ydim} Zdim : {Zdim} Edim : {Edim} signalfile : {signals_target_file} """.format( roi_path = roi_path, data_path_template = data_path_template, monitor_path_template = monitor_path_template, scan_interval = scan_interval, Ydim = Ydim, Zdim = Zdim, Edim = Edim, signals_target_file = signals_target_file) process_input( inputstring, exploit_slurm_mpi = 0) def get_reference( roi_path = None, data_path_template = None, monitor_path_template = None , reference_scan_list = None, extracted_reference_target_file = None, isolate_spot_by = None ): signal_path = extracted_reference_target_file + ":/" input_string = """ loadscan_2Dimages_galaxies_foilscan : roiaddress : {roi_path} expdata : {data_path_template} signalfile : "{extracted_reference_target_file}" isolateSpot : {isolate_spot_by} scan_list : {reference_scan_list} """ s=input_string.format( data_path_template = data_path_template, reference_scan_list = reference_scan_list, roi_path = roi_path, isolate_spot_by = isolate_spot_by, signal_path = signal_path, extracted_reference_target_file = extracted_reference_target_file ) process_input( s , exploit_slurm_mpi = 0) def get_scalars( iE = None , reference_address = None, signals_file = None, target_file = None, use_optional_solution=False, save_factors = False, load_factors_from = None, selected_rois = None, scal_prod_use_optional_solution= False, scal_prod_load_factors = False, scal_prod_load_factors_from = None, ): input_string = """ superR_scal_deltaXimages : sample_address : {signals_file}:/E{iE} delta_address : {reference_address} # roi_keys : [60, 64, 35, 69, 34, 24, 5, 6, 71, 70, 39, 58, 56, 33] roi_keys : {selected_rois} nbin : 1 target_address : {target_file}:/E{iE}/scal_prods """ if scal_prod_use_optional_solution: input_string = input_string+""" optional_solution : {target_file}:/E{iE}/volume """ if True: input_string = input_string+""" save_factors_on : factors_{iE}.json """ if scal_prod_load_factors : input_string = input_string+""" load_factors_from : %s """ % scal_prod_load_factors_from input_string = input_string .format( iE=iE, signals_file = signals_file , reference_address = reference_address, target_file = target_file, selected_rois = list(selected_rois) ) process_input( input_string, exploit_slurm_mpi = 0) def get_volume( iE = None, volumes_file= None, niter = None, beta = None, ): inputstring = """ superR_getVolume : scalprods_address : {volumes_file}:/E{iE}/scal_prods target_address : {volumes_file}:/E{iE}/volume niter : {niter} beta : {beta} eps : 2e-07 debin : [1, 1] """ s=inputstring.format( iE = iE, volumes_file = volumes_file, niter = niter, beta = beta ) process_input(s, exploit_slurm_mpi = 0 ) def collect_factors(pattern="factors_*.json", newfile="newfactors.json"): files = glob.glob(pattern) indexes = [ int( (s.split("_")[1]).replace(".json","" ) ) for s in files ] order = np.argsort(indexes) files = [files[i] for i in order] files = files[1:-1] result = {} result2 = {} Nkeys = None for f in files: factors = json.load(open(f,"r")) if Nkeys is None: Nkeys = len(list(factors.keys())) assert(Nkeys == len(list(factors.keys())) ) for k,val in factors.items(): if k not in result: result[k] = 0.0 result2[k] = 0.0 result[k] += factors[k]/ len(files) result2[k] += (factors[k]*factors[k])/ len(files) json.dump(result,open(newfile,"w") ) keys = list(result.keys() ) keys.sort(key=int) for k in keys: print( k , " ", result[k] , " " , math.sqrt( result2[k] - result[k]*result[k] ) / result[k] ) def get_volume_Esynt( scalarprods_file = None, interpolation_file = None): os.system("mkdir DATASFORCC") inputstring = """ superR_getVolume_Esynt : scalprods_address : {scalarprods_file}:/ dict_interp : {interpolation_file} output_prefix : DATASFORCC/test0_ """.format( scalarprods_file = scalarprods_file , interpolation_file = interpolation_file ) process_input( inputstring, exploit_slurm_mpi = 0) def myOrder(tok): if("volume" not in tok): tokens = tok.split("_") print( tokens) return int(tokens[1])*10000+ int(tokens[2]) else: return 0 def reshuffle( volumefile = "volumes.h5", nick = None ): h5file_root = h5py.File( volumefile ,"r+" ) h5file = h5file_root[nick] scankeys = list( h5file.keys()) scankeys.sort(key=myOrder) print( scankeys) volumes = [] for k in scankeys: if k[:1]!="_": continue print( k) if "volume" in h5file[k]: volumes.append( h5file[k]["volume"] ) # volume = np.concatenate(volumes,axis=0) volume = np.array(volumes) if "concatenated_volume" in h5file: del h5file["concatenated_volume"] h5file["concatenated_volume"]=volume h5file_root.close() def interpolate( peaks_shifts, interp_file_str, interp_file_target_str, energy_exp_grid): interp_file = h5py.File( interp_file_str ,"r+") interp_file_target = h5py.File( interp_file_target_str ,"r+") volum_list = list(interp_file.keys()) scan_num_list = np.array([ int(''.join(filter(str.isdigit, str(t) ))) for t in volum_list]) ene_list = np.array([ energy_exp_grid[t] for t in scan_num_list ] ) order = np.argsort( ene_list ) ene_list = ene_list [order] scan_num_list = scan_num_list [order] volum_list = [ volum_list [ii] for ii in order ] print(ene_list) # raise ene_list_for_search = np.array(ene_list ,"d") ene_list_for_search[0] -=1.0e-6 ene_list_for_search[-1] +=1.0e-6 for t_vn, t_sn, t_ene in list(zip(volum_list, scan_num_list, ene_list ))[0:]: rois_coeffs={} for roi_num, de in enumerate( peaks_shifts ): print ( roi_num, "===== " , t_ene+de , ene_list_for_search .min() , t_ene+de , ene_list_for_search .max() ) if t_ene+de < ene_list_for_search .min() or t_ene+de > ene_list_for_search .max(): continue i0 = np.searchsorted( ene_list_for_search , t_ene+de )-1 assert(i0>=0) i1=i0+1 print (i0, i1, len(ene_list)) print (ene_list) assert(i10 ): comando = (prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: comando = (prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) res = os.system( comando ) assert (res==0) , " something went wrong running command : " + comando def select_rois( data_path_template=None, filter_path=None, roi_target_path=None, scans_to_use=None ): inputstring = """ create_rois_galaxies : expdata : {data_path_template} filter_path : {filter_path} roiaddress : {roi_target_path} # the target destination for rois scans : {scans_to_use} """ .format(data_path_template = data_path_template, filter_path = filter_path, roi_target_path = roi_target_path, scans_to_use = scans_to_use ) process_input( inputstring , exploit_slurm_mpi = 0 ) def extract_sample_givenrois( roi_path = None, data_path_template = None, monitor_path_template = None, scan_interval = None, Ydim = None, Zdim = None, Edim = None, signals_target_file = None ): inputstring = """ loadscan_2Dimages_galaxies : roiaddress : {roi_path} expdata : {data_path_template} monitor_address : {monitor_path_template} scan_interval : {scan_interval} Ydim : {Ydim} Zdim : {Zdim} Edim : {Edim} signalfile : {signals_target_file} """.format( roi_path = roi_path, data_path_template = data_path_template, monitor_path_template = monitor_path_template, scan_interval = scan_interval, Ydim = Ydim, Zdim = Zdim, Edim = Edim, signals_target_file = signals_target_file) process_input( inputstring, exploit_slurm_mpi = 0) def get_reference( roi_path = None, data_path_template = None, monitor_path_template = None , reference_scan_list = None, extracted_reference_target_file = None, isolate_spot_by = None ): signal_path = extracted_reference_target_file + ":/" input_string = """ loadscan_2Dimages_galaxies_foilscan : roiaddress : {roi_path} expdata : {data_path_template} signalfile : "{extracted_reference_target_file}" isolateSpot : {isolate_spot_by} scan_list : {reference_scan_list} """ s=input_string.format( data_path_template = data_path_template, reference_scan_list = reference_scan_list, roi_path = roi_path, isolate_spot_by = isolate_spot_by, signal_path = signal_path, extracted_reference_target_file = extracted_reference_target_file ) process_input( s , exploit_slurm_mpi = 0) def get_scalars( iE = None , reference_address = None, signals_file = None, target_file = None, use_optional_solution=False, save_factors = False, load_factors_from = None, selected_rois = None, scal_prod_use_optional_solution= False, scal_prod_load_factors = False, scal_prod_load_factors_from = None, ): input_string = """ superR_scal_deltaXimages : sample_address : {signals_file}:/E{iE} delta_address : {reference_address} # roi_keys : [60, 64, 35, 69, 34, 24, 5, 6, 71, 70, 39, 58, 56, 33] roi_keys : {selected_rois} nbin : 1 target_address : {target_file}:/E{iE}/scal_prods """ if scal_prod_use_optional_solution: input_string = input_string+""" optional_solution : {target_file}:/E{iE}/volume """ if True: input_string = input_string+""" save_factors_on : factors_{iE}.json """ if scal_prod_load_factors : input_string = input_string+""" load_factors_from : %s """ % scal_prod_load_factors_from input_string = input_string .format( iE=iE, signals_file = signals_file , reference_address = reference_address, target_file = target_file, selected_rois = list(selected_rois) ) process_input( input_string, exploit_slurm_mpi = 0) def get_volume( iE = None, volumes_file= None, niter = None, beta = None, ): inputstring = """ superR_getVolume : scalprods_address : {volumes_file}:/E{iE}/scal_prods target_address : {volumes_file}:/E{iE}/volume niter : {niter} beta : {beta} eps : 2e-07 debin : [1, 1] """ s=inputstring.format( iE = iE, volumes_file = volumes_file, niter = niter, beta = beta ) process_input(s, exploit_slurm_mpi = 0 ) def get_volume_Esynt( scalarprods_file = None, interpolation_file = None): os.system("mkdir DATASFORCC") inputstring = """ superR_getVolume_Esynt : scalprods_address : {scalarprods_file}:/ dict_interp : {interpolation_file} output_prefix : DATASFORCC/test0_ """.format( scalarprods_file = scalarprods_file , interpolation_file = interpolation_file ) process_input( inputstring, exploit_slurm_mpi = 0) def myOrder(tok): if("volume" not in tok): tokens = tok.split("_") print( tokens) return int(tokens[1])*10000+ int(tokens[2]) else: return 0 def reshuffle( volumefile = "volumes.h5", nick = None ): h5file_root = h5py.File( volumefile ,"r+" ) h5file = h5file_root[nick] scankeys = list( h5file.keys()) scankeys.sort(key=myOrder) print( scankeys) volumes = [] for k in scankeys: if k[:1]!="_": continue print( k) if "volume" in h5file[k]: volumes.append( h5file[k]["volume"] ) # volume = np.concatenate(volumes,axis=0) volume = np.array(volumes) if "concatenated_volume" in h5file: del h5file["concatenated_volume"] h5file["concatenated_volume"]=volume h5file_root.close() def interpolate( peaks_shifts, interp_file_str, interp_file_target_str, energy_exp_grid): interp_file = h5py.File( interp_file_str ,"r+") interp_file_target = h5py.File( interp_file_target_str ,"r+") volum_list = list(interp_file.keys()) scan_num_list = np.array([ int(''.join(filter(str.isdigit, str(t) ))) for t in volum_list]) ene_list = np.array([ energy_exp_grid[t] for t in scan_num_list ] ) order = np.argsort( ene_list ) ene_list = ene_list [order] scan_num_list = scan_num_list [order] volum_list = [ volum_list [ii] for ii in order ] print(ene_list) # raise ene_list_for_search = np.array(ene_list ,"d") ene_list_for_search[0] -=1.0e-6 ene_list_for_search[-1] +=1.0e-6 for t_vn, t_sn, t_ene in list(zip(volum_list, scan_num_list, ene_list ))[0:]: rois_coeffs={} for roi_num, de in enumerate( peaks_shifts ): print ( roi_num, "===== " , t_ene+de , ene_list_for_search .min() , t_ene+de , ene_list_for_search .max() ) if t_ene+de < ene_list_for_search .min() or t_ene+de > ene_list_for_search .max(): continue i0 = np.searchsorted( ene_list_for_search , t_ene+de )-1 assert(i0>=0) i1=i0+1 print (i0, i1, len(ene_list)) print (ene_list) assert(i1 np.max(chi[:,0]): print( 'Energy outside of values defined in Chi-table.') return # interpolate chi0 = complex(np.interp(energy,chi[:,0],chi[:,1]),np.interp(energy,chi[:,0],chi[:,2])) chih = complex(np.interp(energy,chi[:,0],chi[:,3]),np.interp(energy,chi[:,0],chi[:,4])) # set the stress tensor values depending on crystal used if crystal.upper() == 'SI': s13 = -0.278 elif crystal.upper() == 'GE': s13 = -0.273 else: print( 'Poisson ratio for this crystal not defined') return s15 = -0.0 # s15/s11 # scattering angle in degree th = xrs_utilities.braggd(hkl,energy,crystal) # dspace in m dsp = xrs_utilities.dspace(hkl,crystal)/10.0*1e-9 # wavelength in m lam = 12.3984191/energy/10.0*1e-9 # debye-waller factor dwf = 1.0 # dwf = 0.899577 # meridional bending radius radius = R # sagittal bending radius rsag = R*np.sin(np.radians(th))**2.0 # thickness in m thick = 500.0*1e-6 # asymmetry in radians alpha = np.radians(alpha) # deviation parameter in arcsec dev = dev/3600.0/180.0*np.pi # gamma0,gammah = cos , n = inward normal of crystal surface, K_0,h = wave vector gammah = -np.sin(np.arcsin(lam/(2.0*dsp)) + alpha) # Krisch et al. convention gamma0 = np.sin(np.arcsin(lam/(2.0*dsp)) - alpha) # Krisch et al. convention gamma = gammah/gamma0 a0 = np.sqrt(1-gamma0**2.0) ah = np.sqrt(1-gammah**2.0) beta = gamma0/np.abs(gammah) # polarization factor cpol = 1.0 # penetration depth mu = -2.0*np.pi/lam*chi0.imag tdepth = 1.0/mu/(1.0/np.abs(gamma0)+1.0/np.abs(gammah)) lex = lam*np.sqrt(gamma0*np.abs(gammah))/(np.pi*chih.real) y0 = chi0.imag*(1.0+beta)/(2.0*np.sqrt(beta)*chih.real) c1 = cpol*dwf* complex(1.0,-chih.imag/chih.real) #abbreviation concerning the deviation parameter y abb0 = -np.sqrt(beta)/2.0/chih.real abb1 = chi0.real*(1.0+beta)/(2.0*np.sqrt(beta)*chih.real) #abbreviations concerning the deformation field abb2 = gamma0*gammah*(gamma0-gammah) abb3 = 1.0 + 1.0/(gamma0*gammah) abb4 = s13*(1.0 + radius/rsag) abb5 = (ah - a0)/(gamma0 - gammah)*s15 abb6 = 1.0/(np.abs(cpol)*chih.real*np.cos(np.arcsin(lam/(2.0*dsp)))*radius) abb7 = 2.0*np.abs(cpol)*chih.real*np.cos(np.arcsin(lam/(2.0*dsp)))/gamma0 # spherical diced crystal, 1-m bending radius, nearly backscattering conditions, strain gradient sgbeta = abb6*(abb2*(abb3 - abb4 + abb5)) # number of steps along reflectivity curve nstep=len(dev) # reflectivity curve refl = np.zeros_like(dev) OLDMETHOD = 0 if OLDMETHOD: # loop over all steps along the reflectivity curve for l in range(nstep): # deviation parameter abb8 = -2.0*np.sin(2.0*np.arcsin(lam/(2.0*dsp)))*dev[l] T = np.arange(np.max([-10.0*tdepth, -thick]),0.0,1e-8) Y = odeint(odefctn,np.array([0.0, 0.0]),T,args=(abb0,abb1,abb7,abb8,lex,sgbeta,y0,c1)) # normalized reflectivity at this point refl[l] = np.sum(Y[-1,:]**2.0) else: # deviation parameter abb8 = -2.0*np.sin(2.0*np.arcsin(lam/(2.0*dsp)))*dev # dev axis (complex) YY = np.zeros([nstep],"D") # small step size ministep = tdepth/10000.0 ssrk = ministep/2 xpoints = np.arange(np.max([-10.0*tdepth, -thick]),0, ministep) for xpos in xpoints[:-1]: Yp0 = odefctn_CN( YY, xpos+0*ssrk, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1) Yp1 = odefctn_CN( YY+1.0*ssrk*Yp0, xpos+1*ssrk, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1) Yp2 = odefctn_CN( YY+1.0*ssrk*Yp1, xpos+1*ssrk, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1) Ypb = odefctn_CN( YY+2.0*ssrk*Yp2, xpos+2*ssrk, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1) YY = YY + ministep*( Yp0 + 2.0*(Yp1+Yp2) + Ypb )/6.0 refl = YY.real*YY.real+YY.imag*YY.imag # deviation in degree dev = dev/4.848136811e-06/3600.0 # deviation in meV e = -(xrs_utilities.energy(xrs_utilities.dspace(hkl,crystal),th+dev)-energy)*1.0e6 # deviation in microrad dev = dev*1.e3 return refl, e, dev def odefctn_CN(yCN,t,abb0,abb1,abb7,abb8N,lex,sgbeta,y0,c1): fcomp = 1.0/(complex(0,-lex)) * (-2.0*((abb0*(abb8N + abb7*sgbeta*t) + abb1) + complex(0,y0))*(yCN) + c1*(1.0 + yCN* yCN) ) return fcomp def odefctn(y,t,abb0,abb1,abb7,abb8,lex,sgbeta,y0,c1): """ #% [T,Y] = ODE23(ODEFUN,TSPAN,Y0,OPTIONS,P1,P2,...) passes the additional #% parameters P1,P2,... to the ODE function as ODEFUN(T,Y,P1,P2...), and to #% all functions specified in OPTIONS. Use OPTIONS = [] as a place holder if #% no options are set. """ #print 'shape of y is ' , np.shape(y), np.shape(t) fcomp = 1.0/(complex(0,-lex)) * (-2.0*((abb0*(abb8 + abb7*sgbeta*t) + abb1) + complex(0,y0))*(y[0] + complex(0,y[1])) + c1*(1.0 + (y[0] + complex(0,y[1]))**2.0)) return fcomp.real,fcomp.imag def taupgen(e, hkl = [6,6,0], crystals = 'Si', R = 1.0, dev = np.arange(-50.0,150.0,1.0), alpha = 0.0): """ % TAUPGEN Calculates the reflectivity curves of bent crystals % % function [refl,e,dev]=taupgen_new(e,hkl,crystals,R,dev,alpha); % % e = fixed nominal energy in keV % hkl = reflection order vector, e.g. [1 1 1] % crystals = crystal string, e.g. 'si' or 'ge' % R = bending radius in meters % dev = deviation parameter for which the % curve will be calculated (vector) (optional) % alpha = asymmetry angle % based on a FORTRAN program of Michael Krisch % Translitterated to Matlab by Simo Huotari 2006, 2007 % Is far away from being good matlab writing - mostly copy&paste from % the fortran routines. Frankly, my dear, I don't give a damn. % Complaints -> /dev/null """ prefix = data_installation_dir+'/' path = '/home/christoph/sources/XRStools/data/chitables/chitable_' # prefix + 'data/chitables/chitable_' # path to chitables # load the according chitable (tabulated) hkl_string = str(int(hkl[0])) + str(int(hkl[1])) + str(int(hkl[2])) filestring = path + crystals.lower() + hkl_string + '.dat' chi = np.loadtxt(filestring) # good for 1 m bent crystals in backscattering ystart = -50.0 # start value of angular range in arcsecs yend = 150.0 # end value of angular range in arcsecs ystep = 1.0 # step width in arcsecs if len(chi[:,0]) == 1: print( ' I will only calculate for the following energy: ' + '%.4f' % chi[0,0] + ' keV!!!') else: if e < np.min(chi[:,0]) or e > np.max(chi[:,0]): print( 'Energy outside of the range in ' + filestring) return chi0r = np.interp(e,chi[:,0],chi[:,1]) chi0i = np.interp(e,chi[:,0],chi[:,2]) chihr = np.interp(e,chi[:,0],chi[:,3]) chihi = np.interp(e,chi[:,0],chi[:,4]) th = braggd(hkl,e,crystals) lam = 12.3984191/e/10.0 # wavelength in nm reflcorr = 0.0 chi0 = complex(chi0r,chi0i) chih = complex(chihr,chihi) if crystals.upper() == 'SI': s13 = -0.278 elif crystals.upper() == 'GE': s13 = -0.273 else: print( 'Poisson ratio for this crystal not defined') return s15 = -0.0 # s15/s11 dsp = dspace(hkl,crystals)/10.0 # dspace dwf = 1.0 # dwf = 0.899577 # debye-waller factor radius = R # meridional bending radius rsag = R*np.sin(np.radians(th))**2.0 # sagittal bending radius thick = 500.0 # thickness in micrometers #rsag = R lam = lam*1e-9 dsp = dsp*1e-9 alpha = np.radians(alpha) # alpha in rad thick = thick*1e-6 ystart = ystart/3600.0/180.0*np.pi yend = yend/3600.0/180.0*np.pi ystep = ystep/3600.0/180*np.pi dev = dev/3600.0/180.0*np.pi reflcorr = reflcorr/3600.0/180.0*np.pi thetab = np.arcsin(lam/(2.0*dsp)) cpol = 1.0 # cpol=0.5*(1+cos(2*thetab).^2) # cpol=cos(2*thetab).^2 # gamma0 = sin(thetab+alpha) # normal convention # gammah = -sin(thetab-alpha) # normal convention gammah = -np.sin(thetab + alpha) # Krisch et al. convention (really!) gamma0 = np.sin(thetab - alpha) # Krisch et al. convention (I'm not kidding!!) beta = gamma0/np.abs(gammah) gamma = gammah/gamma0 a0 = np.sqrt(1-gamma0**2.0) ah = np.sqrt(1-gammah**2.0) mu = -2.0*np.pi/lam*chi0i tdepth = 1.0/mu/(1.0/np.abs(gamma0)+1.0/np.abs(gammah)) lex = lam*np.sqrt(gamma0*np.abs(gammah))/(np.pi*chihr) y0 = chi0i*(1.0+beta)/(2.0*np.sqrt(beta)*chihr) pfried = -chihi/chihr c1 = cpol*dwf* complex(1.0,pfried) #abbreviation concerning the deviation parameter y abb0 = -np.sqrt(beta)/2.0/chihr abb1 = chi0r*(1.0+beta)/(2.0*np.sqrt(beta)*chihr) #abbreviations concerning the deformation field abb2 = gamma0*gammah*(gamma0-gammah) abb3 = 1.0 + 1.0/(gamma0*gammah) abb4 = s13*(1.0 + radius/rsag) abb5 = (ah - a0)/(gamma0 - gammah)*s15 abb6 = 1.0/(np.abs(cpol)*chihr*np.cos(thetab)*radius) abb7 = 2.0*np.abs(cpol)*chihr*np.cos(thetab)/gamma0 # a spectrometer based on a spherical diced analyzer crystal with a 1-m bending radius in nearly backscattering conditions utilizing a strain gradient beta sgbeta = abb6*(abb2*(abb3 - abb4 + abb5)) nstep=len(dev) eta = np.zeros_like(dev) abb8z = np.zeros_like(dev) refl = np.zeros_like(dev) refl1 = np.zeros_like(dev) refl2 = np.zeros_like(dev) for l in range(nstep): # actual value of the deviation angle # dev[l] = ystart + (l - 1)*ystep # deviation parameter abb8 = -2.0*np.sin(2.0*thetab)*dev[l] eta[l] = (dev[l]*np.sin(2.0*thetab)+np.abs(chi0.real)/2.0*(1.0-gamma))/(np.abs(cpol)*np.sqrt(np.abs(gamma))*np.sqrt(chih*chih)) eta[l] = eta[l].real ndiff = 2 xend = 0 x = np.max([-10.0*tdepth, -thick]) y = np.array([0.0, 0.0]) h = xend abb8z[l] = abb8 # in this point call the subroutine # [T,Y] = ODE23(ODEFUN,TSPAN,Y0,OPTIONS,P1,P2,...) passes the additional # parameters P1,P2,... to the ODE function as ODEFUN(T,Y,P1,P2...), and to # all functions specified in OPTIONS. Use OPTIONS = [] as a place holder if # no options are set. #print 'the fucking shape of y is ', np.shape(y) T = np.arange(x,xend,1e-8) Y = odeint(odefctn,y,T,args=(abb0,abb1,abb7,abb8,lex,sgbeta,y0,c1)) # normalized reflectivity at this point refl[l] = np.sum(Y[-1,:]**2.0) refl1[l] = Y[-1,0] refl2[l] = Y[-1,1] de = dev * e * 1.0e6 /np.tan(thetab) lam = lam *1.0e+09 dsp = dsp*1.0e+09 alpha = alpha/np.pi*180.0 ystart = ystart/4.848136811e-06 yend = yend/4.848136811e-06 ystep = ystep/4.848136811e-06 dev = dev/4.848136811e-06 # dev in arcsecs dev = dev/3600.0 # in degrees thb = th th = thb + dev e0 = e e = energy(dspace(hkl,crystals),th)-e0 e = e*1e6 dev = dev*3600.0 # back to arcsecs return refl,e,dev,e0 xrstools-0.15.0+git20210910+c147919d/XRStools/ui.py000066400000000000000000000157261412732462000207110ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function # -*- coding: utf-8 -*- ############################################################################# ## ## This file is part of Taurus, a Tango User Interface Library ## ## http://www.tango-controls.org/static/taurus/latest/doc/html/index.html ## ## Copyright 2011 CELLS / ALBA Synchrotron, Bellaterra, Spain ## ## Taurus is free software: you can redistribute it and/or modify ## it under the terms of the GNU Lesser General Public License as published by ## the Free Software Foundation, either version 3 of the License, or ## (at your option) any later version. ## ## Taurus is distributed in the hope that it will be useful, ## but WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU Lesser General Public License for more details. ## ## You should have received a copy of the GNU Lesser General Public License ## along with Taurus. If not, see . ## ############################################################################# """utilities to load ui files for widgets""" __all__ = ["loadUi", "UILoadable", ] import os import sys import functools #TODO: Change to taurus.external # from PyQt4 import Qt # from PyQt4 import uic from silx.gui import qt as Qt #from taurus.external.qt import Qt #from taurus.external.qt import uic class __UI(object): pass def loadUi(obj, filename=None, path=None, with_ui=None): """ Loads a QtDesigner .ui file into the given widget. If no filename is given, it tries to load from a file name which is the widget class name plus the extension ".ui" (example: if your widget class is called MyWidget it tries to find a MyWidget.ui). If path is not given it uses the directory where the python file which defines the widget is located plus a *ui* directory (example: if your widget is defined in a file /home/homer/workspace/taurusgui/my_widget.py then it uses the path /home/homer/workspace/taurusgui/ui) :param filename: the QtDesigner .ui file name [default: None, meaning calculate file name with the algorithm explained before] :type filename: str :param path: directory where the QtDesigner .ui file is located [default: None, meaning calculate path with algorithm explained before] :type path: str :param with_ui: if True, the objects defined in the ui file will be accessible as submembers of an ui member of the widget. If False, such objects will directly be members of the widget. :type with_ui: bool """ if path is None: obj_file = sys.modules[obj.__module__].__file__ path = os.path.join(os.path.dirname(obj_file)) if filename is None: filename = obj.__class__.__name__ + os.path.extsep + 'ui' full_name = os.path.join(path, filename) if with_ui is not None: ui_obj = __UI() setattr(obj, with_ui, ui_obj) previous_members = set(dir(obj)) Qt.loadUi(full_name, baseinstance=obj) post_members = set(dir(obj)) new_members = post_members.difference(previous_members) for member_name in new_members: member = getattr(obj, member_name) setattr(ui_obj, member_name, member) delattr(obj, member_name) else: Qt.loadUi(full_name, baseinstance=obj) def UILoadable(klass=None, with_ui=None): """ A class decorator intended to be used in a Qt.QWidget to make its UI loadable from a predefined QtDesigner UI file. This decorator will add a :func:`loadUi` method to the decorated class and optionaly a property with a name given by *with_ui* parameter. The folowing example assumes the existence of the ui file :file:`/ui/MyWidget.ui` which is a QWidget panel with *at least* a QPushButton with objectName *my_button* :: from taurus.external.qt import Qt from taurus.qt.qtgui.util.ui import UILoadable @UILoadable class MyWidget(Qt.QWidget): def __init__(self, parent=None): Qt.QWidget.__init__(self, parent) self.loadUi() self.my_button.setText("This is MY button") Another example using a :file:`superUI.ui` file in the same directory as the widget. The widget UI components can be accessed through the widget member *_ui* :: import os.path from taurus.external.qt import Qt from taurus.qt.qtgui.util.ui import UILoadable @UILoadable(with_ui="_ui") class MyWidget(Qt.QWidget): def __init__(self, parent=None): Qt.QWidget.__init__(self, parent) self.loadUi(filename="superUI.ui", path=os.path.dirname(__file__)) self._ui.my_button.setText("This is MY button") :param with_ui: assigns a member to the decorated class from which you can access all UI components [default: None, meaning no member is created] :type with_ui: str .. warning:: the current implementation (Jul14) doesn't prevent Qt from overloading any members you might have defined previously by the widget object names from the UI file. This happens even if *with_ui* parameter is given. For example, if the UI contains a QPushButton with objectName *my_button*:: @UILoadable(with_ui="_ui") class MyWidget(Qt.QWidget): def __init__(self, parent=None): Qt.QWidget.__init__(self, parent) self.my_button = "hello" self.loadUi() widget = MyWidget() print widget.my_button This little problem should be solved in the next taurus version. """ if klass is None: return functools.partial(UILoadable, with_ui=with_ui) klass_name = klass.__name__ klass_file = sys.modules[klass.__module__].__file__ klass_path = os.path.join(os.path.dirname(klass_file)) def _loadUi(self, filename=None, path=None): if filename is None: filename = klass_name + os.path.extsep + 'ui' if path is None: path = klass_path return loadUi(self, filename=filename, path=path, with_ui=with_ui) klass.loadUi = _loadUi return klass def main(): from taurus.qt.qtgui.application import TaurusApplication app = TaurusApplication([]) @UILoadable(with_ui="ui") class A(Qt.QWidget): def __init__(self, parent=None): Qt.QWidget.__init__(self, parent) import taurus.qt.qtgui.panel.ui path = os.path.dirname(taurus.qt.qtgui.panel.ui.__file__) self.loadUi(filename='TaurusMessagePanel.ui', path=path) gui = A() gui.show() app.exec_() if __name__ == "__main__": main() xrstools-0.15.0+git20210910+c147919d/XRStools/xes_read.py000066400000000000000000000251401412732462000220550ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: xes_read.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" from . import xrs_rois, xrs_scans, xrs_utilities, math_functions, xrs_fileIO import sys import os import numpy as np # try to import the fast PyMCA file parsers try: import PyMca5.PyMcaIO.EdfFile as EdfIO import PyMca5.PyMcaIO.specfilewrapper as SpecIO use_PyMca = True except: use_PyMca = False print( " >>>>>>>> use_PyMca " , use_PyMca) __metaclass__ = type # new style classes class read_id20: """ Main class for handling raw data from XES experiments on ESRF's ID20. This class is used to read scans from SPEC files and the according EDF-files, it provides access to all tools from the xrs_rois module for defining ROIs, it can be used to integrate scans, sum them up, stitch them together, and define the energy loss scale. INPUT: absFilename = path and filename of the SPEC-file energyColumn = name (string) of the counter for the energy as defined in the SPEC session (counter mnemonic) monitorColumn = name (string) of the counter for the monitor signals as defined in the SPEC session (counter mnemonic) edfName = name/prefix (string) of the EDF-files (default is the same as the SPEC-file name) single_image = boolean switch, 'True' (default) if all 6 detectors are merged in a single image, 'False' if two detector images per point exist. """ def __init__(self,absFilename,energyColumn='Anal Energy',monitorColumn='kaprixs',edfName=None): self.scans = {} # dictionary of scans try: self.path = os.path.split(absFilename)[0] + '/' self.filename = os.path.split(absFilename)[1] except IOError: print('file does not exist.') if not edfName: self.edfName = os.path.split(absFilename)[1] else: self.edfName = edfName self.scannumbers = [] self.EDF_PREFIX = 'edf/' self.EDF_POSTFIX = '.edf' self.DET_PIXEL_NUMx = 1296 self.DET_PIXEL_NUMy = 256 self.DET_PIXEL_NUM = 256 # which column in the SPEC file to be used for the energy and monitor self.encolumn = energyColumn.lower() self.monicolumn = monitorColumn.lower() # here are the attributes of the old rawdata class self.energy = [] # common energy scale for all analyzers self.signals = [] # signals for all analyzers self.errors = [] # poisson errors self.groups = {} # dictionary of groups (such as 2 'elastic', or 5 'edge1', etc.) self.tth = [] # list of scattering angles (one for each ROI) self.resolution = [] # list of FWHM of the elastic lines for each analyzer self.signals_orig = [] # signals for all analyzers before interpolation # ROI object self.roi_obj = [] # an instance of the roi_object class from the xrs_rois module (new) def set_roiObj(self,roiobj): self.roi_obj = roiobj def readscan(self,scannumber): """ Returns the data, motors, counter-names, and edf-files from the SPEC file defined when the xrs_read object was initiated. There should be an alternative that uses the PyMca module if installed. INPUT: scannumber = number of the scan to be loaded fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) """ # load SPEC-file print( 'Parsing EDF- and SPEC-files of scan No. %s' % scannumber) fn = self.path + self.filename if use_PyMca == True: data, motors, counters = xrs_fileIO.PyMcaSpecRead(fn,scannumber) else: data, motors, counters = xrs_fileIO.SpecRead(fn,scannumber) # load EDF-files edfmats = xrs_fileIO.ReadEdfImages(counters['ccdno'], self.DET_PIXEL_NUMx, self.DET_PIXEL_NUMy, self.path, self.EDF_PREFIX, self.edfName, self.EDF_POSTFIX) # add the scannumber to self.scannumbers, if not already present if not scannumber in self.scannumbers: self.scannumbers.extend([scannumber]) return data, motors, counters, edfmats def loadscan(self,scannumbers,scantype='generic'): """ Loads the files belonging to scan No. "scannumber" and puts it into an instance of the xrs_scan-class 'scan'. The default scantype is 'generic', later the scans will be grouped (and added) based on the scantype. INPUT: scannumbers = integer or list of scannumbers that should be loaded scantype = string describing the scan to be loaded (e.g. 'edge1' or 'K-edge') fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) """ # make sure scannumbers are iterable (list) if not isinstance(scannumbers,list): scannums = [] scannums.append(scannumbers) else: scannums = scannumbers for number in scannums: scanname = 'Scan%03d' % number data, motors, counters, edfmats = self.readscan(number) # can assign some things here already (even if maybe redundant) monitor = counters[self.monicolumn] monoangle = 1 # have to check for this later ... !!! counters['pmonoa'] energy = counters[self.encolumn] # create an instance of "scan" class for every scan onescan = xrs_scans.scan(edfmats,number,energy,monitor,counters,motors,data,scantype) # assign one dictionary entry to each scan self.scans[scanname] = onescan def loadscandirect(self,scannumbers,scantype='generic',scaling=None): """ Loads a scan without saving the edf files in matrices. scannumbers = integer or list of integers defining the scannumbers from the SPEC file scantype = string describing the scan to be loaded (e.g. 'edge1' or 'K-edge') fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) scaling = list of scaling factors to be applied, one for each ROI defined """ # make sure scannumbers are iterable (list) if not isinstance(scannumbers,list): scannums = [] scannums.append(scannumbers) else: scannums = scannumbers # check if there are ROIs defined if not self.roi_obj: print( 'Please define some ROIs first') return for number in scannums: scanname = 'Scan%03d' % number data, motors, counters, edfmats = self.readscan(number) # can assign some things here already (even if maybe redundant) monitor = counters[self.monicolumn] monoangle = 1 # counters['pmonoa'] # this still needs checking energy = counters[self.encolumn] # create an instance of "scan" class for every scan onescan = xrs_scans.scan(edfmats,number,energy,monitor,counters,motors,data,scantype) onescan.applyrois(self.roi_obj.indices,scaling=scaling) print( 'Deleting EDF-files of Scan No. %03d' % number) onescan.edfmats = [] # delete the edfmats self.scans[scanname] = onescan def deletescan(self,scannumbers): """ Deletes scans from the class. INPUT: scannumbers = integer or list of integers (SPEC scan numbers) to delete """ numbers = [] if not isinstance(scannumbers,list): numbers.append(scannumbers) else: numbers = scannumbers for number in numbers: scanname = 'Scan%03d' % number del(self.scans[scanname]) self.scannumbers.remove(number) def SumDirect(self,scannumbers): Sum=None for number in scannumbers: data, motors, counters, edfmats = self.readscan(number) if Sum is None: Sum = np.zeros(edfmats[0].shape ,"f") Sum[:] += edfmats.sum(axis=0) return Sum def getXESspectrum(self): """ Groups the instances of the scan class by their scantype attribute, adds equal scans (each group of equal scans) and appends them. INPUT: include_elastic = boolean flag, skips the elastic line if set to 'False' (default) """ # find the groups allgroups = xrs_scans.findgroups(self.scans) for group in allgroups: # self.makegroup(group) onegroup = xrs_scans.makegroup_nointerp(group) self.groups[onegroup.get_type()] = onegroup self.energy,self.signals,self.errors = xrs_scans.appendXESScans(self.groups) def dump_data(self,filename): data = np.zeros((len(self.eloss),3)) data[:,0] = self.eloss data[:,1] = self.signals data[:,2] = self.errors np.savetxt(filename,data) xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_ComptonProfiles.py000066400000000000000000001011121412732462000242740ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range from six.moves import zip #!/usr/bin/python # Filename: xrs_ComptonProfiles.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import numpy as np from . import xrs_utilities from . import xrs_fileIO from scipy import interpolate, integrate, constants, optimize from re import findall from collections import defaultdict # default valence energy cutoff value VAL_CUTOFF_DEFAULT = 20.0 def list_duplicates(seq): tally = defaultdict(list) for i,item in enumerate(seq): tally[item].append(i) return [(key,locs) for key,locs in tally.items()] # if len(locs)>1] def parseChemFormula(ChemFormula): """ """ # parse single formula all_elements = [] all_stoichios = [] splitted = findall(r'([A-Z][a-z]*)(\d*)',ChemFormula) all_elements.extend([element[0] for element in splitted]) all_stoichios.extend([(int(element[1]) if element[1] else 1) for element in splitted]) # sort out double appearances of elements elements = [] stoichiometries = [] duplicates = list_duplicates(all_elements) for pair in duplicates: elements.append(pair[0]) stoich = 0 stoichiometries.append(sum( [all_stoichios[ii] for ii in pair[1]]) ) return elements, stoichiometries def getAtomicWeight(Z): """Returns the atomic weight. """ return xrs_utilities.myprho(1.0,Z)[2] def getAtomicDensity(Z): """Returns the atomic density. """ return xrs_utilities.myprho(1.0,Z)[1] class SqwPredict: """Class to build a S(q,w) prediction based on HF Compton Profiles. Attributes: * sampleStr (list of strings): one string per compound (e.g. ['C','SiO2']) * concentrations (list of floats): relative compositional weight for each compound """ pass class AtomProfile: """ **AtomProfile** Class to construct and handle Hartree-Fock atomic Compton Profile of a single atoms. Attributes: * filename : string Path and filename to the HF profile table. * element : string Element symbol as in the periodic table. * elementNr : int Number of the element as in the periodic table. * shells : list of strings Names of the shells. * edges : list List of edge onsets (eV). * C_total : np.array Total core Compton profile. * J_total : np.array Total Compton profile. * V_total : np.array Total valence Compton profile. * CperShell : dict. of np.arrays Core Compton profile per electron shell. * JperShell : dict. of np.arrays Total Compton profile per electron shell. * VperShell : dict. of np.arrays Valence Compton profile per electron shell. * stoichiometry : float, optional Stoichiometric weight (default is 1.0). * atomic_weight : float Atomic weight. * atomic_density : float Density (g/cm**3). * twotheta : float Scattering angle 2Th (degrees). * alpha : float Incident angle (degrees). * beta : float Exit angle (degrees). * thickness : float Sample thickness (cm). """ def __init__(self, element, filename, stoichiometry=1.0): self.filename = filename self.element = element self.elementNr = xrs_utilities.element(element) self.CP_profile, self.edges, self.occupation_num, self.shells = PzProfile(element,filename) self.eloss = [] self.C_total = [] self.J_total = [] self.V_total = [] self.q_vals = [] self.CperShell = {} self.JperShell = {} self.VperShell = {} self.stoichiometry = stoichiometry self.atomic_weight = getAtomicWeight(element) self.atomic_density = getAtomicDensity(element) self.twotheta = [] self.alpha = [] self.beta = [] self.thickness = [] def get_stoichiometry(self): return self.stoichiometry def get_elossProfiles(self,E0, twotheta,correctasym=None,valence_cutoff=VAL_CUTOFF_DEFAULT): """ **get_elossProfiles** Convert the HF Compton profile on to energy loss scale. Args: E0 : float Analyzer energy, enery of the scattered r-rays. twotheta : float or list of floats Scattering angle 2Th. correctasym : float, optional Scaling factor to be multiplied to the asymmetry. valence_cutoff : float, optional Energy cut off as to what is considered the boundary between core and valence. """ # save the parameters self.E0 = E0 # reset self.twotheta self.twotheta = [] if isinstance(twotheta, list) or isinstance(twotheta, np.ndarray): self.twotheta.extend(twotheta) elif isinstance(twotheta, float): self.twotheta.append(twotheta) else: print('Unsupported type for twotheta argument') return # do the conversion for the first tth to get the size/shape of things enScale, J_total, C_total, V_total, q, J_shell, C_shell, V_shell = elossProfile(self.element,self.filename,E0,self.twotheta[0],correctasym,valence_cutoff) # save eloss scale self.eloss = enScale # prepare output self.C_total = np.zeros((len(enScale),len(self.twotheta))) self.J_total = np.zeros((len(enScale),len(self.twotheta))) self.V_total = np.zeros((len(enScale),len(self.twotheta))) self.q_vals = np.zeros((len(enScale),len(self.twotheta))) for key in C_shell: self.CperShell[key] = np.zeros((len(enScale),len(self.twotheta))) self.JperShell[key] = np.zeros((len(enScale),len(self.twotheta))) self.VperShell[key] = np.zeros((len(enScale),len(self.twotheta))) # convert everything to eloss scale for tth,ii in zip(self.twotheta,list(range(len(self.twotheta)))): enScale, J_total, C_total, V_total, q, J_shell, C_shell, V_shell = elossProfile(self.element,self.filename,E0,tth,correctasym,valence_cutoff) # save the results (all shell dicts have the same keys) self.C_total[:,ii] = np.interp(self.eloss,enScale,C_total)*self.stoichiometry self.J_total[:,ii] = np.interp(self.eloss,enScale,J_total)*self.stoichiometry self.V_total[:,ii] = np.interp(self.eloss,enScale,V_total)*self.stoichiometry self.q_vals[:,ii] = np.interp(self.eloss,enScale,q) for key in self.CperShell: self.CperShell[key][:,ii] = np.interp(self.eloss,enScale,C_shell[key]) self.JperShell[key][:,ii] = np.interp(self.eloss,enScale,C_shell[key]) self.VperShell[key][:,ii] = np.interp(self.eloss,enScale,C_shell[key]) def absorptionCorrectProfiles(self, alpha, thickness, geometry='transmission'): """ **absorptionCorrectProfiles** Apply absorption correction to the Compton profiles on energy loss scale. Args: * alpha :float Angle of incidence (degrees). * beta : float Exit angle for the scattered x-rays (degrees). If 'beta' is negative, transmission geometry is assumed, if 'beta' is positive, reflection geometry. * thickness : float Sample thickness. """ # save the angles self.alpha = alpha if geometry == 'reflection': beta = 180 - alpha - self.twotheta if geometry == 'transmission': beta = alpha - self.twotheta if geometry == 'sphere': beta = alpha - self.twotheta self.beta = beta # set the sample thickness self.thickness = thickness # in [cm] now # get the mass absorption coefficients mu_in = xrs_utilities.mpr(self.eloss/1.0e3+self.E0,self.element)[0] mu_out = xrs_utilities.mpr(self.E0,self.element)[0] # calculate the absorption factor for several alpha values if isinstance(self.alpha,list) or isinstance(self.alpha,np.ndarray): for alpha,ii in zip(self.alpha,list(range(len(self.alpha)))): abs_corr = xrs_utilities.absCorrection(mu_in,mu_out,alpha,self.beta,self.thickness,geometry=geometry) # apply correction to all profiles self.C_total[:,ii] /= abs_corr self.J_total[:,ii] /= abs_corr self.V_total[:,ii] /= abs_corr for key in self.CperShell: self.CperShell[key][:,ii] /= abs_corr self.JperShell[key][:,ii] /= abs_corr self.VperShell[key][:,ii] /= abs_corr # calculate the absorption factor for several beta values elif isinstance(self.beta,list) or isinstance(self.beta,np.ndarray): for beta,ii in zip(self.beta,list(range(len(self.beta)))): abs_corr = xrs_utilities.absCorrection(mu_in,mu_out,self.alpha,beta,self.thickness,geometry=geometry) # apply correction to all profiles self.C_total[:,ii] /= abs_corr self.J_total[:,ii] /= abs_corr self.V_total[:,ii] /= abs_corr for key in self.CperShell: self.CperShell[key][:,ii] /= abs_corr self.JperShell[key][:,ii] /= abs_corr self.VperShell[key][:,ii] /= abs_corr # calculate the absorption factor for several sample thickness values elif isinstance(self.thickness,list) or isinstance(self.thickness,np.ndarray): for thick,ii in zip(self.thickness,list(range(len(self.thickness)))): abs_corr = xrs_utilities.absCorrection(mu_in,mu_out,self.alpha,self.beta,thick,geometry=geometry) # apply correction to all profiles self.C_total[:,ii] /= abs_corr self.J_total[:,ii] /= abs_corr self.V_total[:,ii] /= abs_corr for key in self.CperShell: self.CperShell[key][:,ii] /= abs_corr self.JperShell[key][:,ii] /= abs_corr self.VperShell[key][:,ii] /= abs_corr # calculate the absorption factor for a single value else: abs_corr = xrs_utilities.absCorrection(mu_in,mu_out,self.alpha,self.beta,self.thickness,geometry=geometry) # apply correction to all profiles self.C_total /= abs_corr self.J_total /= abs_corr self.V_total /= abs_corr for key in self.CperShell: self.CperShell[key] /= abs_corr self.JperShell[key] /= abs_corr self.VperShell[key] /= abs_corr class FormulaProfile: """ **FormulaProfile** Class to construct and handle Hartree-Fock atomic Compton Profile of a single chemical compound. Attributes * filename : string Path and filename to Biggs database. * formula : string Chemical sum formula for the compound of interest (e.g. 'SiO2' or 'H2O'). * elements : list of strings List of atomic symbols that make up the chemical sum formula. * stoichiometries : list of integers List of the stoichimetric weights for each of the elements in the list *elements*. * element_Nrs : list of integers List of atomic numbers for each element in the *elements* list. * AtomProfiles : list of *AtomProfiles* List of instances of the *AtomProfiles* class for each element in the list. * eloss : np.ndarray Energy loss scale for the Compton profiles. * C_total : np.ndarray Core HF Compton profile (one column per 2Th). * J_total : np.ndarray Total HF Compton profile (one column per 2Th). * V_total :np.ndarray Valence HF Compton profile (one column per 2Th). * E0 : float Analyzer energy (keV). * twotheta : float, list, or np.ndarray Value or list/np.ndarray of the scattering angle. """ def __init__(self, formula, filename, weight=1): assert type(formula) is str, "\'formula\' argument should be a string!" self.filename = filename self.formula = formula self.elements, self.stoichiometries = parseChemFormula(formula) self.element_Nrs = [xrs_utilities.element(element) for element in self.elements] self.AtomProfiles = {} for element,stoichio in zip(self.elements,self.stoichiometries): CP = AtomProfile(element,filename,stoichiometry=stoichio) self.AtomProfiles[element] = CP self.eloss = [] self.C_total = [] self.J_total = [] self.V_total = [] self.q_vals = [] self.E0 = 0.0 self.twotheta = [] self.stoich_weight = weight def get_stoichWeight(self): return self.stoich_weight def get_elossProfiles(self,E0, twotheta,correctasym=None,valence_cutoff=VAL_CUTOFF_DEFAULT): self.E0 = E0 # reset self.twotheta self.twotheta = [] if isinstance(twotheta, list): self.twotheta.extend(twotheta) elif isinstance(twotheta, float): self.twotheta.append(twotheta) else: print('Unsupported type for twotheta argument') for key in self.AtomProfiles: self.AtomProfiles[key].get_elossProfiles(self.E0, self.twotheta,correctasym,valence_cutoff) self.eloss = self.AtomProfiles[list(self.AtomProfiles.keys())[0]].eloss # initialize the profiles self.C_total = np.zeros((len(self.eloss),len(self.twotheta))) self.J_total = np.zeros((len(self.eloss),len(self.twotheta))) self.V_total = np.zeros((len(self.eloss),len(self.twotheta))) self.q_vals = np.zeros((len(self.eloss),len(self.twotheta))) # add up all AtomProfiles for key in self.AtomProfiles: AP = self.AtomProfiles[key] for ii in range(len(self.twotheta)): self.C_total[:,ii] += np.interp(self.eloss,AP.eloss,AP.C_total[:,ii])*AP.get_stoichiometry() self.J_total[:,ii] += np.interp(self.eloss,AP.eloss,AP.J_total[:,ii])*AP.get_stoichiometry() self.V_total[:,ii] += np.interp(self.eloss,AP.eloss,AP.V_total[:,ii])*AP.get_stoichiometry() self.q_vals[:,ii] = np.interp(self.eloss,AP.eloss,AP.q_vals[:,ii]) def get_correctecProfiles(self, densities, alpha, beta, samthick ): pass class HFProfile: """ *HFProfile* Class to construct and handle Hartree-Fock atomic Compton Profile of sample composed of several chemical compounds. Attributes """ def __init__(self, formulas, stoich_weights, filename): if isinstance(formulas,list) and isinstance(stoich_weights,list): self.formulas = formulas self.stoich_weights = stoich_weights elif isinstance(formulas,str) and isinstance(stoich_weits,int) or isinstance(formulas,str) and isinstance(stoich_weits,float): self.formulas = [] self.formulas.append(formulas) self.stoich_weights = [] self.stoich_weights.append(stoich_weights) else: print('Unsupported/uncongruent types for formulas/stoich_weights arguments!') return self.filename = filename self.FormulaProfiles = {} for formula,ii in zip(self.formulas,list(range(len(self.formulas)))): CP = FormulaProfile(formula,filename,weight=stoich_weights[ii]) self.FormulaProfiles[formula] = CP self.eloss = [] self.C_total = [] self.J_total = [] self.V_total = [] self.q_vals = [] self.twotheta = [] self.E0 = 0.0 def get_elossProfiles(self,E0, twotheta,correctasym=None,valence_cutoff=VAL_CUTOFF_DEFAULT): # save the E0 value self.E0 = E0 # reset self.twotheta self.twotheta = [] if isinstance(twotheta, list): self.twotheta.extend(twotheta) elif isinstance(twotheta, float): self.twotheta.append(twotheta) else: print('Unsupported type for twotheta argument') # get all Atomic and Formula unit profiles on eloss scale for key in self.FormulaProfiles: self.FormulaProfiles[key].get_elossProfiles(self.E0, self.twotheta,correctasym,valence_cutoff) # save the eloss-scale self.eloss = self.FormulaProfiles[list(self.FormulaProfiles.keys())[0]].eloss # initialize the profiles self.C_total = np.zeros((len(self.eloss),len(self.twotheta))) self.J_total = np.zeros((len(self.eloss),len(self.twotheta))) self.V_total = np.zeros((len(self.eloss),len(self.twotheta))) self.q_vals = np.zeros((len(self.eloss),len(self.twotheta))) # add up all Compton Profiles from the sub-units for key,jj in zip(self.FormulaProfiles,list(range(len(self.twotheta)))): FP = self.FormulaProfiles[key] for ii in range(len(self.twotheta)): self.C_total[:,ii] += np.interp(self.eloss,FP.eloss,FP.C_total[:,ii])*FP.get_stoichWeight() self.J_total[:,ii] += np.interp(self.eloss,FP.eloss,FP.J_total[:,ii])*FP.get_stoichWeight() self.V_total[:,ii] += np.interp(self.eloss,FP.eloss,FP.V_total[:,ii])*FP.get_stoichWeight() self.q_vals[:,ii] = np.interp(self.eloss,FP.eloss,FP.q_vals[:,ii]) class ComptonProfiles: """Class for multiple HF Compton profiles. This class should hold one or more instances of the ComptonProfile class and have methods to return profiles from single atoms, single shells, all atoms. It should be able to apply corrections etc. on those... Attributes: * element (string): Element symbol as in the periodic table. * elementNr (int) : Number of the element as in the periodic table. * shells (list) : * edges (list) : * C (np.array) : * J (np.array) : * V (np.array) : * CperShell (dict. of np.arrays): * JperShell (dict. of np.arrays): * VperShell (dict. of np.arrays): """ def __init__(self, element): self.element = element self.elementNr = xrs_utilities.element(z) self.shells = [] self.edges = [] self.C = [] self.J = [] self.V = [] self.CperShell = {} self.JperShell = {} self.VperShell = {} def trapz_weights(x): dx = np.diff(x) w = np.empty(x.shape) w[1:-1] = (dx[1:] + dx[:-1])/2. w[0] = dx[0] / 2. w[-1] = dx[-1] / 2. return w def PzProfile(element,filename): """Returnes tabulated HF Compton profiles. Reads in tabulated HF Compton profiles from the Biggs paper, interpolates them, and normalizes them to the # of electrons in the shell. Args: * element (string): element symbol (e.g. 'Si', 'Al', etc.) * filename (string): absolute path and filename to tabulated profiles Returns: * CP_profile (np.array): Matrix of the Compton profile * 1. column: pz-scale * 2. ... n. columns: Compton profile of nth shell * binding_energy (list): binding energies of shells * occupation_num (list): number of electrons in the according shells """ # load Biggs data, mirror at pz = 0.0 CP_tab, occupation_num, binding_energies, shell_names = xrs_fileIO.readbiggsdata(filename,element) pz_tab = np.append(-1.0*np.flipud(CP_tab[1::,0]),CP_tab[:,0]) CP_tab = np.append(np.flipud(CP_tab[1::,:]),CP_tab,axis=0) CP_tab[:,0] = pz_tab # pad with zeros as large pos. and large neg. pz for nicer spline CP_tab = np.append(np.zeros((1,CP_tab.shape[1])),CP_tab,axis=0) CP_tab = np.append(CP_tab,np.zeros((1,CP_tab.shape[1])),axis=0) CP_tab[0,0] = -10000.0 CP_tab[-1,0] = 10000.0 # interpolate pz_scale = np.arange(-100.0,100.0,0.01) CP_profile = np.zeros((len(pz_scale),len(binding_energies)+1)) CP_profile[:,0] = pz_scale for n in range(len(binding_energies)): interp_func = interpolate.pchip(CP_tab[:,0], CP_tab[:,n+2]) CP_profile[:,n+1] = interp_func(pz_scale) # normalize to one electron, multiply by number of electrons for n in range(len(binding_energies)): norm = np.trapz(CP_profile[:,n+1],CP_profile[:,0]) CP_profile[:,n+1] = CP_profile[:,n+1]/norm*int(occupation_num[n]) binding_energies = [float(energy) for energy in binding_energies] occupation_num = [float(value) for value in occupation_num] return CP_profile, binding_energies, occupation_num, shell_names def elossProfile(element,filename,E0,tth,correctasym=None,valence_cutoff=20.0): """Returns HF Compton profiles on energy loss scale. Uses the PzProfile function to read read in Biggs HF profiles and converts them onto energy loss scale. The profiles are cut at the respective electron binding energies and are normalized to the f-sum rule (i.e. S(q,w) is in units of [1/eV]). Args: * element (string): element symbol. * filename (string): absolute path and filename to tabulated Compton profiles. * E0 (float): analyzer energy in [keV]. * tth (float): scattering angle two theta in [deg]. * correctasym (np.array): vector of scaling factors to be applied. * valence_cutoff (float): energy value below which edges are considered as valence Returns: * enScale (np.array): energy loss scale in [eV] * J_total (np.array): total S(q,w) in [1/eV] * C_total (np.array): core contribution to S(q,w) in [1/eV] * V_total (np.array): valence contribution to S(q,w) in [1/eV], the valence is defined by valence_cutoff * q (np.array): momentum transfer in [a.u] * J_shell (dict of np.arrays): dictionary of contributions for each shell, the key are defines as in Biggs table. * C_shell (dict of np.arrays): same as J_shell for core contribution * V_shell (dict of np.arrays): same as J_shell for valence contribution """ # read in the Biggs data CP_profile, binding_energies, occupation_num, shell_names = PzProfile(element,filename) # convert pz to energy loss scale enScale = ((np.flipud(xrs_utilities.pz2e1(E0,CP_profile[:,0],tth))-E0)*1.0e3) # define the momentum transfer q = xrs_utilities.momtrans_au(enScale/1000.0 + E0, E0, tth) # calculate asymmetry after Holm and Ribberfors for filles 1s and 2p shells # if correctasym == True asymmetry = np.flipud(HRcorrect(CP_profile, occupation_num, q)) if correctasym: CP_profile[:,1:4] = CP_profile[:1:4] + asymmetry * correctasym # discard profiles, q, and enScale for energy losses smaller than zero HF_profile = CP_profile[np.nonzero(enScale.T>=0.0)[0],:] q = q[np.nonzero(enScale.T>=0)[0]] enScale = enScale[np.nonzero(enScale.T>=0)[0]] HF_profile[:,0] = enScale # discard profiles for energy losses below according binding energies for n in range(len(binding_energies)): HF_profile[np.where(enScale35: [\'K\', \'L1\', \'L2\', \'L3\', \'M1\', \'M2\', \'M3\', \'M4\', \'M5\', \'N1\', \'N2\', \'N3\', \'N4\', \'N5\', \'N6\', \'N7\', \'O1\', \'O2\', \'O3\', \'O4\', \'O5\', \'P1\', \'P2\', \'P3\']') return # 1s, 2s, 2p, 3s, 3p, 4s, 3d, 4p, 5s, 4d, 5p, 6s, 4f, 5d, 6p, 7s, 5f, 6d, 7p, (8s, 5g, 6f, 7d, 8p, and 9s) def HRcorrect(pzprofile,occupation,q): """ Returns the first order correction to filled 1s, 2s, and 2p Compton profiles. Implementation after Holm and Ribberfors (citation ...). Args: * pzprofile (np.array): Compton profile (e.g. tabulated from Biggs) to be corrected (2D matrix). * occupation (list): electron configuration. * q (float or np.array): momentum transfer in [a.u.]. Returns: * asymmetry (np.array): asymmetries to be added to the raw profiles (normalized to the number of electrons on pz scale) """ # prepare output matrix if len(occupation) == 1: asymmetry = np.zeros((len(pzprofile[:,0]),1)) elif len(occupation) == 2: asymmetry = np.zeros((len(pzprofile[:,0]),2)) elif len(occupation) >= 3: asymmetry = np.zeros((len(pzprofile[:,0]),3)) # take care for the cases where 2p levels have spin-orbit split taken into account in the Biggs table if len(occupation)>3 and occupation[2]==2 and occupation[3]==4: pzprofile[:,3] = pzprofile[:,3] + pzprofile[:,4] occupation[2] = 6 # 1s if occupation[0] < 2: pass else: # find gamma1s lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2 fitfct = lambda a: (np.absolute(np.max(pzprofile[:,1])-np.max(occupation[0]*8.0*a**5.0/3.0/np.pi/(a**2.0+pzprofile[:,0]**2.0)**3.0))) res = optimize.leastsq(fitfct,np.sum(occupation)) gamma1s = res[0][0] # calculate j0 and j1 j0 = occupation[0]*8.0*gamma1s**5.0/3.0/np.pi/((gamma1s**2.0+pzprofile[:,0]**2.0)**3.0) j1 = 2.0*gamma1s*np.arctan2(pzprofile[:,0],gamma1s)-3.0/2.0*pzprofile[:,0] j1 = j1/q*j0 asymmetry[:,0] = j1 # 2s if len(occupation)>1: if occupation[1] < 2: pass else: # find gamma2s fitfct = lambda a: (np.absolute(np.max(pzprofile[:,2])-np.max(occupation[1]*((a**4.0-10.0*a**2.0*pzprofile[:,0]**2 + 40.0*pzprofile[:,0]**4.0)*128.0*a**5.0/15.0/np.pi/(a**2.0 + 4.0*pzprofile[:,0]**2.0)**5.0)))) res = optimize.leastsq(fitfct,np.sum(occupation)*2.0/3.0) gamma2s = res[0][0] # calculate j0 and j1 j0 = occupation[1]*(gamma2s**4.0-10.0*gamma2s**2.0*pzprofile[:,0]**2.0+40.0*pzprofile[:,0]**4.0)*128.0*gamma2s**5.0/15.0/np.pi/(gamma2s**2.0 + 4.0*pzprofile[:,0]**2.0)**5.0 j1 = 2.0*gamma2s*np.arctan2(2.0*pzprofile[:,0],gamma2s)-5.0/4.0*(gamma2s**4.0+48.0*pzprofile[:,0]**4.0)/(gamma2s**4.0-10.0*gamma2s**2.0*pzprofile[:,0]**2.0+40.0*pzprofile[:,0]**4.0)*pzprofile[:,0] j1 = j1/q*j0 asymmetry[:,1] = j1 # 2p if len(occupation)>2: if occupation[2] < 6: pass else: forgamma = 3.0*pzprofile[:,3]/np.trapz(pzprofile[:,3],pzprofile[:,0]) # 2p correction is defined for 3 electrons in the 2p shell # find gamma2p fitfct = lambda a: (np.absolute(np.max(forgamma)-np.max(((a**2.0+20.0*pzprofile[:,0]**2.0)*64.0*a**7.0/5.0/np.pi/(a**2.0+4.0*pzprofile[:,0]**2.0)**5.0)))) res = optimize.leastsq(fitfct,np.sum(occupation)*1.0/3.0) gamma2p = res[0][0] # calculate j0 and j1 j0 = 2.0*(gamma2p**2.0+20.0*pzprofile[:,0]**2.0)*64.0*gamma2p**7.0/5.0/np.pi/(gamma2p**2.0+4.0*pzprofile[:,0]**2.0)**5.0 j1 = 2.0*gamma2p*np.arctan2(2.0*pzprofile[:,0],gamma2p)-2.0/3.0*pzprofile[:,0]*(10.0*gamma2p**2.0+60.0*pzprofile[:,0]**2.0)/(gamma2p**2.0+20.0*pzprofile[:,0]**2.0) j1 = j1/q*j0 asymmetry[:,2] = j1 return asymmetry xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_alignment.py000066400000000000000000000406771412732462000231510ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import six from six.moves import range from six.moves import zip #!/usr/bin/python # Filename: xrs_alignment.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group and contains practical functions, # most of which are translated from Matlab functions from the University of # Helsinki Electronic Structure Laboratory. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import numpy as np from . import xrs_scans, xrs_read, roifinder_and_gui, math_functions, xrs_utilities from scipy import interpolate, optimize from matplotlib import pylab as plt scan72_motornames = ['vdtx1','vdtx2','vdtx3','vdtx4','vdtx5','vdtx6','vdtx7','vdtx8','vdtx9','vdtx10','vdtx11','vdtx12', \ 'vutx1','vutx2','vutx3','vutx4','vutx5','vutx6','vutx7','vutx8','vutx9','vutx10','vutx11','vutx12', \ 'vbtx1','vbtx2','vbtx3','vbtx4','vbtx5','vbtx6','vbtx7','vbtx8','vbtx9','vbtx10','vbtx11','vbtx12', \ 'hrtx1','hrtx2','hrtx3','hrtx4','hrtx5','hrtx6','hrtx7','hrtx8','hrtx9','hrtx10','hrtx11','hrtx12', \ 'hltx1','hltx2','hltx3','hltx4','hltx5','hltx6','hltx7','hltx8','hltx9','hltx10','hltx11','hltx12', \ 'hbtx1','hbtx2','hbtx3','hbtx4','hbtx5','hbtx6','hbtx7','hbtx8','hbtx9','hbtx10','hbtx11','hbtx12'] def optimize_analyzer_focus(path, SPECfname, EDFprefix, EDFname, EDFpostfix, roi_obj, scan_number, plotting=True): """Returns position for all 72 TX motors that optimize the analyzer foci. Args: path (str): Absolute path to location of the SPEC-file. SPECfname (str): SPEC-file name. EDFprefix (str): Prefix to where EDF-files are stored. EDFname (str): Base name of the EDF-files. EDFpostfix (str): Post-fix used for the EDF-files. roi_obj (roi_obj): ROI object of the xrs_rois class. Scan_number (int): Scan number of the 72-motor scan. Returns: Dictionary with motorname - position pairs. """ TX_positions = {} fwhm_results = {} scan72 = xrs_scans.Scan() scan72.load(path, SPECfname, EDFprefix, EDFname, EDFpostfix, scan_number) edfmats = scan72.edfmats scan_len = edfmats.shape[0] # get the 2D insets for each ROI scan72.get_raw_signals( roi_obj, method='pixel') # measure the FWHM for each ROI for ii,key in enumerate(sorted(scan72.raw_signals , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) )): motor_scale = scan72.counters[scan72_motornames[ii]] points = [] heights = [] # index 1 widths = [] # index 2 x = np.arange(scan72.raw_signals[key].shape[2]) y = np.arange(scan72.raw_signals[key].shape[1]) for jj in range(scan_len): points.append(jj) data = scan72.raw_signals[key][jj,:,:] print(y, np.sum(data, axis=1), x, np.sum(data, axis=0) ) heights.append( xrs_utilities.fwhm( y, np.sum(data, axis=1))[0] ) widths.append( xrs_utilities.fwhm( x, np.sum(data, axis=0))[0] ) # find minimum fitH = np.polyval( np.polyfit(points, widths, 2), points ) fitV = np.polyval( np.polyfit(points, heights, 2), points ) minH = np.argmin(np.abs( fitH-np.amin(fitH) )) minV = np.argmin(np.abs( fitV-np.amin(fitV) )) min_pos = motor_scale[minH] if plotting: plt.cla() plt.plot(motor_scale, heights, '-o', label='spot-size V') plt.plot(motor_scale, widths, '-o', label='spot-size H') plt.plot(motor_scale, fitH, '-', label='fit H') plt.plot(motor_scale, fitV, '-', label='fit V') plt.xlabel('scan point') plt.ylabel('FWHM [pixel]') plt.legend(frameon=False) plt.title('%s: opt. pos. %s = %0.3f'%(key, scan72_motornames[ii], min_pos )) plt.waitforbuttonpress() # assign results fwhm_results[key] = [ min_pos, minH, minV ] TX_positions[key] = [ min_pos ] #for ii in range(len(scan72_motornames)): # motor_scale = scan72.counters[scan72_motornames[ii]] # try: # TX_pos = findAnalyzerFocus(edfmats, motor_scale, roi_obj, ii) # TX_positions[scan72_motornames[ii]] = TX_pos # except: # print ('Fit failed for ROI No. %d.'%ii) return TX_positions, fwhm_results def fit_foci_2d(edfmats, roi_obj): """ **fit_foci_2d** Finds FWHMs of 2D Gaussians of the content of each ROI for a given stack of EDF-images. Args: edfmats (np.array): Stack of EDF-matrices from a d72scan of all translation motors of the analyzer crystals. roi_obj (obj): ROI object of the xrs_rois class. Returns: sigma_1 (dict): Dictionary containing FWHMs in the vertical direction. sigma_2 (dict): Dictionary containing FWHMs in the horizontal direction. """ sigma_1 = {} # FWHM along dimension 1 (vertical) sigma_2 = {} # FWHM along dimension 2 (horizontal) for key, (pos, M) in six.iteritems(roi_obj.red_rois): S = M.shape inset = (slice( pos[0], pos[0]+(S[0]) ), slice( pos[1], pos[1]+(S[1]) )) ind = 0 sigma_1[key] = np.zeros(edfmats.shape[0]) sigma_2[key] = np.zeros(edfmats.shape[0]) for ii in range(len(edfmats)): sub_mat = edfmats[ii, inset[0], inset[1]] * (M/M.max()) x = np.array(list(range(sub_mat.shape[0]))) y = np.array(list(range(sub_mat.shape[1]))) xx, yy = np.meshgrid(y, x) initial_guess = (np.amax(sub_mat),x.mean(),y.mean(),0.5,0.5,0) try: popt, pcov = optimize.curve_fit(math_functions.flat2DGaussian, (xx, yy), sub_mat.ravel(), p0=initial_guess) if np.abs(popt[3]) < 10.0: sigma_1[key][ii] = popt[3] else: sigma_1[key][ii] = 0.0 if np.abs(popt[4]) < 10.0: sigma_2[key][ii] = popt[4] else: sigma_2[key][ii] = 0.0 except: sigma_1[key][ii] = 0.0 sigma_2[key][ii] = 0.0 ind += 1 return sigma_1, sigma_2 def fit_foci_1d(edfmats, roi_obj): """ **fit_foci_1d** Finds FWHMs of 2D Gaussians of the content of each ROI for a given stack of EDF-images. Args: edfmats (np.array): Stack of EDF-matrices from a d72scan of all translation motors of the analyzer crystals. roi_obj (obj): ROI object of the xrs_rois class. Returns: sigma_1 (dict): Dictionary containing FWHMs in the vertical direction. sigma_2 (dict): Dictionary containing FWHMs in the horizontal direction. """ sigma_1 = {} # FWHM along dimension 1 (vertical) sigma_2 = {} # FWHM along dimension 2 (horizontal) for key, (pos, M) in six.iteritems(roi_obj.red_rois): S = M.shape inset = (slice( pos[0], pos[0]+(S[0]) ), slice( pos[1], pos[1]+(S[1]) )) ind = 0 sigma_1[key] = np.zeros(edfmats.shape[0]) sigma_2[key] = np.zeros(edfmats.shape[0]) for ii in range(len(edfmats)): sub_mat = edfmats[ii, inset[0], inset[1]] * (M/M.max()) y0 = np.sum(sub_mat,axis=0) y1 = np.sum(sub_mat,axis=1) initial_guess0 = (np.where(y0 == y0.max())[0][0], 1.0, 1.0, y0.max(), 0.0) initial_guess1 = (np.where(y1 == y1.max())[0][0], 1.0, 1.0, y1.max(), 0.0) try: popt0, pcov0 = optimize.curve_fit(math_functions.pearson7_forcurvefit, np.arange(len(y0)), y0, p0=initial_guess0) popt1, pcov1 = optimize.curve_fit(math_functions.pearson7_forcurvefit, np.arange(len(y1)), y1, p0=initial_guess1) if np.abs(popt0[1]) < 10.0: sigma_1[key][ii] = popt0[1] else: sigma_1[key][ii] = 0.0 if np.abs(popt1[1]) < 10.0: sigma_2[key][ii] = popt1[1] else: sigma_2[key][ii] = 0.0 except: print (key) sigma_1[key][ii] = 0.0 sigma_2[key][ii] = 0.0 ind += 1 return sigma_1, sigma_2 #for name,key in zip(scan72_motornames,sorted(roifinder.roi_obj.red_rois)): # motor_scale[key] = scan72.counters[name] def findBestFocus( sigma_1, sigma_2, counters, margin=3.0, verbose=False ): for key, ii in zip(sorted(sigma_1), list(range(len(sigma_1)))): motor_scale = counters[scan72_motornames[ii]] # find the best compromise/minimum for sigma_1 and sigma_2 x = np.array(motor_scale) y1 = np.array(sigma_1[key]) y2 = np.array(sigma_2[key]) if show_fits: plt.cla() plt.title(key) plt.plot(x,y1,'-o') plt.plot(x,y2,'-o') plt.plot(x, np.polyval(np.polyfit(x,y1,2),x)) plt.plot(x, np.polyval(np.polyfit(x,y2,2),x)) plt.xlabel('motor position [mm]') plt.ylabel('FWHM [pixels]') plt.legend(['dim1', 'dim2', 'fit1', 'fit2']) plt.waitforbuttonpress() actual_pos = motor_scale[len(motor_scale)//2] min1_ind = np.where(np.polyval(np.polyfit(x,y1,2),x) == np.amin(np.polyval(np.polyfit(x,y1,2),x)))[0] min2_ind = np.where(np.polyval(np.polyfit(x,y2,2),x) == np.amin(np.polyval(np.polyfit(x,y2,2),x)))[0] min1 = motor_scale[min1_ind] min2 = motor_scale[min2_ind] if np.abs(min1 - min2) <= margin: min_pos = np.mean([min1, min2]) else: min_pos = None if min_pos and np.abs(min_pos - actual_pos) <= margin: if verbose: print(key + ' :') print('current motor position is: %0.4f'%actual_pos ) print('optimum focus for estimated to:') print('umv ' + scan72_motornames[ii] + ' %0.4f # current position is: %0.4f '%(min_pos,actual_pos)) print('\n') else: print('umv ' + scan72_motornames[ii] + ' %0.4f # current position is: %0.4f '%(min_pos,actual_pos)) #np.polyval(np.polyfit(x,y2,2),x) def findAnalyzerFocus(edfmats,motor_scale,roi_obj,roiNumber): """ **findAnalyzerFocus** Returns motor position that optimizes the analyzer focus subject to a 2D Gaussian fit. Args: ----- edfmats (np.array): 3D Numpy array containing the EDF-matrices of a scan. motorScale (np.array): Motor positions along the scan (analyzer tx-scan). roi_obj (xrs_rois.roi_object): ROI object, defined from the scan, should have some margins around the spots. roiNumber (int): Number indicating which ROI to be optimized. Returns: -------- optPos (float): Motor position that optimizes the focus of the analyzer in question. """ xmin = min(roi_obj.x_indices[roiNumber]) xmax = max(roi_obj.x_indices[roiNumber]) ymin = min(roi_obj.y_indices[roiNumber]) ymax = max(roi_obj.y_indices[roiNumber]) x = np.arange(xmin,xmax) y = np.arange(ymin,ymax) xx, yy = np.meshgrid(y, x) sigma_1 = [] # FWHM along dimension 1 sigma_2 = [] # FWHM along dimension 2 # go through all images and fit the 2D Gaussian for ii in range(edfmats.shape[0]): initial_guess = (np.amax(edfmats[ii,xmin:xmax,ymin:ymax]),(ymin+ymax)/2.,(xmin+xmax)/2.,1.0,1.0,0) popt, pcov = optimize.curve_fit(math_functions.flat2DGaussian, (xx, yy), edfmats[ii,xmin:xmax,ymin:ymax].ravel(), p0=initial_guess) sigma_1.append(popt[3]) sigma_2.append(popt[4]) # find the best compromise/minimum for sigma_1 and sigma_2 x = np.array(motor_scale) y1 = np.array(sigma_1) y2 = np.array(sigma_2) # popt will have (x,amp,x0,fwhm) popt_1, pcov_1 = optimize.curve_fit( gauss_forcurvefit, x, y1 ) popt_2, pcov_2 = optimize.curve_fit( gauss_forcurvefit, x, y2 ) return np.mean([popt_1[2], popt_2[2]]) def fouc_det_focus(path, scan_number, SPECfname='rixs', EDFprefix='/edf/', EDFname='rixs_', EDFpostfix='.edf'): """ **fouc_det_focus** Returns best focus for FOURC spectrometer. Args: path (str): Absolute path to location of the SPEC-file. roi_obj (roi_obj): ROI object of the xrs_rois class. scan_number (int): Scan number of the 72-motor scan. SPECfname (str): SPEC-file name. EDFprefix (str): Prefix to where EDF-files are stored. EDFname (str): Base name of the EDF-files. EDFpostfix (str): Post-fix used for the EDF-files. Returns: Optimized dtx and dtz position. """ # create xrs_read object fourc_obj = xrs_read.Fourc(path,SPECfname=SPECfname, EDFprefix=EDFprefix, EDFname=EDFname, EDFpostfix=EDFpostfix) im4rois = fourc_obj.SumDirect(scan_number) # creat ROI object roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(im4rois) # create scan object, load scan, use first ROI defined scan = xrs_scans.Scan() scan.load(path, SPECfname, EDFprefix, EDFname, EDFpostfix, scan_number) scan.get_raw_signals(roifinder.roi_obj, method='row') # a2scan motors dtx = scan.counters['detector x'] dtz = scan.counters['detector z'] # fit width of ROI at each point of the a2scan roi_shape = scan.raw_signals['ROI00'].shape fwhms = [] for ii in range(roi_shape[0]): y = scan.raw_signals['ROI00'][ii,:] x = np.arange(len(y)) p0 = ( y.max(), x[np.where(y==y.max())[0][0]], roi_shape[1]/3 ) try: popt, pcov = optimize.curve_fit(math_functions.gauss_forcurvefit, x, y, p0=p0) y_fit = math_functions.gauss_forcurvefit(x, popt[0], popt[1], popt[2]) plt.cla() plt.plot(x, y, '-ok') plt.plot(x, y_fit,'-r') plt.xlabel('detector pixel') plt.ylabel('intensity') plt.legend(['data', 'Gaussian fit']) plt.hold(True) plt.draw() #plt.waitforbuttonpress() plt.pause(0.01) except: print('aaaaaaaaaaahhhhhhhhh') popt = np.zeros((3,)) fwhms.append(popt[2]) # fit quadartic function to all FWHMs try: fit1 = np.polyval( np.polyfit(dtx, fwhms, 2), dtx) fit2 = np.polyval( np.polyfit(dtz, fwhms, 2), dtz) except: print ('whaoooo') fit1 = np.zeros_like(dtx) fit2 = np.zeros_like(dtz) # plot results, should be in one figure with double x-axis plt.figure() plt.plot(dtx, fwhms, '-o') plt.plot(dtx, fit1, '-') #plt.hold(True) plt.draw() plt.figure() plt.plot(dtz, fwhms, '-o') plt.plot(dtz, fit2, '-') #plt.hold(True) plt.show() # return/print out optimal positions dtx_min = dtx[np.where(fit1==fit1.min())[0][0]] dtz_min = dtz[np.where(fit1==fit1.min())[0][0]] print('Minimum position for dtx = %6.4f'%dtx_min ) print('Minimum position for dtz = %6.4f'%dtz_min ) return dtx_min, dtz_min xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_calctools.py000066400000000000000000003775151412732462000231620ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: xrs_calctools.py from . import xrs_utilities import os import warnings from copy import deepcopy import numpy as np import array as arr from itertools import groupby from scipy.integrate import trapz from scipy.interpolate import interp1d from scipy import constants import sys if(sys.argv[0][-12:]!="sphinx-build"): ## otherwise the documentation cannot build : wrong docstrings imported from pylab from pylab import * from scipy import signal from scipy.ndimage import measurements import matplotlib.pyplot as plt from six.moves import range from six.moves import zip __metaclass__ = type # new style classes A2AU_factor = constants.physical_constants['atomic unit of length'][0]*10**10 def gauss1(x,x0,fwhm): """ returns a gaussian with peak value normalized to unity a[0] = peak position a[1] = Full Width at Half Maximum """ y = np.exp(-np.log(2.0)*((x-x0)/fwhm*2.0)**2.0) return y def gauss(x,x0,fwhm): # area-normalized gaussian sigma = fwhm/(2*np.sqrt(2*np.log(2))); y = np.exp(-(x-x0)**2/2/sigma**2)/sigma/np.sqrt(2*np.pi) return y def gauss_areanorm(x,x0,fwhm): """ area-normalized gaussian """ sigma = fwhm/(2.0*np.sqrt(2.0*np.log(2.0))) y = np.exp(-(x-x0)**2.0/2.0/sigma**2)/sigma/np.sqrt(2.0*np.pi) return y def convg(x,y,fwhm): """ Convolution with Gaussian """ dx = np.min(np.absolute(np.diff(x))) x2 = np.arange(np.min(x)-1.5*fwhm, np.max(x)+1.5*fwhm, dx) xg = np.arange(-np.floor(2.0*fwhm/dx)*dx, np.floor(2.0*fwhm/dx)*dx, dx) yg = gauss(xg,[0,fwhm]) yg = yg/np.sum(yg) y2 = spline2(x,y,x2) c = np.convolve(y2,yg, mode='full') n = int( np.floor(np.max(np.shape(xg))/2)) c = c[n:len(c)-n+1] # not sure about the +- 1 here f = interpolate.interp1d(x2,c) return f(x) def spline2(x,y,x2): """ Extrapolates the smaller and larger valuea as a constant """ xmin = np.min(x) xmax = np.max(x) imin = x == xmin imax = x == xmax f = interpolate.interp1d(x,y, bounds_error=False, fill_value=0.0) y2 = f(x2) i = np.where(x2xmax) y2[i] = y[imax] return y2 def readxas(filename): """ function output = readxas(filename)%[e,p,s,px,py,pz] = readxas(filename) % READSTF Load StoBe fort.11 (XAS output) data % % [E,P,S,PX,PY,PZ] = READXAS(FILENAME) % % E energy transfer [eV] % P dipole transition intensity % S r^2 transition intensity % PX dipole transition intensity along x % PY dipole transition intensity along y % PZ dipole transition intensity along z % % as line diagrams. % % T Pylkkanen @ 2011-10-17 """ # Open file f = open(filename,'r') lines = f.readlines() f.close() data = [] for line in lines[1:]: data.append([float(x) for x in line.replace('D', 'e').strip().split()]) data = np.array(data) data[:,0] = data[:,0]*27.211384565719481 # convert from a.u. to eV data[:,3] = 2.0/3.0*data[:,3]**2.0*e/27.211384565719481 # osc(x) data[:,4] = 2.0/3.0*data[:,4]**2.0*e/27.211384565719481 # osc(y) data[:,5] = 2.0/3.0*data[:,5]**2.0*e/27.211384565719481 # osc(z) return data def broaden_diagram(e,s,params=[1.0, 1.0, 537.5, 540.0],npoints=1000): """ function [e2,s2] = broaden_diagram2(e,s,params,npoints) % BROADEN_DIAGRAM2 Broaden a StoBe line diagram % % [ENE2,SQW2] = BROADEN_DIAGRAM2(ENE,SQW,PARAMS,NPOINTS) % % gives the broadened spectrum SQW2(ENE2) of the line-spectrum % SWQ(ENE). Each line is substituted with a Gaussian peak, % the FWHM of which is determined by PARAMS. ENE2 is a linear % scale of length NPOINTS (default 1000). % % PARAMS = [f_min f_max emin max] % % For ENE <= e_min, FWHM = f_min. % For ENE >= e_max, FWHM = f_min. % FWHM increases linearly from [f_min f_max] between [e_min e_max]. % % T Pylkkanen @ 2008-04-18 [17:37] """ f_min = params[0] f_max = params[1] e_min = params[2] e_max = params[3] e2 = np.linspace(np.min(e)-10.0,np.max(e)+10.0,npoints); s2 = np.zeros_like(e2) fwhm = np.zeros_like(e) # FWHM: Constant -- Linear -- Constant A = (f_max-f_min)/(e_max-e_min) B = f_min - A*e_min fwhm = A*e + B inds = e <= e_min fwhm[inds] = f_min inds = e >= e_max fwhm[inds] = f_max for i in range(len(s)): s2 += s[i]*gauss(e2,e[i],fwhm[i]) return e2, s2 def broaden_linear(spec,params=[0.8, 8, 537.5, 550],npoints=1000): evals = spec[:,0] sticks= spec[:,1] f_min = params[0] f_max = params[1] e_min = params[2] e_max = params[3] e2 = np.linspace(np.min(evals)-10.0,np.max(evals)+10.0,npoints) s2 = np.zeros(len(e2)) fwhm = np.zeros(len(evals)) # FWHM: Constant -- Linear -- Constant A = (f_max-f_min)/(e_max-e_min) B = f_min - A*e_min fwhm = A*evals + B fwhm[evals <= e_min] = f_min fwhm[evals >= e_max] = f_max for n in range(len(sticks)): s2 = s2 + sticks[n]*gauss1(e2,evals[n],fwhm[n]) spectrum = np.zeros((len(e2),2)) spectrum[:,0] = e2 spectrum[:,1] = s2 return spectrum def load_stobe_specs(prefix,postfix,fromnumber,tonumber,step,stepformat=2): """ load a bunch of StoBe calculations, which filenames are made up of the prefix, postfix, and the counter in the between the prefix and postfix runs from 'fromnumber' to 'tonumber' in steps of 'step' (number of digits is 'stepformat') """ numbers = np.linspace(fromnumber,tonumber,(tonumber-fromnumber + step)//step) filenames = [] precision = '%0'+str(stepformat)+'d' for number in numbers: thenumber = precision % number thefilename = prefix+thenumber+postfix filenames.append(thefilename) specs = [] for filename in filenames: try: specs.append(readxas(filename)) except: print( 'found no file: ' + filename) return specs def load_erkale_spec(filename): spec = np.loadtxt(filename) return spec def load_erkale_specs(prefix,postfix,fromnumber,tonumber,step,stepformat=2): numbers = np.linspace(fromnumber,tonumber,(tonumber-fromnumber + step)//step) filenames = [] precision = '%0'+str(stepformat)+'d' for number in numbers: thenumber = precision % number thefilename = prefix+thenumber+postfix filenames.append(thefilename) specs = [] for filename in filenames: try: specs.append(load_erkale_spec(filename)) except: print( 'found no file: ' + filename) return specs def cut_spec(spec,emin=None,emax=None): if not emin: emin = spec[0,0] if not emax: emax = spec[-1,0] spec = spec[spec[:,0]>emin] spec = spec[spec[:,0]= e_max, FWHM = f_min. % FWHM increases linearly from [f_min f_max] between [e_min e_max]. % % T Pylkkanen @ 2008-04-18 [17:37] """ f_min = params[0] f_max = params[1] e_min = params[2] e_max = params[3] e2 = np.linspace(np.min(e)-10.0,np.max(e)+10.0,npoints); s2 = np.zeros_like(e2) fwhm = np.zeros_like(e) # FWHM: Constant -- Linear -- Constant A = (f_max-f_min)/(e_max-e_min) B = f_min - A*e_min fwhm = A*e + B inds = e <= e_min fwhm[inds] = f_min inds = e >= e_max fwhm[inds] = f_max for i in range(len(s)): s2 += s[i]*gauss1(e2,e[i],fwhm[i]) return e2, s2 def broaden_linear(spec,params=[0.8, 8, 537.5, 550],npoints=1000): """ broadens a spectrum with a Gaussian of width params[0] below params[2] and width params[1] above params[3], width increases linear in between. returns two-column numpy array of length npoints with energy and the broadened spectrum """ evals = spec[:,0] sticks= spec[:,1] f_min = params[0] f_max = params[1] e_min = params[2] e_max = params[3] e2 = np.linspace(np.min(evals)-10.0,np.max(evals)+10.0,npoints) s2 = np.zeros(len(e2)) fwhm = np.zeros(len(evals)) # FWHM: Constant -- Linear -- Constant A = (f_max-f_min)/(e_max-e_min) B = f_min - A*e_min fwhm = A*evals + B fwhm[evals <= e_min] = f_min fwhm[evals >= e_max] = f_max for n in range(len(sticks)): s2 = s2 + sticks[n]*gauss(e2,evals[n],fwhm[n]) spectrum = np.zeros((len(e2),2)) spectrum[:,0] = e2 spectrum[:,1] = s2 return spectrum def load_stobe_specs(prefix,postfix,fromnumber,tonumber,step,stepformat=2): """ load a bunch of StoBe calculations, which filenames are made up of the prefix, postfix, and the counter in the between the prefix and postfix runs from 'fromnumber' to 'tonumber' in steps of 'step' (number of digits is 'stepformat') """ numbers = np.linspace(fromnumber,tonumber,(tonumber-fromnumber + step)//step) filenames = [] precision = '%0'+str(stepformat)+'d' for number in numbers: thenumber = precision % number thefilename = prefix+thenumber+postfix filenames.append(thefilename) specs = [] for filename in filenames: try: specs.append(readxas(filename)) except: print( 'found no file: ' + filename) return specs def load_erkale_spec(filename): """ returns an erkale spectrum """ spec = np.loadtxt(filename) return spec def load_erkale_specs(prefix,postfix,fromnumber,tonumber,step,stepformat=2): """ returns a list of erkale spectra """ numbers = np.linspace(fromnumber,tonumber,(tonumber-fromnumber + step)//step) filenames = [] precision = '%0'+str(stepformat)+'d' for number in numbers: thenumber = precision % number thefilename = prefix+thenumber+postfix filenames.append(thefilename) specs = [] for filename in filenames: try: specs.append(load_erkale_spec(filename)) except: print( 'found no file: ' + filename) return specs def cut_spec(spec,emin=None,emax=None): """ deletes lines of matrix with first column smaller than emin and larger than emax """ if not emin: emin = spec[0,0] if not emax: emax = spec[-1,0] spec = spec[spec[:,0]>emin] spec = spec[spec[:,0]=emin,self.energy<=emax)) norm = np.trapz(self.signal[inds],self.energy[inds]) self.signal = self.signal/norm class erkale: """ class to analyze ERKALE XRS results. """ def __init__(self,prefix,postfix,fromnumber,tonumber,step,stepformat=2): self.energy = [] # array of final energy scale for all snapshots of this run self.sqw = [] # array of averaged and smoothed results self.rawspecs = load_erkale_specs(prefix,postfix,fromnumber,tonumber,step,stepformat) # list of all raw stick spectra self.broadened= [] self.norm = [] # results of normalization def cut_rawspecs(self,emin=None,emax=None): cutspecs = [] for spec in self.rawspecs: cutspecs.append(cut_spec(spec,emin,emax)) self.rawspecs = cutspecs def cut_broadspecs(self,emin=None,emax=None): cutspecs = [] for spec in self.broadened: cutspecs.append(cut_spec(spec,emin,emax)) self.broadened = cutspecs def broaden_lin(self,params=[0.8, 8, 537.5, 550],npoints=1000): for spec in self.rawspecs: self.broadened.append(broaden_linear(spec,params,npoints)) def sum_specs(self): self.energy = self.broadened[0][:,0] # first spectrum defines energy scale self.sqw = np.zeros(np.shape(self.energy)) for spec in self.broadened: f = interp1d(spec[:,0], spec[:,1],bounds_error=False, kind='cubic', fill_value=0.0) self.sqw += f(self.energy) def norm_area(self,emin=None,emax=None): if not emin: emin = self.energy[0] if not emax: emax = self.energy[-1] inds = np.where(np.logical_and(self.energy>=emin,self.energy<=emax))[0] self.sqw = self.sqw/np.trapz(self.sqw[inds],self.energy[inds]) def norm_max(self): pass def plot_spec(self): plt.plot(self.energy,self.sqw) plt.show() ################################### # reading function for cowan's code output class xyzAtom: """ **xyzAtom** Class to hold information about and manipulate a single atom in xyz-style format. Args. : * name (str): Atomic symbol. * coordinates (np.array): Array of xyz-coordinates. * number (int): Integer, e.g. number of atom in a cluster. """ def __init__(self,name,coordinates,number): self.name = name self.coordinates = np.array(coordinates) self.x_coord = self.coordinates[0] self.y_coord = self.coordinates[1] self.z_coord = self.coordinates[2] self.Z = xrs_utilities.element(name) self.number = number self.spectrum = np.array([]) def getNorm(self): return np.linalg.norm(self.coordinates) def getCoordinates(self): return self.coordinates def translateSelf(self, vector): try: self.coordinates += vector self.x_coord += vector[0] self.y_coord += vector[1] self.z_coord += vector[2] except ValueError: print('Vector must be 3D np.array!') def translateSelf_arb(self, lattice, lattice_inv, vector): rel_coords = np.dot(lattice_inv, np.array(self.coordinates)) rel_coords += vector self.coordinates = np.dot(lattice,rel_coords) self.x_coord = self.coordinates[0] self.y_coord = self.coordinates[1] self.z_coord = self.coordinates[2] def load_spectrum(self, file_name): self.spectrum = np.loadtxt(file_name) def load_spectrum_all_pol(self, prefix, num_pols, printing=False ): data = {} # load all polarization directions for ii in range(num_pols): fname = prefix+'%02d'%(ii+1) if printing: print('loading spectrum from file: %s'%fname) data[fname] = np.loadtxt(fname) # average ene = data[ list(data.keys())[0] ][:,0] dat = np.zeros_like( ene ) for key in data: dat += np.interp( ene, data[key][:,0], data[key][:,1] ) dat /= num_pols self.spectrum = np.array([ene, dat]).T def normalize_spectrum(self, normrange): inds = np.where(np.logical_and(self.spectrum[:,0]>=normrange[0], self.spectrum[:,0]<=normrange[1]))[0] norm = np.trapz(self.spectrum[inds,1], self.spectrum[inds,0]) self.spectrum[:,1] /= norm def getDist( self, atom ): return np.linalg.norm(self.coordinates - atom.coordinates) def getDistPBCarb( self, atom, lattice, lattice_inv ): return getDistancePBC_arb( self, atom, lattice, lattice_inv ) def getAnglePBCarb( self, atom2, atom3, lattice, lattice_inv, degrees=True): """ **get_angle** Return angle between the three given atoms (as seen from atom2). """ vec1 = getDistVectorPBC_arb(atom2, self, lattice, lattice_inv) vec2 = getDistVectorPBC_arb(atom3, self, lattice, lattice_inv) dotp = np.dot(vec1/np.linalg.norm(vec1), vec2/np.linalg.norm(vec2)) if degrees: return np.degrees( np.arccos( np.clip( dotp, -1.0, 1.0 ) ) ) else: return np.arccos( np.clip( dotp, -1.0, 1.0 ) ) class xyzMolecule: """ **xyzMolecule** Class to hold information about and manipulate an xyz-style molecule. Args.: * xyzAtoms (list): List of instances of the xyzAtoms class that make up the molecule. """ def __init__(self,xyzAtoms,title=None): self.xyzAtoms = xyzAtoms self.title = title def getCoordinates(self): """ **getCoordinates** Return coordinates of all atoms in the cluster. """ return [atom.getCoordinates() for atom in self.xyzAtoms] def getCoordinates_name(self,name): """ **getCoordinates_name** Return coordintes of all atoms with 'name'. """ atoms = [] for atom in self.xyzAtoms: if atom.name == name: atoms.append(atom.coordinates) return atoms def get_atoms_by_name(self,name): """ **get_atoms_by_name** Return a list of all xyzAtoms of a given name 'name'. """ atoms = [] for atom in self.xyzAtoms: if atom.name == name: atoms.append(atom) else: pass if len(atoms) == 0: print('Found no atoms with given name in molecule.') return return atoms def getGeometricCenter(self): """ **getGeometricCenter** Return the geometric center of the xyz-molecule. """ for_average = np.zeros((len(self.xyzAtoms),3)) for ii in range(len(self.xyzAtoms)): for_average[ii, :] = self.xyzAtoms[ii].coordinates return np.mean(for_average,axis = 0) def getGeometricCenter_arb(self, lattice, lattice_inv): pass def translateAtomsMinimumImage(self, lattice, lattice_inv, center=np.array([0.0, 0.0, 0.0])): """ **translateAtomsMinimumImage** Brings back all atoms into the original box using periodic boundary conditions and minimal image convention. """ nullatom = xyzAtom('O', center, 0) for atom in self.xyzAtoms: new_vec = getDistVectorPBC_arb(nullatom, atom, lattice, lattice_inv) atom.coordinates = new_vec+center atom.x_coord = atom.coordinates[0]+center[0] atom.y_coord = atom.coordinates[1]+center[1] atom.z_coord = atom.coordinates[2]+center[2] def translateSelf(self,vector): """ **translateSelf** Translate all atoms of the molecule by a vector 'vector'. """ for atom in self.xyzAtoms: atom.translateSelf(vector) def scatterPlot(self): """ **scatterPlot** Opens a plot window with a scatter-plot of all coordinates of the molecule. """ from mpl_toolkits.mplot3d import Axes3D fig = figure() ax = Axes3D(fig) x_vals = [coord[0] for coord in self.getCoordinates()] y_vals = [coord[1] for coord in self.getCoordinates()] z_vals = [coord[2] for coord in self.getCoordinates()] ax.scatter(x_vals, y_vals, z_vals) show() def appendAtom(self,Atom): """ **appendAtom** Add an xzyAtom to the molecule. """ if isinstance(Atom,xyzAtom): self.xyzAtoms.append(Atom) elif isinstance(Atom,list): self.xyzAtoms.extend(Atom) def popAtom(self,xyzAtom): """ **popAtom** Delete an xyzAtom from the molecule. """ self.xyzAtoms.remove(xyzAtom) def writeXYZfile(self,fname): """ **writeXYZfile** Creates an xyz-style text file with all coordinates of the molecule. """ if not self.title: self.title = 'None' writeXYZfile(fname, len(self.xyzAtoms), self.title, self.xyzAtoms) class xyzBox: """ **xyzBox** Class to hold information about and manipulate a xyz-periodic cubic box. Args.: * xyzAtoms (list): List of instances of the xyzAtoms class that make up the molecule. * boxLength (float): Box length. """ def __init__( self, xyzAtoms, boxLength=None, title=None ): self.xyzMolecules = [] self.xyzAtoms = xyzAtoms self.n_atoms = len(self.xyzAtoms) self.title = title self.boxLength = boxLength self.lattice = None self.lattice_inv = None self.relAtoms = None self.av_spectrum = np.array([]) def setBoxLength( self, boxLength, angstrom=True ): """ **setBoxLength** Set the box length. """ if angstrom: self.boxLength = boxLength else: self.boxLength = boxLength*constants.physical_constants['atomic unit of length'][0]*10**10 def writeBox(self, filename): """ **writeBox** Creates an xyz-style text file with all coordinates of the box. """ writeXYZfile(filename, self.n_atoms, self.title, self.xyzAtoms) def writeRelBox(self,filename,inclAtomNames=True): """ **writeRelBox** Writes all relative atom coordinates into a text file (useful as OCEAN input). """ if not self.boxLength: print('Cannot write rel. coordinates without boxLength. Need to set it first.') return else: writeRelXYZfile(filename, self.n_atoms, self.boxLength, self.title, self.xyzAtoms, inclAtomNames) def multiplyBoxPBC(self,numShells): """ **multiplyBoxPBC** Applies the periodic boundary conditions and multiplies the box in shells around the original. """ if not self.boxLength: print('Cannot multiply without boxLength. Need to set it first.') return all_atoms = getPeriodicTestBox(self.xyzAtoms,self.boxLength,numbershells=numShells) self.xyzMolecules = [] self.xyzAtoms = all_atoms self.n_atoms = self.n_atoms*(numShells*2.+1.)**3. self.boxLength = self.boxLength*(numShells*2.0+1.0) def multiplyBoxPBC_arb( self, lx=[-1,1], ly=[-1,1], lz=[-1,1] ): """ **multiplyBoxPBC_arb** Applies the periodic boundary conditions and multiplies the box in shells around the original. Works with arbitrary lattices. """ if not np.any(self.lattice) and not np.any(self.lattice_inv): print('Cannot multiply without lattice. Need to set it first.') return all_atoms = getPeriodicTestBox_arb( self.xyzAtoms, self.lattice, self.lattice_inv, lx, ly, lz ) self.xyzMolecules = [] self.xyzAtoms = all_atoms self.n_atoms = self.n_atoms*((lx[1]-lx[0])+1.)*((lx[1]-lx[0])+1.)*((lx[1]-lx[0])+1.) try: self.lattice[:,0] = self.lattice[:,0]*((lx[1]-lx[0])+1.) self.lattice[:,1] = self.lattice[:,1]*((ly[1]-ly[0])+1.) self.lattice[:,2] = self.lattice[:,2]*((lz[1]-lz[0])+1.) self.lattice_inv = np.linalg.inv(self.lattice) except: pass def translateAtomsMinimumImage(self, lattice, lattice_inv): """ **translateAtomsMinimumImage** Brings back all atoms into the original box using periodic boundary conditions and minimal image convention. """ nullatom = xyzAtom('O', np.array([0.0, 0.0, 0.0]), 0) for atom in self.xyzAtoms: new_vec = getDistVectorPBC_arb(nullatom, atom, lattice, lattice_inv) atom.coordinates = new_vec atom.x_coord = atom.coordinates[0] atom.y_coord = atom.coordinates[1] atom.z_coord = atom.coordinates[2] def deleteTip4pCOM(self): """ **deleteTip4pCOM** Deletes the ficticious atoms used in the TIP4P water model. """ for atom in self.xyzAtoms: if atom.name == 'M': self.xyzAtoms.remove(atom) self.n_atoms -= 1 def writeClusters(self,cenatom_name, number,cutoff,prefix,postfix='.xyz'): """ **writeXYZclusters** Write water clusters into files. """ # find central atom cen_atom = self.get_atoms_by_name(cenatom_name)[number] coor1 = cen_atom.getCoordinates() # cut clusters and write files atoms = [] atoms.append(cen_atom) for atom2 in self.xyzAtoms: coor2 = atom2.getCoordinates() if np.linalg.norm( coor1 - coor2) > 0.0 and np.linalg.norm( coor1 - coor2) <= cutoff: atoms.append(atom2) fname = prefix + '_%03d' % number + postfix box = xyzBox(atoms) box.writeBox(fname) def writeClusters_arb(self,cenatom_name, number,cutoff,prefix, postfix='.xyz', test_box_multiplyer=1 ): """ **writeXYZclusters** Write water clusters into files. """ test_atoms = deepcopy( self.xyzAtoms ) test_box = xyzBox(test_atoms) test_box.lattice = deepcopy(self.lattice) test_box.lattice_inv = deepcopy(self.lattice_inv) test_box.multiplyBoxPBC_arb( test_box_multiplyer ) # find central atom cen_atom = self.get_atoms_by_name(cenatom_name)[number] # cut clusters and write files atoms = [] atoms.append( cen_atom ) for atom2 in test_box.xyzAtoms: dist = np.linalg.norm(cen_atom.coordinates-atom2.coordinates) if dist > 0.0 and dist <= cutoff: # print(dist) atoms.append(atom2) box2 = xyzBox(atoms) fname = prefix + '_%03d' % number + postfix box2.writeBox(fname) def writeH2Oclusters(self,cutoff,prefix,postfix='.xyz',o_name='O',h_name='H'): """ **writeXYZclusters** Write water clusters into files. """ if not self.boxLength: print('Cannot multiply without boxLength. Need to set it first.') return # find H2O molecules self.get_h2o_molecules(o_name,h_name) # get a test box pbcMols = getPeriodicTestBox_molecules(self.xyzMolecules,self.boxLength,numbershells=1) # cut clusters and write files for ii, mol in enumerate(self.xyzMolecules): o_atom = mol.get_atoms_by_name(o_name)[0] #for o_atom, ii in zip(o_atoms,range(len(o_atoms))): cluster = [] for molecule in pbcMols: coor = molecule.getCoordinates_name(o_name) if np.linalg.norm( o_atom.coordinates - coor) <= cutoff: cluster.extend(molecule.xyzAtoms) fname = prefix + '_%03d' % ii + postfix box = xyzBox(cluster) box.writeBox(fname) def writeMoleculeCluster(self,molAtomList,fname,cutoff=None,numH2Omols=None,o_name='O',h_name='H',mol_center=None): """ **writeMoleculeCluster** Careful, this works only for a single molecule in water. """ if not self.boxLength: print('Cannot multiply without boxLength. Need to set it first.') return # find H2O molecules self.get_h2o_molecules(o_name,h_name) # get a test box pbcMols = getPeriodicTestBox_molecules(self.xyzMolecules,self.boxLength,numbershells=1) # find the solute molecule cluster = findMolecule(self.xyzAtoms,molAtomList) # find center of mass of molecule if not mol_center: cenom = cluster.getGeometricCenter() else: cenom = cluster.getCoordinates_name(mol_center) if cutoff: # use cutoff criterium waters = findAllWaters(cenom,pbcMols,o_name,cutoff) cluster.appendAtom(waters) elif numH2Omols: # use number of waters to include dists = getDistsFromMolecule(cenom,pbcMols,o_name=o_name) inds = np.argsort(dists) for ii in range(numH2Omols): cluster.appendAtom(pbcMols[inds[ii]].xyzAtoms) else: print('Something is fishy!') return cluster.writeXYZfile(fname) def writeFDMNESinput(self,fname,Filout,Range,Radius,Edge,NRIXS,Absorber): """ **writeFDMNESinput** Creates an input file to be used for q-dependent calculations with FDMNES. """ writeFDMNESinput_file(self.xyzAtoms,fname,Filout,Range,Radius,Edge,NRIXS,Absorber) def writeOCEANinput(self,fname,headerfile,exatom,edge,subshell): """ **writeOCEANinput** Creates an OCEAN input file based on the headerfile. """ if not self.boxLength: print('Need box size for this function to work!') return writeOCEANinput(fname,headerfile,self,exatom,edge,subshell) def getCoordinates(self): """ **getCoordinates** Return coordinates of all atoms in the cluster. """ return [atom.getCoordinates() for atom in self.xyzAtoms] def get_atoms_by_name(self,name): """ **get_atoms_by_name** Return a list of all xyzAtoms of a given name 'name'. """ # find oxygen atoms atoms = [] for atom in self.xyzAtoms: if atom.name == name: atoms.append(atom) else: pass if len(atoms) == 0: print('Found no atoms with given name in box.') return return atoms def scatterPlot(self): """ **scatterPlot** Opens a plot window with a scatter-plot of all coordinates of the box. """ from mpl_toolkits.mplot3d import Axes3D fig = figure() ax = Axes3D(fig) x_vals = [coord[0] for coord in self.getCoordinates()] y_vals = [coord[1] for coord in self.getCoordinates()] z_vals = [coord[2] for coord in self.getCoordinates()] cla() ax.scatter(x_vals, y_vals, z_vals) draw() def get_OO_neighbors(self,Roocut=3.6): """ **get_OO_neighbors** Returns list of numbers of nearest oxygen neighbors within readius 'Roocut'. """ o_atoms = self.get_atoms_by_name('O') if not self.boxLength: return count_OO_neighbors(o_atoms,Roocut) else: return count_OO_neighbors(o_atoms,Roocut,boxLength=self.boxLength) def get_OO_neighbors_pbc(self,Roocut=3.6): """ **get_OO_neighbors_pbc** Returns a list of numbers of nearest oxygen atoms, uses periodic boundary conditions. """ o_atoms = self.get_atoms_by_name('O') return count_OO_neighbors_pbc(o_atoms,Roocut,boxLength=self.boxLength) def get_h2o_molecules(self,o_name='O',h_name='H'): """ **get_h2o_molecules** Finds all water molecules inside the box and collects them inside the self.xyzMolecules attribute. """ o_atoms = self.get_atoms_by_name(o_name) h_atoms = self.get_atoms_by_name(h_name) if self.boxLength: self.xyzMolecules = find_H2O_molecules(o_atoms,h_atoms,boxLength=self.boxLength) else: self.xyzMolecules = find_H2O_molecules(o_atoms,h_atoms,boxLength=None) def get_h2o_molecules_arb(self, o_name='O',h_name='H'): o_atoms = self.get_atoms_by_name(o_name) h_atoms = self.get_atoms_by_name(h_name) self.xyzMolecules = h2o_mols = find_H2O_molecules_PBC_arb( o_atoms, h_atoms, self.lattice, self.lattice_inv ) def get_atoms_from_molecules(self): """ **get_atoms_from_molecules** Parses all atoms inside self.xyzMolecules into self.xyzAtoms (useful for turning an xyzMolecule into an xyzBox). """ if not self.xyzAtoms: self.xyzAtoms = [] for molecule in self.xyzMolecules: for atom in molecule.xyzAtoms: self.xyzAtoms.append(atom) self.n_atoms = len(self.xyzAtoms) def get_hbonds(self, Roocut=3.6, Rohcut=2.4, Aoooh=30.0): """ **get_hbonds** Counts the hydrogen bonds inside the box, returns the number of H-bond donors and H-bond acceptors. """ hb_accept_sum = 0 # total number of H-bonds in box hb_donor_sum = 0 # total number of donor H-bonds in box dbonds = [] abonds = [] o_atoms = self.get_atoms_by_name('O') h_atoms = self.get_atoms_by_name('H') if self.boxLength: h2o_mols = find_H2O_molecules(o_atoms,h_atoms,boxLength=self.boxLength) self.xyzMolecules = h2o_mols else: h2o_mols = find_H2O_molecules(o_atoms,h_atoms) test_h2o_mols = h2o_mols for mol1 in h2o_mols: dbonds_1 = 0 abonds_1 = 0 for mol2 in h2o_mols: donor, acceptor = countHbonds_pbc(mol1,mol2,self.boxLength,Roocut=Roocut, Rohcut=Rohcut, Aoooh=Aoooh) dbonds_1 += donor abonds_1 += acceptor hb_donor_sum += donor hb_accept_sum += acceptor dbonds.append(dbonds_1) abonds.append(abonds_1) return dbonds, abonds #hb_donor_sum, hb_accept_sum #hbonds, dbonds, abonds, hbondspermol def changeOHBondlength(self,fraction, oName='O', hName='H'): """ **changeOHBondlength** Changes all OH covalent bond lengths inside the box by a fraction. """ o_atoms = self.get_atoms_by_name('O') h_atoms = self.get_atoms_by_name('H') # find all H2O molecules if self.boxLength: h2o_mols = find_H2O_molecules(o_atoms,h_atoms,boxLength=self.boxLength) else: h2o_mols = find_H2O_molecules(o_atoms,h_atoms) # change the bond length new_h2o_mols = [] for mol in h2o_mols: new_h2o_mols.append(changeOHBondLength(mol, fraction, boxLength=self.boxLength, oName=oName, hName=hName)) # redefine all molecules and atoms in box self.xyzMolecules = new_h2o_mols self.xyzAtoms = [] for mol in self.xyzMolecules: for atom in mol.xyzAtoms: self.xyzAtoms.append(atom) def getTetraParameter(self): """ **getTetraParameter** Returns a list of tetrahedrality paprameters, according to NATURE, VOL 409, 18 JANUARY (2001). UNTESTED!!! """ if not self.boxLength: print('This only works with PBC. Need to set boxLength first.') return else: o_atoms = self.get_atoms_by_name('O') return getTetraParameter(o_atoms,self.boxLength) def get_angle_arb( self, atom1, atom2, atom3, degrees=True): """ **get_angle** Return angle between the three given atoms (as seen from atom2). """ vec1 = getDistVectorPBC_arb(atom1, atom2, self.lattice, self.lattice_inv) vec2 = getDistVectorPBC_arb(atom3, atom2, self.lattice, self.lattice_inv) dotp = np.dot(vec1/np.linalg.norm(vec1), vec2/np.linalg.norm(vec2)) if degrees: return np.degrees( np.arccos( np.clip( dotp, -1.0, 1.0 ) ) ) else: return np.arccos( np.clip( dotp, -1.0, 1.0 ) ) def get_angle( self, atom1, atom2, atom3, degrees=True): """ **get_angle** Return angle between the three given atoms (as seen from atom2). """ vec1 = getDistVector(atom1, atom2) vec2 = getDistVector(atom3, atom2) dotp = np.dot(vec1/np.linalg.norm(vec1), vec2/np.linalg.norm(vec2)) if degrees: return np.degrees( np.arccos( np.clip( dotp, -1.0, 1.0 ) ) ) else: return np.arccos( np.clip( dotp, -1.0, 1.0 ) ) def count_neighbors( self, name1, name2, cutoff_low=0.0, cutoff_high=2.0, counter_name='num_OO_shell' ): """ **count_neighbors** Counts number of neighbors (of name2) around atom of name1. Args: * name1 (str): Name of first type of atom. * name2 (str): Name of second type of atom. * cutoff_low (float): Lower cutoff (Angstrom). * cutoff_high (float): Upper cutoff (Angstrom). * counter_name (str): Attribute namer under which the result should be saved. """ for atom in self.xyzAtoms: if atom.name == name1: cou = 0 atoms_2 = self.get_atoms_by_name(name2) for atom_2 in atoms_2: dist = getDistancePBC_arb( atom, atom_2, self.lattice, self.lattice_inv ) if dist >= cutoff_low and dist <= cutoff_high: cou += 1 setattr(atom, counter_name, cou) def count_hbonds( self, Roocut=3.6, Rohcut=2.4, Aoooh=30.0, counter_name='num_H_bonds', counter_name2='H_bond_angles'): """ **count_hbonds** Counts the number of hydrogen bonds around all oxygen atoms and sets that number as attribute to the accorting xyzAtom. """ o_atoms = self.get_atoms_by_name('O') h_atoms = self.get_atoms_by_name('H') h2o_mols = find_H2O_molecules_PBC_arb( o_atoms, h_atoms, self.lattice, self.lattice_inv ) for mol1 in h2o_mols: don = 0 acc = 0 angles = [] for mol2 in h2o_mols: d, a, ang = count_HBonds_pbc_arb( mol1, mol2, self.lattice, self.lattice_inv, Roocut=Roocut, Rohcut=Rohcut, Aoooh=Aoooh ) don += d acc += a angles.append(ang) the_o_atom = mol1.get_atoms_by_name('O')[0] angles = np.array(angles) setattr(the_o_atom, counter_name, (don, acc)) setattr(the_o_atom, counter_name2, angles) #for atom in o_atoms: # acceptor = 0 # donor = 0 # # first molecule # mol_1 = [] # mol_1.append(atom) # for h_atom in h_atoms: # if getDistancePBC_arb(atom, h_atom, self.lattice, self.lattice_inv ) <= 1.2: # mol_1.append(h_atom) # mol1 = xyzMolecule(mol_1) # for o_atom in o_atoms: # # second molecule # mol_2 = [] # mol_2.append(o_atom) # for h_atom in h_atoms: # if getDistancePBC_arb( o_atom, h_atom, self.lattice, self.lattice_inv) <= 1.5: # mol_2.append(h_atom) # mol2 = xyzMolecule(mol_2) # d, a = count_HBonds_pbc_arb( mol1, mol2, self.lattice, self.lattice_inv, Roocut=3.6, Rohcut=2.4, Aoooh=30.0 ) # acceptor += a # donor += d # setattr(atom, counter_name, (donor, acceptor)) def count_contact_pairs( self, name_1, name_2, cutoff, counter_name='contact_pair'): atoms_1 = self.get_atoms_by_name(name_1) atoms_2 = self.get_atoms_by_name(name_2) for atom1 in atoms_1: contact_pair = [] contact_pair.append(atom1) for atom2 in atoms_2: dist = getDistancePBC_arb(atom1, atom2, self.lattice, self.lattice_inv ) if dist <= cutoff: contact_pair.append(atom2) if len(contact_pair) == 2: setattr(atom1, counter_name, 1) else: setattr(atom1, counter_name, 0) def normalize_spectrum(self, normrange): inds = np.where(np.logical_and(self.av_spectrum[:,0]>=normrange[0], self.av_spectrum[:,0]<=normrange[1]))[0] norm = np.trapz(self.av_spectrum[inds,1], self.av_spectrum[inds,0]) self.av_spectrum[:,1] /= norm def normalize_arb_spectrum(self, normrange, attribute): spectrum = getattr(self, attribute) inds = np.where(np.logical_and(spectrum[:,0]>=normrange[0], spectrum[:,0]<=normrange[1]))[0] norm = np.trapz(spectrum[inds,1], spectrum[inds,0]) spectrum[:,1] /= norm setattr(self, attribute, spectrum) def find_hydroniums( self, OH_cutoff=1.5 ): """ **find_hydroniums** Returns a list of hydronium molecules. """ o_atoms = self.get_atoms_by_name('O') h_atoms = self.get_atoms_by_name('H') hydroniums = [] for o_atom in o_atoms: molecule = [] molecule.append( o_atom ) for h_atom in h_atoms: if np.any(self.lattice) and np.any(self.lattice_inv): oh_dist = getDistancePBC_arb( o_atom, h_atom, self.lattice, self.lattice_inv ) elif np.any(self.boxLength): oh_dist = getDistancePbc( o_atom, h_atom, self.boxLength ) else: oh_dist = np.linalg.norm( o_atom.coordinates - h_atom.coordinates ) if oh_dist <= OH_cutoff: molecule.append( h_atom ) if len(molecule) == 4: hydroniums.append( xyzMolecule(molecule) ) return hydroniums def find_tmao_molecules_arb(self, CH_cut=1.2, CN_cut=1.6, NO_cut=1.5, CC_cut=2.5 ): """ **find_tmao_molecules** Returns a list of TMAO molecules. """ o_atoms = self.get_atoms_by_name('O') h_atoms = self.get_atoms_by_name('H') c_atoms = self.get_atoms_by_name('C') n_atoms = self.get_atoms_by_name('N') tmao_mols = [] # for n_atom in n_atoms: molecule = [] molecule.append( n_atom ) # find all C atoms for c_atom in c_atoms: cn_dist = getDistancePBC_arb( n_atom, c_atom, self.lattice, self.lattice_inv ) if cn_dist <= CN_cut: molecule.append(c_atom) # find the O atom for o_atom in o_atoms: on_dist = getDistancePBC_arb( n_atom, o_atom, self.lattice, self.lattice_inv ) if on_dist <= NO_cut: molecule.append(o_atom) # find the H atoms for c_atom in c_atoms: for h_atom in h_atoms: ch_dist = getDistancePBC_arb( c_atom, h_atom, self.lattice, self.lattice_inv ) if ch_dist <= CN_cut: molecule.append(h_atom) # check if molecule is complete if len(molecule) == 14: tmao_mols.append(xyzMolecule(molecule)) # return tmao_mols def find_urea_molecules_arb(self, NH_cut=1.2, CN_cut=1.6, CO_cut=1.5 ): """ **find_urea_molecules** Returns a list of Urea molecules. """ o_atoms = self.get_atoms_by_name('O') h_atoms = self.get_atoms_by_name('H') c_atoms = self.get_atoms_by_name('C') n_atoms = self.get_atoms_by_name('N') urea_mols = [] # for c_atom in c_atoms: molecule = [] molecule.append( c_atom ) # find the O atom for o_atom in o_atoms: oc_dist = getDistancePBC_arb( c_atom, o_atom, self.lattice, self.lattice_inv ) if oc_dist <= CO_cut: molecule.append(o_atom) # find the N atoms mol_n_atoms = [] for n_atom in n_atoms: nc_dist = getDistancePBC_arb( c_atom, n_atom, self.lattice, self.lattice_inv ) if nc_dist <= CN_cut: molecule.append(n_atom) mol_n_atoms.append(n_atom) # find the H atoms for n_atom in mol_n_atoms: for h_atom in h_atoms: nh_dist = getDistancePBC_arb( n_atom, h_atom, self.lattice, self.lattice_inv ) if nh_dist <= NH_cut: molecule.append(h_atom) # check if molecule is complete if len(molecule) == 8: urea_mols.append(xyzMolecule(molecule)) # return urea_mols def find_hydroxides( self, OH_cutoff=1.5 ): """ **find_hydroxides** Returns a list of hydroxide molecules. """ o_atoms = self.get_atoms_by_name('O') h_atoms = self.get_atoms_by_name('H') hydroxides = [] for o_atom in o_atoms: molecule = [] molecule.append( o_atom ) for h_atom in h_atoms: if np.any(self.lattice) and np.any(self.lattice_inv): oh_dist = getDistancePBC_arb( o_atom, h_atom, self.lattice, self.lattice_inv ) elif np.any(self.boxLength): oh_dist = getDistancePbc( o_atom, h_atom, self.boxLength ) else: oh_dist = np.linalg.norm( o_atom.coordinates - h_atom.coordinates ) if oh_dist <= OH_cutoff: molecule.append( h_atom ) if len(molecule) == 2: hydroxides.append( xyzMolecule(molecule) ) return hydroxides def getDistancePBC_arb(self, atom1, atom2): """ **getDistancePBC_arb** Calculates the distance of two atoms from an arbitrary simulation box using the minimum image convention. Args: atom1 (obj): Instance of the xzyAtom class. atom2 (obj): Instance of the xzyAtom class. Returns: The distance between the two atoms. """ return np.linalg.norm( getDistVectorPBC_arb(atom1, atom2, self.lattice, self.lattice_inv) ) def getDistVectorPBC_arb(self, atom1, atom2): """ **getDistVectorPBC_arb** Calculates the distance vector between two atoms from an arbitrary simulation box using the minimum image convention. Args: atom1 (obj): Instance of the xzyAtom class. atom2 (obj): Instance of the xzyAtom class. Returns: The distance vector between the two atoms (np.array). """ dist_vec = np.array(atom2.coordinates) - np.array(atom1.coordinates) dist_vec -= np.dot(self.lattice, np.round(np.dot(self.lattice_inv,dist_vec))) return dist_vec def findMethAndHexMolecules(self, CO_cut=1.6, CH_cut=1.2, OH_cut=1.2, CC_cut=1.7 ): """ **CH3OH** """ meth_molecules, c_atoms = findMethanolMolecules( self, CO_cut, CH_cut, OH_cut ) hex_molecules = findHexaneMolecules( self, c_atoms, CC_cut=1.7, CH_cut=1.2 ) print( 'len molecules: ', len(hex_molecules) ) print( 'len one mol: ', len(hex_molecules[0].xyzAtoms)) return meth_molecules, hex_molecules def findMethanolMolecules(self, CO_cut=1.6, CH_cut=1.2, OH_cut=1.2): """ **CH3OH** """ meth_molecules, c_atoms = findMethanolMolecules( self, CO_cut, CH_cut, OH_cut ) return meth_molecules class xyzTrajectory: def __init__(self,xyzBoxes): self.xyzBoxes = xyzBoxes try: self.boxLength = xyzBoxes[0].boxLength except: self.boxLength = None def writeRandBox(self,filename): ind = np.random.randint(len(self.xyzBoxes)) self.xyzBoxes[ind].writeBox(filename) def loadAXSFtraj(self,filename): self.xyzBoxes = axsfTrajParser(filename) try: self.boxLength = self.xyzBoxes[0].boxLength except: pass def writeXYZtraj(self,filename): writeXYZtrajectory(filename,self.xyzBoxes) def getRDF(self,atom1='O',atom2='O',MAXBIN=1000,DELR=0.01,RHO=1.0): HIST = np.zeros(MAXBIN+1) GR = np.zeros(MAXBIN+1) for box in self.xyzBoxes: atoms = box.get_atoms_by_name(atom1) + box.get_atoms_by_name(atom2) HIST += calculateRIJhist(atoms,self.boxLength,DELR=DELR,MAXBIN=MAXBIN) CONST = 4.0*np.pi*RHO/3.0 for BIN in range(2,MAXBIN): RLOWER = (BIN-1)*DELR RUPPER = RLOWER + DELR NIDEAL = CONST * ( RUPPER**3 - RLOWER**3 ) GR[BIN] = float(HIST[BIN]) / float(len(self.xyzBoxes)) / float(len(atoms)) / float(NIDEAL) return np.arange(0.0, (MAXBIN+1)*DELR, DELR),GR def getRDF_arb(self,atom1='O',atom2='O',MAXBIN=1000,DELR=0.01,RHO=1.0): HIST = np.zeros(MAXBIN+1) GR = np.zeros(MAXBIN+1) for box in self.xyzBoxes: atoms1 = box.get_atoms_by_name(atom1) atoms2 = box.get_atoms_by_name(atom2) HIST += calculateRIJhist_arb(atoms1, atoms2, box.lattice, box.lattice_inv,DELR=DELR,MAXBIN=MAXBIN) CONST = 4.0*np.pi*RHO/3.0 for BIN in range(2,MAXBIN): RLOWER = (BIN-1)*DELR RUPPER = RLOWER + DELR NIDEAL = CONST * ( RUPPER**3 - RLOWER**3 ) GR[BIN] = float(HIST[BIN]) / float(len(self.xyzBoxes)) / float(len(atoms1)+len(atoms2)) / float(NIDEAL) return np.arange(0.0, (MAXBIN+1)*DELR, DELR),GR def getRDF2_arb( self, atom1='O', atom2='O', MAXBIN=1000, DELR=0.01, RHO=1.0 ): HIST = np.zeros(MAXBIN+1) GR = np.zeros(MAXBIN+1) for box in self.xyzBoxes: atoms1 = box.get_atoms_by_name(atom1) atoms2 = box.get_atoms_by_name(atom2) HIST += calculateRIJhist2_arb(atoms1, atoms2, box.lattice, box.lattice_inv, DELR=DELR, MAXBIN=MAXBIN ) V = box.lattice[:,0].dot( np.cross(box.lattice[:,1],box.lattice[:,2])) rhoB = len(atoms2)/V Nconf = float(len(self.xyzBoxes)) Na = len(atoms1) for BIN in range(2,MAXBIN): HistAB = HIST[BIN] Ri2 = (BIN*DELR)**2 GR[BIN] = HistAB/ (4.0*np.pi) / rhoB / Ri2 / DELR / Nconf / Na return np.arange(0.0, (MAXBIN+1)*DELR, DELR),GR def calculateRIJhist(atoms,boxLength,DELR=0.01,MAXBIN=1000): HIST = np.zeros(MAXBIN+1) for ii in range(len(atoms)-1): for jj in range(ii+1,len(atoms)): RIJ = getDistancePbc(atoms[ii],atoms[jj],boxLength) BIN = int(RIJ/DELR) + 1 if BIN <= MAXBIN: HIST[BIN] = HIST[BIN] + 2 return HIST def calculateRIJhist_arb(atoms1, atoms2, lattice, lattice_inv,DELR=0.01,MAXBIN=1000): HIST = np.zeros(MAXBIN+1) for ii in range(len(atoms1)): for jj in range(len(atoms2)): RIJ = getDistancePBC_arb( atoms1[ii], atoms2[jj], lattice, lattice_inv ) BIN = int(RIJ/DELR) + 1 if BIN <= MAXBIN: HIST[BIN] = HIST[BIN] + 2 return HIST def calculateRIJhist2_arb( atoms1, atoms2, lattice, lattice_inv, DELR=0.01, MAXBIN=1000 ): HIST = np.zeros(MAXBIN+1) for ii in range(len(atoms1)): for jj in range(len(atoms2)): RIJ = getDistancePBC_arb( atoms1[ii], atoms2[jj], lattice, lattice_inv ) BIN = int(RIJ/DELR) + 1 if BIN <= MAXBIN: HIST[BIN] = HIST[BIN] + 1 return HIST def getDistsFromMolecule(point,listOfMolecules,o_name=None): dists = [] if not o_name: for mol in listOfMolecules: dists.append(np.linalg.norm(point - mol.getGeometricCenter())) return dists else: for mol in listOfMolecules: coor = mol.getCoordinates_name(o_name)[0] dists.append(np.linalg.norm(point - coor)) return dists def findMethanolMolecules( box, CO_cut=1.6, CH_cut=1.2, OH_cut=1.2): o_atoms = box.get_atoms_by_name('O') c_atoms = box.get_atoms_by_name('C') h_atoms = box.get_atoms_by_name('H') meth_molecules = [] for o_atom in o_atoms: one_mol = [] one_mol.append( o_atom ) for c_atom in c_atoms: dist = box.getDistancePBC_arb( o_atom, c_atom ) if dist < CO_cut: mol_c = c_atom one_mol.append( c_atom ) c_atoms.remove( c_atom ) for h_atom in h_atoms: OHdist = box.getDistancePBC_arb( o_atom, h_atom ) if OHdist < OH_cut: one_mol.append( h_atom ) CHdist = box.getDistancePBC_arb( mol_c, h_atom ) if CHdist < CH_cut: one_mol.append( h_atom ) meth_molecules.append(xyzMolecule( one_mol )) return meth_molecules, c_atoms def findHexaneMolecules( box, c_atoms, CC_cut=1.7, CH_cut=1.2 ): h_atoms = box.get_atoms_by_name('H') hex_molecules = [] print ('c atoms ',len(c_atoms), len(c_atoms)/6.) all_carbons = [] for ii in range(int(len(c_atoms)/6)): molecule = [] test_atom = c_atoms[ii*6] molecule.append(test_atom) for c_atom in c_atoms: CCdist = box.getDistancePBC_arb( test_atom, c_atom ) if CCdist < CC_cut and CCdist > 0.0: molecule.append(c_atom) all_carbons.append(test_atom) if len(molecule)==2: test_atom1 = deepcopy(molecule[-1]) for c_atom in c_atoms: if not c_atom in all_carbons: CCdist = box.getDistancePBC_arb( test_atom1, c_atom ) if CCdist < CC_cut and CCdist > 0.0: molecule.append(c_atom) test_atom1 = deepcopy(c_atom) all_carbons.append(test_atom1) if len(molecule)==3: test_atom1 = deepcopy(molecule[-1]) test_atom2 = deepcopy(molecule[-2]) for c_atom in c_atoms: if not c_atom in all_carbons: CCdist1 = box.getDistancePBC_arb( test_atom1, c_atom ) CCdist2 = box.getDistancePBC_arb( test_atom2, c_atom ) if CCdist1 < CC_cut and CCdist1 > 0.0: molecule.append(c_atom) test_atom1 = deepcopy(c_atom) all_carbons.append(test_atom1) if CCdist2 < CC_cut and CCdist2 > 0.0: molecule.append(c_atom) test_atom2 = deepcopy(c_atom) all_carbons.append(test_atom2) hex_molecules.append(xyzMolecule(molecule)) for molecule in hex_molecules: c_atoms = molecule.get_atoms_by_name('C') for c_atom in c_atoms: for h_atom in h_atoms: CHdist = box.getDistancePBC_arb( c_atom, h_atom ) if CHdist < CH_cut and CHdist > 0.0: molecule.xyzAtoms.append(h_atom) return hex_molecules def findAllWaters(point,waterMols,o_name,cutoff): atoms = [] for mol in waterMols: coor = mol.getCoordinates_name(o_name) if np.linalg.norm( point - coor) <= cutoff: atoms.extend(mol.xyzAtoms) return atoms def findMolecule(xyzAtoms,molAtomList): molecule = [] for atom in xyzAtoms: if atom.name in molAtomList: molecule.append(atom) return xyzMolecule(molecule) def calculateCOMlist(atomList): """ **calculateCOMlist** Calculates center of mass for a list of atoms. """ r = np.array([0.,0.,0.]) cou = 0 for atom in atomList: r += atom.coordinates cou += 1 return r/cou def getPeriodicTestBox_molecules(Molecules,boxLength,numbershells=1): vectors = [] for l in range(-numbershells,numbershells+1): for m in range(-numbershells,numbershells+1): for n in range(-numbershells,numbershells+1): vectors.append(np.array([l, m, n])) pbc_molecules = [] for vector in vectors: for molecule in Molecules: cpAtoms = [] for atom in molecule.xyzAtoms: cpAtom = deepcopy(atom) cpAtom.translateSelf(vector*boxLength) cpAtoms.append(cpAtom) pbc_molecules.append(xyzMolecule(cpAtoms)) return pbc_molecules def getPeriodicTestBox(xyzAtoms,boxLength,numbershells=1): vectors = [] for l in range(-numbershells,numbershells+1): for m in range(-numbershells,numbershells+1): for n in range(-numbershells,numbershells+1): vectors.append(np.array([l, m, n])) pbc_atoms = [] for vector in vectors: for atom in xyzAtoms: cpAtom = deepcopy(atom) cpAtom.translateSelf(vector*boxLength) pbc_atoms.append(cpAtom) return pbc_atoms def getPeriodicTestBox_arb( xyzAtoms, lattice, lattice_inv, lx=[-1,1], ly=[-1,1], lz=[-1,1] ): vectors = [] for l in range(lx[0], lx[1]+1): for m in range( ly[0], ly[1]+1): for n in range( lz[0], lz[1]+1): vectors.append(np.array([l, m, n])) pbc_atoms = [] for vector in vectors: for atom in xyzAtoms: cpAtom = deepcopy(atom) cpAtom.translateSelf_arb(lattice, lattice_inv, vector) pbc_atoms.append(cpAtom) return pbc_atoms def getTranslVec(atom1,atom2,boxLength): """ **getTranslVec** Returns the translation vector that brings atom2 closer to atom1 in case atom2 is further than boxLength away. """ xcoord1 = atom1.coordinates[0] xcoord2 = atom2.coordinates[0] ycoord1 = atom1.coordinates[1] ycoord2 = atom2.coordinates[1] zcoord1 = atom1.coordinates[2] zcoord2 = atom2.coordinates[2] translVec = np.zeros(atom2.coordinates.shape) if xcoord1-xcoord2 > boxLength/2.0: translVec[0] = -boxLength/2.0 if ycoord1-ycoord2 > boxLength/2.0: translVec[1] = -boxLength/2.0 if zcoord1-zcoord2 > boxLength/2.0: translVec[2] = -boxLength/2.0 return translVec def getTranslVec_geocen(mol1COM,mol2COM,boxLength): """ **getTranslVec_geocen** """ translVec = np.zeros((len(mol1COM),)) if mol1COM[0]-mol2COM[0] > boxLength/2.0: translVec[0] = -boxLength/2.0 if mol1COM[1]-mol2COM[0] > boxLength/2.0: translVec[1] = -boxLength/2.0 if mol1COM[2]-mol2COM[0] > boxLength/2.0: translVec[2] = -boxLength/2.0 return translVec def getDistancePbc(atom1,atom2,boxLength): xdist = atom1.coordinates[0] - atom2.coordinates[0] xdist -= boxLength*round(xdist/boxLength) ydist = atom1.coordinates[1] - atom2.coordinates[1] ydist -= boxLength*round(ydist/boxLength) zdist = atom1.coordinates[2] - atom2.coordinates[2] zdist -= boxLength*round(zdist/boxLength) return np.sqrt(xdist**2.0 + ydist**2.0 + zdist**2.0) def getDistancePBC_arb(atom1, atom2, lattice, lattice_inv): """ **getDistancePBC_arb** Calculates the distance of two atoms from an arbitrary simulation box using the minimum image convention. Args: atom1 (obj): Instance of the xzyAtom class. atom2 (obj): Instance of the xzyAtom class. lattice (np.array): Array with lattice vectors as columns. lattice_inv (np.array): Inverse of lattice. Returns: The distance between the two atoms. """ return np.linalg.norm(getDistVectorPBC_arb(atom1, atom2, lattice, lattice_inv)) def getDistVectorPBC_arb(atom1, atom2, lattice, lattice_inv): """ **getDistVectorPBC_arb** Calculates the distance vector between two atoms from an arbitrary simulation box using the minimum image convention. Args: atom1 (obj): Instance of the xzyAtom class. atom2 (obj): Instance of the xzyAtom class. lattice (np.array): Array with lattice vectors as columns. lattice_inv (np.array): Inverse of lattice. Returns: The distance vector between the two atoms (np.array). """ #frac_coords1 = np.dot( lattice_inv, atom1.coordinates ) #frac_coords2 = np.dot( lattice_inv, atom2.coordinates ) #red_frac1 = np.array(frac_coords1) - np.floor(frac_coords1) #red_frac2 = np.array(frac_coords2) - np.floor(frac_coords2) #red_dist = np.array(red_frac2) - np.array(red_frac1) #return np.dot(lattice, red_dist) dist_vec = np.array(atom2.coordinates) - np.array(atom1.coordinates) dist_vec -= np.dot(lattice, np.round(np.dot(lattice_inv,dist_vec))) return dist_vec def getDistance(atom1, atom2): return np.linalg.norm(atom2.getCoordinates()-atom2.getCoordinates()) def getDistVectorPbc(atom1,atom2,boxLength): xdist = atom1.coordinates[0] - atom2.coordinates[0] xdist -= boxLength*round(xdist/boxLength) ydist = atom1.coordinates[1] - atom2.coordinates[1] ydist -= boxLength*round(ydist/boxLength) zdist = atom1.coordinates[2] - atom2.coordinates[2] zdist -= boxLength*round(zdist/boxLength) return np.array([xdist, ydist, zdist]) def getDistVector(atom1,atom2): xdist = atom1.coordinates[0] - atom2.coordinates[0] ydist = atom1.coordinates[1] - atom2.coordinates[1] zdist = atom1.coordinates[2] - atom2.coordinates[2] return np.array([xdist, ydist, zdist]) def count_HBonds_pbc_arb( mol1, mol2, lattice, lattice_inv, Roocut=3.6, Rohcut=2.4, Aoooh=30.0 ): hbond_angle = 0 hb_donor = 0 hb_accept = 0 # get atoms mol1_o = mol1.get_atoms_by_name('O') mol1_h = mol1.get_atoms_by_name('H') mol2_o = mol2.get_atoms_by_name('O') mol2_h = mol2.get_atoms_by_name('H') # O-O dist: OO_dist = getDistancePBC_arb(mol1_o[0],mol2_o[0], lattice, lattice_inv) if OO_dist <= Roocut and OO_dist > 0.0: # donor bond through first hydrogen atom of mol1 OH_dist = getDistancePBC_arb(mol1_h[0],mol2_o[0], lattice, lattice_inv) if OH_dist <= Rohcut: vec1 = getDistVectorPBC_arb(mol1_h[0], mol1_o[0], lattice, lattice_inv) vec2 = getDistVectorPBC_arb(mol2_o[0], mol1_o[0], lattice, lattice_inv) angle = np.degrees( np.arccos( np.clip( np.dot(vec2,vec1)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)), -1, 1 ) )) if angle <= Aoooh: hb_donor += 1.0 hbond_angle = angle # donor bond through second hydrogen atom of mol1 try: # catch if there is a OH- involved OH_dist = getDistancePBC_arb(mol1_h[1],mol2_o[0], lattice, lattice_inv) if OH_dist <= Rohcut: vec1 = getDistVectorPBC_arb(mol1_h[1], mol1_o[0], lattice, lattice_inv) vec2 = getDistVectorPBC_arb(mol2_o[0], mol1_o[0], lattice, lattice_inv) angle = np.degrees( np.arccos( np.clip( np.dot(vec2,vec1)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)), -1, 1 ) )) if angle <= Aoooh: hb_donor += 1.0 hbond_angle = angle except: pass # acceptor bond through first hydrogen atom of mol2 OH_dist = getDistancePBC_arb(mol1_o[0],mol2_h[0], lattice, lattice_inv) if OH_dist <= Rohcut: vec1 = getDistVectorPBC_arb(mol2_h[0], mol2_o[0], lattice, lattice_inv) vec2 = getDistVectorPBC_arb(mol1_o[0], mol2_o[0], lattice, lattice_inv) angle = np.degrees( np.arccos( np.clip( np.dot(vec2,vec1)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)), -1, 1 ) )) if angle <= Aoooh: hb_accept += 1.0 hbond_angle = angle # acceptor bond through second hydrogen atom of mol2 try: # catch if there is a OH- involved OH_dist = getDistancePBC_arb(mol1_o[0],mol2_h[1], lattice, lattice_inv) if OH_dist <= Rohcut: vec1 = getDistVectorPBC_arb(mol2_h[1], mol2_o[0], lattice, lattice_inv) vec2 = getDistVectorPBC_arb(mol1_o[0], mol2_o[0], lattice, lattice_inv) angle = np.degrees( np.arccos( np.clip( np.dot(vec2,vec1)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)), -1, 1 ) )) if angle <= Aoooh: hb_accept += 1.0 hbond_angle = angle except: pass # try/catch if there is a OH3+ involved # acceptor bond through THIRD hydrogen atom of mol1 try: # catch if there is a OH- involved OH_dist = getDistancePBC_arb(mol1_o[0],mol2_h[2], lattice, lattice_inv) if OH_dist <= Rohcut: vec1 = getDistVectorPBC_arb(mol2_h[2], mol2_o[0], lattice, lattice_inv) vec2 = getDistVectorPBC_arb(mol1_o[0], mol2_o[0], lattice, lattice_inv) angle = np.degrees( np.arccos( np.clip( np.dot(vec2,vec1)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)), -1, 1 ) )) if angle <= Aoooh: hb_accept += 1.0 hbond_angle = angle except: pass # donor bond through THIRD hydrogen atom of mol2 try: # catch if there is a OH- involved OH_dist = getDistancePBC_arb(mol1_h[2],mol2_o[0], lattice, lattice_inv) if OH_dist <= Rohcut: vec1 = getDistVectorPBC_arb(mol1_h[2], mol1_o[0], lattice, lattice_inv) vec2 = getDistVectorPBC_arb(mol2_o[0], mol1_o[0], lattice, lattice_inv) angle = np.degrees( np.arccos( np.clip( np.dot(vec2,vec1)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)), -1, 1 ) )) if angle <= Aoooh: hb_donor += 1.0 hbond_angle = angle except: pass return hb_donor, hb_accept, hbond_angle def countHbonds_pbc(mol1,mol2,boxLength,Roocut=3.6, Rohcut=2.4, Aoooh=30.0): hb_donor = 0.0 hb_accept = 0.0 # get atoms mol1_o = mol1.get_atoms_by_name('O') mol1_h = mol1.get_atoms_by_name('H') mol2_o = mol2.get_atoms_by_name('O') mol2_h = mol2.get_atoms_by_name('H') if getDistancePbc(mol1_o[0],mol2_o[0],boxLength) <= Roocut and getDistancePbc(mol1_o[0],mol2_o[0],boxLength) > 0.0: # donor bond through first hydrogen atom of mol1 if getDistancePbc(mol1_h[0],mol2_o[0],boxLength) <= Rohcut: vec1 = getDistVectorPbc(mol1_h[0],mol1_o[0],boxLength) vec2 = getDistVectorPbc(mol2_o[0],mol1_o[0],boxLength) if np.degrees(np.arccos( np.dot(vec2,vec1)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)))) <= Aoooh: hb_donor += 1.0 # donor bond through second hydrogen atom of mol1 if getDistancePbc(mol1_h[1],mol2_o[0],boxLength) <= Rohcut: vec1 = getDistVectorPbc(mol1_h[1],mol1_o[0],boxLength) vec2 = getDistVectorPbc(mol2_o[0],mol1_o[0],boxLength) if np.degrees(np.arccos( np.dot(vec2,vec1)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)))) <= Aoooh: hb_donor += 1.0 # acceptor bond through first hydrogen atom of mol2 if getDistancePbc(mol1_o[0],mol2_h[0],boxLength) <= Rohcut: vec1 = getDistVectorPbc(mol2_h[0],mol2_o[0],boxLength) vec2 = getDistVectorPbc(mol1_o[0],mol2_o[0],boxLength) if np.degrees(np.arccos( np.dot(vec2,vec1)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)))) <= Aoooh: hb_accept += 1.0 # acceptor bond through second hydrogen atom of mol2 if getDistancePbc(mol1_o[0],mol2_h[1],boxLength) <= Rohcut: vec1 = getDistVectorPbc(mol2_h[1],mol2_o[0],boxLength) vec2 = getDistVectorPbc(mol1_o[0],mol2_o[0],boxLength) if np.degrees(np.arccos( np.dot(vec2,vec1)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)))) <= Aoooh: hb_accept += 1.0 return hb_donor, hb_accept def countHbonds(mol1,mol2, Roocut=3.6, Rohcut=2.4, Aoooh=30.0): hb_donor = 0 hb_accept = 0 # get the coordinates Aoooh = np.radians(Aoooh) mol1_o = mol1.getCoordinates_name('O') mol1_h = mol1.getCoordinates_name('H') mol2_o = mol2.getCoordinates_name('O') mol2_h = mol2.getCoordinates_name('H') # check O-O distance if np.linalg.norm(mol2_o[0] - mol1_o[0]) > 0.0 and np.linalg.norm(mol2_o[0] - mol1_o[0]) <= Roocut: # check Roocut is met # check for donor H-bonds if np.linalg.norm(mol2_o[0] - mol1_h[0]) > 0.0 and np.linalg.norm(mol2_o[0] - mol1_h[0]) <= Rohcut: # check Rohcut, first H vec1 = mol1_h[0]-mol1_o[0] vec2 = mol2_o[0]-mol1_o[0] if np.arccos((np.dot(vec1,vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2)))) <= Aoooh: hb_donor += 1 if np.linalg.norm(mol2_o[0] - mol1_h[1]) > 0.0 and np.linalg.norm(mol2_o[0] - mol1_h[1]) <= Rohcut: # check Rohcut, second H vec1 = mol1_h[1]-mol1_o[0] vec2 = mol2_o[0]-mol1_o[0] if np.arccos((np.dot(vec1,vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2)))) <= Aoooh: hb_donor += 1 # check for acceptor H-bonds if np.linalg.norm(mol2_h[0] - mol1_o[0]) > 0.0 and np.linalg.norm(mol2_h[0] - mol1_o[0]) <= Rohcut: # check Rohcut, first H vec1 = mol1_o[0]-mol2_o[0] vec2 = mol2_h[0]-mol2_o[0] if np.arccos((np.dot(vec1,vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2)))) <= Aoooh: hb_accept += 1 if np.linalg.norm(mol2_h[1] - mol1_o[0]) > 0.0 and np.linalg.norm(mol2_h[1] - mol1_o[0]) <= Rohcut: # check Rohcut, socond H vec1 = mol1_o[0]-mol2_o[0] vec2 = mol2_h[1]-mol2_o[0] if np.arccos((np.dot(vec1,vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2)))) <= Aoooh: hb_accept += 1 return hb_donor, hb_accept def countHbonds_orig(mol1,mol2, Roocut=3.6, Rohcut=2.4, Aoooh=30.0): mol1_o = mol1.getCoordinates_name('O') mol1_h = mol1.getCoordinates_name('H') mol2_o = mol2.getCoordinates_name('O') mol2_h = mol2.getCoordinates_name('H') hbnoD1 = 0 if np.linalg.norm(mol2_o[0]-mol1_o[0]) > 0.0 and np.linalg.norm(mol2_o[0]-mol1_o[0]) <= Roocut: if np.linalg.norm(mol2_h[0] - mol1_o[0]) <= Rohcut: vec1 = mol2_h[0]-mol2_o[0] vec2 = mol1_o[0]-mol2_o[0] if np.degrees(np.arccos( np.dot(vec1,vec2)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)))) <= Aoooh: hbnoD1 += 1 hbnoD2 = 0 if np.linalg.norm(mol2_o[0]-mol1_o[0]) > 0.0 and np.linalg.norm(mol2_o[0]-mol1_o[0]) <= Roocut: if np.linalg.norm(mol2_h[1]-mol1_o[0]) <= Rohcut: vec1 = mol2_h[1]-mol2_o[0] vec2 = mol1_o[0]-mol2_o[0] if np.degrees(np.arccos( np.dot(vec1,vec2)/(np.linalg.norm(vec2)*np.linalg.norm(vec1)))) <= Aoooh: hbnoD2 += 1 hbnoA1 = 0 if np.linalg.norm(mol2_o[0]-mol1_o[0]) > 0.0 and np.linalg.norm(mol2_o[0]-mol1_o[0]) <= Roocut: if np.linalg.norm(mol2_o[0]-mol1_h[0]) <= Rohcut: vec1 = mol2_o[0]-mol1_o[0] vec2 = mol1_h[0]-mol1_o[0] if np.degrees(np.arccos( np.dot(vec1,vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2)))) <= Aoooh: hbnoA1 += 1 hbnoA2 = 0 if np.linalg.norm(mol2_o[0]-mol1_o[0]) > 0.0 and np.linalg.norm(mol2_o[0]-mol1_o[0]) <= Roocut: if np.linalg.norm(mol2_o[0]-mol1_h[1]) <= Rohcut: vec1 = mol2_o[0]-mol1_o[0] vec2 = mol1_h[1]-mol1_o[0] if np.degrees(np.arccos( np.dot(vec1,vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2)))) <= Aoooh: hbnoA2 += 1 dbonds = hbnoD1 + hbnoD2 abonds = hbnoA1 + hbnoA2 return dbonds, abonds def getTetraParameter(o_atoms,boxLength=None): """ according to NATURE, VOL 409, 18 JANUARY 2001 """ tetra_params = [] for atom1 in o_atoms: NN_dists = [] NN_atoms = [] for atom2 in o_atoms: NN_dists.append(np.linalg.norm(atom1.coordinates - atom2.coordinates)) order = np.argsort(NN_dists) for ii in order[1:5]: NN_atoms.append(o_atoms[ii]) tetra_param = 0 for j in range(0,3): for k in range(j+1,4): vec1 = getDistVectorPbc(atom1,NN_atoms[j],boxLength) vec2 = getDistVectorPbc(atom1,NN_atoms[k],boxLength) psi = np.arccos(np.dot(vec1,vec2)/np.linalg.norm(vec1)/np.linalg.norm(vec2)) tetra_param += (np.cos(psi) + 1./3.)**2 tetra_params.append(1-3./8.*tetra_param) return tetra_params def repair_h2o_molecules_pbc(h2o_mols,boxLength): new_mols = [] for mol in h2o_mols: o_atom = mol.get_atoms_by_name('O')[0] h_atoms = mol.get_atoms_by_name('H') new_mol = [o_atom] for h_atom in h_atoms: cpAtom = deepcopy(h_atom) xdist = o_atom.coordinates[0] - cpAtom.coordinates[0] ydist = o_atom.coordinates[1] - cpAtom.coordinates[1] zdist = o_atom.coordinates[2] - cpAtom.coordinates[2] cpAtom.translateSelf([boxLength*round(xdist/boxLength),boxLength*round(ydist/boxLength),boxLength*round(zdist/boxLength)]) new_mol.append(cpAtom) new_mols.append(xyzMolecule(new_mol)) return new_mols def find_H2O_molecules(o_atoms,h_atoms,boxLength=None): h2o_molecules = [] if not boxLength: warnings.warn('No box length provided, will not take PBC into account!') for o_atom in o_atoms: ho_dists = [] for h_atom in h_atoms: ho_dists.append(np.linalg.norm(o_atom.coordinates - h_atom.coordinates)) order = np.argsort(ho_dists) if np.linalg.norm(o_atom.coordinates - h_atoms[order[0]].getCoordinates()) <= 1.5 and np.linalg.norm(o_atom.coordinates - h_atoms[order[1]].getCoordinates()) <= 1.5: h2o_molecules.append(xyzMolecule([o_atom,h_atoms[order[0]],h_atoms[order[1]]])) return h2o_molecules else: for o_atom in o_atoms: ho_dists = [] for h_atom in h_atoms: ho_dists.append(getDistancePbc(o_atom,h_atom,boxLength)) order = np.argsort(ho_dists) h2o_molecules.append(xyzMolecule([o_atom,h_atoms[order[0]],h_atoms[order[1]]])) return h2o_molecules def find_H2O_molecules_PBC_arb( o_atoms, h_atoms, lattice, lattice_inv, OH_cutoff=1.5 ): h2o_molecules = [] for o_atom in o_atoms: ho_dists = [] for h_atom in h_atoms: ho_dists.append(getDistancePBC_arb(o_atom, h_atom, lattice, lattice_inv)) inds = np.where(np.array(ho_dists) <= OH_cutoff)[0] molecule = [] molecule.append(o_atom) for ind in inds: molecule.append(h_atoms[ind]) h2o_molecules.append(xyzMolecule(molecule)) return h2o_molecules def writeXYZfile(filename,numberOfAtoms, title, list_of_xyzAtoms): # create file xyz = open(filename,'w+') # write number of atoms xyz.write(str(numberOfAtoms) + ' \n') # write title if not title: title = 'None' xyz.write(title + ' \n') # write coordinates for atom in list_of_xyzAtoms: xyz.write('%4s %12.8f %12.8f %12.8f \n' % (atom.name, atom.x_coord, atom.y_coord, atom.z_coord)) xyz.close() def writeXYZtrajectory(filename,boxes): # create file xyz = open(filename,'w+') cou = 0 for box in boxes: xyz.write(str(len(box.xyzAtoms)) + ' \n') xyz.write('Step: ' + str(cou) + ' , boxLength = ' + str(box.boxLength) + ' \n') for atom in box.xyzAtoms: xyz.write('%6.4s %14.8f %14.8f %14.8f \n' % (atom.name, atom.x_coord, atom.y_coord, atom.z_coord)) cou += 1 xyz.close() def writeRelXYZfile(filename, n_atoms, boxLength, title, xyzAtoms, inclAtomNames=True): # create file xyz = open(filename,'w+') # write number of atoms xyz.write(str(n_atoms) + ' \n') # write title if not title: title = 'None' xyz.write(title + ' \n') # write coordinates for atom in xyzAtoms: if inclAtomNames: xyz.write('%4s %8f %8f %8f \n' % (atom.name, atom.x_coord/boxLength, atom.y_coord/boxLength, atom.z_coord/boxLength)) else: xyz.write('%8f %8f %8f \n' % (atom.x_coord/boxLength, atom.y_coord/boxLength, atom.z_coord/boxLength)) xyz.close() def count_OO_neighbors(list_of_o_atoms,Roocut,boxLength=None): noo = [] if not boxLength: warnings.warn('No box length provided, will not take PBC into account!') for atom1 in list_of_o_atoms: dists = [] for atom2 in list_of_o_atoms: dists.append(np.linalg.norm(atom1.coordinates - atom2.coordinates)) noo.append(len(np.where(np.logical_and(np.sort(np.array(dists))>0.0, np.sort(np.array(dists))<=Roocut))[0])) return noo else: for atom1 in list_of_o_atoms: dists = [] for atom2 in list_of_o_atoms: dists.append(getDistancePbc(atom1,atom2,boxLength)) noo.append(len(np.where(np.logical_and(np.sort(np.array(dists))>0.0, np.sort(np.array(dists))<=Roocut))[0])) return noo def count_OO_neighbors_pbc(list_of_o_atoms,Roocut,boxLength,numbershells=1): noo = [] vectors = [] for l in range(-numbershells,numbershells+1): for m in range(-numbershells,numbershells+1): for n in range(-numbershells,numbershells+1): vectors.append(np.array([l, m, n])) pbc_atoms = [] for vector in vectors: for atom in list_of_o_atoms: cpAtom = deepcopy(atom) cpAtom.translateSelf(vector*boxLength) pbc_atoms.append(cpAtom) for atom1 in list_of_o_atoms: dists = [] for atom2 in pbc_atoms: dists.append(np.linalg.norm(atom1.coordinates - atom2.coordinates)) sdists = np.sort(np.array(dists)) noo.append(len(np.where(np.logical_and(sdists>0.0, sdists<=Roocut))[0])) return noo def boxParser(filename): """**parseXYZfile** Reads an xyz-style file. """ atoms = [] coordinates = [] xyz = open(filename) n_atoms = int(xyz.readline()) title = xyz.readline() for line in xyz: if len(line.split())==4: atom,x,y,z = line.split()[0:4] atoms.append(atom) coordinates.append([float(x), float(y), float(z)]) else: pass xyz.close() xyzAtoms = [] for ii in range(n_atoms): xyzAtoms.append(xyzAtom(atoms[ii],coordinates[ii],ii)) return xyzBox(xyzAtoms) def keithBoxParser(cell_fname, coord_fname): """ **keithBoxParser** Reads structure files from Keith's SiO2 simulations. """ # lattice in Angstr. lattice = np.loadtxt(cell_fname)*A2AU_factor absCoords = [] atoms = [] xyz = open(coord_fname) for line in xyz: atom,x,y,z = line.split()[0:4] atoms.append(atom) absCoords.append([float(x), float(y), float(z)]) xyz.close() xyzAtoms = [] relAtoms = [] for ii in range(len(atoms)): xyzAtoms.append(xyzAtom( atoms[ii], np.array(absCoords[ii])*A2AU_factor, ii)) relAtoms.append(xyzAtom( atoms[ii], np.dot(np.array(absCoords[ii]),np.linalg.inv(lattice)) , ii)) box = xyzBox(xyzAtoms) box.lattice = lattice box.lattice_inv = np.linalg.inv(lattice) box.relAtoms = relAtoms return box def axsfTrajParser(filename): """ **axsfTrajParser** """ boxes = [] boxLength = None alat = [[0,0,0],[0,0,0],[0,0,0]] title = None # open the file xyz = open(filename) counter = 0 line = xyz.readline() while line: if 'ANIMSTEPS' in line: numBoxes = int(line.split()[-1]) if 'CRYSTAL' in line: line = xyz.readline() for ii in range(3): latt = xyz.readline().split() alat[ii] = [float(latt[0]), float(latt[1]), float(latt[2])] if 'PRIMCOORD' in line: title = line.strip() line = xyz.readline() n_atoms = int(line.split()[0]) xyzAtoms = [] atoms = [] coordinates = [] # read in coordinates for ii in range(n_atoms): line = xyz.readline() atoms.append(xrs_utilities.element(int(line.split()[0]))) x = float(line.split()[1]) y = float(line.split()[2]) z = float(line.split()[3]) coordinates.append([x,y,z]) xyzAtoms.append(xyzAtom(atoms[ii],coordinates[ii],ii)) boxes.append(xyzBox(xyzAtoms,np.amax(alat),title)) line = xyz.readline() return boxes def writeWFN1waterInput(fname,box,headerfile,exatomNo=0): """ **writeWFN1input** Writes an input for cp.x by Quantum espresso for electronic wave function minimization. """ header = open(headerfile) inputf = open(fname,'w+') for line in header: inputf.write(line) inputf.write('CELL_PARAMETERS cubic \n') A2Bfac = constants.physical_constants['atomic unit of length'][0]*10**10 inputf.write('%20.16f %20.16f %20.16f \n' % (box.boxLength/A2Bfac, 0.0, 0.0)) inputf.write('%20.16f %20.16f %20.16f \n' % (0.0, box.boxLength/A2Bfac, 0.0)) inputf.write('%20.16f %20.16f %20.16f \n' % (0.0, 0.0, box.boxLength/A2Bfac)) inputf.write('ATOMIC_POSITIONS crystal \n') for atom,ii in zip(box.xyzAtoms,list(range(len(box.xyzAtoms)))): if ii == exatomNo: if atom.name == 'O': inputf.write('%4s %20.16f %20.16f %20.16f \n' % ('Ox', atom.x_coord/box.boxLength, atom.y_coord/box.boxLength, atom.z_coord/box.boxLength)) else: print('Not writing Ox, since chosen atom is not an oxygen atom.') inputf.write('%4s %20.16f %20.16f %20.16f \n' % (atom.name, atom.x_coord/box.boxLength, atom.y_coord/box.boxLength, atom.z_coord/box.boxLength)) else: inputf.write('%4s %20.16f %20.16f %20.16f \n' % (atom.name, atom.x_coord/box.boxLength, atom.y_coord/box.boxLength, atom.z_coord/box.boxLength)) inputf.close() header.close() def writeMD1Input(fname,box,headerfile,exatomNo=0): """ **writeWFN1input** Writes an input for cp.x by Quantum espresso for electronic wave function minimization. """ header = open(headerfile) inputf = open(fname,'w+') for line in header: inputf.write(line) inputf.write('CELL_PARAMETERS cubic \n') A2Bfac = constants.physical_constants['atomic unit of length'][0]*10**10 inputf.write('%20.16f %20.16f %20.16f \n' % (box.boxLength/A2Bfac, 0.0, 0.0)) inputf.write('%20.16f %20.16f %20.16f \n' % (0.0, box.boxLength/A2Bfac, 0.0)) inputf.write('%20.16f %20.16f %20.16f \n' % (0.0, 0.0, box.boxLength/A2Bfac)) inputf.write('ATOMIC_POSITIONS crystal \n') for atom,ii in zip(box.xyzAtoms,list(range(len(box.xyzAtoms)))): if ii == exatomNo: if atom.name == 'O': inputf.write('%4s %20.16f %20.16f %20.16f \n' % ('C', atom.x_coord/box.boxLength, atom.y_coord/box.boxLength, atom.z_coord/box.boxLength)) else: print('Not writing Ox, since chosen atom is not an oxygen atom.') inputf.write('%4s %20.16f %20.16f %20.16f \n' % (atom.name, atom.x_coord/box.boxLength, atom.y_coord/box.boxLength, atom.z_coord/box.boxLength)) else: inputf.write('%4s %20.16f %20.16f %20.16f \n' % (atom.name, atom.x_coord/box.boxLength, atom.y_coord/box.boxLength, atom.z_coord/box.boxLength)) inputf.close() header.close() def writeOCEAN_XESInput(fname,box,headerfile,exatomNo=0): """ **writeOCEAN_XESInput** Writes an input for ONEAN XES calculation for 17 molecule water boxes. """ header = open(headerfile) inputf = open(fname,'w+') for line in header: inputf.write(line) inputf.write('xred { \n') for atom,ii in zip(box.xyzAtoms,list(range(len(box.xyzAtoms)))): inputf.write('%20.16f %20.16f %20.16f \n' % ( atom.x_coord/box.boxLength, atom.y_coord/box.boxLength, atom.z_coord/box.boxLength)) inputf.write('} \n') def groBoxParser(filename,nanoMeter=True): """ **groBoxParser** Parses an gromacs GRO-style file for the xyzBox class. """ atoms = [] coordinates = [] boxLength = None if nanoMeter: scale = 10.0 else: scale = 1.0 xyz = open(filename) title = xyz.readline() n_atoms = int(xyz.readline()) for line in xyz: if len(line.split()) == 9: name = line.split()[1][0] x = float(line.split()[3])*scale y = float(line.split()[4])*scale z = float(line.split()[5])*scale atoms.append(name) coordinates.append([x, y, z]) elif len(line.split()) == 8: name = line.split()[1][0] x = float(line.split()[2])*scale y = float(line.split()[3])*scale z = float(line.split()[4])*scale atoms.append(name) coordinates.append([x, y, z]) elif line[0] == '#': pass elif len(line.split()) == 3: boxLength = float(line.split()[0])*scale else: print('Something is fishy!') xyz.close() AllxyzAtoms = [] for ii in range(n_atoms): AllxyzAtoms.append(xyzAtom(atoms[ii],coordinates[ii],ii)) return xyzBox(AllxyzAtoms,boxLength=boxLength) def xyzTrajecParser( filename, boxLength, firstBox=0, lastBox=-1 ): """Parses a Trajectory of xyz-files. Args: filename (str): Filename of the xyz Trajectory file. Returns: A list of xzyBoxes. """ boxes = [] # read the file xyz = open(filename) n_atoms = int(xyz.readline()) title = xyz.readline() # headerlines headerlines = 2 lines_per_box = n_atoms + headerlines # reopen file and start from scratch xyz.close() xyz = open(filename) counter = 0 startLine = lines_per_box*firstBox if lastBox < 0: endLine = np.inf else: endLine = lines_per_box*(lastBox+1) for line in xyz: if counter >=startLine and counter<=endLine: if counter%lines_per_box == 0: # new box n_atoms = int(line) atoms = [] coordinates = [] cou = 0 if counter%lines_per_box == 1: title = line if counter%lines_per_box in list(range(lines_per_box))[2::]: atom,x,y,z = line.split()[0:4] #print(atom,x,y,z) atoms.append(xyzAtom(atom,[float(x), float(y), float(z)],cou)) #coordinates.append([float(x), float(y), float(z)]) cou += 1 if counter%lines_per_box == lines_per_box-1: boxes.append(xyzBox(atoms,boxLength=boxLength)) counter += 1 return boxes def vaspBoxParser(filename): """ **groTrajecParser** Parses an gromacs GRO-style file for the xyzBox class. """ all_lines = open(filename).readlines() atom_kinds = [ii for ii in all_lines[5].split()] num_atom_kind = len(atom_kinds) num_atoms = np.array([int(ii) for ii in all_lines[6].split()]) headerlines = 2 box_latt_lines = 3 headerlines2 = 3 linesPerBox = headerlines + box_latt_lines + headerlines2 + np.sum(num_atoms) cell1 = np.array([float(ii) for ii in all_lines[2].split()]) cell2 = np.array([float(ii) for ii in all_lines[3].split()]) cell3 = np.array([float(ii) for ii in all_lines[4].split()]) cell = np.array([cell1, cell2, cell3]) coordinates = [] atoms = [] coor_start = headerlines + box_latt_lines + headerlines2 coor_stop = headerlines + box_latt_lines + headerlines2+np.sum(num_atoms) cou = 0 for zz,kind in enumerate(atom_kinds): for ind in range(num_atoms[zz]): # print(ind, kind, cou) rel_coords = np.array([float(ii) for ii in all_lines[coor_start+cou].split()]) abs_coords = cell.T.dot(rel_coords) atoms.append(xyzAtom(kind, abs_coords, cou )) cou += 1 box = xyzBox(atoms,boxLength=0.0) box.lattice = np.array([cell1, cell2, cell3]).T box.lattice_inv = np.linalg.inv(box.lattice) return box def vaspTrajecParser(filename, min_boxes=0, max_boxes=1000): """ **groTrajecParser** Parses an gromacs GRO-style file for the xyzBox class. """ boxes = [] atoms = [] coordinates = [] all_lines = open(filename).readlines() atom_kinds = [ii for ii in all_lines[5].split()] num_atom_kind = len(atom_kinds) num_atoms = np.array([int(ii) for ii in all_lines[6].split()]) headerlines = 2 box_latt_lines = 3 headerlines2 = 3 linesPerBox = headerlines + box_latt_lines + headerlines2 + np.sum(num_atoms) del(all_lines) xyz = open(filename) counter = 0 for line in xyz: if counter//linesPerBox>= min_boxes: if counter%linesPerBox == 0: #print('hehe1') # write a new box if there is something to write if len(coordinates) > 0: coorcou = 0 for ii_kind in range(num_atom_kind): for jj_num in range(num_atoms[ii_kind]): atoms.append(xyzAtom( ([atom_kinds[ii_kind]]*num_atoms[ii_kind])[jj_num] ,coordinates[coorcou],counter)) coorcou += 1 box = xyzBox(atoms,boxLength=0.0) box.lattice = np.array([cell1, cell2, cell3]).T box.lattice_inv = np.linalg.inv(box.lattice) boxes.append(box) # if first box, start here else: pass # new box title = line.strip() atoms = [] coordinates = [] atom_type_counter = np.zeros_like(num_atoms) if counter/linesPerBox >= max_boxes: return boxes if counter%linesPerBox == 1: title2 = line.strip() if counter%linesPerBox == 2: cell1 = np.array([float(ii) for ii in line.split()]) if counter%linesPerBox == 3: cell2 = np.array([float(ii) for ii in line.split()]) if counter%linesPerBox == 4: cell3 = np.array([float(ii) for ii in line.split()]) if counter%linesPerBox == 5: atom_kinds = [ii for ii in line.split()] if counter%linesPerBox == 6: num_atoms = np.array([int(ii) for ii in line.split()]) if counter%linesPerBox == 7: title3 = line.strip() if not counter%linesPerBox in [0,1,2,3,4,5,6,7]: #print(counter, line.split()) cell = np.array([cell1, cell2, cell3]) rel_coords = np.array([float(ii) for ii in line.split()]) abs_coords = cell.T.dot(rel_coords) coordinates.append(abs_coords) counter += 1 return boxes def groTrajecParser(filename,nanoMeter=True): """ **groTrajecParser** Parses an gromacs GRO-style file for the xyzBox class. """ boxes = [] atoms = [] coordinates = [] residuals = [] boxLength = None if nanoMeter: scale = 10.0 else: scale = 1.0 # read the header xyz = open(filename) title = xyz.readline() n_atoms = int(xyz.readline()) xyz.close() # how many lines per box headerlines = 2 taillines = 1 linesPerBox = headerlines + n_atoms + taillines # read the rest xyz = open(filename) counter = 0 for line in xyz: if counter%linesPerBox == 0: # new box title = line atoms = [] coordinates = [] if counter%linesPerBox == 1: n_atoms = int(line) if counter in list(range(linesPerBox))[2::]: if len(line.split()) == 9: atoms.append(line.split()[1][0]) x = float(line.split()[3])*scale y = float(line.split()[4])*scale z = float(line.split()[5])*scale coordinates.append([x, y, z]) residuals.append(line.split()[0]) elif len(line.split()) == 8: atoms.append(line.split()[1][0]) x = float(line.split()[2])*scale y = float(line.split()[3])*scale z = float(line.split()[4])*scale coordinates.append([x, y, z]) residuals.append(line.split()[0]) elif len(line.split()) == 6: atoms.append(line.split()[1][0]) x = float(line.split()[3])*scale y = float(line.split()[4])*scale z = float(line.split()[5])*scale coordinates.append([x, y, z]) residuals.append(line.split()[0]) elif len(line.split()) == 5: atoms.append(line.split()[1][0]) x = float(line.split()[2])*scale y = float(line.split()[3])*scale z = float(line.split()[4])*scale coordinates.append([x, y, z]) residuals.append(line.split()[0]) if counter > 1 and counter%(linesPerBox-1) == 0: boxLength = float(line.split()[0])*scale AllxyzAtoms = [] # print('>>>>>>>>> n_atoms ', n_atoms) # print('>>>>>>>>> atoms ',len(atoms)) for ii in range(n_atoms): atom = xyzAtom(atoms[ii],coordinates[ii],ii) atom.residual = residuals[ii] AllxyzAtoms.append(atom) boxes.append(xyzBox(AllxyzAtoms,boxLength=boxLength)) counter = -1 counter += 1 return boxes def changeOHBondLength(h2oMol, fraction, boxLength=None, oName='O', hName='H'): o_atom = h2oMol.get_atoms_by_name(oName) h_atom = h2oMol.get_atoms_by_name(hName) # get OH bond-vectors ohVectors = [] if not boxLength: warnings.warn('No box length provided, will not take PBC into account!') for atom in h_atom: ohVectors.append(getDistVector(atom,o_atom[0])) else: for atom in h_atom: ohVectors.append(getDistVectorPbc(atom,o_atom[0],boxLength)) # get new OH bond vectors new_ohVectors = [] for vector in ohVectors: new_ohVectors.append( vector*fraction) # make new molecule newmol =[deepcopy(o_atom[0])] for vector in new_ohVectors: newmol.append(xyzAtom(hName,o_atom[0].getCoordinates()+vector,1)) return xyzMolecule(newmol) def parseXYZfile(filename): """**parseXYZfile** Reads an xyz-style file. """ atoms = [] coordinates = [] xyz = open(filename) n_atoms = int(xyz.readline()) title = xyz.readline() for line in xyz: atom,x,y,z = line.split() atoms.append(atom) coordinates.append([float(x), float(y), float(z)]) xyz.close() return n_atoms, title, atoms, coordinates def alterGROatomNames(filename,oldName, newName): import re with open(filename, "r") as sources: lines = sources.readlines() with open(filename, "w") as sources: for line in lines: sources.write(re.sub(r'%s'%oldName, '%s'%newName, line)) def writeFDMNESinput_file(xyzAtoms,fname,Filout,Range,Radius,Edge,NRIXS,Absorber,Green=False,SCF=False): """ **writeFDMNESinput_file** Writes an input file to be used for FDMNES. """ # create file inp = open(fname,'w+') # write some header line inp.write('! Fdmnes indata file \n') inp.write('\n') # Filout inp.write(' Filout \n') inp.write(' ' + Filout + ' \n') inp.write('\n') # Range inp.write(' Range \n') inp.write(' ') for rr in [str(ii) for ii in Range]: inp.write(rr + ' ') inp.write(' \n') inp.write('\n') # Radius inp.write(' Radius \n') inp.write(' ' + str(Radius) + ' \n') inp.write('\n') # Edge inp.write(' Edge \n') inp.write(' ' + Edge + ' \n') inp.write('\n') # Green if Green: inp.write(' Green \n') inp.write('\n') # SCF if SCF: inp.write(' SCF \n') inp.write('\n') # NRIXS if NRIXS: inp.write(' NRIXS \n') inp.write(' ') for nn in [str(ii) for ii in NRIXS]: inp.write(nn + ' ') inp.write(' \n') inp.write('\n') # Absorber inp.write(' Absorber \n') inp.write(' ' + str(Absorber) + ' \n') inp.write('\n') # molecule inp.write(' molecule \n') inp.write(' 1.0 1.0 1.0 90.0 90.0 90.0 \n') # Atoms for atom in xyzAtoms: inp.write('%4d %10f %10f %10f \n' % (xrs_utilities.element(atom.name), atom.x_coord, atom.y_coord, atom.z_coord)) inp.write('\n') # Convolution inp.write(' Convolution \n') inp.write('\n') # End inp.write(' End \n') inp.write('\n') def writeOCEANinput(fname,headerfile,xyzBox,exatom,edge,subshell): """ **writeOCEANinput** """ # write everything that is in the header header = open(headerfile) inputf = open(fname,'w+') for line in header: inputf.write(line) inputf.write('\n') # write the cell const = constants.physical_constants['atomic unit of length'][0]*10**10 inputf.write('acell { %10.8f %10.8f %10.8f } \n'%(xyzBox.boxLength/const, xyzBox.boxLength/const, xyzBox.boxLength/const)) inputf.write('\n') # write natoms inputf.write('natom %d \n' %len(xyzBox.xyzAtoms)) inputf.write('\n') # write typat inputf.write('typat { \n') for atom in xyzBox.xyzAtoms: if atom.name == 'H': inputf.write('%d ' %1) if atom.name == 'O': inputf.write('%d ' %2) if atom.name == 'N': inputf.write('%d ' %3) if atom.name == 'C': inputf.write('%d ' %4) if atom.name == 'Cl': inputf.write('%d ' %3) if atom.name == 'Na': inputf.write('%d ' %4) inputf.write('\n') inputf.write('} \n') inputf.write('\n') # write nedges ind = 0 for atom in xyzBox.xyzAtoms: if atom.name == exatom: ind += 1 inputf.write('nedges %d \n' %ind) inputf.write('\n') # write edges inputf.write('edges { \n') ind = 1 for atom in xyzBox.xyzAtoms: if atom.name == exatom: inputf.write('%d %d %d \n' %(ind,edge,subshell)) ind += 1 inputf.write('} \n') inputf.write('\n') # write coordinates inputf.write('xred { \n') for atom in xyzBox.xyzAtoms: inputf.write('%16f %16f %16f \n' % (atom.x_coord/xyzBox.boxLength, atom.y_coord/xyzBox.boxLength, atom.z_coord/xyzBox.boxLength)) inputf.write('} \n') inputf.write('\n') inputf.close() def writeOCEANinput_new(fname,headerfile,xyzBox,exatom,edge,subshell): """ **writeOCEANinput** """ # write everything that is in the header header = open(headerfile) inputf = open(fname,'w+') for line in header: inputf.write(line) inputf.write('\n') # write the cell const = constants.physical_constants['atomic unit of length'][0]*10**10 inputf.write('acell { %10.8f %10.8f %10.8f } \n'%(xyzBox.boxLength/const, xyzBox.boxLength/const, xyzBox.boxLength/const)) inputf.write('\n') # write natoms inputf.write('natom %d \n' %len(xyzBox.xyzAtoms)) inputf.write('\n') # write typat inputf.write('typat { \n') all_atom_kinds = [] for atom in xyzBox.xyzAtoms: if not atom.name in all_atom_kinds: all_atom_kinds.append(atom.name) inputf.write('%d '%(all_atom_kinds.index(atom.name)+1)) # if atom.name == 'H': # inputf.write('%d ' %1) # if atom.name == 'O': # inputf.write('%d ' %2) # if atom.name == 'N': # inputf.write('%d ' %6) # if atom.name == 'C': # inputf.write('%d ' %5) # if atom.name == 'Cl': # inputf.write('%d ' %3) # if atom.name == 'Na': # inputf.write('%d ' %4) inputf.write('\n') inputf.write('} \n') inputf.write('\n') # write nedges #ind = 0 #for atom in xyzBox.xyzAtoms: # if atom.name == exatom: # ind += 1 #inputf.write('nedges %d \n' %ind) #inputf.write('\n') # write edges inputf.write('edges { \n') #ind = 1 #for atom in xyzBox.xyzAtoms: # if atom.name == exatom: inputf.write('%d %d %d \n' %(-xrs_utilities.element(exatom),edge,subshell)) # ind += 1 inputf.write('} \n') inputf.write('\n') # write coordinates inputf.write('xred { \n') for atom in xyzBox.xyzAtoms: inputf.write('%16f %16f %16f \n' % (atom.x_coord/xyzBox.boxLength, atom.y_coord/xyzBox.boxLength, atom.z_coord/xyzBox.boxLength)) inputf.write('} \n') inputf.write('\n') inputf.close() def writeOCEANinput_arb(fname, headerfile, xyzBox, exatom, edge,subshell): """ **writeOCEANinput** """ # write everything that is in the header header = open(headerfile) inputf = open(fname,'w+') for line in header: inputf.write(line) inputf.write('\n') # write the cell const = constants.physical_constants['atomic unit of length'][0]*10**10 inputf.write('acell { 1.0 1.0 1.0 }' ) inputf.write('\n') # write rprim inputf.write('rprim { \n') inputf.write('%10.8f %10.8f %10.8f \n'%(xyzBox.lattice[0,0]/const, xyzBox.lattice[0,1]/const, xyzBox.lattice[0,2]/const)) inputf.write('%10.8f %10.8f %10.8f \n'%(xyzBox.lattice[1,0]/const, xyzBox.lattice[1,1]/const, xyzBox.lattice[1,2]/const)) inputf.write('%10.8f %10.8f %10.8f \n'%(xyzBox.lattice[2,0]/const, xyzBox.lattice[2,1]/const, xyzBox.lattice[2,2]/const)) inputf.write('}') inputf.write('\n') # write natoms inputf.write('natom %d \n' %len(xyzBox.xyzAtoms)) inputf.write('\n') # write typat inputf.write('typat { \n') all_atom_kinds = [] for atom in xyzBox.xyzAtoms: if not atom.name in all_atom_kinds: all_atom_kinds.append(atom.name) inputf.write('%d '%(all_atom_kinds.index(atom.name)+1)) #inputf.write('typat { \n') #for atom in xyzBox.xyzAtoms: # if atom.name == 'H': # inputf.write('%d ' %4) # if atom.name == 'O': # inputf.write('%d ' %1) # if atom.name == 'N': # inputf.write('%d ' %2) # if atom.name == 'C': # inputf.write('%d ' %3) # if atom.name == 'Cl': # inputf.write('%d ' %5) # if atom.name == 'Na': # inputf.write('%d ' %3) inputf.write('\n') inputf.write('} \n') inputf.write('\n') # write ntypat inputf.write('ntypat %d \n' %len(all_atom_kinds)) inputf.write('\n') # inputf.write('znucl { ' ) for ii in all_atom_kinds: inputf.write(' %d '%xrs_utilities.element(ii)) inputf.write('} \n') inputf.write('\n') # write nedges #ind = 0 #for atom in xyzBox.xyzAtoms: # if atom.name == exatom: # ind += 1 #inputf.write('nedges %d \n' %ind) #inputf.write('\n') # write edges inputf.write('edges { \n') #ind = 1 #for atom in xyzBox.xyzAtoms: # if atom.name == exatom: inputf.write('%d %d %d \n' %(-xrs_utilities.element(exatom),edge,subshell)) # ind += 1 inputf.write('} \n') inputf.write('\n') # write coordinates inputf.write('xred { \n') for atom in xyzBox.xyzAtoms: coords = np.dot(xyzBox.lattice_inv,atom.coordinates) inputf.write('%16f %16f %16f \n' % (coords[0], coords[1], coords[2])) inputf.write('} \n') inputf.write('\n') inputf.close() def writeOCEANinput_full(fname,xyzBox,exatom,edge,subshell): """ Writes a complete OCEAN input file. Args: * fname (str): Filename for the input file to be written. * xyzBox (xyzBox): Instance of the xyzBox class to be converted into an OCEAN input file. * exatom (str): Atomic symbol for the excited atom. * edge (int): Integer defining which shell to excite (e.g. 0 for K-shell, 1 for L, etc.). * subshell (int): Integer defining which sub-shell to excite ( e.g. 0 for s, 1 for p, etc.). """ # some pre-defined input parameters std_input = {} std_input['control'] = 'control 0 \n' std_input['core'] = 'core 28 \n' std_input['para_prefix'] = 'para_prefix{ mpirun -machinefile ../nodelist -n 28} \n' std_input['dft'] = 'dft { obf } \n' std_input['trace_tol'] = 'trace_tol{ 1.0d-10 } \n' std_input['core_off'] = 'core_offset 130 \n' std_input['ngkpt'] = 'ngkpt{ 1 1 1 } \n' std_input['paw.nkpt'] = 'paw.nkpt{ 1 1 1 } \n' std_input['nkpt'] = 'nkpt{ 2 2 2 } \n' std_input['obkpt'] = 'obkpt{ 1 1 1 } \n' std_input['ham_kpoints'] = 'ham_kpoints{ 2 2 2 } \n' std_input['nbands'] = 'nbands 1600 \n' std_input['paw.nbands'] = 'paw.nbands 1600 \n' std_input['obf.nbands'] = 'obf.nbands 1000 \n' std_input['rprim'] = 'rprim { \n 1 0 0 \n 0 1 0 \n 0 0 1 \n } \n' std_input['ppdir'] = 'ppdir { \n \'/users/sahle/pseudos\' \n } \n' std_input['ecut'] = 'ecut 70 \n' std_input['toldfe'] = 'toldfe 1.0d-8 \n' std_input['toldfr'] = 'tolwfr 1.0d-16 \n' std_input['nstep'] = 'nstep 600 \n' std_input['mixing'] = 'mixing 0.1 \n' std_input['diemac'] = 'diemac 2.0 \n' std_input['atomic_paw'] = 'paw.fill {8 o.fill } \n' +'paw.opts {8 o.opts } \n' std_input['paw.shells'] = 'paw.shells { 4.5 } \n' std_input['cnbse.rad'] = 'cnbse.rad { 4.5 } \n' std_input['cnbse.broaden'] = 'cnbse.broaden { 0.05 } \n' std_input['scfac'] = 'scfac 1.0 \n' std_input['cks.normal'] = 'cks.normal { .true. } \n' std_input['xas'] = 'cnbse.mode { xas } \n' std_input['cnbse.ways'] = 'cnbse.ways { 3 } \n' std_input['cnbse.xmesh'] = 'cnbse.xmesh{ 24 24 24 } \n' # open the filename f = open(fname,'w') # start with printing all standard input parameters for key in std_input: f.write(std_input[key]) # write the cell parameters in atomic units bl = xyzBox.boxLength/0.52917721092 f.write( 'acell { ' + str(bl) + ' ' + str(bl) + ' ' + str(bl) + '}\n' ) # write number of types of atoms unique_atoms = [] for atom in xyzBox.xyzAtoms: if atom.name not in unique_atoms: unique_atoms.append(atom.name) f.write( 'ntypat ' + str(len(unique_atoms)) + ' \n' ) # write znucl znucl_str = 'znucl { ' for ii in range(len(unique_atoms)): znucl_str = znucl_str + str(xrs_utilities.element(unique_atoms[ii])) + ' ' f.write( znucl_str + '}\n' ) # write pp_list f.write( 'pp_list { \n \n } \n' ) # write number of atoms in calculation f.write( 'natom ' + str(len(xyzBox.xyzAtoms)) + '\n' ) # write typat typat_str = 'typat { ' list_of_unique_atomic_numbers = [xrs_utilities.element(ii) for ii in unique_atoms] for atom in xyzBox.xyzAtoms: ind = int(np.where(np.array(list_of_unique_atomic_numbers) == xrs_utilities.element(atom.name))[0] +1) typat_str = typat_str + str(ind) + ' ' f.write( typat_str + '}\n' ) # write nedges (number of edges to be calculated) excited_inds = list(np.where(np.array([atom.name for atom in xyzBox.xyzAtoms]) == exatom)[0]) f.write( 'nedges ' + str(len(excited_inds)) + ' \n' ) # write edges excited_inds = list(np.where(np.array([atom.name for atom in xyzBox.xyzAtoms]) == exatom)[0]) edges_str = 'edges { ' for ind in excited_inds: edges_str = edges_str + str(ind+1) +' '+ str(int(edge))+' '+ str(int(subshell))+' '+ '\n'+' ' f.write( edges_str + '}\n' ) # write reduced atomic coordinates xred_str = 'xred { \n' for atom in xyzBox.xyzAtoms: xred_str = xred_str + str(atom.x_coord) +' '+ str(atom.y_coord) +' '+ str(atom.z_coord) + ' \n' f.write( xred_str + '}\n' ) def parsePwscfFile(fname): """ **parsePwscfFile** Parses a PWSCF file and returns a xyzBox object. Args: fname (str): Absolute filename of OCEAN input file. Returns: xyzBox object """ nat = 0 ntyp = 0 celldm = 0 infile = open(fname) while True: line = infile.readline() if not line: break if '&SYSTEM' in line: system_read = True while system_read: line = infile.readline() if 'nat' in line: nat = int(line.split()[-1]) if 'ntyp' in line: ntyp = int(line.split()[-1]) if 'celldm' in line: celldm = float(line.split()[-1]) if nat>0 and ntyp>0 and celldm>0: system_read = False if 'CELL_PARAMETERS' in line: rprim1 = infile.readline() rprim2 = infile.readline() rprim3 = infile.readline() lattice = np.array([[rprim1.split()[0], rprim1.split()[1], rprim1.split()[2]], [rprim2.split()[0], rprim2.split()[1], rprim2.split()[2]], [rprim3.split()[0], rprim3.split()[1], rprim3.split()[2]] ] ,dtype=float) # lattice = np.array([[rprim1.split()[0], rprim2.split()[0], rprim3.split()[0]], # [rprim1.split()[1], rprim2.split()[1], rprim3.split()[1]], # [rprim1.split()[2], rprim2.split()[2], rprim3.split()[2]] ] ,dtype=float) if 'ATOMIC_POSITIONS' in line: xred = [] atomType = [] for ii in range(nat): line = infile.readline() atomType.append(str(line.split()[0])) xred.append(np.array( [line.split()[1], line.split()[2], line.split()[3] ],dtype=float )) lattice[:,0] *= celldm * A2AU_factor lattice[:,1] *= celldm * A2AU_factor lattice[:,2] *= celldm * A2AU_factor xabs = [np.dot(lattice, xred[ii]) for ii in range(len(xred))] xyzAtoms = [] for ii in range(len(xabs)): xyzAtoms.append( xyzAtom(atomType[ii],[xabs[ii][0], xabs[ii][1], xabs[ii][2]], ii) ) box = xyzBox( xyzAtoms ) box.lattice = lattice box.lattice_inv = np.linalg.inv(lattice) box.relAtoms = xred return box def writePWinuptFile( fname, box, param_dict ): """ **writePWinuptFile** """ # define standard default values params = {} # control block params['control'] = {} params['control']['calculation'] = '\'scf\'' params['control']['prefix'] = '\'scratch\'' params['control']['pseudo_dir'] = '\'./\'' params['control']['outdir'] = '\'./\'' params['control']['tstress'] = '.false.' params['control']['tprnfor'] = '.false.' params['control']['wf_collect'] = '.true.' params['control']['verbosity'] = '\'high\'' # system block params['system'] = {} params['system']['ibrav'] = 0 params['system']['nat'] = len(box.xyzAtoms) params['system']['ntyp'] = len( set( [atom.name for atom in box.xyzAtoms]) ) params['system']['noncolin'] = '.false.' params['system']['lspinorb'] = '.false.' params['system']['ecutwfc'] = 60 params['system']['occupations'] = '\'fixed\'' params['system']['smearing'] = '\'gaussian\'' params['system']['degauss'] = 0.02 params['system']['nspin'] = 1 params['system']['tot_charge'] = 0.0 params['system']['nosym'] = '.true.' params['system']['noinv'] = '.true.' # electrons block params['electrons'] = {} params['electrons']['conv_thr'] = 1.0e-7 params['electrons']['mixing_beta'] = 0.2 params['electrons']['electron_maxstep'] = 600 params['electrons']['startingwfc'] = '\'atomic+random\'' params['electrons']['diagonalization'] = '\'david\'' # ions block params['ions'] = {} # k-points params['k_points'] = {} params['k_points']['option'] = 'AUTOMATIC' params['k_points']['points'] = '1 1 1' params['k_points']['offset'] = '0 0 0' # atomic species params['atomic_species'] = {} params['atomic_species']['O'] = [15.9994, '08-o.lda.fhi.UPF'] # overwrite those defined in the "param_dict" if len(param_dict)>0: for key1 in param_dict: for key2 in param_dict[key1]: params[key1][key2] = param_dict[key1][key2] else: print('no parameters defined, using default.') # cell parameters params['cell_parameters'] = {} params['cell_parameters']['option'] = 'angstrom' params['cell_parameters']['lattice'] = box.lattice # atomic positions params['atomic_positions'] = {} params['atomic_positions']['option'] = 'crystal' params['atomic_positions']['atom_names'] = [atom.name for atom in box.xyzAtoms] params['atomic_positions']['atom_coords'] = [np.dot(box.lattice_inv,atom.coordinates) for atom in box.xyzAtoms] # write the input blocks f = open( fname, 'w+' ) # control f.write('&control \n') for key in params['control'].keys(): if type(params['control'][key]) == str: f.write(' %15s = %s \n'%(key, params['control'][key])) else: f.write(' %15s = %s \n'%(key, params['control'][key])) f.write('/ \n') # system f.write('&system \n') for key in params['system'].keys(): f.write(' %15s = %s \n'%(key, params['system'][key])) f.write('/ \n') # electrons f.write('&electrons \n') for key in params['electrons'].keys(): f.write(' %15s = %s \n'%(key, params['electrons'][key])) f.write('/ \n') # ions f.write('&ions \n') for key in params['ions'].keys(): f.write(' %15s = %s \n'%(key, params['ions'][key])) f.write('/ \n') # atomic species f.write('ATOMIC_SPECIES \n') for key in params['atomic_species'].keys(): f.write('%3s %10f %s \n'%(key, params['atomic_species'][key][0], params['atomic_species'][key][1])) # cell parameters f.write('CELL_PARAMETERS %s \n'%params['cell_parameters']['option']) latt = params['cell_parameters']['lattice'] for ii in range(3): f.write('%10f %10f %10f \n'%(latt[ii,0], latt[ii,1], latt[ii,2] )) # atomic positions f.write('ATOMIC_POSITIONS %s \n'%params['atomic_positions']['option']) for ii in range(len(params['atomic_positions']['atom_names'])): name = params['atomic_positions']['atom_names'][ii] posx = params['atomic_positions']['atom_coords'][ii][0] posy = params['atomic_positions']['atom_coords'][ii][1] posz = params['atomic_positions']['atom_coords'][ii][2] f.write('%5s %10f %10f %10f \n'%( name, posx, posy, posz )) # kpoints f.write('K_POINTS %s \n'%params['k_points']['option']) f.write('%s %s \n'%( params['k_points']['points'], params['k_points']['offset'] )) f.close() def parseVaspFile(fname): """ **parseVaspFile** Parses a VASPS file and returns a xyzBox object. Args: fname (str): Absolute filename of VASP file. Returns: xyzBox object """ nat = 0 ntyp = 0 celldm = 0 headerLines = 1 scalingLines = 1 cellLines = 3 elementLines = 1 atomNumLines = 1 headerLines2 = 1 AllLines = open(fname).readlines() cou = 0 # parse scale scale = float(AllLines[headerLines].split()[0]) # parse box lattice = np.zeros((3,3)) for ii in range(3): lattice[ii,:] = [ float(AllLines[headerLines+scalingLines+ii].split()[jj]) for jj in range(3) ] # scale the lattice lattice *= scale # parse the element types elements = AllLines[headerLines+scalingLines+cellLines].split() ntyp = len(elements) # parse the number of atoms line = AllLines[headerLines+scalingLines+cellLines+elementLines].split() ntypat = [ int(ii) for ii in line ] nat = np.sum(ntypat) # parse relative positions xred = [] atomType = [] for ii in range(nat): lineNum = headerLines+scalingLines+cellLines+elementLines+atomNumLines+headerLines2+ii line = AllLines[lineNum].split() xred.append( np.array( [line[0], line[1], line[2] ],dtype=float ) ) for ii in range(len(ntypat)): for jj in range(ntypat[ii]): atomType.append(elements[ii]) # get absolute atom positions xabs = [np.dot(lattice.T, xred[ii]) for ii in range(len(xred))] # create the box xyzAtoms = [] for ii in range(len(xabs)): xyzAtoms.append( xyzAtom( atomType[ii], [xabs[ii][0], xabs[ii][1], xabs[ii][2]], ii) ) box = xyzBox( xyzAtoms ) box.lattice = lattice box.lattice_inv = np.linalg.inv(lattice) box.relAtoms = xred return box def parseOCEANinputFile(fname): """ **parseOCEANinputFile** Parses an OCEAN input file and returns lattice vectors, atom names, and relative atom positions. Args: * fname (str): Absolute filename of OCEAN input file. * atoms (list): List of elemental symbols in the same order as they appear in the input file. Returns: * lattice (np.array): Array of lattice vectors. * rel_coords (np.array): Array of relative atomic coordinates. * oceaatoms (list): List of atomic names. """ infile = open(fname) while True: line = infile.readline() if not line: break if 'acell' in line: acell = np.array( [line.split()[2], line.split()[3], line.split()[4]] ,dtype=float ) if 'rprim' in line: rprim1 = infile.readline() rprim2 = infile.readline() rprim3 = infile.readline() lattice = np.array([[rprim1.split()[0], rprim1.split()[1], rprim1.split()[2]], [rprim2.split()[0], rprim2.split()[1], rprim2.split()[2]], [rprim3.split()[0], rprim3.split()[1], rprim3.split()[2]] ] ,dtype=float) if 'xred' in line: xred = [] if len(line.split())==1 or len(line.split())==2: line = infile.readline() elif len(line.split())== 5: xred.append(np.array( [line.split()[-3], line.split()[-2], line.split()[-1] ],dtype=float )) line = infile.readline() while True: if not '}' in line: xred.append(np.array( [line.split()[0], line.split()[1], line.split()[2] ],dtype=float )) line =infile.readline() elif not line.split()[0]=='}' and line.split()[-1] == '}': xred.append(np.array( [line.split()[0], line.split()[1], line.split()[2] ],dtype=float )) break elif line.split()[0]=='}' and line.split()[-1] == '}': break else: print('>>>>>> check your OCEAN input file!') if 'ntypat' in line: ntypat = line.split()[-1] if 'znucl' in line and not line.strip().startswith('#'): t1 = [ii for ii in line.split()[1:] if ii!='{'] znucl = np.array([ii for ii in t1 if ii!='}'], dtype=int) if 'typat' in line and 'ntypat' not in line: typat = [] if len(line.split())==1 or len(line.split())==2: line = infile.readline() elif len(line.split())> 2: print(line) # line_list = line.split() # try: # line_list.remove('typat') # except: # pass # try: # line_list.remove('{') # except: # pass # try: # line_list.remove('}') # except: # pass typat.append(np.array( [line.split()[ii] for ii in range(len(line.split())) ],dtype=int )) #typat = np.asarray(line_list, dtype=int) line = infile.readline() break while True: if not '}' in line: typat.append(np.array( [line.split()[ii] for ii in range(len(line.split())) ],dtype=int )) line =infile.readline() elif not line.split()[0]=='}' and line.split()[-1] == '}': typat.append(np.array( [line.split()[ii] for ii in range(len(line.split())-1) ],dtype=int )) break elif line.split()[0]=='}' and line.split()[-1] == '}': break else: print('>>>>>> check your OCEAN input file!') typat=typat[0] lattice[:,0] *= acell[0] * A2AU_factor lattice[:,1] *= acell[1] * A2AU_factor lattice[:,2] *= acell[2] * A2AU_factor atoms_name = [xrs_utilities.element(int(znucl[ii])) for ii in range(len(znucl))] atoms = [] #print('>>>>>>>> ' , typat ) #print('========', atoms_name) for at in typat: atoms.append(atoms_name[at-1]) xabs = [np.dot(lattice.T, xred[ii]) for ii in range(len(xred))] xyzAtoms = [] for ii in range(len(xabs)): xyzAtoms.append( xyzAtom(atoms[ii],[xabs[ii][0], xabs[ii][1], xabs[ii][2]], ii) ) box = xyzBox( xyzAtoms ) box.lattice = lattice.T box.lattice_inv = np.linalg.inv(lattice.T) box.relAtoms = xred return box def translateOcean2FDMNES_p1(ocean_in, fdmnes_out, header_file): # parse the OCEAN input box = parseOCEANinputFile( ocean_in ) # read the header header = open(header_file) inputf = open(fdmnes_out,'w+') for line in header: inputf.write(line) inputf.write('\n') # unit cell lengths a = np.linalg.norm(box.lattice[0,:])#/0.52917721092 b = np.linalg.norm(box.lattice[1,:])#/0.52917721092 c = np.linalg.norm(box.lattice[2,:])#/0.52917721092 # unit cell angles alpha = xrs_utilities.vangle(box.lattice[1,:], box.lattice[2,:]) beta = xrs_utilities.vangle(box.lattice[0,:], box.lattice[2,:]) gamma = xrs_utilities.vangle(box.lattice[0,:], box.lattice[1,:]) # write the cell size and angles # write the atom numbers and relative coordinates inputf.write(' %10.8f %10.8f %10.8f %8.6f %8.6f %8.6f \n'%(a,b,c,alpha,beta,gamma)) for ii in range(len(box.xyzAtoms)): name = xrs_utilities.element(box.xyzAtoms[ii].name) xyz = np.dot(box.lattice_inv,box.xyzAtoms[ii].getCoordinates()) inputf.write( '%4d %10.8f %10.8f %10.8f \n'%(name, xyz[0], xyz[1], xyz[2] ) ) inputf.write('\n') inputf.write(' Convolution \n') inputf.write('\n') inputf.write(' End \n') inputf.write('\n') def sorter(elem): return elem.dist_to_center def writeFEFFinput_arb(fname, headerfile, xyzBox, exatom, edge): """ **writeFEFFinput_arb** """ # write everything that is in the header header = open(headerfile) inputf = open(fname,'w+') for line in header: inputf.write(line) inputf.write('\n') # find some numbers numat = len([atom.name for atom in xyzBox.xyzAtoms]) numex = len(xyzBox.get_atoms_by_name(exatom)) tags = list( set([atom.name for atom in xyzBox.xyzAtoms])) pots = np.arange(len(tags)) + 1 exatom_ind = tags.index(exatom) + 1 # write ATOMS inputf.write('ATOMS \n') inputf.write(' * x y z ipot tag distance \n') cen_atom = xyzBox.xyzAtoms[0] dists = [] for atom in xyzBox.xyzAtoms: atom.dist_to_center = np.linalg.norm(atom.coordinates - cen_atom.coordinates) dists.append(atom.dist_to_center) inds = np.argsort(dists) for ind in inds: atom = xyzBox.xyzAtoms[ind] name = atom.name tag = tags.index(name)+1 coords = atom.coordinates dist = atom.dist_to_center if ind == 0: tag = 0 inputf.write('%16f %16f %16f %d %s %5f\n' % (coords[0], coords[1], coords[2], tag, name, dist)) if dist>0.0: inputf.write('%16f %16f %16f %d %s %5f\n' % (coords[0], coords[1], coords[2], tag, name, dist)) inputf.write('END \n') inputf.write('\n') inputf.close() xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_extraction.py000066400000000000000000000711131412732462000233400ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range from six.moves import zip #!/usr/bin/python # Filename: xrs_extraction.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import os import copy import numpy as np from . import xrs_utilities, xrs_fileIO, math_functions, xrs_ComptonProfiles import matplotlib.pyplot as plt import matplotlib.pyplot as pyplot from scipy import optimize from .math_functions import * installation_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "resources" ) debug = 0 if not debug: # when testing it from the source you can create a link in ./ to ../data/ HFCP_PATH = os.path.join(installation_dir,'data/ComptonProfiles.dat') LOGTABLE_PATH = os.path.join(installation_dir,'data/logtable.dat') else: HFCP_PATH = 'data/ComptonProfiles.dat' #'/home/christoph/sources/XRStools/data/ComptonProfiles.dat' LOGTABLE_PATH = 'data/logtables.dat' #'/home/christoph/sources/XRStools/data/logtable.dat' def map_chamber_names(name): """ **map_chamber_names** Maps names of chambers to range of ROI numbers. """ chamberNames = {'VD':list(range(0,12)), 'VU':list(range(12,24)), 'VB':list(range(24,36)), 'HR':list(range(36,48)), 'HL':list(range(48,60)), 'HB':list(range(60,72))} return chamberNames[name.upper()] class HF_dataset: """ **dataset** A class to hold all information from HF Compton profiles necessary to subtract background from the experiment. """ def __init__(self, data, formulas, stoich_weights, edges): self.formulas = formulas self.stoich_weights = stoich_weights self.edges = edges #e.g. {'Li':['K','L23'], 'O':'K'} self.E0 = data.E0 self.cenom = data.cenom self.tth = data.tth self.eloss = data.eloss self.HFProfile = xrs_ComptonProfiles.HFProfile(formulas, stoich_weights, HFCP_PATH) self.HFProfile.get_elossProfiles(self.E0,self.tth) # interpolate total HF profiles onto experimental eloss scale self.J_total = np.zeros((len(self.eloss),len(self.tth))) self.C_total = np.zeros((len(self.eloss),len(self.tth))) self.V_total = np.zeros((len(self.eloss),len(self.tth))) self.q_vals = np.zeros((len(self.eloss),len(self.tth))) for ii in range(len(self.tth)): self.J_total[:,ii] = np.interp(self.eloss, self.HFProfile.eloss,self.HFProfile.J_total[:,ii]) self.C_total[:,ii] = np.interp(self.eloss, self.HFProfile.eloss,self.HFProfile.C_total[:,ii]) self.V_total[:,ii] = np.interp(self.eloss, self.HFProfile.eloss,self.HFProfile.V_total[:,ii]) self.q_vals[:,ii] = np.interp(self.eloss, self.HFProfile.eloss,self.HFProfile.q_vals[:,ii]) # initialize double dicts for {'element1':{'edge1','edge2',...}, 'element2':{'edge1','edge2',...} } self.C_edges = {} for key in self.edges: self.C_edges[key] = {} for edge in self.edges[key]: self.C_edges[key][edge] = np.zeros((len(self.eloss),len(self.tth))) # interpolate the core profiles for the desired elements for key in self.edges: for edge in self.edges[key]: edge_keyword = xrs_ComptonProfiles.mapShellNames(edge,xrs_utilities.element(key)) for formula in self.formulas: if key in formula: # cp core-edge profile atom_profile = self.HFProfile.FormulaProfiles[formula].AtomProfiles[key] for jj in range(len(self.tth)): self.C_edges[key][edge][:,jj] = np.interp(self.eloss,atom_profile.eloss,atom_profile.CperShell[edge_keyword][:,jj]) else: print('Could not find ' + key + ' in any of the provided formulas.') def get_J_total_av(self,columns): return np.mean(self.J_total[:,columns]) def get_C_total(self,columns): return np.mean(self.J_total[:,columns]) def get_C_edges_av(self,element,edge,columns): return np.mean(self.C_edges[element][edge][:,columns]) class valence_CP: """ **valence_CP** Class to organize information about extracted experimental valence Compton profiles. """ def __init__(self): self.pzscale = np.flipud(np.arange(-10,10,0.05)) # definition according to Huotari et al, JPCS 62 (2001) 2205 self.valencepz = np.array([]) self.valasymmetrypz = np.array([]) self.valence = np.array([]) self.valasymmetry = np.array([]) def get_asymmetry(self): pass def get_pzscale(self): pass class functorObjectV: def __init__( self, y, eloss, hfcore, lam ): self.y = y self.eloss = eloss self.hfcore = hfcore self.lam = lam def funct( self, a, eloss ): pear = math_functions.pearson7_zeroback( eloss, a ) poly = np.polyval( a[4:6], eloss ) tot = pear+poly return tot def __call__( self, x ): pea = math_functions.pearson7_zeroback( self.eloss, x[0:4] ) if len(x)==7: pol = np.polyval(x[4:6],self.eloss) hf = self.hfcore else: pol = 0 hf = 0 self.hf_fit = hf self.fit = pea+pol+hf self.peapol = pea+pol diff = self.y*x[6]-self.fit # with extra cost for large linear background res = diff/np.sqrt(self.y*x[6]) + self.lam*(x[4]**2+x[5]**2) return res class edge_extraction: """ **edge_extraction** Class to destill core edge spectra from x-ray Raman scattering experiments. """ def __init__(self,exp_data, formulas, stoich_weights, edges ,prenormrange=[5,np.inf]): # input self.eloss = copy.deepcopy( exp_data.eloss ) self.signals = copy.deepcopy( exp_data.signals ) self.errors = copy.deepcopy( exp_data.errors ) self.E0 = exp_data.E0 self.tth = exp_data.tth self.prenormrange = prenormrange self.HF_dataset = HF_dataset(exp_data, formulas, stoich_weights, edges) # output self.background = np.zeros(np.shape(exp_data.signals)) self.sqw = np.zeros(np.shape(exp_data.signals)) self.sqwav = np.zeros(np.shape(exp_data.eloss)) self.sqwaverr = np.zeros(np.shape(exp_data.eloss)) self.valence_CP = valence_CP() # some variables for averaging rawdata over analyzers/whole chambers self.avsignals = np.array([]) self.averrors = np.array([]) self.av_C = {} self.av_J = np.array([]) self.avqvals = np.array([]) # rough normalization over range given by prenormrange scales = [] if prenormrange: for n in range(self.signals.shape[1]): HFnorm = np.trapz(self.HF_dataset.J_total[:,n], self.eloss) inds = np.where(np.logical_and(self.eloss >= prenormrange[0], self.eloss <= prenormrange[1]))[0] EXPnorm = np.trapz(self.signals[inds,n], self.eloss[inds]) scales.append(EXPnorm*HFnorm) #print 'HFnorm = ', HFnorm, ', EXPnorm = ', EXPnorm #self.signals[:,n] /= EXPnorm #self.signals[:,n] *= HFnorm #self.errors[:,n] /= EXPnorm #self.errors[:,n] *= HFnorm scale = np.mean(scales) for n in range(self.signals.shape[1]): self.signals[:,n] *= scale self.errors[:,n] *= scale def analyzerAverage(self,roi_numbers,errorweighing=True): """ **analyzerAverage** Averages signals from several crystals before background subtraction. Args: * roi_numbers : list, str list of ROI numbers to average over of keyword for analyzer chamber (e.g. 'VD','VU','VB','HR','HL','HB') * errorweighing : boolean (True by default) keyword if error weighing should be used for the averaging or not """ if isinstance(roi_numbers,list): columns = roi_numbers elif isinstance(roi_numbers,str): columns = map_chamber_names(roi_numbers) else: print('Unsupported type for keyword \'roi_numbers\'.') return self.avsignals = np.zeros_like(self.eloss) self.averror = np.zeros_like(self.eloss) self.av_J = np.zeros_like(self.eloss) self.avqvals = np.zeros_like(self.eloss) # build matricies to sum over av = np.zeros((len(self.eloss),len(columns))) averr = np.zeros((len(self.eloss),len(columns))) avqvals = np.zeros((len(self.eloss),len(columns))) avJ = np.zeros((len(self.eloss),len(columns))) avcvals = {} for key in self.HF_dataset.edges: avcvals[key] = {} for edge in self.HF_dataset.edges[key]: avcvals[key][edge] = np.zeros((len(self.eloss),len(columns))) # assign the signals to sum over for column,ii in zip(columns,list(range(len(columns)))): # find data points with error = 0.0 and replace error by 1.0 inds = np.where(self.errors[:,column] == 0.0)[0] self.errors[inds,column] = 1.0 av[:,ii] = self.signals[:,column] averr[:,ii] = self.errors[:,column] avqvals[:,ii] = self.HF_dataset.q_vals[:,column] for key in self.HF_dataset.edges: for edge in self.HF_dataset.edges[key]: avcvals[key][edge][:,ii] = self.HF_dataset.C_edges[key][edge][:,column] # sum things up if errorweighing: self.avsignals = np.sum( av/(averr**2.0) ,axis=1)/( np.sum(1.0/(averr**2.0),axis=1)) self.averrors = np.sqrt( 1.0/np.sum(1.0/(averr**2.0),axis=1) ) else: self.avsignals = np.mean(av,axis=1) self.averrors = np.sqrt(np.sum(np.absolute(averr)**2.0,axis=1))/np.sqrt(averr.shape[1]*(averr.shape[1]-1)) # check this again # average over HF core profiles self.avqvals = np.mean(avqvals,axis=1) self.av_J = np.mean(self.HF_dataset.J_total[:,columns],axis=1) for key in self.HF_dataset.edges: self.av_C[key] = {} for edge in self.HF_dataset.edges[key]: self.av_C[key][edge] = np.mean(avcvals[key][edge],axis=1) def removePolyCoreAv(self,element,edge,range1,range2,weights=[1,1],guess=[1.0,0.0,0.0],ewindow=100.0): """ **removePolyCoreAv** Subtract a polynomial from averaged data guided by the HF core Compton profile. Args * element : str String (e.g. 'Si') for the element you want to work on. * edge: str String (e.g. 'K' or 'L23') for the edge to extract. * range1 : list List with start and end value for fit-region 1. * range2 : list List with start and end value for fit-region 2. * weigths : list of ints List with weights for the respective fit-regions 1 and 2. Default is [1,1]. * guess : list List of starting values for the fit. Default is [1.0,0.0,0.0] (i.e. a quadratic function. Change the number of guess values to get other degrees of polynomials (i.e. [1.0, 0.0] for a constant, [1.0,0.0,0.0,0.0] for a cubic, etc.). The first guess value passed is for scaling of the experimental data to the HF core Compton profile. * ewindow: float Width of energy window used in the plot. Default is 100.0. """ # check that there are averaged signals available if not np.any(self.avsignals): print('Found no averaged signals. Use \'analyzerAverage\'-method first to create some averages.') return # check that desired edge is available if not element in list(self.HF_dataset.edges.keys()): print('Cannot find HF profiles for desired atom.') return if not edge in self.HF_dataset.edges[element]: print('Cannot find HF core profiles for desired edge.' ) return # define fitting ranges region1 = np.where(np.logical_and(self.eloss >= range1[0], self.eloss <= range1[1])) region2 = np.where(np.logical_and(self.eloss >= range2[0], self.eloss <= range2[1])) region = np.append(region1*weights[0],region2*weights[1]) # prepare plotting window # plt.ion() #plt.cla() # get the HF core spectrum HF_core = self.av_C[element][edge] # estimate start value for scaling parameter HF_core_norm = np.trapz(HF_core[region2],self.eloss[region2]) exp_norm = np.trapz(self.avsignals[region2],self.eloss[region2]) self.avsignals *= HF_core_norm/exp_norm # define fit-function, boundaries, and constraints cons = ({'type': 'eq', 'fun': lambda x: np.trapz(HF_core[region2],self.eloss[region2]) - np.trapz(x[0]*self.avsignals[region2]-HF_core[region2] - np.polyval(x[1::],self.eloss[region2]),self.eloss[region2] ) }, {'type': 'eq', 'fun': lambda x: np.trapz( np.abs( x[0]*self.avsignals[region1] - HF_core[region1] - np.polyval(x[1::],self.eloss[region1]),self.eloss[region1] )) }, {'type': 'ineq', 'fun': lambda x: x[0]}) fitfct = lambda a: np.sum( (a[0]*self.avsignals[region] - HF_core[region] - np.polyval(a[1::],self.eloss[region]) )**2.0 ) res = optimize.minimize(fitfct, guess, method='SLSQP',constraints=cons).x print( 'The fit parameters are: ', res) yres = np.polyval(res[1::], self.eloss) plt.plot(self.eloss,HF_core) plt.plot(self.eloss,self.avsignals*res[0], self.eloss,yres+HF_core, self.eloss,self.avsignals*res[0]-yres) plt.legend(['HF core','scaled signal','poly-fit + core','scaled signal - poly']) plt.xlabel('energy loss [eV]') plt.ylabel('signal [a.u.]') plt.xlim(range1[0]-ewindow,range2[1]+ewindow) plt.autoscale(enable=True, axis='y') plt.draw() self.sqwav = self.avsignals*res[0] - yres self.sqwaverr = self.averrors*res[0] def removeCorePearsonAv(self,element,edge,range1,range2,weights=[2,1],HFcore_shift=0.0,guess=None,scaling=None,return_background=False, show_plots = True): """ **removeCorePearsonAv** guess (list): [position, FWHM, shape, intensity, ax, b, scale ] """ # check that there are averaged signals available if not np.any(self.avsignals): print('Found no averaged signals. Use \'analyzerAverage\'-method first to create some averages.') return # check that desired edge is available if not element in list(self.HF_dataset.edges.keys()): print('Cannot find HF profiles for desired atom.') # @@@@@@@@@@@@@@@@@@@ raise return if not edge in self.HF_dataset.edges[element]: print('Cannot find HF core profiles for desired edge.' ) # @@@@@@@@@@@@ raise return # define fitting ranges region1 = np.where(np.logical_and(self.eloss >= range1[0], self.eloss <= range1[1])) region2 = np.where(np.logical_and(self.eloss >= range2[0], self.eloss <= range2[1])) region = np.append(region1*weights[0],region2*weights[1]) # find indices for guessing start values from HF J_total fitfct = lambda a: np.sum( (self.av_J[region] - pearson7_zeroback(self.eloss,a)[region] - np.polyval(a[4:6],self.eloss[region]) )**2.0 ) guess1 = optimize.minimize(fitfct, [1.0,1.0,1.0,1.0], method='SLSQP').x #guessregion = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] #if not guess: # guess = [] # ind = self.avsignals[guessregion].argmax(axis=0) # find index of maximum of signal in "prenormrange" (defalt [5,inf]) # guess.append(self.eloss[guessregion][ind]) # max of signal (in range of prenorm from __init__) # guess.append(guess[0]*1.0) # once the position of the peason maximum # guess.append(1.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian # guess.append(self.avsignals[guessregion][ind]) # Peak intensity # guess.append(0.0) # linear slope # guess.append(0.0) # no background # guess.append(1.0) # scaling factor for exp. data if not guess: guess = guess1 guess = np.append(guess,[0.0,0.0,1.0]) # append starting values for linear and scaling # manage some plotting things # plt.ion() # plt.cla() # get the HF core spectrum HF_core = np.interp(self.eloss,self.eloss+HFcore_shift,self.av_C[element][edge]) # approximately scale data in post-edge region HF_core_norm = np.trapz(HF_core[region2],self.eloss[region2]) exp_norm = np.trapz(self.avsignals[region2],self.eloss[region2]) the_signals = self.avsignals*HF_core_norm/exp_norm if scaling: bnds = ((None, None), (0, None), (0, None), (0, None), (0, None), (0, None), (scaling-scaling*0.001, scaling+scaling*0.001)) else: scaling = 1.0 bnds = ((None, None), (0, None), (0, None), (0, None), (0, None), (0, None), (0, None)) cons = (#{'type': 'ineq', 'fun': lambda x: x[2] }, #{'type': 'ineq', 'fun': lambda x: x[3] }, #{'type': 'ineq', 'fun': lambda x: x[6] }, {'type': 'eq', 'fun': lambda x: np.trapz(np.abs(scaling*the_signals[region1] - pearson7_zeroback(self.eloss[region1],x[0:4]) - np.polyval(x[4:6],self.eloss[region1]) - HF_core[region1] ), self.eloss[region1] ) }, {'type': 'eq', 'fun': lambda x: np.trapz(scaling*the_signals[region2] - pearson7_zeroback(self.eloss[region2],x[0:4]) - np.polyval(x[4:6],self.eloss[region2])-HF_core[region2], self.eloss[region2])}) fitfct = lambda a: np.sum( (scaling*the_signals[region] - pearson7_zeroback(self.eloss[region],a[0:4]) - np.polyval(a[4:6],self.eloss[region]) - HF_core[region] )**2.0 ) res = optimize.minimize(fitfct, guess, method='SLSQP', bounds=bnds, constraints=cons).x print( 'The fit parameters are: ', res) yres = pearson7_zeroback(self.eloss,res[0:4]) + np.polyval(res[4:6],self.eloss) if show_plots: plt.plot(self.eloss,the_signals*scaling,self.eloss,yres+HF_core,self.eloss,the_signals*scaling-yres,self.eloss,HF_core) plt.legend(('scaled data','pearson + linear + core','data - (pearson + linear)','core')) plt.draw() pyplot.show() self.sqwav = the_signals*scaling - yres self.sqwaverr = self.averrors*scaling*HF_core_norm/exp_norm if return_background: return self.eloss, yres, HF_core def removePearsonAv(self,element,edge,range1,range2=None,weights=[2,1],guess=None,scale=1.0,HFcore_shift=0.0): """ **removePearsonAv** """ # check that there are averaged signals available if not np.any(self.avsignals): print('Found no averaged signals. Use \'analyzerAverage\'-method first to create some averages.') return # define fitting ranges region1 = np.where(np.logical_and(self.eloss >= range1[0], self.eloss <= range1[1])) if range2: region2 = np.where(np.logical_and(self.eloss >= range2[0], self.eloss <= range2[1])) region = np.append(region1*weights[0],region2*weights[1]) else: region = region1 guessregion = np.where(np.logical_and(self.eloss>=self.prenormrange[0],self.eloss<=self.prenormrange[1]))[0] if not guess: guess = [] ind = self.avsignals[guessregion].argmax(axis=0) # find index of maximum of signal in "prenormrange" (defalt [5,inf]) guess.append(self.eloss[guessregion][ind]) # max of signal (in range of prenorm from __init__) guess.append(guess[0]*2.0) # once the position of the peason maximum guess.append(1.0) # pearson shape, 1 = Lorentzian, infinite = Gaussian guess.append(self.avsignals[guessregion][ind]) # Peak intensity guess.append(0.0) # no background # scale data by hand thespec = self.avsignals * scale # get the HF core spectrum HF_core = np.interp(self.eloss,self.eloss+HFcore_shift,self.av_C[element][edge]) # define fitfunction fitfct = lambda a: np.sum( (thespec[region] - pearson7_zeroback(self.eloss[region],a[0:4]) - np.polyval(a[4:6],self.eloss[region]) - HF_core[region])**2.0 ) res = optimize.minimize(fitfct,guess).x print( 'the fitting results are: ', res) yres = pearson7_zeroback(self.eloss,res[0:4]) + np.polyval(res[4:6],self.eloss) plt.cla() plt.plot(self.eloss,thespec,self.eloss,yres,self.eloss,thespec-yres,self.eloss,HF_core) plt.legend(('data','pearson fit','data - pearson','core')) plt.draw() self.sqwav = thespec-yres self.sqwaverr = self.averrors * scale def save_average_Sqw(self,filename, emin=None, emax=None, normrange=None): """ **save_average_Sqw** Save the S(q,w) into a ascii file (energy loss, S(q,w), Poisson errors). Args: * filename : str Filename for the ascii file. * emin : float Use this to save only part of the spectrum. * emax : float Use this to save only part of the spectrum. * normrange : list of floats E_start and E_end for possible area-normalization before saving. """ # check that there are is an extracted S(q,w) available if not np.any(self.sqwav): print('Found no extracted S(q,w).') return if emin and emax: inds = np.where(np.logical_and(self.eloss>=emin,self.eloss<=emax))[0] data = np.zeros((len(inds),3)) data[:,0] = self.eloss[inds] data[:,1] = self.sqwav[inds] data[:,2] = self.sqwaverr[inds] else: data = np.zeros((len(self.eloss),3)) data[:,0] = self.eloss data[:,1] = self.sqwav data[:,2] = self.sqwaverr if normrange: assert type(normrange) is list and len(normrange) is 2, "normrange has to be a list of length two!" inds = np.where(np.logical_and(data[:,0]>=normrange[0],data[:,0]<=normrange[1]))[0] norm = np.trapz(data[inds,1],data[inds,0]) data[:,1] /= norm data[:,2] /= norm np.savetxt(filename,data) def removeCorePearsonAv_new( self, element, edge, range1, range2, HFcore_shift=0.0, guess=None, scaling=None, return_background=False, reg_lam=10): """ **removeCorePearsonAv_new** """ # check that there are averaged signals available if not np.any(self.avsignals): raise ValueError('Found no averaged signals. Use \'analyzerAverage\'-method first to create some averages.') # check that desired edge is available if not element in list(self.HF_dataset.edges.keys()): raise ValueError('Cannot find HF profiles for desired atom.') if not edge in self.HF_dataset.edges[element]: raise ValueError('Cannot find HF core profiles for desired edge.') # define fitting ranges region1 = np.where(np.logical_and(self.eloss >= range1[0], self.eloss <= range1[1])) region2 = np.where(np.logical_and(self.eloss >= range2[0], self.eloss <= range2[1])) region = np.append(region1, region2) # get the HF core spectrum HF_core = np.interp( self.eloss, self.eloss+HFcore_shift, self.av_C[element][edge] ) y_reg1 = self.avsignals[region1] eloss_reg1 = self.eloss[region1] HF_core_reg1 = HF_core[region1] y_reg2 = self.avsignals[region2] eloss_reg2 = self.eloss[region2] HF_core_reg2 = HF_core[region2] y_reg = self.avsignals[region] eloss_reg = self.eloss[region] HF_core_reg = HF_core[region] HF_reg_max = HF_core[region].max() y_reg_max = y_reg.max() fact = HF_reg_max/y_reg_max if not guess: fitfct = functorObjectV( y_reg, eloss_reg, HF_core_reg , reg_lam ) bndsa = [ -np.inf]+ [ 0 for tmp in range(6)] bndsa[5] = -np.inf bndsb = [ np.inf for tmp in range(7)] solution = optimize.least_squares(fitfct, np.random.rand(7),method='trf', bounds=[bndsa,bndsb] ) guess = solution.x # print ( "Using following guess parameters: ", solution.x) # manage some plotting things # plt.ion() # plt.cla() # do the actual fit using the guess parameters fitfct_res = functorObjectV( y_reg, eloss_reg, HF_core_reg , reg_lam ) bndsa = [ -np.inf]+ [ 0 for tmp in range(6)] bndsa[5] = -np.inf bndsb = [ np.inf for tmp in range(7)] solution = optimize.least_squares(fitfct_res, guess, method='trf', bounds=[bndsa,bndsb] ) print ( "Best fit parameters: ", solution.x) # plot results fitfct_res = functorObjectV( self.avsignals, self.eloss, HF_core , reg_lam ) res = fitfct_res(solution.x) x = self.eloss # eloss y1 = self.avsignals*solution.x[6] # scaled data y2 = fitfct_res.fit # pearson + linear + core y3 = y1 - fitfct_res.peapol # data - (pearson + linear) y4 = HF_core # core plt.plot( x, y1, x, y2, x, y3, x, y4 ) plt.legend(('scaled data','pearson + linear + core','data - (pearson + linear)','core')) plt.draw() self.sqwav = y3 self.sqwaverr = self.averrors*solution.x[6] if return_background: return yres xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_fileIO.py000066400000000000000000000355531412732462000223370ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import map from six.moves import range from six.moves import zip #!/usr/bin/python # Filename: xrs_utilities.py __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import numpy as np import array as arr import collections import os # # try to import the fast PyMCA parsers # try: # import PyMca.EdfFile as EdfIO # # use as: data = EdfIO.EdfFile(fname,"r").GetData(0) # import PyMca.specfilewrapper as SpecIO # # use as: # # sf = SpecIO.Specfile(filename) # # sf.scanno() # returns the number of scans in the SPEC file # # scan = sf.select('1') # returns the first scan in the specfile # # scan.data() # np.array of the data # # scan.alllabels() # Python list of all counter names # use_PyMca = True # except: # use_PyMca = False try: import PyMca5.PyMcaIO.EdfFile as EdfIO import PyMca5.PyMcaIO.specfilewrapper as SpecIO import fabio from silx.io.specfile import SpecFile use_PyMca = True except: use_PyMca = False try: from silx.io.specfile import SpecFile except: pass print( " >>>>>>>> use_PyMca " , use_PyMca) __metaclass__ = type # new style classes SHOW_LOADED_FILES = False # this is useful when you want to take home a limited number # of files for an experiment : you run the analysis at the lab : # you look a the filenames that are printed and the you tar gz # that ones for home. This without having to bring home the # whole shift if "SHOW_LOADED_FILES" in os.environ: SHOW_LOADED_FILES = True if SHOW_LOADED_FILES : open("list_of_used_files.txt","w") def dump_on_file_list(filename): f = open("list_of_used_files.txt","a") f.write( filename+"\n") f.close() def SpecRead(filename,nscan): """Parses a SPEC file and returns a specified scan. Args: * filename (string): SPEC file name (inlc. path) * nscan (int): Number of the desired scan. Returns: * data (np.array): array of the data from the specified scan. * motors (list): list of all motor positions from the header of the specified scan. * counters (dict): all counters in a dictionary with the counter names as keys. """ scannid = '#S' countid = '#L' motorid = '#P' data = [] motors = [] counterss = [] f = open(filename,'r') if SHOW_LOADED_FILES: dump_on_file_list(filename) while True: line = f.readline() if not line: break if line[0:2] == scannid: if int(line.split()[1]) == nscan: line = '##'+line while line and line[0:2]!='#S': line = f.readline() if not line: break if line[0:2] == countid: cline = ' '+line[2:] counterss = [n.strip() for n in [_f for _f in cline.split(' ')[1:] if _f]] if line[0:2] == motorid: motors.append([float(n) for n in line.strip().split()[1:]]) if line[0] != '#': data.append([float(n) for n in line.strip().split()]) data.pop(-1) # the trailing empty line f.close() # put the data into a dictionary with entries from the counterss counters = {} for n in range(len(counterss)): counters[counterss[n].lower()] = [row[n] for row in data] # data[:,n] return data, motors, counters def myEdfRead(filename): """ Returns EDF-data, if PyMCA is not installed (this is slow). """ # get some info from header f = open(filename,'rb').readlines() if SHOW_LOADED_FILES: dump_on_file_list(filename) counter = 0 predata = [] for entry in f: counter += 1 if entry.strip().split()[0] == '}': break for entry in f[:counter]: if entry.strip().split()[0] == 'Dim_1': dim1 = int(entry.strip().split()[2]) if entry.strip().split()[0] == 'Dim_2': dim2 = int(entry.strip().split()[2]) if entry.strip().split()[0] == 'Size': size = int(entry.strip().split()[2]) length = 0 for line in f: length += len(line) headerlength = (length-size)//2 # get the data f = open(filename,'rb') predata = arr.array('H') predata.fromfile(f,(headerlength+dim1*dim2)) data = np.reshape(predata[headerlength:],(dim2,dim1)) f.close() return data def PyMcaSpecRead_my(filename,nscan): """ Returns data, counter-names, and EDF-files using PyMCA. """ if SHOW_LOADED_FILES: dump_on_file_list(filename) sf = SpecIO.Specfile(filename) scan = sf.select(str(nscan)) data = scan.data() lables = scan.alllabels() counters = {} cou = 0 for lable in lables: counters[lable.lower()] = data[cou,:] cou += 1 #motors = [np.array(ii.split()[1::]).astype(np.float) for ii in scan.header('P')] #motors = collections.OrderedDict( zip(scan.allmotors(), map( scan.motorpos, scan.allmotors())) ) motors = dict( list(zip(scan.allmotors(), list(map( scan.motorpos, scan.allmotors())))) ) return data.T, motors, counters, lables def SilxSpecRead( filename, nscan ): """ Returns data, motors, counter-names, and labels using Silx. """ if SHOW_LOADED_FILES: dump_on_file_list(filename) nscan -= 1 sf = SpecFile( filename ) data = sf.data( nscan ) lables = sf.labels( nscan ) counters = {} cou = 0 for lable in lables: counters[lable.lower()] = data[:,cou] cou += 1 motors = dict( list(zip(sf.motor_names(nscan), list( map( sf.motor_position_by_name, [nscan]*len(sf.motor_names(nscan)), sf.motor_names( nscan ))))) ) return data, motors, counters, lables def PyMcaSpecRead(filename,nscan): """ Returns data, counter-names, and EDF-files using PyMCA. """ sf = SpecIO.Specfile(filename) if SHOW_LOADED_FILES: dump_on_file_list(filename) scan = sf.select(str(nscan)) data = scan.data() lables = scan.alllabels() counters = {} cou = 0 for lable in lables: counters[lable.lower()] = data[cou,:] cou += 1 #motors = [np.array(ii.split()[1::]).astype(np.float) for ii in scan.header('P')] #motors = collections.OrderedDict( zip(scan.allmotors(), map( scan.motorpos, scan.allmotors())) ) motors = dict( list(zip(scan.allmotors(), list(map( scan.motorpos, scan.allmotors())))) ) return data.T, motors, counters def PyMcaEdfRead(fname): """ Returns the EDF-data using PyMCA. """ if SHOW_LOADED_FILES: dump_on_file_list(fname) if not os.path.exists(fname): os.system("gzip -d %s.gz"%fname) data = EdfIO.EdfFile(fname,"r").GetData(0) return data def FabioEdfRead(fname): """ Returns the EDF-data using FabIO. """ if SHOW_LOADED_FILES: dump_on_file_list(fname) return fabio.edfimage.EdfImage().read(fname).data def ReadScanFromFile(fname): """ Returns a scan stored in a Numpy archive. """ try: print( 'Trying to load scan from file.') scanname = 'Scan%03d' % scannumber scan = np.load(fname) data = list(scan['data']) motors = list(scan['motors']) counters = scan['counters'].item() edfmats = scan['edfmats'] return data, motors, counters, edfmats except: print( 'Failed loading scan from file, will read EDF- and SPEC-file.') pass def WriteScanToFile(fname,data,motors,counters,edfmats): """ Writes a scan into a Numpy archive. """ try: np.savez(fname, data=data, motors=motors, counters=counters, edfmats=edfmats) print( 'trying to save file in numpy-archive.') except: print( 'Could not write ' + fname + '.') pass def PrepareEdfMatrix_TwoImages(scan_length,num_pix_x,num_pix_y): """ Returns np.zeros for old data (horizontal and vertical Maxipix images in different files). """ edfmats_h = np.zeros((scan_length,num_pix_y//2,num_pix_x)) edfmats_v = np.zeros((scan_length,num_pix_y//2,num_pix_x)) edfmats = np.zeros((scan_length,num_pix_y,num_pix_x)) return edfmats_h, edfmats_v, edfmats def PrepareEdfMatrix(scan_length,num_pix_x,num_pix_y): """ Returns np.zeros of the shape of the detector. """ edfmats = np.zeros((scan_length,num_pix_y,num_pix_x)) return edfmats def ReadEdfImages_TwoImages(ccdcounter,num_pix_x,num_pix_y,path,EdfPrefix_h,EdfPrefix_v, EdfNmae, EdfPostfix): """ Reads a series of EDF-images and returs them in a 3D Numpy array (horizontal and vertical Maxipix images in different files). """ edfmats_h, edfmats_v, edfmats = PrepareEdfMatrix_TwoImages(len(ccdcounter),num_pix_x,num_pix_y) for m in range(len(ccdcounter)): ccdnumber = ccdcounter[m] edfnameh = path + EDF_PREFIXh + edfName + '_' + "%04d" % ccdnumber + EDF_POSTFIX edfnamev = path + EDF_PREFIXv + edfName + '_' + "%04d" % ccdnumber + EDF_POSTFIX if use_PyMca == True: edfmatsh[m,:,:] = PyMcaEdfRead(edfnameh) edfmatsv[m,:,:] = PyMcaEdfRead(edfnamev) edfmats[m,0:num_pix_y//2,:] = edfmatsv[m,:,:] edfmats[m,num_pix_y//2:,:] = edfmatsh[m,:,:] else: edfmatsh[m,:,:] = myEdfRead(edfnameh) edfmatsv[m,:,:] = myEdfRead(edfnamev) edfmats[m,0:num_pix_y//2,:] = edfmatsv[m,:,:] edfmats[m,num_pix_y//2:,:] = edfmatsh[m,:,:] return edfmats def EdfRead(fname): if use_PyMca == True: # print " using pymca " edfmat = PyMcaEdfRead(fname) else: # print "NOT using pymca " edfmat = myEdfRead(fname) return edfmat def ReadEdf_justFirstImage(ccdcounter, path, EdfPrefix, EdfName, EdfPostfix): m=0 ccdnumber = ccdcounter[m] fname = path + EdfPrefix + EdfName + '_' + "%04d" % ccdnumber + EdfPostfix if use_PyMca == True: print( " using pymca " ) edfmat = PyMcaEdfRead(fname) else: print( "NOT using pymca " ) edfmat = myEdfRead(fname) return edfmat def ReadEdfImages(ccdcounter, num_pix_x, num_pix_y, path, EdfPrefix, EdfName, EdfPostfix): """ Reads a series of EDF-images and returs them in a 3D Numpy array (horizontal and vertical Maxipix images in different files). """ edfmats = PrepareEdfMatrix(len(ccdcounter),num_pix_x,num_pix_y) for m in range(len(ccdcounter)): ccdnumber = ccdcounter[m] fname = path + EdfPrefix + EdfName + '_' + "%04d" % ccdnumber + EdfPostfix print( " for file ", fname) if use_PyMca == True: print( " using pymca " ) edfmats[m,:,:] = PyMcaEdfRead(fname) else: print( "NOT using pymca " ) edfmats[m,:,:] = myEdfRead(fname) return edfmats def ReadEdfImages_my(ccdcounter, path, EdfPrefix, EdfName, EdfPostfix): """ Reads a series of EDF-images and returs them in a 3D Numpy array (horizontal and vertical Maxipix images in different files). """ fname = path + EdfPrefix + EdfName + "%04d" % ccdcounter[0] + EdfPostfix xyShape = np.shape(myEdfRead(fname)) edfmats = PrepareEdfMatrix(len(ccdcounter),xyShape[1],xyShape[0]) for m in range(len(ccdcounter)): ccdnumber = ccdcounter[m] fname = path + EdfPrefix + EdfName + "%04d" % ccdnumber + EdfPostfix if SHOW_LOADED_FILES : dump_on_file_list(fname) print( " LEGGO ", fname) edfmats[m,:,:] = myEdfRead(fname) return edfmats def ReadEdfImages_PyMca(ccdcounter, path, EdfPrefix, EdfName, EdfPostfix): """ Reads a series of EDF-images and returs them in a 3D Numpy array (horizontal and vertical Maxipix images in different files). """ fname = path + EdfPrefix + EdfName + "%04d" % ccdcounter[0] + EdfPostfix xyShape = np.shape(PyMcaEdfRead(fname)) edfmats = PrepareEdfMatrix(len(ccdcounter),xyShape[1],xyShape[0]) for m in range(len(ccdcounter)): ccdnumber = ccdcounter[m] fname = path + EdfPrefix + EdfName + "%04d" % ccdnumber + EdfPostfix if SHOW_LOADED_FILES : dump_on_file_list(fname) print( " LEGGO ", fname) edfmats[m,:,:] = PyMcaEdfRead(fname) return edfmats def readbiggsdata(filename,element): """ Reads Hartree-Fock Profile of element 'element' from values tabulated by Biggs et al. (Atomic Data and Nuclear Data Tables 16, 201-309 (1975)) as provided by the DABAX library (http://ftp.esrf.eu/pub/scisoft/xop2.3/DabaxFiles/ComptonProfiles.dat). input: filename = path to the ComptonProfiles.dat file (the file should be distributed with this package) element = string of element name returns: data = the data for the according element as in the file: #UD Columns: #UD col1: pz in atomic units #UD col2: Total compton profile (sum over the atomic electrons #UD col3,...coln: Compton profile for the individual sub-shells occupation = occupation number of the according shells bindingen = binding energies of the accorting shells colnames = strings of column names as used in the file """ elementid = '#S' sizeid = '#N' occid = '#UOCCUP' bindingid = '#UBIND' colnameid = '#L' data = [] f = open(filename,'r') istrue = True while istrue: line = f.readline() if line[0:2] == elementid: if line.split()[-1] == element: line = f.readline() while line[0:2] != elementid: if line[0:2] == sizeid: arraysize = int(line.split()[-1]) line = f.readline() if line[0:7] == occid: occupation = line.split()[1:] line = f.readline() if line[0:6] == bindingid: bindingen = line.split()[1:] line = f.readline() if line[0:2] == colnameid: colnames = line.split()[1:] line = f.readline() if line[0]== ' ': data.append([float(n) for n in line.strip().split()]) #data = np.zeros((31,arraysize)) line = f.readline() break length = len(data) data = (np.reshape(np.array(data),(length,arraysize))) return data, occupation, bindingen, colnames xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_imaging.py000066400000000000000000000732751412732462000226060ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: id20_imaging.py import numpy as np import matplotlib.pyplot as plt from scipy import io from itertools import groupby from scipy.interpolate import Rbf, RectBivariateSpline from scipy.optimize import leastsq, fmin import scipy import scipy.ndimage from . import xrs_rois from .helpers import * #from xrs_read import rois from . import xrs_read import PyMca5.PyMcaIO.specfilewrapper as SpecIO from six.moves import map from six.moves import range from six.moves import zip class oneD_imaging(xrs_read.read_id20): """ **oneD_imaging** Class to construct images using the 1D piercing mode. """ def __init__(self,absfilename,energycolumn='sty',monitorcolumn='kapraman', monitor_divider = 1.0, edfName=None,single_image=True, sumto1D = 1, recenterings=None ): self.sumto1D = sumto1D try: self.path = os.path.split(absfilename)[0] + '/' self.filename = os.path.split(absfilename)[1] except IOError: print('IOError! No such SPEC file, please check SPEC-filename.') if not edfName: self.edfName = os.path.split(absfilename)[1] else: self.edfName = edfName self.single_image = single_image self.scannumbers = [] self.EDF_PREFIXh = 'edf/h_' self.EDF_PREFIXv = 'edf/v_' self.EDF_PREFIX = 'edf/' self.EDF_POSTFIX = '.edf' self.DET_PIXEL_NUMx = 768 self.DET_PIXEL_NUMy = 512 self.DET_PIXEL_NUM = 256 self.encolumn = energycolumn.lower() self.monicolumn = monitorcolumn.lower() self.monitor_divider = monitor_divider self.eloss = [] # common eloss scale for all analyzers self.energy = [] # common energy scale for all analyzers self.signals = [] # signals for all analyzers self.errors = [] # poisson errors self.qvalues = [] # for all analyzers self.groups = {} # dictionary of groups (such as 2 'elastic', or 5 'edge1', etc.) self.cenom = [] # list of center of masses of the elastic lines self.E0 = [] # energy value, mean value of self.cenom self.tth = [] # list of scattering angles (one for each ROI) self.VDtth = [] self.VUtth = [] self.VBtth = [] self.HRtth = [] self.HLtth = [] self.HBtth = [] self.resolution = [] # list of FWHM of the elastic lines for each analyzer self.signals_orig = [] # signals for all analyzers before interpolation self.errors_orig = [] # errors for all analyzers before interpolation # TTH offsets from center of V and H modules # tth offsets in one direction (horizontal for V-boxes, vertical for H-boxes) self.TTH_OFFSETS1 = np.array([5.0, 0.0, -5.0, 5.0, 0.0, -5.0, 5.0, 0.0, -5.0, 5.0, 0.0, -5.0]) # tth offsets in the other direction (horizontal for H-boxes, vertical for V-boxes) self.TTH_OFFSETS2 = np.array([-9.71, -9.75, -9.71, -3.24, -3.25, -3.24, 3.24, 3.25, 3.24, 9.71, 9.75, 9.71]) # input self.roi_obj = [] # an instance of the roi_object class from the xrs_rois module (new) # images self.twoDimages = {} # dictionary: one entry per scan, each scan has a list of one image per ROI self.motorDict = {} # additional dictionary : for every scan a dictionary with values associated to motor names self.recenterings = recenterings def loadscan_2Dimages(self, scannumbers,scantype='sty', isolateSpot = 0): print( " SONO IN loadscan_2Dimages monicolumns ", self.monicolumn) edfmat = self.read_just_first_scanimage(scannumbers[0]) if edfmat.shape[1] != self.DET_PIXEL_NUMx : self.DET_PIXEL_NUMy ,self.DET_PIXEL_NUMx = edfmat.shape # check if there are ROIs defined if not self.roi_obj: print( 'Please define some ROIs first.') return # make sure scannumbers are iterable (list) if not isinstance(scannumbers,list): scannums = [] scannums.append(scannumbers) else: scannums = scannumbers maxvalue = 0 for number in scannums: # go through all scans scanname = 'Scan%03d' % number data, motors, counters, edfmats = self.readscan(number) if self.monicolumn in counters: intensities = counters[self.monicolumn] np.multiply( edfmats, (self.monitor_divider/np.array(intensities))[:,None,None],edfmats ) monitor = intensities else: monitor = np.ones( edfmats.shape[0],"f") self.monitor_divider = 1.0 maxvalue = max(maxvalue, edfmats.max()) position = counters[scantype.lower()] self.twoDimages[scanname] = [] ####### fn = self.path + self.filename spc = SpecIO.Specfile(fn) spcscn = spc.select(str(number)) motodict=dict( list(zip(spcscn.allmotors(), list(map( spcscn.motorpos, spcscn.allmotors())))) ) self.motorDict[scanname] = motodict ####### for ii in range(len(self.roi_obj.x_indices)): # go through all ROIs # construct the image if len( self.roi_obj.x_indices[ii]): roixinds = self.roi_obj.x_indices[ii] roiyinds = self.roi_obj.y_indices[ii] minx = np.amin(roixinds) miny = np.amin(roiyinds) if self.recenterings is not None: if self.recenterings[ii].shape ==(2,2): ## to be refined by comparaison : zb,xb is the gol for new baricenter [[zb, za],[xb,xa]] = self.recenterings[ii] sx = xb-xa sz = zb-za else: ## good shift is known in advance sz,sx = self.recenterings[ii] if sx != 0.0: print( " SHIFTO DI " , sx) isx = int(1000+sx)-1000 dsx = sx-isx edfmats2use = (1.0-dsx) * np.roll(edfmats , isx , axis=-1) + dsx * np.roll(edfmats , isx+1 , axis=-1) if sz != 0.0: isz = int(1000+sz)-1000 dsz = sz-isz edfmats2use = (1.0-dsz) * np.roll(edfmats2use , isz , axis=-2) + dsz * np.roll(edfmats2use , isz+1 , axis=-1) tmp = np.zeros_like(edfmats2use) tmp[:,roixinds,roiyinds] = edfmats2use[:,roixinds,roiyinds] print( " ENERGIA adesso " , tmp.sum()) print( " ENERGIA prima " ,edfmats[:,roixinds,roiyinds] .sum()) # # When recentering.shape ==(2,2) it contains the previously measure (bari_Y, bari_X) of the reference scan ( with the reference ROI) in the first column # while the second columns is the previously measured baricenter of the sample scan ( with the sample ROI). # The goal now is to have a shift ( confirmed shift) which brings the sample baricenter ( with the reference ROI) over the scan baricenter if self.recenterings[ii].shape ==(2,2): dimz,dimy,dimx = tmp.shape BX = (tmp*np.arange(dimx)).sum()/tmp.sum() print( " original barix for zone ", ii, " : " , xa) print( " actual / goal : ", BX, xb) sx = xb-xa + xb-BX if sx != 0.0: print( " SHIFTO DI " , sx) isx = int(1000+sx)-1000 dsx = sx-isx edfmats2use = (1.0-dsx) * np.roll(edfmats , isx , axis=-1) + dsx * np.roll(edfmats , isx+1 , axis=-1) tmp = np.zeros_like(edfmats2use) tmp[:,roixinds,roiyinds] = edfmats2use[:,roixinds,roiyinds] print( "E ENERGIA adesso " , tmp.sum()) dimz,dimy,dimx = tmp.shape BX = (tmp*np.arange(dimx)).sum()/tmp.sum() print( " original barix for zone ", ii, " : " , xa) print( " NOW actual / goal : ", BX, xb) ############################################################# BY = (tmp*(np.arange(dimy)[:,None])).sum()/tmp.sum() print( " original bariy for zone ", ii, " : " , za ) print( " actual / goal : ", BY, zb) sz = zb-za + zb-BY if sz != 0.0: print( " SHIFTO DI " , sz) isz = int(1000+sz)-1000 dsz = sz-isz edfmats2use = (1.0-dsz) * np.roll(edfmats2use , isz , axis=-2) + dsz * np.roll(edfmats2use , isz+1 , axis=-2) shifts = np.array([sz,sx]) self.recenterings[ii] = shifts tmp = np.zeros_like(edfmats2use) tmp[:,roixinds,roiyinds] = edfmats2use[:,roixinds,roiyinds] print( "E ENERGIA adesso " , tmp.sum()) dimz,dimy,dimx = tmp.shape BX = (tmp*np.arange(dimx)).sum()/tmp.sum() BY = (tmp*(np.arange(dimy)[:,None])).sum()/tmp.sum() print( " original barix for zone ", ii, " : " , xa, za) print( " NOW actual / goal : ", BX, xb ," and ", BY, zb ) else: edfmats2use = edfmats axesrange = [0,roiyinds[-1],position[-1],position[0]] inset = np.zeros([edfmats2use.shape[0], np.amax(roixinds)+1-minx, np.amax(roiyinds)+1-miny ]) inset [:,roixinds-minx, roiyinds-miny ] =edfmats2use[:, roixinds , roiyinds ] if self.sumto1D: imageMat = (np.sum(inset,axis=1)) else: if isolateSpot: imageLines = np.sum(inset,axis=1) imageLines =imageLines- scipy.ndimage.filters.gaussian_filter( imageLines ,[0,isolateSpot],mode='constant',cval=0) poss = np.argmax(imageLines,axis=1) for i in range(len(poss)): inset[i,:, : max(0,poss[i]-isolateSpot) ]=0 inset[i,:, poss[i]+isolateSpot : ]=0 imageMat = inset imageInst = LRimage(imageMat,position, roiyinds, cornerpos = (minx, miny), monitor = monitor ) self.twoDimages[scanname].append(imageInst) else: imageInst = LRimage([],[],[], monitor = monitor) self.twoDimages[scanname].append(imageInst) return maxvalue def save_state_hdf5(self, filename, groupname, comment="", myrank=0, factor = 1.0, save_also_roi = False): import h5py h5 = h5py.File(filename,"a") if (myrank==0): if groupname in h5: print( " levo " , groupname) del h5[groupname] h5.flush() h5.close() h5 = h5py.File(filename,"a") h5.require_group(groupname) h5group = h5[groupname] if(myrank==0): h5group["README"] = " Some metadata concerning the way data have been extracted \n" "comment : contains the containt of input file give to XRS_swissknife\n" "path : the path to the aquisition ditrectory \n" "filename : the spec file \n" "encolumn : the spec column with the scan energy\n" "monicolumn : the column containing the beam reference (not used if not found) \n" "edfName : the prefix of edf files contained in edf directory ( the aquired images)\n" h5group["path"] = self.path h5group["filename"] = self.filename h5group["edfName"] = self.edfName h5group["monicolumn"] = self.monicolumn h5group["encolumn"] = self.encolumn h5group["comment"] = comment h5group.require_group("scans") h5group = h5group["scans"] if save_also_roi and self.roi_obj is not None: xrs_rois.write_rois_toh5(h5group , self.roi_obj.red_rois ) elif save_also_roi=="for_resynth" : print(" dovre salvare ", h5group , self.roi_obj.red_rois ) xrs_rois.write_rois_toh5_for_resynth(h5group , self.roi_obj.red_rois ) for scanname, imageInst in self.twoDimages.items(): h5group.require_group(scanname) h5group_scan = h5group[scanname] if scanname in self.motorDict: h5_md = h5group_scan.require_group("motorDict") for mn, mv in self.motorDict[scanname].items(): h5_md[mn] = mv for ii, ima in enumerate(imageInst): print(" ii ima ", ii, ima) if ima is not None and ima.matrix !=[]: h5group_scan.require_group( str(ii) ) h5group_scan_ii = h5group_scan[ str(ii) ] h5group_scan_ii["README"]=("barix : the x(fast index) component of the baricenter. May be used to change displacement relative to the roi. \n" "bariy : the y component of the baricenter. May be used to change displacement relative to the roi. \n" "monitor : by what the data have been divided. The denominator. If the monitor name was not given correctly it is a list of ones\n" "monitor_divider : a renormalisation for the monitor. Can be used to avoid numerically extreme situations where the monitor is huge or too small.\n" "matrix : the stack of image roi data.\n" "cornerpos : where in the image the roi begins.\n" "xscale : may have several meaning. In general the variable which changes during the scan\n" "yscal : a variable which changes inside each image of the scan. For example the pixel horizontal position\n") h5group_scan_ii["name"] = ima.name h5group_scan_ii["matrix"] = ima.matrix*factor h5group_scan_ii["xscale"] = ima.xscale h5group_scan_ii["yscale"] = ima.yscale h5group_scan_ii["tth"] = ima.tth h5group_scan_ii["cornerpos"] = ima.cornerpos barix = ( ( ima.matrix*np.arange(ima.matrix.shape[-1] ) ).sum() )/( ima.matrix.sum() ) h5group_scan_ii["barix"] = barix bariy = ( ( ima.matrix* ( np.arange(ima.matrix.shape[-2])[:,None] ) ).sum() )/( ima.matrix.sum() ) h5group_scan_ii["bariy"] = bariy h5group_scan_ii["monitor_divider"] = self.monitor_divider h5group_scan_ii["monitor"] = ima.monitor h5.flush() h5.close() def load_state_hdf5(self, filename, groupname): import h5py h5 = h5py.File(filename,"r") h5group = h5[groupname] self.path = h5group["path" ] self.filename = h5group["filename" ] self.edfName = h5group["edfName" ] self.monicolumn = h5group["monicolumn" ] self.monitor_divider = h5group["monitor_divider"] self.encolumn = h5group["encolumn" ] h5group = h5[groupname+"/scans"] self.twoDimages = {} self.motorDict = {} for scanname, group in h5group.items(): if "motorDict" in group: h5group_scan = group["motorDict"] md = {} for mn, mv in h5group_scan.items(): md[mn] = mv self.motorDict[ scanname ] = md iis = np.array(list(map( int , list(group.keys())))) N = iis.max()+1 self.twoDimages[scanname]=N*["This items must be filled by load_state_hdf5 " +__file__ ] for i in iis: group_i = group[str(i)] name = np.array(group_i["name"]) matrix = np.array(group_i["matrix"]) xscale = np.array(group_i["xscale"]) yscale = np.array(group_i["yscale"]) tth = np.array(group_i["tth"]) cornerpos = np.array(group_i["cornerpos"]) ima = image(matrix, xscale, yscale, ) ima.name = name ima.tth = tth ima.cornerpos=cornerpos self.twoDimages[scanname][i] = ima h5.close() class imageset: """ class to make SR-images from list of LR-images """ def __init__(self): self.list_of_images = [] self.xshifts = [] self.yshifts = [] self.shifts = [] self.srimage = [] self.srxscale = [] self.sryscale = [] self.refimagenum = [] def estimate_xshifts(self,whichimage=None): if not whichimage: ind = 0 # first image in list_of_images is the reference image else: ind = whichimage origx = self.list_of_images[ind].xscale origy = self.list_of_images[ind].yscale origim = self.list_of_images[ind].matrix xshifts = [] for image in self.list_of_images: newx = image.xscale newy = image.yscale newim = image.matrix xshifts.append(estimate_xshift(origx,origy,origim,newx,newy,newim)) self.refimagenum = ind self.xshifts = xshifts def estimate_yshifts(self,whichimage=None): if not whichimage: ind = 0 # first image in list_of_images is the reference image else: ind = whichimage origx = self.list_of_images[ind].xscale origy = self.list_of_images[ind].yscale origim = self.list_of_images[ind].matrix yshifts = [] for image in self.list_of_images: newx = image.xscale newy = image.yscale newim = image.matrix yshifts.append(estimate_yshift(origx,origy,origim,newx,newy,newim)) self.refimagenum = ind self.yshifts = yshifts def estimate_shifts(self,whichimage=None): if not whichimage: ind = 0 # first image in list_of_images is the reference image else: ind = whichimage origx = self.list_of_images[ind].xscale origy = self.list_of_images[ind].yscale origim = self.list_of_images[ind].matrix shifts = [] for image in self.list_of_images: newx = image.xscale newy = image.yscale newim = image.matrix shifts.append(estimate_shift(origx,origy,origim,newx,newy,newim)) self.refimagenum = ind self.shifts = shifts def interpolate_xshift_images(self,scaling,whichimages=None): if not whichimages: inds = list(range(len(self.list_of_images))) elif not isinstance(whichimages,list): inds = [] inds.append(whichimages) else: inds = whichimages newim = np.zeros((len(inds),np.shape(self.list_of_images[inds[0]].matrix)[0]*scaling,np.shape(self.list_of_images[inds[0]].matrix)[1])) newx = np.linspace(self.list_of_images[inds[0]].xscale[0]-self.xshifts[inds[0]],self.list_of_images[inds[0]].xscale[-1]-self.xshifts[inds[0]],len(self.list_of_images[inds[0]].xscale)*scaling) newy = self.list_of_images[inds[0]].yscale for n in range(len(inds)): print( self.xshifts[inds[n]]) oldim = self.list_of_images[inds[n]].matrix oldx = self.list_of_images[inds[n]].xscale-self.xshifts[inds[n]] oldy = self.list_of_images[inds[n]].yscale newim[n,:,:] = interpolate_image(oldx,oldy,oldim,newx,newy) self.srimage = np.sum(newim,axis=0) self.srxscale = newx self.sryscale = newy def interpolate_yshift_images(self,scaling,whichimages=None): if not whichimages: inds = list(range(len(self.list_of_images))) elif not isinstance(whichimages,list): inds = [] inds.append(whichimages) else: inds = whichimages newim = np.zeros((len(inds),np.shape(self.list_of_images[inds[0]].matrix)[0]*scaling,np.shape(self.list_of_images[inds[0]].matrix)[1])) newx = self.list_of_images[0].xscale newy = np.linspace(self.list_of_images[inds[0]].yscale[0]-self.yshifts[inds[0]],self.list_of_images[inds[0]].yscale[-1]-self.yshifts[inds[0]],len(self.list_of_images[inds[0]].yscale)*scaling) for n in range(len(inds)): oldim = self.list_of_images[inds[n]].matrix oldx = self.list_of_images[inds[n]].xscale oldy = self.list_of_images[inds[n]].yscale-self.yshifts[inds[n]] newim[n,:,:] = interpolate_image(oldx,oldy,oldim,newx,newy) self.srimage = np.sum(newim,axis=0) self.srxscale = newx self.sryscale = newy def interpolate_shift_images(self,scaling,whichimages=None): if not whichimages: inds = list(range(len(self.list_of_images))) elif not isinstance(whichimages,list): inds = [] inds.append(whichimages) else: inds = whichimages if len(scaling)<2: scaling = [scaling, scaling] print( inds, self.list_of_images[inds[0]].xscale[0], self.shifts[inds[0]], self.list_of_images[inds[0]].xscale[-1]) newim = np.zeros((len(self.list_of_images),np.shape(self.list_of_images[inds[0]].matrix)[0]*scaling[0],np.shape(self.list_of_images[inds[0]].matrix)[1]*scaling[1])) newx = np.linspace(self.list_of_images[inds[0]].xscale[0]-self.shifts[inds[0]][0],self.list_of_images[inds[0]].xscale[-1]-self.shifts[inds[0]][0],len(self.list_of_images[inds[0]].xscale)*scaling[0]) newy = np.linspace(self.list_of_images[inds[0]].yscale[0]-self.shifts[inds[0]][1],self.list_of_images[inds[0]].yscale[-1]-self.shifts[inds[0]][1],len(self.list_of_images[inds[0]].yscale)*scaling[1]) for n in range(len(inds)): oldim = self.list_of_images[inds[n]].matrix oldx = self.list_of_images[inds[n]].xscale-self.shifts[inds[n]][0] oldy = self.list_of_images[inds[n]].yscale-self.shifts[inds[n]][1] newim[n,:,:] = interpolate_image(oldx,oldy,oldim,newx,newy) self.srimage = np.sum(newim,axis=0) self.srxscale = newx self.sryscale = newy def plotSR(self): X,Y = pylab.meshgrid(self.srxscale,self.sryscale) pylab.pcolor(X,Y,np.transpose(self.srimage)) pylab.show(block=False) def plotLR(self,whichimage): if not isinstance(whichimage,list): inds = [] inds.append(whichimage) else: inds = list(whichimage) for ind in inds: X,Y = pylab.meshgrid(self.list_of_images[ind].xscale,self.list_of_images[ind].yscale) pylab.figure(ind) pylab.pcolor(X,Y,np.transpose(self.list_of_images[ind].matrix)) pylab.show(block=False) def save(self): pass def load(self): pass def loadkimberlite(self,matfilename): data_dict = io.loadmat(matfilename) sorted_keys = sorted(data_dict.keys() , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) sy = data_dict['sy'][0] allsx = [] for key in sorted_keys[3:12]: allsx.append(data_dict[key][0]) allmats = [] for key in sorted_keys[13:]: allmats.append(data_dict[key]) alllengths = [] for sx in allsx: alllengths.append(len(sx)) ind = np.where(alllengths == np.max(alllengths))[0][0] # spline everything onto longest sx-scale for n in range(len(allmats)): print( np.shape(allsx[n]), np.shape(sy), np.shape(allmats[n])) ip = RectBivariateSpline(allsx[n],sy,allmats[n]) allmats[n] = ip(allsx[ind],sy) allsx[n] = allsx[ind] allimages = [] for n in range(len(allmats)): allimages.append(image(allmats[n],allsx[n],sy)) self.list_of_images = allimages def loadhe3070(self,matfilename): data_dict = io.loadmat(matfilename) sy = data_dict['det'][0][0]['sy'][0][0][0] allsx = [] allmats = [] for n in range(9): allsx.append(np.reshape(data_dict['det'][0][n]['sx'][0][0]-data_dict['det'][0][n]['sx'][0][0][0],len(data_dict['det'][0][n]['sx'][0][0],))) allmats.append(data_dict['det'][0][n]['img'][0][0]) alllengths = [] for sx in allsx: alllengths.append(len(sx)) ind = np.where(alllengths == np.max(alllengths))[0][0] for n in range(len(allmats)): print( np.shape(allsx[n]), np.shape(sy), np.shape(np.transpose(allmats[n]))) ip = RectBivariateSpline(allsx[n],sy,np.transpose(allmats[n])) allmats[n] = ip(allsx[ind],sy) allsx[n] = allsx[ind] allimages = [] for n in range(len(allmats)): allimages.append(image(allmats[n],allsx[n],sy)) self.list_of_images = allimages class image: """ Container class to hold info of a single LR-image to be put togther in a SR-image by the imageset class """ def __init__(self,matrix,xscale,yscale): self.name = [] self.matrix = matrix self.xscale = xscale self.yscale = yscale self.tth = [] def plotimage(self): pass def shiftx(self): pass def shifty(self): pass def save(self): pass def load(self): pass def interpolate_image(oldx,oldy,oldIM,newx,newy): """ 2d interpolation """ interp = RectBivariateSpline(oldx,oldy,oldIM) return interp(newx,newy) def estimate_xshift(x1,y1,im1,x2,y2,im2): """ estimate shift in x-direction only by stepwise shifting im2 by precision and thus minimising the sum of the difference between im1 and im2 """ funct = lambda a: np.sum((interpolate_image(x1,y1,im1,x1,y1) - interpolate_image(x2,y2,im2,x2+a,y2))**2.0) res = leastsq(funct,0.0) return res[0] def estimate_yshift(x1,y1,im1,x2,y2,im2): """ estimate shift in x-direction only by stepwise shifting im2 by precision and thus minimising the sum of the difference between im1 and im2 """ funct = lambda a: np.sum((interpolate_image(x1,y1,im1,x1,y1) - interpolate_image(x2,y2,im2,x2,y2+a))**2.0) res = leastsq(funct,0.0) return res[0] def estimate_shift(x1,y1,im1,x2,y2,im2): """ estimate shift in x-direction only by stepwise shifting im2 by precision and thus minimising the sum of the difference between im1 and im2 """ funct = lambda a: np.sum((interpolate_image(x1,y1,im1,x1,y1) - interpolate_image(x2,y2,im2,x2+a[0],y2+a[1]))**2.0) res = fmin(funct,[0.0,0.0],disp=0) return res class LRimage: """ container class to hold info of a single LR-image to be put togther in a SR-image by the imageset class """ def __init__(self,matrix,xscale,yscale, cornerpos = [0,0], monitor = 1.0): self.name = [] self.matrix = matrix self.xscale = xscale self.yscale = yscale self.tth = [] self.cornerpos = cornerpos self.monitor = monitor def plotimage(self): pass def shiftx(self): pass def shifty(self): pass def save(self): pass def load(self): pass xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_lerixs.py000066400000000000000000000634551412732462000225000ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import os, sys, re, time, h5py, glob import numpy as np from XRStools import xrs_utilities, xrs_scans from dateutil.parser import parse as dateparse TINY = 1.e-7 MAX_FILESIZE = 100*1024*1024 # 100 Mb limit COMMENTCHARS = '#;%*!$' NAME_MATCH = re.compile(r"[a-zA-Z_][a-zA-Z0-9_]*(\.[a-zA-Z_][a-zA-Z0-9_]*)*$").match VALID_SNAME_CHARS = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_' VALID_NAME_CHARS = '.%s' % VALID_SNAME_CHARS VALID_CHARS1 = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ_' BAD_FILECHARS = ';~,`!%$@$&^?*#:"/|\'\\\t\r\n (){}[]<>' GOOD_FILECHARS = '_'*len(BAD_FILECHARS) RESERVED_WORDS = ('and', 'as', 'assert', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'eval', 'except', 'exec', 'execfile', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'not', 'or', 'pass', 'print', 'raise', 'return', 'try', 'while', 'with', 'group', 'end', 'endwhile', 'endif', 'endfor', 'endtry', 'enddef', 'True', 'False', 'None') ################################################################################ # Functions to get the header attributes ################################################################################ class Lerix: def __init__(self): self.scans = {} self.keys = {} self.elastic_scans = [] self.elastic_name = 'elastic' self.nixs_scans = [] self.nixs_name = 'nixs' self.wide_scans = [] self.wide_name = 'wide' self.scan_name = [] self.eloss_avg = [] #elastic eloss average self.signals_avg = [] #elastic signals average used to plot analyzer resolutions at the end self.energy = [] self.signals = [] self.errors = [] #self.groups = {} self.tth = [] self.resolution = {} self.E0 = [] self.cenom = [] self.cenom_dict = {} def isValidName(self,filename): "input is a valid name" if filename in RESERVED_WORDS: return False tnam = filename[:].lower() return NAME_MATCH(tnam) is not None def fixName(self,filename, allow_dot=True): "try to fix string to be a valid name" if self.isValidName(filename): return filename if self.isValidName('_%s' % filename): return '_%s' % filename chars = [] valid_chars = VALID_SNAME_CHARS if allow_dot: valid_chars = VALID_NAME_CHARS for s in filename: if s not in valid_chars: s = '_' chars.append(s) filename = ''.join(chars) # last check (name may begin with a number or .) if not self.isValidName(filename): filename = '_%s' % filename return filename def getfloats(self,txt, allow_times=True): words = [w.strip() for w in txt.replace(',', ' ').split()] mktime = time.mktime for i, w in enumerate(words): val = None try: val = float(w) except ValueError: try: val = mktime(dateparse(w).timetuple()) except ValueError: pass words[i] = val return words def colname(self,txt): return fixName(txt.strip().lower()).replace('.', '_') def attributes(self,fn): fh = open(fn, 'r') text = fh.read() text = text.replace('\r\n', '\n').replace('\r', '\n').split('\n') _labelline = None ncol = None data, footers, headers = [], [], [] text.reverse() section = 'FOOTER' for line in text: line = line.strip() if len(line) < 1: continue # look for section transitions (going from bottom to top) if section == 'FOOTER' and not None in self.getfloats(line): section = 'DATA' elif section == 'DATA' and None in self.getfloats(line): section = 'HEADER' _labelline = line if _labelline[0] in COMMENTCHARS: _labelline = _labelline[1:].strip() # act of current section: if section == 'FOOTER': footers.append(line) elif section == 'HEADER': headers.append(line) elif section == 'DATA': rowdat = self.getfloats(line) if ncol is None: ncol = len(rowdat) if ncol == len(rowdat): data.append(rowdat) # reverse header, footer, data, convert to array footers.reverse(headers.reverse(data.reverse(data = np.array(data).transpose() # try to parse attributes from header text header_attrs = {} for hline in headers: hline = hline.strip().replace('\t', ' ') if len(hline) < 1: continue if hline[0] in COMMENTCHARS: hline = hline[1:].strip() keywds = [] if ':' in hline: # keywords in 'x: 22' words = hline.split(':', 1) keywds = words[0].split() elif '=' in hline: # keywords in 'x = 22' words = hline.split('=', 1) keywds = words[0].split() if len(keywds) == 1: key = self.colname(keywds[0]) if key.startswith('_'): key = key[1:] if len(words) > 1: header_attrs[key] = words[1].strip() return header_attrs ################################################################################ # Get Scan Info - returns the number, name, type and f.ext of the scan ################################################################################ def scan_info(self, file): """get the scan number, name, type and file extention from the title of the scan assuming typical format e.g. elastic.0001, nixs.0001""" fn,fext = os.path.splitext(file) if str.lower(fn)==str.lower(self.nixs_name): scan_type = 'nixs' elif str.lower(fn)==str.lower(self.elastic_name): scan_type = 'elastic' elif str.lower(fn)==str.lower(self.wide_name): scan_type = 'wide' scan_number = fext.lstrip('.') scan_number = int(scan_number) scan_name = scan_type + '%04d' %scan_number return scan_number, scan_name, scan_type, fext, file def sort_dir(self, dir): """Returns a list of directory contents after filtering out scans without the correct format or size e.g. 'elastic.0001, nixs.0001 '""" dir_scans = [] for file in os.listdir(dir): file_lc = str.lower(file) fn,fext = os.path.splitext(file_lc) if not file_lc.startswith('.'): if fext.lstrip('.').isdigit(): if not os.stat(dir + '/' + file).st_size > 8000: print("{} {}".format(">> >> Warning!! skipped empty scan (<8KB): ", file)) continue elif not os.stat(dir + '/' + file).st_size < MAX_FILESIZE: print("{} {}".format(">> >> Warning!! skipped huge scan (>100MB): ", file)) continue else: if fn==self.nixs_name: dir_scans.append(file) elif fn==self.elastic_name: dir_scans.append(file) elif fn==self.wide_name: dir_scans.append(file) sorted_dir = sorted(dir_scans, key=lambda x: os.path.splitext(x)[1]) return sorted_dir def isValidDir(self,dir): """Show that the scan directory is valid, that the directory holds a scan with the correct elastic name, nixs name and then let the user know if it has not found a wide scan. Returns True if valid directory.""" if not os.path.isdir(dir): print('Check the directory you have supplied') return False elif not os.path.isfile(dir+'/'+self.elastic_name+'.0001'): print("The directory you supplied does not have a elastic.0001 file!!! \n If your elastic scan has a different name, please specify as: 'elastic_name'") return False elif not os.path.isfile(dir+'/'+self.nixs_name+'.0001'): print("The directory you supplied does not have a NIXS.0001 file!!! \n If your raman scan has a different name, please specify as: 'NIXS_name'") return False elif not os.path.isfile(dir+'/'+self.wide_name+'.0001'): print("No wide scans found. Continuing...") return True else: return True def plot_data(self,analyzer=False): """.plot_data() Function that can be called to plot the eloss data for each channel and build an average by clicking a button. Requires matplotlib >2.1""" import matplotlib.pyplot as plt from matplotlib.widgets import CheckButtons, Cursor channels = [] for analyzer in self.resolution: if analyzer.startswith('Analyzer'): if self.resolution[analyzer] < 1.0: channels.append(int(analyzer.lstrip('Analyzer'))-1) data = np.average(self.signals[:,channels],axis=1) fig, ax = plt.subplots() ax.plot(self.eloss, data, lw=2) ax.set_xlabel('Energy Loss (eV)') ax.set_title('Plotting Raman Analysers') plt.subplots_adjust(left=0.3) rax = plt.axes([0.02, 0.1, 0.2, 0.8]) check = CheckButtons(rax, ('Analyzer1', 'Analyzer2', 'Analyzer3','Analyzer4','Analyzer5', 'Analyzer6','Analyzer7','Analyzer8','Analyzer9','Analyzer10','Analyzer11','Analyzer12' ,'Analyzer13','Analyzer14','Analyzer15','Analyzer16','Analyzer17','Analyzer18','Analyzer19'), (False, False, False, False, False, False, False, False, False, False, False, False, False ,False,False,False,False,False,False)) def func(label): is_checked = [] on = check.get_status() for ii in range(19): if on[ii]: is_checked.append(ii) #errors.append(np.average(noodle.errors[:,ii],axis=0)) data = np.average(self.signals[:,is_checked],axis=1) ax.clear() ax.plot(self.eloss, data, lw=2) ax.autoscale(True) ax.set_xlabel('Energy Loss (eV)') ax.set_title('Plotting Raman Analysers') plt.draw() cursor = Cursor(ax, useblit=False, color='red', linewidth=2) plt.show() #print("{} {}".format("Average Error: ", )) check.on_clicked(func) plt.show() def write_H5scanData(self,dir,H5file,averaged='False'): """Writes all the scan information into a H5 file named after the sample name. inside this H5 directory scans are split into elastic and NIXS and the averaged scans. No support yet for wide scans""" sample_name = os.path.basename(dir) g = H5file.create_group(sample_name) #H5 subgroup with the name of the sample H5_ela = g.create_group('elastic') #H5 subgroup for elastics H5_xrs = g.create_group('XRS') #H5 subgroup for NIXS all_scans = self.elastic_scans+self.nixs_scans for file in all_scans: scan_info = self.scan_info(file) if scan_info[2] == 'elastic': h5group = H5_ela.create_group(scan_info[1]) h5group.create_dataset("energy",data=self.scans[scan_info[1]].energy) h5group.create_dataset("signals",data=self.scans[scan_info[1]].signals) h5group.create_dataset("errors",data=self.scans[scan_info[1]].errors) h5group.create_dataset("cenoms",data=self.scans[scan_info[1]].cenom) elif scan_info[2]=='nixs': h5group = H5_xrs.create_group(scan_info[1]) h5group.create_dataset("energy",data=self.scans[scan_info[1]].energy) h5group.create_dataset("signals",data=self.scans[scan_info[1]].signals) h5group.create_dataset("eloss",data=self.scans[scan_info[1]].eloss) h5group.create_dataset("errors",data=self.scans[scan_info[1]].errors) h5group.create_dataset("tth",data=self.scans[scan_info[1]].tth) g.create_dataset("energy",data=self.energy) g.create_dataset("signals",data=self.signals) g.create_dataset("eloss",data=self.eloss) g.create_dataset("errors",data=self.errors) g.create_dataset("tth",data=self.tth) g.create_dataset("Mean Resolutions", data=np.array(self.resolution.items())) #Never forget to close an open H5 file!!! H5file.close() ################################################################################ # Read Scan ################################################################################ def get_cenoms(self, scan_info): """Internal Function to get the centre of mass of the elastic peak and the E0 for each elastic scan using XRStools""" cenom_list = [] for analyzer in range(19): #The analyzer channels in the scan ASCII self.scans[scan_info[1]].cenom.append(xrs_utilities.find_center_of_mass(self.scans[scan_info[1]].energy,self.scans[scan_info[1]].signals[:,analyzer])) cenom_list.append(self.scans[scan_info[1]].cenom) self.cenom = [sum(a)/len(a) for a in zip(*cenom_list)] self.E0 = np.mean(self.cenom)/1e3 def get_resolutions(self,scan_numbers): """Internal function to get the average resolution of each analyzer and average to give a self.resolution over the 19 analyzers. Returns a Dictionary of resolutions the mean and each analyser""" eloss_running_elastic = [] signals_running_elastic = [] if scan_numbers=='all': chosen_scans = [] for number in range(len(self.elastic_scans)): scan_info = self.scan_info(self.elastic_scans[number]) chosen_scans.append(scan_info[4]) elif type(scan_numbers) is list: scan_numbers[:] = [x - 1 for x in scan_numbers] #scan 1 will be the 0th item in the list chosen_scans = [] for number in scan_numbers: scan_info = self.scan_info(self.elastic_scans[number]) chosen_scans.append(scan_info[4]) else: print('scan numbers must be a list of the scans with correct length') return #populate lists with eloss and signals and then find the average over the whole range for file in chosen_scans: scan_info = self.scan_info(file) eloss_running_elastic.append(self.scans[scan_info[1]].eloss) signals_running_elastic.append(self.scans[scan_info[1]].signals) self.eloss_avg = np.array([sum(a)/len(a) for a in zip(*eloss_running_elastic)]) self.signals_avg = np.array([sum(a)/len(a) for a in zip(*signals_running_elastic)]) #take these average values and find the average FWHM for each analyzer and then find the total average for file in chosen_scans: resolution = [] skipped = [] for analyzer in range(19): try: resolution.append(xrs_utilities.fwhm(self.eloss_avg, self.signals_avg[:,analyzer])[0]) self.resolution['Analyzer%s'%analyzer] = resolution[analyzer] except: skipped.append(analyzer+1) continue if len(skipped) > 1: print("{} {}".format("Skipped resolution for analyzer/s: ", list(set(skipped)))) self.resolution['Resolution'] = round(np.mean(resolution),3) def read_scans(self,dir,file,valid_elastic='True'): """Internal Function that reads the APS data using numpy and finds the cenoms for each elastic scan ready to be passed to read_nixs to get eloss""" scan_info = self.scan_info(file) analyzers = [range(19)] try: scan_data = np.loadtxt(dir+'/'+file, comments='#') except: print("{} {}".format("NumPy failed to load scan name: ", file)) pass self.scans[scan_info[1]].energy = np.array(scan_data[:,0]) #this format for np.repeat to work self.scans[scan_info[1]].signals = np.array(scan_data[:,5:24]) self.scans[scan_info[1]].errors = np.array(np.sqrt(np.absolute(self.scans[scan_info[1]].signals))) if scan_info[2]=='elastic': self.get_cenoms(scan_info) for analyzer in range(19): self.scans[scan_info[1]].eloss = np.subtract(self.scans[scan_info[1]].energy,self.scans[scan_info[1]].cenom[analyzer]) elif scan_info[2]=='nixs' or scan_info[2]=='wide': #create empty array with shape energy.v.signals eloss = np.zeros(self.scans[scan_info[1]].signals.shape) self.scans[scan_info[1]].tth = list(range(9,180,9)) #assign tth to each scan self.tth = list(range(9,180,9)) #assign tth to self if valid_elastic=='True': for analyzer in range(19): self.scans[scan_info[1]].eloss = np.subtract(self.scans[scan_info[1]].energy,self.scans['elastic%04d'%scan_info[0]].cenom[analyzer]) elif valid_elastic=='False': for analyzer in range(19): self.scans[scan_info[1]].eloss = np.subtract(self.scans[scan_info[1]].energy,self.cenom[analyzer]) else: print('valid_elastic is a boolean') sys.exit() def average_scans(self,scan_numbers='all'): """Function to calculate the average eloss, energy, signals and errors over all the read scans (default) or over a list of scans e.g. [1,3,5]""" energy_running = [] signals_running = [] eloss_running = [] errors_running = [] if scan_numbers=='all': for file in self.nixs_scans: scan_info = self.scan_info(file) energy_running.append(self.scans[scan_info[1]].energy) signals_running.append(self.scans[scan_info[1]].signals) eloss_running.append(self.scans[scan_info[1]].eloss) errors_running.append(self.scans[scan_info[1]].errors) self.energy = np.array([sum(a)/len(a) for a in zip(*energy_running)]) self.signals = np.array([sum(a)/len(a) for a in zip(*signals_running)]) self.eloss = np.array([sum(a)/len(a) for a in zip(*eloss_running)]) self.errors = np.array([sum(a)/len(a) for a in zip(*errors_running)]) elif type(scan_numbers) is list: scan_numbers[:] = [x - 1 for x in scan_numbers] #scan 1 will be the 0th item in the list chosen_scans = [] for number in scan_numbers: scan_info = self.scan_info(self.nixs_scans[number]) chosen_scans.append(scan_info[1]) print("{} {}".format("Averaging scan numbers: ", chosen_scans)) for scan in chosen_scans: energy_running.append(self.scans[scan_info[1]].energy) signals_running.append(self.scans[scan_info[1]].signals) eloss_running.append(self.scans[scan_info[1]].eloss) errors_running.append(self.scans[scan_info[1]].errors) self.energy = np.array([sum(a)/len(a) for a in zip(*energy_running)]) self.signals = np.array([sum(a)/len(a) for a in zip(*signals_running)]) self.eloss = np.array([sum(a)/len(a) for a in zip(*eloss_running)]) self.errors = np.array([sum(a)/len(a) for a in zip(*errors_running)]) else: print("scan_numbers must be blank, 'all' or a list of scan numbers e.g.[1,3,5]") sys.exit() ################################################################################ # Begin the reading ################################################################################ def load_scan(self,dir,nixs_name='NIXS',wide_name='wide',elastic_name='elastic',scan_numbers='all',H5=False,H5path=None): """Function to load scan data from a typical APS 20ID Non-Resonant inelastic X-ray scattering experiment. With data in the form of elastic.0001, allign.0001 and NIXS.0001. Function reteurns the averaged energy loss, signals, errors, E0 and 2theta angles for the scans in the chosen directory.""" self.nixs_name = str.lower(nixs_name) self.wide_name = str.lower(wide_name) self.elastic_name = str.lower(elastic_name) #check dir location if not self.isValidDir(dir): print('IO Error - sorry about that!') sys.exit() else: pass #sort the directory so that scans are in order, determine number of scans #open list to be filled with the elastic/nixs scan names sorted_dir = self.sort_dir(dir) #returns all the scan names number_of_scans = len(glob.glob(dir+'/'+self.nixs_name+'*'))-1 #number of nixs scans self.elastic_scans = [] self.nixs_scans = [] self.wide_scans = [] #self.keys = {"eloss":np.array, "energy":np.array, "signals":np.array, "errors":np.array,"E0":np.float, "tth":np.array} #,"resolution":array } #split scans into NIXS and elastic and begin instance of XRStools scan class for each scan for file in sorted_dir: scan_info = self.scan_info(file) scan = xrs_scans.Scan() if scan_info[2]=='elastic': self.elastic_scans.append(file) self.scans[scan_info[1]] = scan #self.scans {} in class _init_ self.scans[scan_info[1]].scan_type = scan_info[2] self.scans[scan_info[1]].scan_number = scan_info[0] if scan_info[2]=='nixs': self.nixs_scans.append(file) self.scans[scan_info[1]] = scan #self.scans {} in class _init_ self.scans[scan_info[1]].scan_type = scan_info[2] self.scans[scan_info[1]].scan_number = scan_info[0] if scan_info[2]=='wide': self.wide_scans.append(file) self.scans[scan_info[1]] = scan #self.scans {} in class _init_ self.scans[scan_info[1]].scan_type = scan_info[2] self.scans[scan_info[1]].scan_number = scan_info[0] else: continue #read elastic scans first to calculate cenom # reading all the elastics in the file to improve E0 accuracy and get a # good grasp on the scan resolution for file in self.elastic_scans: scan_info = self.scan_info(file) print("{} {}".format("Reading elastic scan named: ", file)) self.read_scans(dir,file) print('I always read all the Elastic scans to improve Resolution and E0 Accuracy\n >> Type .resolution to see the analyzer resolutions.') #Read NIXS scans - if there isn't a corresponing elastic scan, subtract the #running average cenoms and tell the user. for file in self.nixs_scans: scan_info = self.scan_info(file) corresponding_elastic = dir+'/'+self.elastic_name+scan_info[3] if os.path.isfile(corresponding_elastic): print("{} {}".format("Reading NIXS scan name: ", file)) self.read_scans(dir,file) elif not os.path.isfile(corresponding_elastic): print("{} {} {}".format(">> >> WARNING:", scan_info[1],"has no corresponding elastic - finding eloss by average elastic values!")) print("{} {}".format("Reading NIXS scan named: ", file)) self.read_scans(dir,file,valid_elastic='False') for file in self.wide_scans: scan_info = self.scan_info(file) print("{} {}".format("Reading wide scan named: ", file)) self.read_scans(dir,file) #call function to calculate the average values over the scans - all by default self.average_scans(scan_numbers) self.get_resolutions(scan_numbers) #if the user asks, call function to write all info to H5 file if H5: if H5path==None: H5path = dir elif H5path: if os.path.isdir(H5path): H5path = H5path else: print('H5 path directory does not exist!') H5name = '20ID_APS_data.H5' saveloc = H5path+'/'+H5name if os.path.isfile(saveloc): H5file = h5py.File(saveloc, "a") else: H5file = h5py.File(saveloc, "w") self.write_H5scanData(dir,H5file) print("{} {}".format("Wrote scan data to H5 file: ", saveloc)) #let the user know the program has finished print('Finished Reading!') """To do: 1) Check if the cenoms are close to the E0 specified in the ASCII header, and if not, do not average that scan_name 2) Make the H5 file location more interactive and allow many different samples to be read into the H5file - e.g. check if it exists and if so write into it. DONE 3) read wide scans - Proving hard, can't do this until I've fixed the header attrbs reading, to pull out the NIXS scan region 4) Header reading and H5 attributes 5) Maybe a file size check to make sure the input file isn't crazy - DONE 6) If file size is less than 5KB, then ignore it from the list - DONE """ xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_prediction.py000066400000000000000000001223311412732462000233170ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range from six.moves import zip #!/usr/bin/python # Filename: theory2.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" from . import xrs_utilities import numpy as np import math import shelve import os import pylab import h5py from scipy import interpolate, signal, integrate, constants, optimize from re import findall # from pylab import * import pylab from optparse import OptionParser __metaclass__ = type # new style classes installation_dir = os.path.dirname(os.path.abspath(__file__)) def cla(): pass class detector: """ Class to describe detector related things. All default values are meant for the ESRF MAXIPIX detector. """ def __init__(self, energy=9.68, thickness=500, material='Si', pixel_size=[256,768]): self.energy = np.array(energy) # analyzer energy [keV] self.thickness = np.array(thickness) # thickness of the active material [microns] self.material = material # detector active material self.efficiency = [] def set_energy(self,energy): self.energy = energy def get_energy(self): if np.any(self.energy): return self.energy else: print( 'No energy set, please set an enegy first!') return def set_thickness(self,thickness): self.thickness = thickness def get_thickness(self): if np.any(self.thickness): return self.thickness else: print( 'No thickness set, please set a thickness first!') def set_material(self,material): if isinstance(material,str): self.material = material.upper() else: print( 'material must be passed as a string. Default is \'Si\'!') def get_material(self): if any(self.material): return self.material else: print( 'No material set, please set the material first!') def set_size(self,size): if np.shape(np.array(size)) == (2,): self.size = np.array(size) else: print( 'size must be passed as a 2x0 numpy array or list of two entries.') def get_size(self): if np.any(self.size): return self.size else: print( 'No size has been set') def get_efficiency(self,energy=None): """ calculates the detector efficiency at the given energy (simply given by the absorption of the detector active material). """ if not energy: energy = self.energy thickness = self.thickness*1e-4 # conversion to cm (needed for mpr routine) material = self.material murho,rhov,mv = xrs_utilities.mpr(energy,material) return 1.0 - np.exp(-thickness*murho) class analyzer: """ Class to describe things related to the analyzer crystal used. Default values are for a Si(660) crystal. """ def __init__(self,material='Si', hkl=[6,6,0], mask_d=60.0, bend_r=1.0, energy_resolution = 0.5, diced=False, thickness=500.0, database_dir=installation_dir): self.material = material # analyzer material self.hkl = np.array(hkl) # [hkl] indices of reflection used (shape (3,) numpy array) self.mask_d = mask_d # analyzer mask diameter in [mm] self.bend_r = bend_r # bending radius of the crystal [mm] self.diced = diced # boolean (True or False) if a diced crystal is used or not (defalt is False) self.thickness = thickness # thickness of the analyzer crystal self.energy_resolution = energy_resolution # energy resolution [eV] self.energy_of_refl_calculation = None # energy(dspace(hkl,material)) !!! check this again !!! may be obsolete or misleading self.database_dir = database_dir # path to a folder, where once calculated reflectivities are stored to spead up second time usage # output self.solid_angle = [] # solid angle of the analyzers self.efficiency = [] # factor, to be calculated self.reflectivity = [] # analyzer reflectivity (to be calculated) self.deviation_meV = [] # x-axis for reflectivity [meV] self.deviation_arcsec = [] # x-axis for reflectivity [arc seconds] def set_material(self,material): if isinstance(material,str): self.material = material.upper() else: print( 'material must be passed as a string. Default is \'Si\'!') def get_material(self): if self.material: return self.material else: print( 'No material set, please set the material first!') def set_hkl(self,hkl): if np.shape(np.array(hkl)) == (3,): self.hkl = np.array(hkl) def get_hkl(self): if np.any(self.hkl): return self.hkl else: print( 'No hkl set, please set a hkl first!') def set_mask_d(self,mask_d): if isinstance(mask_d,int) or isinstance(mask_d,float): self.mask_d = np.array(mask_d) else: print( 'mask_d (analyzer mask diameter in mm) must be passed as either integer or float!') def get_mask_d(self): if np.any(self.mask_d): return self.mask_d else: print( 'No mask_d has been set') def set_bend_r(self,bend_r): if isinstance(bend_r,int) or isinstance(bend_r,float): self.bend_r = np.array(bend_r) else: print( 'bend_r (analyzer bending radius in m) must be passed as integer or float!') def get_bend_r(self): if np.any(self.bend_r): return self.bend_r else: print( 'No bend_r has been set') def set_diced(self,diced): if diced == True or diced == False: self.diced = diced else: print( 'diced must be either True or False') def get_diced(self): if self.diced: return self.diced else: print( 'diced has not been set') def set_thickness(self,thickness): self.thickness = thickness def get_thickness(self): if np.any(self.thickness): return self.thickness else: print( 'No thickness set, please set a thickness first!') return def get_energy_resolution(self): return self.energy_resolution def get_energy_resolution_keV(self): return self.energy_resolution*1.0e-3 def get_solid_angle(self): det_area = 2.0*np.pi*(self.mask_d/2)**2.0 sample_det_distance = self.bend_r*1.0e3/2 return det_area/(4.0*np.pi*sample_det_distance**2.0) def get_reflectivity(self,energy,dev = np.arange(-50.0,150.0,1.0), alpha = 0.0): """ Calculates the reflectivity curve for a given analyzer crystal. Checks in the directory self.database_dir, if desired reflectivity curve has been calculated before. IN: energy = energy at which the reflectivity is to be calculated in [keV] dev = deviation parameter for which the curve is to be calculated alpha = deviation angle from exact Bragg angle [deg] """ hkl = self.get_hkl() material = self.get_material() bend_r = self.get_bend_r() # print energy, hkl, material, bend_r, type(dev), type(alpha) try: raise # try opening reflectivity from file filename = self.database_dir + material + '_' + 'hkl' + str(hkl) + '_'+ str(energy) + 'keV' + '.dat' database = shelve.open(filename) self.reflectivity = database['reflectivity'] self.deviation_meV = database['deviation_meV'] self.deviation_arcsec = database['deviation_arcsec'] self.energy_of_refl_calculation = database['energy_of_refl_calculation'] except: # if no file exists, calculate reflectivity from scratch #print ('>>>>>>>>>>>>>>>', energy, hkl, material, bend_r, dev, alpha) reflectivity, e_scale, dev, e0 = xrs_utilities.taupgen(energy,hkl,material, bend_r,dev,alpha) self.reflectivity = reflectivity self.deviation_meV = e_scale self.deviation_arcsec = dev self.energy_of_refl_calculation = e0 # save reflectivity for next time use # filename = self.database_dir + material + '_' + 'hkl' + str(hkl) + '_'+ str(energy) + 'keV' + '.dat' filename = material + '_' + 'hkl' + str(hkl) + '_'+ str(energy) + 'keV' + '.dat' # database = shelve.open(filename) # database['reflectivity'] = reflectivity # database['deviation_meV'] = e_scale # database['deviation_arcsec'] = dev # database['energy_of_refl_calculation'] = e0 def plot_reflectivity(self,mode='energy'): """ Generates and opens a plot of the calculated reflectivity curve. mode = keyword for which x-axis is to be used, can be 'energy' or 'angle' """ cla() dev_energy = self.deviation_meV dev_arcsec = self.deviation_arcsec reflectivity = self.reflectivity hkl = self.get_hkl() E0 = self.energy_of_refl_calculation if mode == 'energy': pylab.plot(dev_energy,reflectivity) pylab.xlabel(['deviation from Bragg angle [meV]']) pylab.ylabel(['reflectivity [arb. units]']) titlestring = 'Takagi-Taupin curve for the ' + str(hkl) + ' reflection at %.2f' %E0 + ' keV.' pylab.title(titlestring) pylab.show() elif mode == 'angle': pylab.plot(dev_arcsec,reflectivity) pylab.xlabel(['deviation from Bragg angle [arcsec]']) pylab.ylabel(['reflectivity [arb. units]']) titlestring = 'Takagi-Taupin curve for the ' + str(hkl) + ' reflection at %.2f' %E0 + ' keV.' pylab.title(titlestring) pylab.show() else: print( 'mode unknown, please select either \'energy\' or \'angle\'.') return def get_efficiency(self,energy=None): """ Calculates the efficiency of the analyzer crystal based on the calculated reflectivity curve. The efficiency is calculated by averaging over the energy resolution set upon class initialization. energy = energy (in [keV]) for wich the efficiency is to be calculated """ if not energy: energy = self.energy_of_refl_calculation # print type(energy) energy_resolution = self.energy_resolution * 1.0e3 # resolution in meV if not np.any(self.reflectivity): self.get_reflectivity(energy) dev_energy = self.deviation_meV reflectivity = self.reflectivity # average over the FWHM of the reflectivity curve for an estimate of the efficiency fwhm, x0 = xrs_utilities.fwhm(dev_energy,reflectivity) inds = np.where(np.logical_and(dev_energy>=x0-fwhm/2.0,dev_energy<=x0+fwhm/2.0))[0] self.efficiency = np.mean(reflectivity[inds]) self.energy_resolution_meV = energy_resolution return self.efficiency class sample: """ Class to describe a sample. """ def __init__(self,chem_formulas,concentrations,densities,angle_tth,sample_thickness,angle_in=None,angle_out=None,shape='sphere',molar_masses=None): self.chem_formulas = chem_formulas # list of strings of chemical sum formulas self.concentrations = concentrations # list of concentrations, should contain values between 0.0 and 1.0 self.densities = densities # list of densities of the constituents [g/cm^3] self.molar_masses = molar_masses # list of molar masses of all constituents self.shape = shape # keyword, can be 'slab' or 'sphere' self.tth = angle_tth # scattering angle [deg] self.alpha = angle_in # incident beam angle in [deg] relative to sample surface normal self.beta = angle_out # beam exit angle in [deg] relatice to sample surface normal (negative for transmission geometry) self.thickness = sample_thickness # sample thickness/diameter in [cm] self.energy1 = [] self.energy2 = [] def get_formulas(self): return self.chem_formulas def get_concentrations(self): return self.concentrations def get_densities(self): return self.densities def get_average_densities(self): return np.sum(self.densities)/len(self.densities) def get_shape(self): return self.shape def get_tth(self): return self.tth def get_thickness(self): return self.thickness def get_molar_masses(self): return self.molar_masses def get_energy1(self): return self.energy1 def get_energy2(self): return self.energy2 def get_alpha(self): if self.alpha: return self.alpha else: print( 'alpha has not been set!') def get_beta(self): if self.beta: return self.beta else: print( 'beta has not been set!') def get_murho(self,energy1,energy2=None): """ Calculates the total photoelectric absorption coefficient of the sample for the two energies given. Returns only one array, if only one energy axis is defined. energy1 = numpy array of energies in [keV] energy2 = numpy array of energies in [keV] (defalt is None, i.e. only one mu is returned) """ self.energy1 = energy1 self.energy2 = energy2 energy = energy1 formulas = self.get_formulas() concentrations = self.get_concentrations() E0 = energy2 rho_formu = self.get_densities() if energy2: return xrs_utilities.mpr_compds(energy,formulas,concentrations,E0,rho_formu) # returns mu_in and mu_out else: E0 = energy[-1] mu_tot_in, mu_tot_out = xrs_utilities.mpr_compds(energy,formulas,concentrations,E0,rho_formu) return mu_tot_in # returns only mu_in def get_absorption_correction(self,energy1,energy2,thickness=None): """ Calculates the absorption correction factor for the sample to be multiplied with experimental data to correct for absorption effects. energy1 = numpy array of energies in [keV] for which the factor is to be calculated energy2 = numpy array of energies in [keV] for which the factor is to be calculated """ alpha = self.alpha beta = self.beta tth = self.tth if not thickness: thickness = self.thickness mu_tot_in, mu_tot_out = self.get_murho(energy1,energy2) if isinstance(tth,list): self.shape == 'sphere' # list of tth only for sphere geometry ac = (mu_tot_in + mu_tot_out)/(1.0 - np.exp(-mu_tot_in*thickness -mu_tot_out*thickness)) else: if self.shape == 'slab' and alpha and beta: if tth: if self.beta<0: # transmission geometry test_tth = alpha-beta else: # reflection geometry test_tth = 180.0 - (alpha+beta) if tth == test_tth: pass else: print( 'the alpha and beta values set are not congruent to the tth value set!') absorption_correction_factor = abscorr2(mu_tot_in,mu_tot_out,alpha,beta,thickness) elif self.shape == 'sphere': ac = (mu_tot_in + mu_tot_out)/(1.0 - np.exp(-mu_tot_in*thickness -mu_tot_out*thickness)) #1.0/np.exp(-thickness*mu_tot_in -thickness*mu_tot_out) # spherical sample just add up in and outgoing absorption else: print( 'please provide either shape=\'sphere\' (default) or \'slab\' and alpha and beta!') return ac def plot_inv_absorption(self,energy1,energy2,range_of_thickness = np.arange(0.0,0.5,0.01)): """ Generates a figure which plots 1/Abscorr for the sample as a function of different thicknesses. This is usefull for finding optimum sample thicknesses for an experiment. energy1 = energy in [keV] at the desired edge energy2 = energy in [keV] at the elastic range_of_thickness = numpy array of sample thicknesses in [cm] !!! right now all samples are treates as if spherical !!! """ ac_range = np.zeros_like(range_of_thickness) for ii in range(len(ac_range)): ac_range[ii] = self.get_absorption_correction(energy1,energy2,range_of_thickness[ii]) cla() pylab.plot(range_of_thickness,1/ac_range) pylab.xlabel('sample thickness [cm]') pylab.ylabel('1/(absorption correction factor) [arb. units]') pylab.show() class thomson: """ Class to take care of the Thomson scattering cross section. """ def __init__(self,omega_1,omega_2,tth,scattering_plane='vertical',polarization=0.99): self.omega_1 = omega_1 # numpy array of primary energy in [keV] self.omega_2 = omega_2 # analyzer energy in [keV] self.tth = tth # scattering angle in [deg] self.scattering_plane = scattering_plane # keyword to indicate scattering plane relative to lab frame ('vertical' or 'horizontal') self.polarization = polarization # degree of polarization (close to 1.0 for undulator radiation) self.r0 = constants.physical_constants['classical electron radius'][0]*1e2 # classical electron radius in [cm] def get_thomson_factor(self): """ Calculates the Thomson scattering factor. """ # mutiple tth values in a list if isinstance(self.tth,list): thomson = np.zeros((self.omega_1.shape[0], len(self.tth))) if self.scattering_plane == 'vertical': for ii in range(len(self.tth)): thomson[:,ii] = self.polarization * 1.0 * self.omega_2/self.omega_1 * self.r0**2.0 elif self.scattering_plane == 'horizontal': for ii in range(len(self.tth)): thomson[:,ii] = self.polarization * np.cos(np.radians(self.tth[ii]))**2.0 * self.omega_2/self.omega_1 * self.r0**2.0 else: print( 'the scattering plane can only be \'vertical\' or \'horizontal\'.') return # just one tth value else: tth = [self.tth] thomson = np.zeros((self.omega_1.shape[0], len(tth))) if self.scattering_plane == 'vertical': thomson[:,0] = self.omega_2/self.omega_1 * self.r0**2.0 * self.polarization elif self.scattering_plane == 'horizontal': thomson[:,0] = self.omega_2/self.omega_1 * self.r0**2.0 * self.polarization * np.cos(np.radians(tth[0]))**2.0 else: print( 'the scattering plane can only be \'vertical\' or \'horizontal\'.') return return thomson class beam: """ Class to describe incident beam related things. """ def __init__(self,i0_intensity,beam_height,beam_width,divergence=None): self.i0_intensity = i0_intensity # number of incident photons [1/sec] self.beam_height = beam_height # in micron self.beam_width = beam_width # in micron self.divergence = divergence # in milli-rad def get_i0_intensity(self): return self.i0_intensity def get_beam_height(self): return self.beam_height def get_beam_height_cm(self): return self.beam_height * 1.0e-4 def get_beam_width(self): return self.beam_height def get_beam_width_cm(self): return self.beam_width * 1.0e-4 def get_divergence(self): return self.divergence def get_beam_cross_section_area(self): """ Calculates the beam cross section area. """ return self.beam_height * self.beam_width # in [microns^2] class compton_profiles: """ Class to hold construct HF Compton profiles for an object of the sample class. """ def __init__(self,sample_obj,eloss_range=np.arange(0.0,1000.0,0.1),E0=9.7): self.chem_formulas = sample_obj.get_formulas() self.concentrations = sample_obj.get_concentrations() self.densities = sample_obj.get_densities() self.mean_density = 0.0 if len(self.densities)>1: for ii in range(len(self.densities)): self.mean_density += self.densities[ii]*self.concentrations[ii] else: self.mean_density = self.densities self.E0 = E0 # elastic line energy in [keV] self.eloss_range = eloss_range # desired energy loss range in [eV] self.sample_shape = sample_obj.get_shape() self.tth = sample_obj.get_tth() self.thickness = sample_obj.get_thickness() self.ac_factor = sample_obj.get_absorption_correction(E0+eloss_range,E0) # output if isinstance(self.tth,list): self.J = np.zeros((len(self.eloss_range),len(self.tth))) self.C = np.zeros((len(self.eloss_range),len(self.tth))) self.V = np.zeros((len(self.eloss_range),len(self.tth))) self.q = np.zeros((len(self.eloss_range),len(self.tth))) else: self.J = np.array([]) self.C = np.array([]) self.V = np.array([]) self.q = np.array([]) def get_E0(self): return self.E0 def get_energy_in_keV(self): return self.eloss_range/1e3 + self.E0 def get_tth(self): return self.tth def calc_pure_HF_profiles(self): if isinstance(self.tth,list): for tth,ii in zip(self.tth,list(range(len(self.tth)))): eloss,J,C,V,q = xrs_utilities.makeprofile_compds(self.chem_formulas,concentrations=self.concentrations,E0=self.E0,tth=tth) self.J[:,ii] = np.interp(self.eloss_range,eloss,J) self.C[:,ii] = np.interp(self.eloss_range,eloss,C) self.V[:,ii] = np.interp(self.eloss_range,eloss,V) self.q[:,ii] = np.interp(self.eloss_range,eloss,q) else: eloss,J,C,V,q = xrs_utilities.makeprofile_compds(self.chem_formulas,concentrations=self.concentrations,E0=self.E0,tth=self.tth) self.J = np.interp(self.eloss_range,eloss,J) self.C = np.interp(self.eloss_range,eloss,C) self.V = np.interp(self.eloss_range,eloss,V) self.q = np.interp(self.eloss_range,eloss,q) def calc_HF_profiles(self): if isinstance(self.tth,list): if not np.any(self.J) and not np.any(self.C) and not np.any(self.V) and not np.any(self.q): self.calc_pure_HF_profiles() for tth,ii in zip(self.tth,list(range(len(self.tth)))): self.J[:,ii] = self.J[:,ii]/self.ac_factor*self.mean_density self.C[:,ii] = self.C[:,ii]/self.ac_factor*self.mean_density self.V[:,ii] = self.V[:,ii]/self.ac_factor*self.mean_density else: if not np.any(self.J) and not np.any(self.C) and not np.any(self.V) and not np.any(self.q): self.calc_pure_HF_profiles() # calculate uncorrected profiles self.J = self.J/self.ac_factor*self.mean_density self.C = self.C/self.ac_factor*self.mean_density self.V = self.V/self.ac_factor*self.mean_density def get_HF_profiles(self): if not np.any(self.J) and not np.any(self.C) and not np.any(self.V) and not np.any(self.q): self.calc_HF_profiles() # calculate uncorrected profiles return self.eloss_range, self.J, self.C, self.V, self.q def plot_HF_profile(self): if not np.any(self.J) and not np.any(self.C) and not np.any(self.V) and not np.any(self.q): self.calc_HF_profiles() cla() pylab.plot(self.eloss_range,self.J) pylab.plot(self.eloss_range,self.C) pylab.plot(self.eloss_range,self.V) pylab.xlabel('energy loss [eV]') pylab.ylabel('intensity [1/eV]') pylab.title('sample absorption corrected HF Compton profile') pylab.show() class absolute_cross_section: """ Class to calculate an expected cross section in absolute counts using objects of the 'beam', 'sample', 'analyzer', 'detector', 'thomson', and 'compton_profile' classes. """ def __init__(self,beam_obj, sample_obj, analyzer_obj, detector_obj, thomson_obj, compton_profile_obj): self.eloss,self.J,self.C,self.V,self.q = compton_profile_obj.get_HF_profiles() self.thomson = thomson_obj.get_thomson_factor() self.I0 = beam_obj.get_i0_intensity() self.beam_h = beam_obj.get_beam_width_cm() self.beam_v = beam_obj.get_beam_height_cm() self.sample_tth = sample_obj.get_tth() self.sample_densities = sample_obj.get_densities() self.sample_molar_masses = sample_obj.get_molar_masses() self.sample_formulas = sample_obj.get_formulas() self.sample_thickness = sample_obj.get_thickness() self.sample_concentrations = sample_obj.get_concentrations() self.sample_abs_in = sample_obj.get_murho(compton_profile_obj.get_energy_in_keV()) self.det_efficiency = detector_obj.get_efficiency(compton_profile_obj.get_E0()) self.ana_efficiency = analyzer_obj.get_efficiency(compton_profile_obj.get_E0()) self.ana_solid_angle = analyzer_obj.get_solid_angle() self.ana_energy_resolution = analyzer_obj.get_energy_resolution() self.energy_in_keV = compton_profile_obj.get_energy_in_keV() # output self.absolute_counts = [] def calc_num_scatterers(self): """ Calculates number of scatterers/atoms using beam size, sample thickness, sample densites, sample molar masses (so far does not differentiate between target atoms and random sample atoms) """ sample_volume = self.beam_h * self.beam_v * self.sample_thickness sample_weight = np.zeros(len(self.sample_densities)) sample_amount = np.zeros(len(self.sample_densities)) for ii in range(len(self.sample_densities)): sample_weight[ii] += self.sample_densities[ii] * sample_volume * self.sample_concentrations[ii] sample_amount[ii] += sample_weight[ii] * self.sample_molar_masses[ii] return np.sum(sample_amount)*constants.physical_constants['Avogadro constant'][0] def calc_abs_cross_section(self): num_of_scatterers = self.calc_num_scatterers() if isinstance(self.sample_tth, list): self.absolute_counts = np.zeros_like(self.J) for ii in range(len(self.sample_tth)): self.absolute_counts[:,ii] = self.I0 * self.thomson[:,ii] * self.J[:,ii] * self.ana_solid_angle * self.ana_energy_resolution * num_of_scatterers * self.sample_thickness * self.sample_abs_in * self.ana_efficiency * self.det_efficiency else: self.absolute_counts = self.I0 * self.thomson[:,0] * self.J * self.ana_solid_angle * self.ana_energy_resolution * num_of_scatterers * self.sample_thickness * self.sample_abs_in * self.ana_efficiency * self.det_efficiency def plot_abs_cross_section(self): if not np.any(self.absolute_counts): self.calc_abs_cross_section() cla() pylab.plot(self.eloss,self.absolute_counts) pylab.xlabel('energy loss') pylab.ylabel('absolute counts [1/sec]') pylab.show() def save_txt(self, file_name, header=''): data = np.zeros((len(self.eloss),self.absolute_counts.shape[1]+1)) data[:,0] = self.eloss data[:,1::] = self.absolute_counts np.savetxt(file_name, data, header=header) def save_hdf5( self, fname, group_name="sample1" ): """ **save_hdf5** Save the results in an HDF5 file. Note: HDF5 files are strange for overwriting files. Args: fname (str): Path and filename for the HDF5 file. """ if isinstance(fname, h5py.Group): f=fname else: f = h5py.File(fname, "w") h5_group = f.require_group(group_name) # absolute counts for plotting if( len(self.absolute_counts.shape)==1): self.absolute_counts = np.array([self.absolute_counts] ).T data = np.zeros((len(self.eloss),self.absolute_counts.shape[1]+1)) data[:,0] = self.eloss data[:,1::] = self.absolute_counts h5_group["abs_counts"] = data # # for attr in ['eloss', 'J', 'C', 'V', 'q', 'thomson', 'I0', 'beam_h', 'beam_v', 'sample_tth', # 'sample_densities', 'sample_molar_masses', 'sample_formulas', 'sample_thickness', # 'sample_concentrations', 'sample_abs_in', 'det_efficiency', 'ana_efficiency', # 'ana_solid_angle', 'ana_energy_resolution', 'energy_in_keV' ]: # h5_group[attr] = eval( 'self.' + attr ) f.flush() f.close() def input_file_parser(filename): """ Parses an input file, which has a structure like the example input file ('prediction.inp') provided in the examples/ folder. (Python lists and numpy arrays have to be profived without white spaces in their definitions, e.g. 'hkl = [6,6,0]' instead of 'hkl = [6, 6, 0]') """ try: lines = open(filename,'r').readlines() #f = open(filename,'r') except IOError: print( 'No input file ' + filename + ' found.') return input_parameters = {} # dictionary of input parameters section_names = ['detector','analyzer','sample','thomson','beam','compton_profiles'] for name in section_names: input_parameters[name] = {} # cempty list for all sections, fill them up from file, add defaults for missing values # parse all given parameters lineindex = 0 while lineindex < len(lines): line = lines[lineindex] if line[0:4] == '####': thekey = line.split()[1] lineindex += 1 while lines[lineindex][0:4] != '####' and lineindex < len(lines): if not lines[lineindex] == '\n': input_parameters[thekey][lines[lineindex].split()[0]] = eval(lines[lineindex].split()[2]) lineindex += 1 else: lineindex += 1 if lineindex == len(lines): break return input_parameters, section_names def get_all_input(filename = 'prediction.inp'): """ Adds default values if input is missing in the input-file and a default value exists for the missing one. """ # parse the input file input_parameters, section_names = input_file_parser(filename) # create something for all possible inputs all_input = {} for name in section_names: all_input[name] = {} # detector all_input['detector']['energy'] = 9.7 # analyzer energy in keV all_input['detector']['thickness'] = 500.0 # detector thickness all_input['detector']['material'] = 'Si' # detector material all_input['detector']['pixel_size']= [256,768] #detector pixel size # analyzer all_input['analyzer']['material'] = 'Si' # analyzer crystal material all_input['analyzer']['hkl'] = [6,6,0] # analyzer crystal reflection all_input['analyzer']['mask_d'] = 60.0 # analyzer mask diameter in mm all_input['analyzer']['bend_r'] = 1.0 # analyzer bending radius in m all_input['analyzer']['energy_resolution'] = 0.5 # resolution in eV all_input['analyzer']['diced'] = False # keyword, if bent or diced analyzer is used all_input['analyzer']['thickness'] = 500.0 # analyzer bending radius in m all_input['analyzer']['database_dir'] = installation_dir # directory to tabulated chi tables (for calculation of reflectivities) # sample all_input['sample']['chem_formulas'] = [] all_input['sample']['concentrations'] = [] all_input['sample']['densities'] = [] all_input['sample']['angle_tth'] = [] all_input['sample']['sample_thickness'] = [] all_input['sample']['angle_in'] = None # up to now, only spherical samples possible !!! all_input['sample']['angle_out'] = None # same here all_input['sample']['shape'] = 'sphere' # sample shape, right now, only this works, should be 'slab' or 'sphere' in the future all_input['sample']['molar_masses'] = None # this is needed for the estimation of number of scatterers. should be mandatory, acutally # thomson all_input['thomson']['omega_1'] = [] all_input['thomson']['omega_2'] = [] all_input['thomson']['tth'] = [] all_input['thomson']['scattering_plane'] = 'vertical' # 'vertical' or 'horizontal', for polarization purposes all_input['thomson']['polarization'] = 0.99 # polarization factor # beam all_input['beam']['i0_intensity'] = [] all_input['beam']['beam_height'] = [] all_input['beam']['beam_width'] = [] all_input['beam']['divergence'] = None # this is just a dummy parameter # compton_profiles all_input['compton_profiles']['eloss_range'] = np.arange(0.0,1000.0,0.1) # energy range in eV all_input['compton_profiles']['E0'] = 9.7 # analyzer energy in keV # if present, replace all_input variable by values provided in the input file: for key in input_parameters: for key2 in input_parameters[key]: all_input[key][key2] = input_parameters[key][key2] return all_input def run(filename='prediction.inp'): """ Function to create a spectrum prediction from input parameters provided in the input file filename. Generates a figure with the result. """ # parse all input parameters inp = get_all_input(filename) # create all the instances beam_obj = beam(inp['beam']['i0_intensity'],inp['beam']['beam_height'],inp['beam']['beam_width'], inp['beam']['divergence']) sample_obj = sample(inp['sample']['chem_formulas'],inp['sample']['concentrations'],inp['sample']['densities'], inp['sample']['angle_tth'],inp['sample']['sample_thickness'],inp['sample']['angle_in'], inp['sample']['angle_out'],inp['sample']['shape'],inp['sample']['molar_masses']) analyzer_obj = analyzer(inp['analyzer']['material'],inp['analyzer']['hkl'],inp['analyzer']['mask_d'], inp['analyzer']['bend_r'],inp['analyzer']['energy_resolution'], inp['analyzer']['diced'], inp['analyzer']['thickness'],inp['analyzer']['database_dir']) detector_obj = detector(inp['detector']['energy'],inp['detector']['thickness'],inp['detector']['material'],inp['detector']['pixel_size']) compton_profile_obj = compton_profiles(sample_obj,inp['compton_profiles']['eloss_range'],inp['compton_profiles']['E0']) thomson_obj = thomson(compton_profile_obj.get_energy_in_keV(),compton_profile_obj.get_E0(),compton_profile_obj.get_tth()) abs_cross_section_obj = absolute_cross_section(beam_obj, sample_obj, analyzer_obj, detector_obj, thomson_obj, compton_profile_obj) abs_cross_section_obj.plot_abs_cross_section() import sys if(sys.argv[0][-12:]!="sphinx-build"): # parse input arguments, i.e. input file parser = OptionParser() parser.add_option("-f", "--file", dest="filename",help="read input from FILE", metavar="FILE") (options, args) = parser.parse_args() if __name__ == '__main__': run(options.filename) def main(): run(options.filename) ####################################################### # plot q vs. matrix elements ####################################################### class radial_wave_function: def __init__(self): self.element = None self.Z = None self.n = None self.l = None self.s = None self.hydrogen_like = False self.spin_polarized = False self.R_nl_numeric = np.array([]) self.r = np.array([]) def load_from_sympy( self, Z, n, l ): try: from sympy.physics.hydrogen import R_nl from sympy import var except: print('Did not find sympy module, will end here.') return var("rr ZZ") R_nl_function = R_nl(n, l, rr, ZZ) r = np.logspace(1e-6, 2., 1000)-1.0 R_nl_numeric = np.zeros_like(r) for ii in range(r.shape[0]): R_nl_numeric[ii] = R_nl(n, l, rr, ZZ).evalf(subs={rr: r[ii] , ZZ:Z}) self.r = r self.R_nl_numeric = R_nl_numeric self.n = n self.l = l self.Z = Z self.element = xrs_utilities.element(Z) self.hydrogen_like = True self.spin_polarized = False def load_from_PP( self, Z, n, l, path='/home/christoph/programs/atomic_wavefunctions/', spin_polarized=False ): l_str = ['s', 'p', 'd', 'f', 'g', 'h'][l] fname_up = path + xrs_utilities.element(Z).lower() + '/' + 'ae/wf-'+str(n)+l_str+'_up' fname_dn = path + xrs_utilities.element(Z).lower() + '/' + 'ae/wf-'+str(n)+l_str+'_dn' # wfcn in form u(r) = R(r)/r # for checking norm: calculate dr r^2 u(r) u*(r) raw_up = np.loadtxt(fname_up) raw_dn = np.loadtxt(fname_dn) class matrix_element: def __init__( self, R1, R2 ): self.wfn1 = R1.R_nl_numeric self.wfn2 = R2.R_nl_numeric self.k = np.array([]) self.r = np.linspace(0.0, 15.0, 1000) self.Mel = np.array([]) self.q = np.array([]) def compute( self, k ): """ **compute** Calculates the matrix elements for a given k or range of k. """ all_k = [] if not isinstance(k,list): all_k.append(k) else: all_k = k self.k = np.array(all_k) self.q = np.zeros_like(self.r) self.Mel = np.zeros((len(self.r),len(k))) for ii in range(len(k)): self.q, self.Mel[:,ii] = xrs_utilities.compute_matrix_elements( self.wfn1, self.wfn2, all_k[ii], self.r ) def write_H5( self, filename ): """ **write_H5** Creates an HDF5 file to store the matrix elements. Args: * fname (str) : Full path and filename for the HDF5 file to be created. """ if np.any(self.q): # check if file already exists if os.path.isfile( filename ): os.remove( filename ) f = h5py.File( fname, "w" ) f.require_group( "matrix_elements" ) f["matrix_elements"]["r"] = self.r f["matrix_elements"]["q"] = self.q f["matrix_elements"]["M"] = self.Mel f.close() else: print('There are no matrix elements to save.') def write_ascii( self, filename ): """ **write_ascii** Creates an ascii-file and writes matrix elements. Args: * fname (str) : Full path and filename for the ascii file to be created. """ if np.any(self.q): # check if file already exists if os.path.isfile( filename ): os.remove( filename ) the_data = np.zerso((len(self.q), len(k+1))) the_data[:,0] = self.q for ii in range(len(self.k)): the_data[:,ii+1] = self.Mel[:,ii] np.savetxt( filename, the_data ) xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_read.py000066400000000000000000004070501412732462000220760ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range from six.moves import zip from six.moves import input #!/usr/bin/python # Filename: xrs_read.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" from . import xrs_rois, xrs_scans, xrs_utilities, math_functions, xrs_fileIO, roifinder_and_gui import h5py import scipy.io import traceback import sys import os import numpy as np import array as arr import pickle import matplotlib.pyplot as plt from numpy import array from itertools import groupby from scipy.integrate import trapz from scipy.interpolate import interp1d, Rbf from scipy import signal, optimize from scipy.ndimage import measurements # try to import the fast PyMCA parsers use_pymca = True if use_pymca: try: import PyMca5.PyMcaIO.EdfFile as EdfIO import PyMca5.PyMcaIO.specfilewrapper as SpecIO use_PyMca = True except: use_PyMca = False else: use_PyMca = False print( " >>>>>>>> use_PyMca " , use_PyMca) __metaclass__ = type # new style classes def print_citation_message(): """Prints plea for citing the XRStools article when using this software. """ print (' ') print (' ############################# Welcome to XRStools #############################') print (' # If you are using this software, please cite the following work: #') print (' # Ch.J. Sahle, A. Mirone, J. Niskanen, J. Inkinen, M. Krisch, and S. Huotari: #') print (' # "Planning, performing, and analyzing X-ray Raman scattering experiments." #') print (' # Journal of Synchrotron Radiation 22, No. 2 (2015): 400-409. #') print (' ###############################################################################') print (' ') print_citation_message() class Hydra: """Main class for handling XRS data from ID20's multi-analyzer spectrometer 'Hydra'. This class is intended to read SPEC- and according EDF-files and generate spectra from multiple individual energy loss scans. Note: Hydra is the name of the multi-analyzer x-ray Raman scattering spectrometer at ESRF's ID20 beamline. This class has been adopted specifically for this spectrometer. If you are using this program, please cite the following work: Sahle, Ch J., A. Mirone, J. Niskanen, J. Inkinen, M. Krisch, and S. Huotari. "Planning, performing and analyzing X-ray Raman scattering experiments." Journal of Synchrotron Radiation 22, No. 2 (2015): 400-409. Args: * path (str): Absolute path to directory holding the data. * SPECfname (str): Name of the SPEC-file ('hydra' is the default). * EDFprefix (str): Prefix for the EDF-files ('/edf/' is the default). * EDFname (str): Filename of the EDF-files ('hydra_' is the default). * EDFpostfix (str): Postfix for the EDF-files ('.edf' is the default). * en_column (str): Counter mnemonic for the energy motor ('energy' is the default). * moni_column (str): Mnemonic for the monitor counter ('izero' is the default). Attributes: * path (str): Absolute path to directory holding the data. * SPECfname (str): Name of the SPEC-file ('hydra' is the default). * EDFprefix (str): Prefix for the EDF-files ('/edf/' is the default). * EDFname (str): Filename of the EDF-files ('hydra_' is the default). * EDFpostfix (str): Postfix for the EDF-files ('.edf' is the default). * en_column (str): Counter mnemonic for the energy motor ('energy' is the default). * moni_column (str): Mnemonic for the monitor counter ('izero' is the default). * scans (dict): Dictionary holding all loaded scans. * scan_numbers (list): List with scan number of all loaded scans. * eloss (np.array): Array with the common energy loss scale for all analyzers. * energy (np.array): Array with the common energy scale for all analyzers. * signals (np.array): Array with the signals for all analyzers (one column per anayzer). * errors (np.array): Array with the poisson errors for all analyzers (one column per anayzer). * qvalues (list): List of momentum transfer values for all analyzers. * groups (dict): Dictionary of groups of scans (instances of the 'scangroup' class, such as 2 'elastic', or 5 'edge1', etc.). * cenom (list): List of center of masses of the elastic lines. * E0 (int): Elastic line energy value, mean value of all center of masses. * tth (list): List of all scattering angles (one value for each ROI). * resolution (list): List of FWHM of the elastic lines (one for each analyzer). * comp_factor (float): Compensation factor for line-by-line energy dispersion compensation. * cenom_dict (dict): Dictionary holding center-of-masses for of the elastic line. * raw_signals (dict): Dictionary holding pixel- or line-wise signals. * raw_errors (dict): Dictionary holding pixel- or line-wise Poisson errors. * TTH_OFFSETS1 (np.array): Two-Theta offsets between individual analyzers inside each analyzer module in one direction (horizontal for V-boxes, vertical for H-boxes). * TTH_OFFSETS2 (np.array): Two-Theta offsets between individual analyzers inside each analyzer module in one direction (horizontal for H-boxes, vertical for V-boxes). * roi_obj (instance): Instance of the roi_object class from the xrs_rois module defining all ROIs for the current dataset (default is 'None'). """ def __init__( self, path, SPECfname='hydra', EDFprefix='/edf/', EDFname='hydra_', \ EDFpostfix='.edf', en_column='energy', moni_column='izero' ): self.path = path self.SPECfname = SPECfname if not os.path.isfile(os.path.join(path, SPECfname)): raise Exception('IOError! No such file or directory.') self.EDFprefix = EDFprefix self.EDFname = EDFname self.EDFpostfix = EDFpostfix self.en_column = en_column.lower() self.moni_column = moni_column.lower() self.scans = {} self.scan_numbers = [] self.eloss = np.array([]) self.energy = np.array([]) self.signals = np.array([]) self.errors = np.array([]) self.q_values = [] self.groups = {} self.cenom = [] self.E0 = [] self.tth = [] self.resolution = [] self.comp_factor = None self.cenom_dict = {} self.raw_signals = {} self.raw_errors = {} self.TTH_OFFSETS1 = np.array([5.0, 0.0, -5.0, 5.0, 0.0, -5.0, 5.0, 0.0, -5.0, 5.0, 0.0, -5.0]) self.TTH_OFFSETS2 = np.array([-9.71, -9.75, -9.71, -3.24, -3.25, -3.24, 3.24, 3.25, 3.24, 9.71, 9.75, 9.71]) self.PIXEL_SIZE = 0.055 # pixel size in mm self.roi_obj = None print_citation_message() def save_state_hdf5( self, file_name, group_name, comment="" ): """ **save_state_hdf5** Save the status of the current instance in an HDF5 file. Args: file_name (str): Path and file name for the HDF5-file to be created. group_name (str): Group name under which to store status in the HDF5-file. comment (str): Optional comment (no comment is default). """ h5 = h5py.File(file_name,"a") h5.require_group(group_name) h5group = h5[group_name] for key in self.__dict__.keys(): if key in h5group.keys(): raise Exception( 'Data \'' + key + '\' already present in ' + file_name + ':' + group_name ) else: h5group[key] = getattr( self, key ) h5group["comment"] = comment h5.flush() h5.close() def load_state_hdf5( self, file_name, group_name ): """ **load_state_hdf5** Load the status of an instance from an HDF5 file. Args: file_name (str): Path and filename for the HDF5-file to be created. group_name (str): Group name under which to store status in the HDF5-file. """ h5 = h5py.File( file_name,"r" ) h5group = h5[group_name] keys = {"eloss":array, "energy":array, "signals":array, "errors":array, "q_values":array, "cenom":array, "E0":float, "tth":array, "resolution":array } for key in keys: setattr(self, key, keys[key](array(h5group[key]))) h5.flush() h5.close() def set_roiObj( self,roiobj ): """ **set_roiObj** Assign an instance of the 'roi_object' class to the current data set. Args: roiobj (instance): Instance of the 'roi_object' class holding all information about the definition of the ROIs. """ self.roi_obj = roiobj def load_scan( self, scan_numbers, scan_type='generic', direct=True, scaling=None, method='sum' ): """**load_scan** Load a single or multiple scans. Note: When composing a spectrum later, scans of the same scan_type will be averaged over. Scans with scan type 'elastic' or long in their names are recognized and will be treated specially. Args: * scan_numbers (int or list): Integer or iterable of scan numbers to be loaded. * scan_type (str): String describing the scan to be loaded (e.g. 'edge1' or 'K-edge'). * direct (boolean): Flag, 'True' if the EDF-files should be deleted after loading/integrating the scan. """ # make sure scannumbers are iterable (list) numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers # make sure there is a cenom_dict available if direct=True AND method='sum' if direct and method=='pixel' and not self.cenom_dict: print('Please run the get_compensation_factor method first for pixel-wise compensation.') return # go throught list of scan_numbers and load scans for number in numbers: # create a name for each scan scan_name = 'Scan%03d' % number # create an instance of the Scan class scan = xrs_scans.Scan() # load the scan scan.load( self.path, self.SPECfname, self.EDFprefix, self.EDFname, self.EDFpostfix, number, \ direct=direct, roi_obj=self.roi_obj, scaling=scaling, scan_type=scan_type, \ en_column=self.en_column, moni_column=self.moni_column, method=method, \ cenom_dict=self.cenom_dict, comp_factor=self.comp_factor ) # add it to the scans dict self.scans[scan_name] = scan # add the number to the list of scan numbers if not number in self.scan_numbers: self.scan_numbers.extend([number]) def load_loop( self, beg_nums, num_of_regions, direct=True, method='sum' ): """ **load_loop** Loads a whole loop of scans based on their starting numbers and the number of single scans in the loop. Args: beg_nums (list): List of scan numbers of the first scans in each loop. num_of_regions (int): Number of scans in each loop. """ type_names = [] for n in range(num_of_regions): type_names.append('edge'+str(n+1)) numbers = [] for n in range(len(beg_nums)): for m in range(num_of_regions): numbers.append((beg_nums[n]+m)) type_names = type_names*len(beg_nums) for n in range(len(type_names)): number = [] number.append(numbers[n]) self.load_scan( number, type_names[n], direct=True, method=method ) def delete_scan( self, scan_numbers ): """ **delete_scan** Deletes scans from the dictionary of scans. Args: scan_numbers (int or list): Integer or list of integers (SPEC scan numbers) to be deleted. """ # make sure scannumbers are iterable (list) numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers # delete the scan for number in numbers: scan_name = 'Scan%03d' % number del(self.scans[scan_name]) self.scan_numbers.remove(number) def get_raw_data( self, method='sum', scaling=None ): """ **get_raw_data** Applies the ROIs to the EDF-files. This extracts the raw-data from the EDF-files subject to three different methods: 'sum' will simply sum up all pixels inside a ROI, 'row' will sum up over the dispersive direction, and 'pixel' will not sum at all. Args: method (str): Keyword specifying the selected choice of data treatment: can be 'sum', 'row', or 'pixel'. Default is 'sum'. """ if not self.roi_obj: print ( 'Did not find a ROI object, please set one first.' ) return for scan in self.scans: if len(self.scans[scan].edfmats): print ( "Integrating " + scan + " using method \'" + method + '\'.') self.scans[scan].get_raw_signals( self.roi_obj , method=method, scaling=scaling ) def get_data_new(self, method='sum', scaling=None): """ **get_data_new** Applies the ROIs to the EDF-files. This extracts the raw-data from the EDF-files subject to three different methods: 'sum' will simply sum up all pixels inside a ROI, 'row' will sum up over the dispersive direction, and 'pixel' will not sum at all. Args: method (str): Keyword specifying the selected choice of data treatment: can be 'sum', 'row', or 'pixel'. Default is 'sum'. """ if not self.cenom_dict: print ( 'No compensation factor/center-of-mass info found, please provide first.' ) return for scan in self.scans: self.scans[scan].get_signals( method=method, cenom_dict=self.cenom_dict, comp_factor=self.comp_factor, scaling=scaling ) def get_data(self): """ **get_data** Applies the ROIs and sums up intensities. Returns: 'None', if no ROI object is available. """ if not self.roi_obj: print ( 'Did not find a ROI object, please set one first.' ) return for scan in self.scans: if len(self.scans[scan].edfmats): print ( "Integrating " + scan ) self.scans[scan].apply_rois( self.roi_obj ) def get_data_pw(self): """ **get_data_pw** Extracts intensities for each pixel in each ROI. Returns: 'None', if no ROI object is available. """ if not np.any(self.roi_obj.indices): print ( 'Did not find a ROI object, please set one first.' ) return for scan in self.scans: if len(self.scans[scan].edfmats): print ( "Integrating pixelwise " + scan ) self.scans[scan].apply_rois_pw(self.roi_obj) def SumDirect( self, scan_numbers, index=None ): """ **SumDirect** Creates a summed 2D image of a given scan or list of scans. Args: scan_numbers (int or list): Scan number or list of scan numbers to be added up. Returns: A 2D np.array of the same size as the detector with the summed image. """ # make sure scannumbers are iterable (list) numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers im_sum = None en_column = None # uses first column in SPEC file as scanned motor for number in numbers: scan = xrs_scans.Scan() scan.load(self.path, self.SPECfname, self.EDFprefix, self.EDFname, self.EDFpostfix, number, \ direct=False, roi_obj=None, scaling=None, scan_type='generic', \ en_column=en_column, moni_column=self.moni_column) if im_sum is None: im_sum1 = np.zeros(scan.edfmats[0].shape ,"f") im_sum2 = np.zeros(scan.edfmats[0].shape ,"f") if not index: im_sum1[:] += scan.edfmats.sum(axis=0) else: im_sum1[:] += scan.edfmats[0:index,:,:].sum(axis=0) im_sum2[:] += scan.edfmats[index:,:,:].sum(axis=0) if not index: return im_sum1 else: return im_sum2-im_sum1 def get_eloss_new(self, method='sum'): """ **get_eloss_new** Defines the energy loss scale for all ROIs and applies dispersion compensation if applicable. Args: method (str): Keyword describing which dispersion compensation method to use. Possible choices are 'sum' (no compensation), 'pixel' (pixel-by-pixel compensation), or 'row' (line-by-line) compensation. """ # make sure an elastic line was loaded if not 'elastic' in self.groups: print( 'Please load/integrate at least one elastic scan first!' ) return # make sure there is data elif not np.any(self.groups['elastic'].raw_signals): self.get_raw_data( method=method ) # make sure there is center of masses available elif not self.cenom_dict and method != 'row': for scan in self.scans: if self.scans[scan].get_type() == 'elastic': print('GETIING COMP FACTOR!!!') self.get_compensation_factor( self.scans[scan].scan_number, method=method ) first_key = list(self.raw_signals.keys())[0] # 'sum' if method == 'sum': # master eloss scale in eV is the one of the first ROI self.signals = np.zeros((len(self.energy),len(self.cenom_dict))) self.errors = np.zeros((len(self.energy),len(self.cenom_dict))) master_eloss = (self.energy - np.median([self.cenom_dict[key] for key in self.cenom_dict]))*1.0e3 self.E0 = np.median([self.cenom_dict[key] for key in self.cenom_dict]) for key,ii in zip(sorted(self.cenom_dict, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ), range(len(self.cenom_dict))): # signals x = ( self.energy - self.cenom_dict[key] )*1.0e3 y = self.raw_signals[key][:] #try: # rbfi = Rbf( x, y, function='linear' ) # self.signals[:,ii] = rbfi( master_eloss ) #except: self.signals[:,ii] = np.interp( master_eloss, x, y ) # errors y = self.raw_errors[key][:] #try: # rbfi = Rbf( x, y, function='linear' ) # self.errors[:,ii] = rbfi( master_eloss ) #except: self.errors[:,ii] = np.interp( master_eloss, x, y ) self.eloss = master_eloss # 'pixel' elif method == 'pixel': # master eloss scale in eV is the one of central pixel in first ROI self.signals = np.zeros((len(self.energy),len(self.cenom_dict))) self.errors = np.zeros((len(self.energy),len(self.cenom_dict))) master_eloss = ( self.energy - np.median(self.cenom_dict[first_key][self.cenom_dict[first_key] > 0.0]) )*1.0e3 self.E0 = np.median(self.cenom_dict[first_key][self.cenom_dict[first_key] > 0.0]) for key,ii in zip(sorted(self.cenom_dict, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ), range(len(self.cenom_dict))): print ('Pixel-by-pixel compensation for ' + key +'.') signal = np.zeros(len(master_eloss)) error = np.zeros(len(master_eloss)) for dim1 in range(self.cenom_dict[key].shape[0]): for dim2 in range(self.cenom_dict[key].shape[1]): x = ( self.energy - self.cenom_dict[key][dim1, dim2] )*1.0e3 # signals y = self.raw_signals[key][:, dim1, dim2] if np.any(y)>0.0: #print "Y AMAX", signal.max() #rbfi = Rbf( x, y, function='linear' ) rbfi = interp1d(x, y,bounds_error=False, fill_value=0.0) #print "rbf AMAX", Rbf( x, y, function='linear' ) #signal += rbfi( master_eloss ) signal += rbfi( master_eloss ) #print "SIGNAL AMAX", signal.max() # errors y = self.raw_errors[key][:, dim1, dim2] rbfi = Rbf( x, y, function='linear' ) error += rbfi( master_eloss )**2 self.signals[:,ii] = signal self.errors[:,ii] = np.sqrt(error) self.eloss = master_eloss # 'row' elif method == 'row': self.signals = np.zeros((len(self.energy),len(self.roi_obj.red_rois))) self.errors = np.zeros((len(self.energy),len(self.roi_obj.red_rois))) energy = self.energy * 1e3 # energy in eV for key,ii in zip(sorted(self.raw_signals, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ), range(len(self.raw_signals))): y = self.raw_signals[key] meanii = len(range(y.shape[1]))/2 yc = np.zeros_like(y) for jj in range(y.shape[1]): sort = np.argsort(energy) yc[:,jj] = np.interp( energy[sort] + (jj-meanii)*self.comp_factor*self.PIXEL_SIZE, energy[sort], y[sort,jj], left=float('nan'), right=float('nan') ) self.signals[:,ii] = xrs_utilities.nonzeroavg(yc) self.errors[:,ii] = np.sqrt(self.signals[:,ii]) else: print('Method \''+method+'\' not supported, use either \'sum\', \'pixel\', or \'row\'.') return def get_eloss( self ): """ **get_eloss** Finds the energy loss scale for all ROIs by calculating the center of mass (COM) for each ROI's elastic line. Calculates the resolution function (FWHM) of the elastic lines. """ # make sure an elastic line was loaded if not 'elastic' in self.groups: print( 'Please load/integrate at least one elastic scan first!' ) return # make sure there is data elif not self.groups['elastic'].signals.shape[1] == len(self.roi_obj.indices): self.get_data() else: # reset values, in case this is run several times self.cenom = [] self.resolution = [] Origin = None valid_cenoms = [] for n in range(self.groups['elastic'].signals.shape[1]): # find the center of mass for each ROI cofm = xrs_utilities.find_center_of_mass(self.groups['elastic'].energy,self.groups['elastic'].signals[:,n]) self.cenom.append(cofm) if self.there_is_a_valid_roi_at( n ): valid_cenoms.append(cofm) if Origin is None: Origin = cofm # find the FWHM/resolution for each ROI en_scale = (self.groups['elastic'].energy - self.cenom[n])*1e3 intensity = self.groups['elastic'].signals_orig[:,n] FWHM, x0 = xrs_utilities.fwhm(en_scale,intensity) # find master E0 self.E0 = np.mean(valid_cenoms) # define last eloss scale as the 'master' scale for all ROIs self.eloss = (self.energy - cofm)*1e3 # energy loss in eV # define eloss-scale for each ROI and interpolate onto the 'master' eloss-scale for n in range(self.signals.shape[1]): # inserting zeros at beginning and end of the vectors to avoid interpolation errors x = (self.energy-self.cenom[n])*1e3 x = np.insert(x,0,-1e10) x = np.insert(x,-1,1e10) y = self.signals[:,n] y = np.insert(y,0,0) y = np.insert(y,-1,0) f = interp1d(x,y, bounds_error=False,fill_value=0.0) self.signals[:,n] = f(self.eloss) # do the same for the errors for n in range(self.signals.shape[1]): # inserting zeros at beginning and end of the vectors to avoid interpolation errors x = (self.energy-self.cenom[n])*1e3 x = np.insert(x,0,-1e10) x = np.insert(x,-1,1e10) y = self.errors[:,n] y = np.insert(y,0,0) y = np.insert(y,-1,0) f = interp1d(x,y, bounds_error=False,fill_value=0.0) self.errors[:,n] = f(self.eloss) def get_spectrum( self, include_elastic=False, abs_counts=False ): """ **get_spectrum** Constructs a spectrum based on the scans loaded so far. Defines the energy loss scale based on the elastic lines. Args: include_elastic (boolean): Boolean flag, does not include the elastic line if set to 'False' (this is the default). abs_counts (boolean): Boolean flag, constructs the spectrum in absolute counts if set to 'True' (default is 'False') """ # find the groups all_groups = xrs_scans.findgroups(self.scans) # append scans for group in all_groups: one_group = xrs_scans.makegroup_nointerp(group, absCounts=abs_counts) self.groups[one_group.get_type()] = one_group self.energy, self.signals, self.errors = xrs_scans.appendScans(self.groups,include_elastic) # define the energy loss scale self.get_eloss() def get_spectrum_new( self, method='sum', include_elastic=False, abs_counts=False, interpolation=False ): """ **get_spectrum_new** Constructs a spectrum from the scans loaded so far based on the chosen method: detector pixels can either be summed up (method='sum'), pixel-by-pixel compensation can be applied (method='pixel'), or a line-by-line compensation scheme can be applied (method='row'). The energy loss scale will be defined in the process and the data will be interpolated onto this scale using norm-conserving wavelet interpolation. Args: method (str): Keyword describing the kind of integration scheme to be used (possible values are 'sum', 'pixel', or 'row'), default is 'sum'. include_elastic (boolean): Boolean flag, does not include the elastic line if set to 'False' (this is the default). abs_counts (boolean): Boolean flag, constructs the spectrum in absolute counts if set to 'True' (default is 'False') interpolation (boolean): Boolean flag, if True, signals are interpolated onto energy grid of the first scan in each group of scans. """ if not method in ['pixel', 'sum', 'row']: print('Unknown integration method. Use either \'pixel\', \'sum\', or \'row\'') return # make sure there is an elastic line available elastic_number = None for key in self.scans: if self.scans[key].scan_type == 'elastic': elastic_number = self.scans[key].scan_number print ('Using scan No. %d for CENOMs.'%elastic_number) break else: pass if not elastic_number: print( 'Please load/integrate at least one elastic scan first!' ) return # get compensation factor/CENOM for an elastic line if method in [ 'sum' , 'pixel'] and not self.cenom_dict: self.get_compensation_factor( elastic_number, method=method ) if method == 'row' and not self.comp_factor: self.get_compensation_factor( elastic_number, method=method ) # get raw data for key in self.scans: if not self.scans[key].raw_signals: self.scans[key].get_raw_signals( self.roi_obj, method=method ) # sum up similar scans # find all groups of scans all_groups = xrs_scans.findgroups(self.scans) # initiate groups self.groups = {} # sum up similar scans for group in all_groups: self.groups[group[0].get_type()] = xrs_scans.sum_scans_to_group( group, method=method, interp=interpolation ) # stitch groups together into a spectrum spectrum = xrs_scans.stitch_groups_to_spectrum( self.groups, method=method, include_elastic=include_elastic ) self.energy = spectrum.energy self.signals = spectrum.signals self.errors = spectrum.errors self.raw_signals = spectrum.raw_signals self.raw_errors = spectrum.raw_errors # define energy loss scale and apply compensation if applicable self.get_eloss_new( method=method ) def get_q_values( self, inv_angstr=False, energy_loss=None ): """ **get_q_values** Calculates the momentum transfer for each analyzer. Args: inv_angstr (boolean): Boolean flag, if 'True' momentum transfers are calculated in inverse Angstroms. energy_loss (float): Energy loss value at which the momentum transfer is to be calculated. If 'None' is given, the momentum transfer is calculated for every energy loss point of the spectrum. Returns: If an energy loss value is passed, the function returns the momentum transfers at this energy loss value for each analyzer crystal. """ # one q-value per analyzer and energy loss point q_vals = np.zeros_like(self.signals) if inv_angstr: for n in range( self.signals.shape[1] ): q_vals[:,n] = xrs_utilities.momtrans_inva(self.E0+self.eloss/1e3,self.E0,self.tth[n]) else: for n in range( self.signals.shape[1] ): q_vals[:,n] = xrs_utilities.momtrans_au(self.E0+self.eloss/1e3,self.E0,self.tth[n]) self.q_values = q_vals # return all q-values if a specific energy loss is given if energy_loss: ind = np.abs(self.eloss - energy_loss).argmin() return self.q_values[ind,:] def copy_edf_files( self, scan_numbers, dest_dir ): """ **copy_edf_files** Copies all EDF-files from given scan_numbers into given directory. Args: * scan_numbers (int or list) = Integer or list of integers defining the scan numbers of scans to be copied. * dest_dir (str) = String with absolute path for the destination. """ import shutil # make scan_numbers iterable numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers fname = self.path + self.SPECfname # go through the scans, find the EDF-files and copy them for nscan in numbers: if use_PyMca: data, motors, counters = xrs_fileIO.PyMcaSpecRead(fname,nscan) else: data, motors, counters = xrs_fileIO.SpecRead(fname,nscan) for m in range(len(counters['ccdno'])): ccdnumber = counters['ccdno'][m] edfname = self.path + self.EDFprefix + self.EDFname + "%04d" % ccdnumber + self.EDFpostfix shutil.copy2(edfname, dest_dir) def dump_spectrum_ascii( self, file_name, header='' ): """ **dump_spectrum_ascii** Stores the energy loss and signals in a txt-file. Args: filename (str): Path and filename to the file to be written. """ data = np.zeros((len(self.eloss),self.signals.shape[1]*2+1)) data[:,0] = self.eloss col = 1 for ii in range(self.signals.shape[1]): data[:,col] = self.signals[:,ii] col += 1 data[:,col] = self.errors[:,ii] col += 1 np.savetxt( file_name, data, header=header ) def dump_spectrum_hdf5( self, file_name, group_name, comment='' ): """ **dump_spectrum_hdf5** Writes the summed spectrum into an HDF5 file. Args: file_name (str): Path and file name for the HDF5-file to be created. group_name (str): Group name under which to store status in the HDF5-file. comment (str): Optional comment (no comment is default). """ h5 = h5py.File(file_name,"a") h5.require_group(group_name) h5group = h5[group_name] keys = ['energy', 'eloss', 'signals', 'errors'] for key in keys: h5group[key] = getattr( self, key ) h5group["comment"] = comment h5.flush() h5.close() def dump_scans_ascii( self, scan_numbers, pre_fix, f_name, post_fix='.dat', header='' ): """ **dump_scans_ascii** Produce ASCII-type files with columns of energy, signal, and Poisson error. Args: scan_numbers (int or list): SPEC scan numbers of scans to be safed in ASCII format. pre_fix (str): Path to directory where files should be written into. f_name (str): Base name for the files to be written. post_fix (str): Extention for the files to be written (default is '.dat'). """ # make sure scan_numbers are iterable numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers for number in numbers: scan_name = 'Scan%03d' % number if not scan_name in self.scans.keys(): print ('Scan No. %03d is currently not loaded.'% number) return x = self.scans[scan_name].energy y = self.scans[scan_name].signals z = self.scans[scan_name].errors data = np.zeros((x.shape[0], y.shape[1]+z.shape[1]+1 )) data[:,0] = x data[:,1:1+y.shape[1]] = y data[:,1+y.shape[1]:1+y.shape[1]+z.shape[1]] = z file_name = pre_fix + f_name + '_' + scan_name + post_fix np.savetxt(file_name, data, header=header) def print_scan_length( self,scan_numbers ): """ **print_scan_length** Print out the numper of points in given scans. Args: scan_numbers (int or list): Scan number or list of scan numbers. """ # make scan_numbers iterable numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers # print out the number of points for all scan_numbers for ii in numbers: name = 'Scan%03d' % ii print( 'Length of scan %03d ' %ii + ' is ' + str(len(self.scans[name].energy)) + '.') def get_tths( self, rvd=None, rvu=None, rvb=None, rhl=None, rhr=None, rhb=None, order=[0,1,2,3,4,5] ): """ **get_tths** Calculates the scattering angles for all analyzer crystals based on the mean angle of the analyzer modules. Args: * rhl (float): Mean scattering angle of the HL module (default is 0.0). * rhr (float): Mean scattering angle of the HR module (default is 0.0). * rhb (float): Mean scattering angle of the HB module (default is 0.0). * rvd (float): Mean scattering angle of the VD module (default is 0.0). * rvu (float): Mean scattering angle of the VU module (default is 0.0). * rvb (float): Mean scattering angle of the VB module (default is 0.0). * order (list): List of integers (0-5) that describe the order of modules in which the ROIs were defined (default is VD, VU, VB, HR, HL, HB; i.e. [0,1,2,3,4,5]). """ # reset all tth values self.tth = [] # try to grab from positions from SPEC-file (first scan in list) scan_name = list(self.scans.keys())[0] if not rvd: rvd = self.scans[scan_name].motors['RVD'] if not rvu: rvu = self.scans[scan_name].motors['RVU'] if not rvb: rvb = self.scans[scan_name].motors['RVB'] if not rhr: rhr = self.scans[scan_name].motors['RHR'] if not rhl: rhl = self.scans[scan_name].motors['RHL'] if not rhb: rhb = self.scans[scan_name].motors['RHB'] # horizontal modules # HL (motor name rhl) v_angles = self.TTH_OFFSETS1 h_angles = self.TTH_OFFSETS2 + rhl HLtth = [] for n in range(len(h_angles)): HLtth.append( np.arccos(np.cos(np.radians(h_angles[n]))*np.cos(np.radians(v_angles[n])))*180.0/np.pi) # HR (motor name rhr) v_angles = self.TTH_OFFSETS1 h_angles = self.TTH_OFFSETS2 + rhr HRtth = [] for n in range(len(h_angles)): HRtth.append( np.arccos(np.cos(np.radians(h_angles[n]))*np.cos(np.radians(v_angles[n])))*180.0/np.pi) # HB (motor name rhb) v_angles = self.TTH_OFFSETS1 h_angles = self.TTH_OFFSETS2 + rhb HBtth = [] for n in range(len(h_angles)): HBtth.append( np.arccos(np.cos(np.radians(h_angles[n]))*np.cos(np.radians(v_angles[n])))*180.0/np.pi) # vertical modules # VD v_angles = self.TTH_OFFSETS2 + rvd h_angles = self.TTH_OFFSETS1 VDtth = [] for n in range(len(h_angles)): VDtth.append( np.arccos(np.cos(np.radians(v_angles[n]))*np.cos(np.radians(h_angles[n])))*180.0/np.pi) # VU v_angles = self.TTH_OFFSETS2 + rvu h_angles = self.TTH_OFFSETS1 VUtth = [] for n in range(len(h_angles)): VUtth.append( np.arccos(np.cos(np.radians(v_angles[n]))*np.cos(np.radians(h_angles[n])))*180.0/np.pi) # VB v_angles = self.TTH_OFFSETS2 + rvb h_angles = self.TTH_OFFSETS1 VBtth = [] for n in range(len(h_angles)): VBtth.append( np.arccos(np.cos(np.radians(v_angles[n]))*np.cos(np.radians(h_angles[n])))*180.0/np.pi) # list of TTH values tth = [VDtth, VUtth, VBtth, HRtth, HLtth, HBtth] # list all TTH values in one long list ordered by the 'order'-keyword for n in order: self.tth.extend(tth[n]) def there_is_a_valid_roi_at( self,n ): """ **there_is_a_valid_roi_at** Checks if n is a valid ROI index. Args: n (int): Index to be checked. Returns: True, if n is a valid ROI index. """ return n= limits[0], center <= limits[1]))[0] inds2 = np.where(np.logical_and(energy >= limits[2], energy <= limits[3]))[0] # make sure to set the correct window (vertical and horizontal) inds = [] for ind in inds1: if ind in inds2: inds.append(ind) inds = np.array(inds) # do the fit fact = np.polyfit( center[inds], energy[inds], 1) # plot the result plt.cla() plt.plot(center,energy,center,np.polyval(fact, center), center[inds], energy[inds]) plt.legend(['dispersion', 'fit', 'data-points used']) # assign compensation factor self.comp_factor = comp_factor = fact[0] print (' >>>> The compensation factor is: %0.6f [eV/mm].' %self.comp_factor ) plt.ioff() class Hydra_imaging(Hydra): """ **Hydra_imaging** """ def __init__( self, path, SPECfname='hydra', EDFprefix='/edf/', EDFname='hydra_', \ EDFpostfix='.edf', en_column='sty', moni_column='izero' ): Hydra.__init__(self, path, SPECfname='hydra', EDFprefix='/edf/', EDFname='hydra_', \ EDFpostfix='.edf', en_column='sty', moni_column='izero') def load_scan( self, scan_numbers, scan_type='imaging', direct=True, scaling=None, method='column', scan_motor='STZ' ): """ **load_scan** Load a single or multiple scans. Note: When composing a spectrum later, scans of the same 'scan_type' will be averaged over. Scans with scan type 'elastic' or 'long' in their names are recognized and will be treated specially. Args: * scan_numbers (int or list): Integer or iterable of scan numbers to be loaded. * scan_type (str): String describing the scan to be loaded (e.g. 'edge1' or 'K-edge'). * direct (boolean): Flag, 'True' if the EDF-files should be deleted after loading/integrating the scan. """ # make sure scannumbers are iterable (list) numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers # make sure there is a cenom_dict available if direct=True AND method='sum' if direct and method=='pixel' and not self.cenom_dict: print('Please run the get_compensation_factor method first for pixel-wise compensation.') return # keep track of the y- and z-axes y_scale = np.array([]) z_scale = [] # go throught list of scan_numbers and load scans for number in numbers: # create a name for each scan scan_name = 'Scan%03d' % number # create an instance of the Scan class scan = xrs_scans.Scan() # load the scan scan.load( self.path, self.SPECfname, self.EDFprefix, self.EDFname, self.EDFpostfix, number, \ direct=direct, roi_obj=self.roi_obj, scaling=scaling, scan_type=scan_type, \ en_column=self.en_column, moni_column=self.moni_column, method=method, cenom_dict=self.cenom_dict ) # keep track of scales y_scale = scan.energy z_scale.append(scan.motors[scan_motor]) # add it to the scans dict self.scans[scan_name] = scan # add the number to the list of scan numbers if not number in self.scan_numbers: self.scan_numbers.extend([number]) self.y_scale = y_scale self.z_scale = np.sort(np.array(list(set(z_scale)))) def load_reference_scan(self, scan_number, scan_type='image_ref', direct=True, scaling=None,): scan.load( self.path, self.SPECfname, self.EDFprefix, self.EDFname, self.EDFpostfix, number, \ direct=direct, roi_obj=self.roi_obj, scaling=scaling, scan_type=scan_type, \ en_column='stx', moni_column=self.moni_column, method=method, cenom_dict=self.cenom_dict ) def get_compensation_factor( self, el_scan_numbers, scan_motor='sty', plotting=False): """ **get_compensation_factor** Calculates the compensation factor for the case of imaging: Args: scan_number (int): Scan number of elastic line scan to be used for finding the compensation factors. method (str): Keyword describing what kind of compensation to be used. Can be \'sum\', \'row\', or \'pixel\'. roi_number (int): ROI number (first ROI is Nr. 0) for which to calculate the line-by-line compensation factor. """ # make sure there is a ROI defined if not self.roi_obj: print( 'Please set a ROI object first.' ) return # load the scans around the elastic line self.load_scan( el_scan_numbers, method='column', direct=True, scan_type='elastic') ## parse energy scale and signals for each ROI #energy = {} #np.zeros(len(range(440,589))) #signals = {} #np.zeros(len(range(440,589))) #for roi_key in sorted(self.roi_obj.red_rois): # energy[roi_key] = np.zeros(len(el_scan_numbers)) # signals[roi_key] = np.zeros(len(el_scan_numbers)) # for scan_number, step in zip(el_scan_numbers, range(len(el_scan_numbers))): # scan_name = 'Scan%03d'%scan_number # energy[roi_key][step] = self.scans[scan_name].motors['energy'] # signals[roi_key][step] = np.sum(self.scans[scan_name].raw_signals[roi_key]) # parse energy scale energy = np.array([]) for scan_key in sorted(self.scans, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): energy = np.append(energy, self.scans[scan_key].motors['energy']) energy = np.array(list(set(energy))) # parse signals signals = {} for roi_key, (pos,M) in sorted(self.roi_obj.red_rois.items()): signals[roi_key] = np.zeros( (len(energy), ) ) for en,estep in zip(energy, range(len(energy))): for scan_key in sorted(self.scans, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): if self.scans[scan_key].motors['energy'] == en: signals[roi_key][estep] += np.sum(self.scans[scan_key].raw_signals[roi_key]) # get centers of mass for each ROI and save in self.cenom_dict for key in signals: inds = np.argsort(energy) cofm = xrs_utilities.find_center_of_mass(energy[inds],signals[key][inds]) self.cenom_dict[key] = cofm if plotting: plt.cla() plt.plot(energy[inds],signals[key][inds],'b-') plt.plot([cofm, cofm], [np.amin(signals[key]), np.amax(signals[key])], 'k-') plt.xlabel('energy [keV]') plt.ylabel('intensity [arb. units]') plt.title('CENOM for %s'%(key)) plt.waitforbuttonpress() self.E0 = np.mean( [self.cenom_dict[key] for key in self.cenom_dict if self.cenom_dict[key]>0.0] ) def interpolate_scans(self, step_motor='STZ'): """ **compensate_scans** Interpolate signals onto common energy-loss grid. """ # define master energy-loss scale energy = np.array([]) for scan_key in sorted(self.scans, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): energy = np.append(energy, self.scans[scan_key].motors['energy']) energy = np.sort(np.array(list(set(energy)))) master_eloss = ( energy - self.E0 )*1.0e3 self.eloss = master_eloss # define master sty scale scan_scale_1 = self.scans[scan_key].energy # define master stz scale dmy = [] for scan_key in self.scans: dmy.append(self.scans[scan_key].motors[step_motor]) scan_scale_2 = np.array(list(set(dmy))) # concatenate scans to volume for roi_key, (pos,M) in sorted(self.roi_obj.red_rois.items()): self.raw_signals[roi_key] = np.zeros( (len(energy), len(scan_scale_1), M.shape[1], len(scan_scale_2)) ) for en,estep in zip(energy, range(len(energy))): for scan_key in sorted(self.scans, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): if self.scans[scan_key].motors['energy'] == en: for zstep in range(len(scan_scale_2)): if self.scans[scan_key].motors[step_motor] == scan_scale_2[zstep]: self.raw_signals[roi_key][estep,:,:,zstep] = self.scans[scan_key].raw_signals[roi_key] # interpolate everything onto master energy-loss scale self.raw_signals_int = {} for roi_key in sorted(self.raw_signals, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): x = (energy - self.cenom_dict[roi_key])*1.0e3 y = self.raw_signals[roi_key] f = interp1d(x, y, kind='linear', axis=0, bounds_error=False, fill_value=0.0) self.raw_signals_int[roi_key] = f(master_eloss) class Fourc: """Main class for handling RIXS data from ID20's high-resolution spectrometer 'Fourc'. This class is intended to read SPEC- and according EDF-files and perform dispersion compensations. Note: 'Fourc' is the name of the high-energy-resolution spectrometer at ESRF's ID20 beamline. This class has been adopted specifically for this spectrometer. If you are using this program, please cite the following work: Sahle, Ch J., A. Mirone, J. Niskanen, J. Inkinen, M. Krisch, and S. Huotari. "Planning, performing and analyzing X-ray Raman scattering experiments." Journal of Synchrotron Radiation 22, No. 2 (2015): 400-409. Args: * path (str): Absolute path to directory holding the data. * SPECfname (str): Name of the SPEC-file ('rixs' is the default). * EDFprefix (str): Prefix for the EDF-files ('/edf/' is the default). * EDFname (str): Filename of the EDF-files ('rixs_' is the default). * EDFpostfix (str): Postfix for the EDF-files ('.edf' is the default). * en_column (str): Counter mnemonic for the energy motor ('energy' is the default). * moni_column (str): Mnemonic for the monitor counter ('izero' is the default). * EinCoor (list): Coordinates, where to find the incident energy value in the SPEC-file (default is [9,0]) Attributes: * path (str): Absolute path to directory holding the data. * SPECfname (str): Name of the SPEC-file ('hydra' is the default). * EDFprefix (str): Prefix for the EDF-files ('/edf/' is the default). * EDFname (str): Filename of the EDF-files ('hydra_' is the default). * EDFpostfix (str): Postfix for the EDF-files ('.edf' is the default). * en1_column (str): Counter mnemonic for the energy motor ('anal energy' is the default). * en2_column (str): Counter mnemonic for the energy motor ('energy' is the default). * moni_column (str): Mnemonic for the monitor counter ('izero' is the default). * EinCoor (list): Coordinates, where to find the incident energy value in the SPEC-file (default is [9,0]) * scans (dict): Dictionary holding all loaded scans. * scan_numbers (list): List with scan number of all loaded scans. * energy (np.array): Array with the common energy scale. * energy2 (np.array): Array with the common energy2 scale. * signals (np.array): Array with the signals for all analyzers (one column per anayzer). * errors (np.array): Array with the poisson errors for all analyzers (one column per anayzer). * groups (dict): Dictionary of groups of scans (instances of the 'scangroup' class, such as 2 'elastic', or 5 'edge1', etc.). * tth (list): List of all scattering angles (one value for each ROI). * resolution (list): List of FWHM of the elastic lines (one for each analyzer). * roi_obj (instance): Instance of the roi_object class from the x0.055 # pixel size in mmrs_rois module defining all ROIs for the current dataset (default is 'None'). * comp_factor (float): Compensation factor used for the dispersion correction. * PIXEL_SIZE (float): Pixel size of the used Maxipix detector (in mm). """ def __init__( self, path, SPECfname='rixs', EDFprefix='/edf/', EDFname='rixs_', \ EDFpostfix='.edf', moni_column='izero', EinCoor='energy' ): self.path = path self.SPECfname = SPECfname if not os.path.isfile( path + SPECfname ): raise Exception( 'IOError! No such file or directory.' ) self.EDFprefix = EDFprefix self.EDFname = EDFname self.EDFpostfix = EDFpostfix #self.en1_column = en1_column.lower() #self.en2_column = en2_column.lower() self.en_column = None self.moni_column = moni_column.lower() self.EinCoor = EinCoor self.scans = {} self.scan_numbers = [] self.energy = [] self.energy2 = [] self.signals = [] self.errors = [] self.groups = {} self.tth = [] self.resolution = [] self.cenom_dict = {} self.raw_signals = {} self.raw_errors = {} self.roi_obj = [] self.comp_factor = 0.0 self.comp_type = None self.PIXEL_SIZE = 0.055 # pixel size in mm print_citation_message( ) def SumDirect( self,scan_numbers , clean_edf_stack=False ): """ **SumDirect** Creates a summed 2D image of a given scan or list of scans. Args: scan_numbers (int or list): Scan number or list of scan numbers to be added up. Returns: A 2D np.array of the same size as the detector with the summed image. """ # make sure scannumbers are iterable (list) numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers # sum up all EDF-images from the given scans im_sum = None en_column = None # uses first column in SPEC file as scanned motor for number in numbers: scan = xrs_scans.Scan() scan.load(self.path, self.SPECfname, self.EDFprefix, self.EDFname, self.EDFpostfix, number, \ direct=False, roi_obj=None, scaling=None, scan_type='generic', \ en_column=en_column, moni_column=self.moni_column, clean_edf_stack=clean_edf_stack) if im_sum is None: im_sum = np.zeros(scan.edfmats[0].shape ,"f") im_sum[:] += scan.edfmats.sum(axis=0) return im_sum def set_roiObj( self, roiobj ): """ **set_roiObj** Assign an instance of the 'roi_object' class to the current data set. Args: roiobj (instance): Instance of the 'roi_object' class holding all information about the definition of the ROIs. """ self.roi_obj = roiobj def load_scan( self, scan_numbers, direct=True, comp_factor=None, scan_type='generic', scaling=None, method='sum', rot_angles=None, clean_edf_stack=False ): """ **load_scan** Loads given scans and applies the dispersion compensation. Args: scan_numbers (int or list): Scan number(s) of scans to be loaded. direct (boolean): Flag, if set to 'True', EDF-files are deleted after loading the scan (this is the default). comp_factor (float): Compensation factor to be used. If 'None', the global compensation factor will be used. If provided, the global compensation factor will be overwritten. scan_type (str): String describing the scan to be loaded. Note: If a compensation factor is passed to this function, the classes 'globel' compensation factor is overwritten. """ # make sure scan_numbers are iterable if not isinstance(scan_numbers,list): numbers = [] numbers.append(scan_numbers) else: numbers = scan_numbers # load scan/scans for number in numbers: # create a name for each scan scan_name = 'Scan%03d' % number # create an instance of the Scan class scan = xrs_scans.Scan() # load scan, first column in SPEC file will be scanned motor scan.load( self.path, self.SPECfname, self.EDFprefix, self.EDFname, \ self.EDFpostfix, number, direct=direct, roi_obj=self.roi_obj, \ scaling=scaling, scan_type=scan_type, en_column=self.en_column, \ moni_column=self.moni_column, method=method, cenom_dict=self.cenom_dict,\ comp_factor=comp_factor,rot_angles=rot_angles, clean_edf_stack=clean_edf_stack ) # assign one dictionary entry to each scan self.scans[scan_name] = scan if not number in self.scan_numbers: self.scan_numbers.extend([number]) # save the incident energy try: self.scans[scan_name].Ein = scan.motors[self.EinCoor] except: self.scans[scan_name].Ein = None def get_compensation_factor( self, scan_number, method='sum', roi_number=None, rot_angles=None ): """ **get_compensation_factor** Calculates the compensation factor from a given elastic line scan: - a pixel-wise center of mass for 'pixel' compensation. - a slope (eV/mm) for row-by-row compensation. - the center of mass for each ROI for no compensation. Args: scan_number (int): Scan number of elastic line scan to be used for finding the compensation factors. method (str): Keyword describing what kind of compensation to be used. Can be \'sum\', \'row\', or \'pixel\'. roi_number (int): ROI number (first ROI is Nr. 0) for which to calculate the line-by-line compensation factor. """ # make sure there is a ROI defined if not self.roi_obj: print( 'Please set a ROI object first.' ) return # simple sum: find center of mass for each ROI if method == 'sum': # reset values self.cenom_dict = {} self.resolution_dict = {} # get EDF-files and pw_data elastic_scan = xrs_scans.Scan() elastic_scan.load( self.path, self.SPECfname, self.EDFprefix, \ self.EDFname, self.EDFpostfix, scan_number, \ direct=False, scan_type='elastic', \ moni_column=self.moni_column, method='sum' ) elastic_scan.get_raw_signals( self.roi_obj, method='sum' ) # find CENOM of each ROI for key in elastic_scan.raw_signals: cofm = xrs_utilities.find_center_of_mass( elastic_scan.energy,elastic_scan.raw_signals[key] ) self.cenom_dict[key] = cofm # set compensation factor self.comp_type = 'sum' # pixel-by-pixel: find center of mass for each pixel in each ROI elif method == 'pixel': # reset values self.cenom_dict = {} self.resolution_dict = {} # get EDF-files and pw_data elastic_scan = xrs_scans.Scan() elastic_scan.load( self.path, self.SPECfname, self.EDFprefix, \ self.EDFname, self.EDFpostfix, scan_number, \ direct=False, scan_type='elastic', \ moni_column=self.moni_column, method='pixel' ) elastic_scan.get_raw_signals( self.roi_obj, method='pixel' ) # find CENOM for each pixel of each ROI for key in sorted(elastic_scan.raw_signals, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): self.cenom_dict[key] = np.zeros_like(self.roi_obj.red_rois[key][1]) for ii in range(self.cenom_dict[key].shape[0]): for jj in range(self.cenom_dict[key].shape[1]): cofm = xrs_utilities.find_center_of_mass(elastic_scan.energy, elastic_scan.raw_signals[key][:,ii,jj]) self.cenom_dict[key][ii,jj] = cofm # set compensation factor self.comp_type = 'pixel' # line-by-line: find a RIXS-type compensation factor elif method == 'row': # reset values self.cenom_dict = {} self.resolution_dict = {} self.comp_type = 'row' # which ROI should the compensation factor be calculated for: if not roi_number: roi_number = int(raw_input("Which ROI to calculate the factor (Python counting)? ")) self.comp_factor = 0.0 # get EDF-files and pw_data elastic_scan = xrs_scans.Scan() elastic_scan.load( self.path, self.SPECfname, self.EDFprefix, \ self.EDFname, self.EDFpostfix, scan_number, \ direct=False, scan_type='elastic', \ moni_column=self.moni_column, method='row', rot_angles=rot_angles ) elastic_scan.get_raw_signals( self.roi_obj, method='row' ) # fit the response for each energy plt.ion() raw_signals = elastic_scan.raw_signals['ROI%02d' % roi_number] el_positions = np.zeros(raw_signals.shape[0] ) for ii in range(el_positions.shape[0]): x = np.arange(raw_signals.shape[1])*self.PIXEL_SIZE+self.PIXEL_SIZE y = raw_signals[ii,:] try: p0 = [ np.amax(y), xrs_utilities.find_center_of_mass(x,y), xrs_utilities.fwhm(x,y)[1]] popt = optimize.curve_fit(math_functions.gauss_forcurvefit, x, y, p0=p0)[0] g = math_functions.gauss_forcurvefit(x,popt[0],popt[1],popt[2]) el_positions[ii] = (popt[1]) except: g = np.zeros_like(x) el_positions[ii] = 0.0 plt.cla() plt.plot(x,y,x,g) plt.draw() # fit the dispersion (eV/mm) plt.cla() # activate the zoom function already thismanager = plt.get_current_fig_manager() thismanager.toolbar.zoom() # make sure the energy vector is ascending sort = np.argsort(np.array(elastic_scan.energy)*1e3) energy = elastic_scan.energy*1e3 center = el_positions[sort] energy = energy[sort] # make user zoom into fig to define fitting-range plt.ion() plt.plot(center,energy) plt.xlabel('x_0 [mm]') plt.ylabel('energy [eV]') plt.draw() raw_input('Zoom in and press enter to continue.') limits = plt.axis() inds1 = np.where(np.logical_and(center >= limits[0], center <= limits[1]))[0] inds2 = np.where(np.logical_and(energy >= limits[2], energy <= limits[3]))[0] # make sure to set the correct window (vertical and horizontal) inds = [] for ind in inds1: if ind in inds2: inds.append(ind) inds = np.array(inds) # do the fit fact = np.polyfit( center[inds], energy[inds], 1) # plot the result plt.cla() plt.plot(center,energy,center,np.polyval(fact, center), center[inds], energy[inds]) plt.legend(['dispersion', 'fit', 'data-points used']) # assign compensation factor self.comp_factor = fact[0] print (' >>>> The compensation factor is: %0.6f [eV/mm].' %self.comp_factor ) plt.ioff() # if method is unknown else: print ( 'Method \''+method+'\' not supported, use either \'sum\', \'row\', or \'pixel\'.') return def get_raw_data( self, method='sum', scaling=None ): """ **get_raw_data** Applies the ROIs to extract the raw signals from the EDF-files. """ # make sure there are some ROIs set if not self.roi_obj: print ( 'Did not find a ROI object, please set one first.' ) return # call the get_raw_signals function for each scan for scan in self.scans: if len(self.scans[scan].edfmats): print ( "Integrating " + scan + " using method \'" + method + '\'.') self.scans[scan].get_raw_signals( self.roi_obj , method=method, scaling=scaling ) def get_data( self, method='sum', scaling=None ): """ **get_data** Applies the ROIs to the EDF-files. This extracts the raw-data from the EDF-files subject to three different methods: 'sum' will simply sum up all pixels inside a ROI, 'row' will sum up over the dispersive direction, and 'pixel' will not sum at all. Args: method (str): Keyword specifying the selected choice of data treatment: can be 'sum', 'row', or 'pixel'. Default is 'sum'. scaling (list): Optional scaling factors (one per ROI) to scale the data with. """ if not self.cenom_dict: print ( 'No compensation factor/center-of-mass info found, please provide first.' ) return for scan in self.scans: # make sure there is raw data available if not self.scans[scan].raw_signals: self.get_raw_data( self, method=method, scaling=scaling ) # apply compensation / get signals self.scans[scan].get_signals( method=method, cenom_dict=self.cenom_dict, comp_factor=self.comp_factor, scaling=scaling ) def get_XES_spectrum( self, method='sum', interpolation=False ): """ **get_XES_spectrum** Constructs an emission spectrum based on the loaded single spectra. NOTE: this needs support for all methods ('sum', 'pixel', 'row') """ # cenom_dict for XES is zeros everywhere if method == 'sum': self.cenom_dict = {} for key in self.roi_obj.red_rois: self.cenom_dict[key] = 0.0 # make sure there is data available for scan in self.scans: if not np.any(self.scans[scan].signals): self.get_data( method=method ) # find the groups all_groups = xrs_scans.findgroups( self.scans ) for group in all_groups: self.groups[group[0].get_type()] = xrs_scans.sum_scans_to_group( group, method=method, interp=interpolation ) if method == 'sum': self.groups[group[0].get_type()].get_signals(method='sum', cenom_dict=self.cenom_dict ) self.energy, self.signals, self.errors = xrs_scans.get_XES_spectrum( self.groups ) def get_Ein_RIXS_map( self, scan_numbers, roi_number, logscaling=False, file_name=None): """ **get_Ein_RIXS_map** Returns a RIXS map that plots energy loss vs inciden energy for the specified scans. Args: * scan_numbers (int or list): SPEC scan numbers to be deleted. * logscaling (boolean): If true numbers are returned on logarithmic scale (False by default) * file_name (str): Absolute path, if map should also be written into an ascii-file. * roi_number (int): ROI to use for creating the RIXS map (Python counting). """ # make sure scan_numbers are iterable numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers # create the matrix scanname = 'Scan%03d' % numbers[0] pre_map = np.zeros( ( len(self.scans[scanname].energy), len(numbers) ) ) rixs_map = np.zeros( ( len(self.scans[scanname].energy), len(numbers) ) ) e_transfer = np.flipud(self.scans[scanname].energy - self.scans[scanname].Ein)*1e3 # save the incident energies e_incident = [] # fill the matrix scan-by-scan for number,ii in zip(numbers, range(len(numbers))): scanname = 'Scan%03d' % number e_incident.append(self.scans[scanname].Ein) pre_map[:,ii] = np.interp( e_transfer, np.flipud(self.scans[scanname].energy - self.scans[scanname].Ein)*1e3, np.flipud(self.scans[scanname].signals[:,roi_number])) sort_inds = np.argsort(e_incident) e_incident_sort = [] for ii in range(pre_map.shape[1]): rixs_map[:,ii] = pre_map[:,sort_inds[ii]] e_incident_sort.append(e_incident[sort_inds[ii]]) return np.array(rixs_map), np.array(e_incident_sort), -np.flipud(np.array(e_transfer)) def delete_scan( self, scan_numbers ): """ **delete_scan** Deletes scans of given scan numbers. Args: scan_numbers (int or list): SPEC scan numbers to be used for the RIXS map. """ # make sure scan_numbers are iterable numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers # delete scans for number in numbers: scanname = 'Scan%03d' % number del(self.scans[scanname]) self.scan_numbers.remove(number) def dump_scans_ascii( self, scan_numbers, pre_fix, f_name, post_fix='.dat', header='' ): """ **dum_scans_ascii** Produce ASCII-type files with columns of energy, signal, and Poisson error. Args: scan_numbers (int or list): SPEC scan numbers of scans to be safed in ASCII format. pre_fix (str): Path to directory where files should be written into. f_name (str): Base name for the files to be written. post_fix (str): Extention for the files to be written (default is '.dat'). """ # make sure scan_numbers are iterable numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers for number in numbers: scan_name = 'Scan%03d' % number if not scan_name in self.scans.keys(): print ('Scan No. %03d is currently not loaded.'% number) return x = self.scans[scan_name].energy y = self.scans[scan_name].signals z = self.scans[scan_name].errors data = np.zeros((x.shape[0], y.shape[1]+z.shape[1]+1 )) data[:,0] = x data[:,1:1+y.shape[1]] = y data[:,1+y.shape[1]:1+y.shape[1]+z.shape[1]] = z file_name = pre_fix + f_name + '_' + scan_name + post_fix if len(header) == 0: header = ' Ein = ' + str(self.scans[scan_name].Ein) + ' keV' np.savetxt(file_name, data, header=header) def copy_edf_files( self, scan_numbers, dest_dir ): """ **copy_edf_files** Copies all EDF-files from given scan_numbers into given directory. Args: * scan_numbers (int or list) = Integer or list of integers defining the scan numbers of scans to be copied. * dest_dir (str) = String with absolute path for the destination. """ import shutil # make sure scan_numbers is iterable numbers = [] if not isinstance(scan_numbers,list): numbers.append(scan_numbers) else: numbers = scan_numbers fname = self.path + self.SPECfname # find EDF-file names and copy them for nscan in numbers: if use_PyMca: data, motors, counters = xrs_fileIO.PyMcaSpecRead(fname,nscan) else: data, motors, counters = xrs_fileIO.SpecRead(fname,nscan) for m in range(len(counters['ccdno'])): ccdnumber = counters['ccdno'][m] edfname = self.path + self.EDFprefix + self.EDFname + "%04d" % ccdnumber + self.EDFpostfix shutil.copy2( edfname, dest_dir ) def get_pw_matrices( self, scan_numbers, method='pixel' ): """ **get_pw_matrices** Sums scans from pixelwise ROI integration for use in the pixel-wise ROI refinement. Args: * scan_numbers (int, list): Integer or list of scan numbers to be added. * method (str): Keyword describing how to return the data (possible values are: 'pixel' for pixel-wise integration, 'column' for column-wise summation, and 'row' for row-wise summation. Returns: * Raw data in pixel-wise format. """ # make scan_numbers iterable if isinstance(scan_numbers,list): scannums = scan_numbers elif isinstance(scan_numbers,int): scannums = [scan_numbers] else: print( 'Please provide keyword \'scan_numbers\' as integer or list of integers.' ) return # make sure method is known if not method in ['pixel', 'column', 'row']: print('Unknown integration method. Use either \'pixel\', \'column\', or \'row\'') return # make sure scan exists for scannum in scannums: if not scannum in self.scan_numbers: self.load_scan(scannum, direct=False, method=method) # make sure raw_signals is existing if not self.scans['Scan%03d' % scannums[0]].raw_signals: self.get_raw_data( method=method ) # one scan only if len(scannums)==1: scanname = 'Scan%03d' % scannums[0] raw_signals = self.scans[scanname].raw_signals # dict with raw_signals monitor = self.scans[scanname].monitor # normalize data pw_matrices_norm = [] for key in sorted(raw_signals, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): if method == 'pixel': unrav_mat = np.zeros((raw_signals[key].shape[0], raw_signals[key].shape[1]*raw_signals[key].shape[2])) elif method in [ 'column' , 'row']: unrav_mat = np.zeros((raw_signals[key].shape[0], raw_signals[key].shape[1])) else: print('Method \''+method+'\' not supported, use either \'pixel\', \'column\', or \'row\'.') return for ii in range(raw_signals[key].shape[0]): if method == 'pixel': unrav_mat[ii,:] = raw_signals[key][ii,:,:].ravel() / monitor[ii] else: unrav_mat[ii,:] = raw_signals[key][ii,:] / monitor[ii] pw_matrices_norm.append(unrav_mat) # multiple scans to be added else: # start with first scan scanname = 'Scan%03d' % scannums[0] raw_signals = self.scans[scanname].raw_signals # dict with raw_signals monitor = self.scans[scanname].monitor # normalize data (first scan) pw_matrices_norm = [] for key in sorted(raw_signals, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ): if method == 'pixel': unrav_mat = np.zeros((raw_signals[key].shape[0], raw_signals[key].shape[1]*raw_signals[key].shape[2])) elif method in [ 'column' , 'row']: unrav_mat = np.zeros((raw_signals[key].shape[0], raw_signals[key].shape[1])) else: print('Method \''+method+'\' not supported, use either \'pixel\', \'column\', or \'row\'.') return for ii in range(raw_signals[key].shape[0]): if method == 'pixel': unrav_mat[ii,:] = raw_signals[key][ii,:,:].ravel() / monitor[ii] else: unrav_mat[ii,:] = raw_signals[key][ii,:] / monitor[ii] pw_matrices_norm.append(unrav_mat) # add all other scans for ii in scannums[1:]: scanname = 'Scan%03d' % ii raw_signals = self.scans[scanname].raw_signals # dict with raw_signals monitor = self.scans[scanname].monitor for key,jj in zip(sorted(raw_signals, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ), range(len(raw_signals))): if method == 'pixel': unrav_mat = np.zeros((raw_signals[key].shape[0], raw_signals[key].shape[1]*raw_signals[key].shape[2])) elif method in [ 'column' ,'row'] : unrav_mat = np.zeros((raw_signals[key].shape[0], raw_signals[key].shape[1])) for ii in range(raw_signals[key].shape[0]): if method == 'pixel': unrav_mat[ii,:] = raw_signals[key][ii,:].ravel() / monitor[ii] else: unrav_mat[ii,:] = raw_signals[key][ii,:] / monitor[ii] pw_matrices_norm[jj] += unrav_mat return pw_matrices_norm class read_id20: """ Main class for handling raw data from XRS experiments on ESRF's ID20. This class is used to read scans from SPEC files and the according EDF-files, it provides access to all tools from the xrs_rois module for defining ROIs, it can be used to integrate scans, sum them up, stitch them together, and define the energy loss scale. INPUT: * absfilename = path and filename of the SPEC-file * energycolumn = name (string) of the counter for the energy as defined in the SPEC session (counter mnemonic) * monitorcolumn = name (string) of the counter for the monitor signals as defined in the SPEC session (counter mnemonic) * edfName = name/prefix (string) of the EDF-files (default is the same as the SPEC-file name) * single_image = boolean switch, 'True' (default) if all 6 detectors are merged in a single image, 'False' if two detector images per point exist. """ def __init__(self,absfilename,energycolumn='energy',monitorcolumn='kap4dio',edfName=None,single_image=True): self.scans = {} # was a dictionary before if absfilename is not None: if not os.path.isfile(absfilename): raise Exception('IOError! No such file %s, please check filename.' % absfilename ) self.path = os.path.split(absfilename)[0] + '/' self.filename = os.path.split(absfilename)[1] if not edfName: self.edfName = os.path.split(absfilename)[1] else: self.edfName = edfName self.single_image = single_image self.scannumbers = [] self.EDF_PREFIXh = 'edf/h_' self.EDF_PREFIXv = 'edf/v_' self.EDF_PREFIX = 'edf/' self.EDF_POSTFIX = '.edf' self.DET_PIXEL_NUMx = 768 self.DET_PIXEL_NUMy = 512 self.DET_PIXEL_NUM = 256 # which column in the SPEC file to be used for the energy and monitor self.encolumn = energycolumn.lower() self.monicolumn = monitorcolumn.lower() # here are the attributes of the old rawdata class self.eloss = [] # common eloss scale for all analyzers self.energy = [] # common energy scale for all analyzers self.signals = [] # signals for all analyzers self.errors = [] # poisson errors self.qvalues = [] # for all analyzers self.groups = {} # dictionary of groups (such as 2 'elastic', or 5 'edge1', etc.) self.cenom = [] # list of center of masses of the elastic lines self.E0 = [] # energy value, mean value of self.cenom self.tth = [] # list of scattering angles (one for each ROI) self.VDtth = [] self.VUtth = [] self.VBtth = [] self.HRtth = [] self.HLtth = [] self.HBtth = [] self.resolution = [] # list of FWHM of the elastic lines for each analyzer self.signals_orig = [] # signals for all analyzers before interpolation self.errors_orig = [] # errors for all analyzers before interpolation # TTH offsets from center of V and H modules # tth offsets in one direction (horizontal for V-boxes, vertical for H-boxes) self.TTH_OFFSETS1 = np.array([5.0, 0.0, -5.0, 5.0, 0.0, -5.0, 5.0, 0.0, -5.0, 5.0, 0.0, -5.0]) # tth offsets in the other direction (horizontal for H-boxes, vertical for V-boxes) self.TTH_OFFSETS2 = np.array([-9.71, -9.75, -9.71, -3.24, -3.25, -3.24, 3.24, 3.25, 3.24, 9.71, 9.75, 9.71]) # input self.roi_obj = [] # an instance of the roi_object class from the xrs_rois module (new) def save_state_hdf5(self, filename, groupname, comment=""): import h5py h5 = h5py.File(filename,"a") h5.require_group(groupname) h5group = h5[groupname] if( "eloss" in h5group.keys() ): raise Exception(" Read data already present in " + filename+":"+groupname) for key in [ "eloss", "energy", "signals", "errors", "qvalues", ########### "groups", "cenom", "E0", "tth", "VDtth", "VUtth", "VBtth", "HRtth", "HLtth", "HBtth", "resolution", "signals_orig", "errors_orig" ]: h5group[key] = getattr(self,key) h5group["comment"] = comment h5.flush() h5.close() def load_state_hdf5(self, filename, groupname): import h5py print ( "filename " , filename ) print ( "groupname " , groupname ) h5 = h5py.File(filename,"r") h5group = h5[groupname] chiavi = {"eloss":array, "energy":array, "signals":array, "errors":array, "qvalues":array, ########### "groups":array, "cenom":array, "E0":float, "tth":array, "VDtth":array, "VUtth":array, "VBtth":array, "HRtth":array, "HLtth":array, "HBtth":array, "resolution":array, "signals_orig":array, "errors_orig":array } for key in chiavi: # print key setattr(self,key,chiavi[key]( array(h5group[key]))) h5.flush() h5.close() def save_state(self): d={} for key in [ "eloss", "energy", "signals", "errors", "qvalues", "groups", "cenom", "E0", "tth", "VDtth", "VUtth", "VBtth", "HRtth", "HLtth", "HBtth", "resolution", "signals_orig", "errors_orig" ]: d[key] = getattr(self,key) f=open("datas.pick","w") pickle.dump(d,f) f.close() def there_is_a_valid_roi_at(self,n): return n def loadscan(self,scannumbers,scantype='generic',fromtofile=False): """ Loads the files belonging to scan No. "scannumber" and puts it into an instance of the xrs_scan-class 'scan'. The default scantype is 'generic', later the scans will be grouped (and added) based on the scantype. INPUT: * scannumbers = integer or list of scannumbers that should be loaded * scantype = string describing the scan to be loaded (e.g. 'edge1' or 'K-edge') * fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) """ # make sure scannumbers are iterable (list) if not isinstance(scannumbers,list): scannums = [] scannums.append(scannumbers) else: scannums = scannumbers for number in scannums: scanname = 'Scan%03d' % number data, motors, counters, edfmats = self.readscan(number,fromtofile) # can assign some things here already (even if maybe redundant) monitor = counters[self.monicolumn] monoangle = 1 # have to check for this later ... !!! counters['pmonoa'] energy = counters[self.encolumn] # create an instance of "scan" class for every scan onescan = xrs_scans.scan(edfmats,number,energy,monitor,counters,motors,data,scantype) # assign one dictionary entry to each scan self.scans[scanname] = onescan def loadloop(self,begnums,numofregions,fromtofile=False): """ Loads a whole loop of scans based on their starting scannumbers and the number of single scans in the loop. INPUT: * begnums = list of scannumbers of the first scans of each loop (is a list) * numofregions = number of scans in each loop (integer) """ typenames = [] for n in range(numofregions): typenames.append('edge'+str(n+1)) numbers = [] for n in range(len(begnums)): for m in range(numofregions): numbers.append((begnums[n]+m)) typenames = typenames*len(begnums) for n in range(len(typenames)): thenumber = [] thenumber.append(numbers[n]) # make the scannumber into an interable list, sorry it's messy self.loadscan(thenumber,typenames[n],fromtofile) def loadelastic(self,scann,fromtofile=False): """ Loads a scan using the loadscan function and sets the scantype attribute to 'elastic'. I.e. shorthand for 'obj.loadscan(scannumber,type='elastic')'. INPUT: * scann = integer or list of integers * fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) """ self.loadscan(scann,'elastic',fromtofile) def loadlong(self,scann,fromtofile=False): """ Loads a scan using the loadscan function and sets the scantype attribute to 'long'. I.e. shorthand for 'obj.loadscan(scannumber,type='long')'. INPUT: * scann = integer or list of integers * fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) """ self.loadscan(scann,'long',fromtofile) def set_roiObj(self,roiobj): self.roi_obj = roiobj def orderrois(self,arrangement='vertical',missing=None): """ order the rois in an order provided such that e.g. autorois have the correct order """ if not self.roi_obj: print( 'Please select some ROIs first!' ) return # get some detector infos all_det_names = ['VD','VU','VB','HR','HL','HB'] all_dets = [] for name in all_det_names: all_dets.append(xrs_utilities.maxipix_det(name,arrangement)) # find the ROIs for each detector # go through all detectors and ROIs defined and see which one has centers in which detector for det in all_dets: det_x_min = det.get_pixel_range()[0] det_x_max = det.get_pixel_range()[1] det_y_min = det.get_pixel_range()[2] det_y_max = det.get_pixel_range()[3] for ii in range(len(self.roi_obj.indices)): x_mean = np.mean(self.roi_obj.x_indices[ii]) y_mean = np.mean(self.roi_obj.y_indices[ii]) if x_mean >= det_x_min and x_mean <= det_x_max and y_mean >= det_y_min and y_mean <= det_y_max: det.roi_indices.append(self.roi_obj.indices[ii]) det.roi_x_indices.append(self.roi_obj.x_indices[ii]) det.roi_y_indices.append(self.roi_obj.y_indices[ii]) det.roi_x_means.append(x_mean) det.roi_y_means.append(y_mean) # count, check with missing, break if something is wrong for det in all_dets: if not len(det.roi_indices) == 12: print( 'WARNING! Module ' + det.name + ' only has ' + '%d' % len(det.roi_indices) + ' ROIs defined, your numbering will be messed up!' ) # rearrange according to 'arrangement' keyword if arrangement == 'vertical': verticalIndex = [0,3,6,9,1,4,7,10,2,5,8,11] for det in all_dets: # order from low to high y-mean roi_coords = np.array(det.roi_indices) roi_y_means = np.array(det.roi_x_means) sorting_inds = roi_y_means.argsort() roi_coords_increasing = [roi_coords[i] for i in sorting_inds] if det.get_det_name() in ['VD','VU','VB']: # sort from high to low y-center det.roi_indices = [roi_coords_increasing[i] for i in verticalIndex[::-1]] elif det.get_det_name() in ['HR','HL','HB']: # sort from low to high y-center det.roi_indices = [roi_coords_increasing[i] for i in verticalIndex] else: print( 'Sorry, no such module.' ) # reassign all roi_obj variables... allrois = [] for det in all_dets: allrois.extend(det.roi_indices) self.roi_obj.indices = allrois self.roi_obj.roi_matrix = xrs_rois.convert_inds_to_matrix(self.roi_obj.indices,(512,768)) self.roi_obj.red_rois = xrs_rois.convert_matrix_to_redmatrix(self.roi_obj.roi_matrix) self.roi_obj.x_indices = xrs_rois.convert_inds_to_xinds(self.roi_obj.indices) self.roi_obj.y_indices = xrs_rois.convert_inds_to_yinds(self.roi_obj.indices) self.roi_obj.masks = xrs_rois.convert_roi_matrix_to_masks(self.roi_obj.roi_matrix) self.roi_obj.number_of_rois = np.amax(self.roi_obj.roi_matrix) def getrawdata(self): """ Goes through all instances of the scan class and calls it's applyrois method to sum up over all rois. """ if not np.any(self.roi_obj.indices): print( 'Please define some ROIs first.' ) return for scan in self.scans: if len(self.scans[scan].edfmats): print ("integrating "+scan) self.scans[scan].applyrois(self.roi_obj.indices) def getrawdata_pixelwise(self): """ Goes through all instances of the scan class and calls it's applyrois_pw method to extract intensities for all pixels in each ROI. """ if not np.any(self.roi_obj.indices): print( 'Please define some ROIs first.' ) return for scan in self.scans: if len(self.scans[scan].edfmats): print ("integrating pixelwise "+scan) self.scans[scan].applyrois_pw(self.roi_obj.indices) def SumDirect(self,scannumbers): sum=None for number in scannumbers: data, motors, counters, edfmats = self.readscan(number) if sum is None: sum = np.zeros(edfmats[0].shape ,"f") sum[:] += edfmats.sum(axis=0) return sum def loadscandirect(self,scannumbers,scantype='generic',fromtofile=False,scaling=None): """ Loads a scan without saving the edf files in matrices. scannumbers = integer or list of integers defining the scannumbers from the SPEC file scantype = string describing the scan to be loaded (e.g. 'edge1' or 'K-edge') fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) scaling = list of scaling factors to be applied, one for each ROI defined """ # make sure scannumbers are iterable (list) if not isinstance(scannumbers,list): scannums = [] scannums.append(scannumbers) else: scannums = scannumbers # check if there are ROIs defined if not self.roi_obj: print( 'Please define some ROIs first' ) return for number in scannums: scanname = 'Scan%03d' % number data, motors, counters, edfmats = self.readscan(number,fromtofile) # can assign some things here already (even if maybe redundant) monitor = counters[self.monicolumn] monoangle = 1 # counters['pmonoa'] # this still needs checking energy = counters[self.encolumn] # create an instance of "scan" class for every scan onescan = xrs_scans.scan(edfmats,number,energy,monitor,counters,motors,data,scantype) onescan.applyrois(self.roi_obj.indices,scaling=scaling) print( 'Deleting -- EDF-files of Scan No. %03d' % number ) onescan.edfmats = [] # delete the edfmats self.scans[scanname] = onescan def loadloopdirect(self,begnums,numofregions,fromtofile=False,scaling=None): """ Loads a whole loop of scans based on their starting scannumbers and the number of single INPUT: * begnums = list of scannumbers of the first scans of each loop (is a list) * numofregions = number of scans in each loop (integer) * fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) * scaling = list of scaling factors to be applied, one for each ROI defined """ typenames = [] for n in range(numofregions): typenames.append('edge'+str(n+1)) numbers = [] for n in range(len(begnums)): for m in range(numofregions): numbers.append((begnums[n]+m)) typenames = typenames*len(begnums) for n in range(len(typenames)): thenumber = [] thenumber.append(numbers[n]) # make the scannumber into an interable list, sorry it's messy self.loadscandirect(thenumber,typenames[n],fromtofile,scaling=scaling) def loadelasticdirect(self,scann,fromtofile=False): """ Loads a scan using the loadscan function and sets the scantype attribute to 'elastic'. I.e. shorthand for 'obj.loadscan(scannumber,type='elastic')'. INPUT: * scann = integer or list of integers * fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) """ self.loadscandirect(scann,'elastic',fromtofile) def loadlongdirect(self,scann,fromtofile=False,scaling=None): """ Loads a scan using the loadscan function and sets the scantype attribute to 'long'. I.e. shorthand for 'obj.loadscan(scannumber,type='long')'. INPUT: * scann = integer or list of integers * fromtofile = boolean flag, 'True' if the scan should be saved in a pickle-file (this is developmental) """ self.loadscandirect(scann,'long',fromtofile,scaling=scaling) def deletescan(self,scannumbers): """ Deletes scans from the class. INPUT: * scannumbers = integer or list of integers (SPEC scan numbers) to delete """ numbers = [] if not isinstance(scannumbers,list): numbers.append(scannumbers) else: numbers = scannumbers for number in numbers: scanname = 'Scan%03d' % number del(self.scans[scanname]) self.scannumbers.remove(number) def getspectrum(self, include_elastic=False, absCounts=False): """ Groups the instances of the scan class by their scantype attribute, adds equal scans (each group of equal scans) and appends them. INPUT: * include_elastic = boolean flag, skips the elastic line if set to 'False' (default) """ # find the groups allgroups = xrs_scans.findgroups(self.scans) for group in allgroups: # self.makegroup(group) onegroup = xrs_scans.makegroup_nointerp(group, absCounts=absCounts) self.groups[onegroup.get_type()] = onegroup self.energy,self.signals,self.errors = xrs_scans.appendScans(self.groups,include_elastic) self.signals_orig = self.signals self.errors_orig = self.errors def geteloss(self): """ Defines the energy loss scale for all ROIs by finding the center of mass for each ROI's elastic line. Interpolates the signals and errors onto a commom energy loss scale. Finds the resolution (FWHM) of the 'elastic' groups. """ if not 'elastic' in self.groups: print( 'Please load/integrate at least one elastic scan first!' ) return else: # reset values, in case this is run several times self.cenom = [] self.resolution = [] Origin=None valid_cenoms = [] for n in range(len(self.roi_obj.indices)): # find the center of mass for each ROI cofm = xrs_utilities.find_center_of_mass(self.groups['elastic'].energy,self.groups['elastic'].signals_orig[:,n]) self.cenom.append(cofm) if self.there_is_a_valid_roi_at(n) : valid_cenoms.append(cofm) if Origin is None : Origin = cofm # try finden the FWHM/resolution for each ROI FWHM,x0 = xrs_utilities.fwhm((self.groups['elastic'].energy - self.cenom[n])*1e3,self.groups['elastic'].signals_orig[:,n]) # try: # FWHM,x0 = xrs_utilities.fwhm((self.groups['elastic'].energy - self.cenom[n])*1e3,self.groups['elastic'].signals_orig[:,n]) # self.resolution.append(FWHM) # # append a zero if the FWHM routine fails # except: # exc_type, exc_value, exc_traceback = sys.exc_info() # print "*** print_tb:" # traceback.print_tb(exc_traceback, limit=None, file=sys.stdout) # print " need a more sofisticated way of finding the FWHM " # self.resolution.append(0.0) # need a more sofisticated way of finding the FWHM self.E0 = np.mean(valid_cenoms) # define the first eloss scale as the 'master' scale for all ROIs self.eloss = (self.energy - cofm)*1e3 # energy loss in eV # define eloss-scale for each ROI and interpolate onto the 'master' eloss-scale for n in range(len(self.roi_obj.indices)): # inserting zeros at beginning and end of the vectors to avoid interpolation errors x = (self.energy-self.cenom[n])*1e3 x = np.insert(x,0,-1e10) x = np.insert(x,-1,1e10) y = self.signals_orig[:,n] y = np.insert(y,0,0) y = np.insert(y,-1,0) f = interp1d(x,y, bounds_error=False,fill_value=0.0) self.signals[:,n] = f(self.eloss) # do the same for the errors for n in range(len(self.roi_obj.indices)): # inserting zeros at beginning and end of the vectors to avoid interpolation errors x = (self.energy-self.cenom[n])*1e3 x = np.insert(x,0,-1e10) x = np.insert(x,-1,1e10) y = self.errors_orig[:,n] y = np.insert(y,0,0) y = np.insert(y,-1,0) f = interp1d(x,y, bounds_error=False,fill_value=0.0) self.errors[:,n] = f(self.eloss) def gettths(self,rvd=0.0,rvu=0.0,rvb=0.0,rhl=0.0,rhr=0.0,rhb=0.0,order=[0,1,2,3,4,5]): """ Uses the defined TT_OFFSETS of the read_id20 class to set all scattering angles tth from the mean angle avtth of the analyzer modules. INPUT: * rhl = mean tth angle of HL module (default is 0.0) * rhr = mean tth angle of HR module (default is 0.0) * rhb = mean tth angle of HB module (default is 0.0) * rvd = mean tth angle of VD module (default is 0.0) * rvu = mean tth angle of VU module (default is 0.0) * rvb = mean tth angle of VB module (default is 0.0) * order = list of integers (0-5) which describes the order of modules in which the ROIs were defined (default is VD, VU, VB, HR, HL, HB; i.e. [0,1,2,3,4,5]) """ # reset all values, just in case mean angles are redefined (values are otherwise appended to existing values) self.VDtth = [] self.VUtth = [] self.VBtth = [] self.HRtth = [] self.HLtth = [] self.HBtth = [] self.tth = [] # horizontal modules # HL (motor name rhl) v_angles = self.TTH_OFFSETS1 h_angles = self.TTH_OFFSETS2 + rhl for n in range(len(h_angles)): self.HLtth.append( np.arccos(np.cos(np.radians(h_angles[n]))*np.cos(np.radians(v_angles[n])))*180.0/np.pi) # HR (motor name rhr) v_angles = self.TTH_OFFSETS1 h_angles = self.TTH_OFFSETS2 + rhr for n in range(len(h_angles)): self.HRtth.append( np.arccos(np.cos(np.radians(h_angles[n]))*np.cos(np.radians(v_angles[n])))*180.0/np.pi) # HB (motor name rhb) v_angles = self.TTH_OFFSETS1 h_angles = self.TTH_OFFSETS2 + rhb for n in range(len(h_angles)): self.HBtth.append( np.arccos(np.cos(np.radians(h_angles[n]))*np.cos(np.radians(v_angles[n])))*180.0/np.pi) # vertical modules # VD v_angles = self.TTH_OFFSETS2 + rvd h_angles = self.TTH_OFFSETS1 for n in range(len(h_angles)): self.VDtth.append( np.arccos(np.cos(np.radians(v_angles[n]))*np.cos(np.radians(h_angles[n])))*180.0/np.pi) # VU v_angles = self.TTH_OFFSETS2 + rvu h_angles = self.TTH_OFFSETS1 for n in range(len(h_angles)): self.VUtth.append( np.arccos(np.cos(np.radians(v_angles[n]))*np.cos(np.radians(h_angles[n])))*180.0/np.pi) # VB v_angles = self.TTH_OFFSETS2 + rvb h_angles = self.TTH_OFFSETS1 for n in range(len(h_angles)): self.VBtth.append( np.arccos(np.cos(np.radians(v_angles[n]))*np.cos(np.radians(h_angles[n])))*180.0/np.pi) # list of TTH values tth = [self.VDtth, self.VUtth, self.VBtth, self.HRtth, self.HLtth, self.HBtth] # list all TTH values in one long list ordered by the 'order'-keyword for n in order: self.tth.extend(tth[n]) def getqvals(self,invangstr=False): """ Calculates q-values from E0 and tth values in either atomic units (defalt) or inverse angstroms. """ theqvals = np.zeros_like(self.signals) if invangstr: for n in range(len(self.signals[0,:])): theqvals[:,n] = xrs_utilities.momtrans_inva(self.E0+self.eloss/1e3,self.E0,self.tth[n]) else: for n in range(len(self.signals[0,:])): theqvals[:,n] = xrs_utilities.momtrans_au(self.E0+self.eloss/1e3,self.E0,self.tth[n]) self.qvalues = theqvals def getqvals_energy(self,energy): """ Returns all q-values at a certain energy loss. INPUT: * energy = energy loss value for which all q-values are stored """ ind = np.abs(self.eloss - energy).argmin() return self.qvalues[ind,:] def copy_edf_files(self,scannumbers,destdir): """ Copies all edf-files from scan with scannumber or scannumbers into directory 'destdir' INPUT: * scannumbers = integer or list of integers defining the scannumbers from the SPEC file * destdir = string with absolute path for the destination """ import shutil numbers = [] if not isinstance(scannumbers,list): numbers.append(scannumbers) else: numbers = scannumbers fn = self.path + self.filename if not self.single_image: for n in range(len(numbers)): data, motors, counters = xrs_utilities.specread(fn,numbers[n]) for m in range(len(counters['ccdno'])): ccdnumber = counters['ccdno'][m] edfnameh = self.path + self.EDF_PREFIXh + self.filename + '_' + "%04d" % ccdnumber + self.EDF_POSTFIX edfnamev = self.path + self.EDF_PREFIXv + self.filename + '_' + "%04d" % ccdnumber + self.EDF_POSTFIX shutil.copy2(edfnameh, destdir) shutil.copy2(edfnamev, destdir) if self.single_image: for n in range(len(numbers)): data, motors, counters = xrs_utilities.specread(fn,numbers[n]) for m in range(len(counters['ccdno'])): ccdnumber = counters['ccdno'][m] edfname = self.path + self.EDF_PREFIX + self.filename + '_' + "%04d" % ccdnumber + self.EDF_POSTFIX shutil.copy2(edfname, destdir) def printlength(self,scannumbers): """ Prints the number of energy points in a scan or a number of scans. INPUT: * scannumbers = integer or list of integers """ numbers = [] if not isinstance(scannumbers,list): numbers.append(scannumbers) else: numbers = scannumbers for i in numbers: name = 'Scan%03d' % i print( 'length of scan %03d ' %i + ' is ' + str(len(self.scans[name].energy))) def removeBackgroundRoi(self,backroinum,estart=None,estop=None): if not estart: estart = self.eloss[0] if not estop: estop = self.elsos[-1] if not self.tth: print( 'Please define the scattering angles first using the gettth method!' ) return for ii in range(len(self.tth)): if ii != backroinum: inds = np.where(np.logical_and(self.eloss>=estart, mpc96.eloss<=estop)) expnorm = np.trapz(self.signals[inds,ii],self.eloss[inds]) backnorm = np.trapz(self.signals[inds,backroinum],self.eloss[inds]) background = self.signals[:,backroinum]/backnorm*expnorm self.signals[:,ii] -= background # subtract background roi def save_raw_data(self,filename): data = np.zeros((len(self.eloss),len(self.signals[0,:]))) data[:,0] = self.eloss data[:,1::] = self.signals np.savetxt(filename,data) def animation(id20read_object,scannumber,logscaling=True,timeout=-1,colormap='jet'): """ Shows the edf-files of a scan as a 'movie'. INPUT: * scannumber = integer/scannumber * logscaling = set to 'True' (default) if edf-images are to be shown on logarithmic-scale * timeout = time in seconds defining pause between two images, if negative (default) images are renewed by mouse clicks * colormap = matplotlib color scheme used in the display """ if isinstance(scannumber,list): if len(scannumber)>1: print( 'this only works for a single scan, sorry' ) return else: scannumber = scannumber[0] scanname = 'Scan%03d' % scannumber edfmats = id20read_object.scans[scanname].edfmats scanlen = np.shape(edfmats)[0] plt.ion() plt.clf() for n in range(scanlen): plt.clf() if logscaling: theimage = plt.imshow(np.log(edfmats[n,:,:])) else: theimage = plt.imshow(edfmats[n,:,:]) plt.xlabel('detector x-axis [pixel]') plt.ylabel('detector y-axis [pixel]') if timeout<0: titlestring = 'Frame No. %d' % (n+1) + ' of %d' % scanlen + ', press key or mouse botton to continue' plt.title(titlestring) else: titlestring = 'Frame No. %d' % (n+1) + ' of %d' % scanlen + ', updating every %2.2f ' % timeout + ' seconds' plt.title(titlestring) theimage.set_cmap(colormap) plt.draw() plt.waitforbuttonpress(timeout=timeout) def alignment_image(id20read_object,scannumber,motorname,filename=None): """ Loads a scan from a sample position scan (x-scan, y-scan, z-scan), lets you choose a zoomroi and constructs a 2D image from this INPUT: * scannumber = number of the scan * motorname = string that contains the motor name (must be the same as in the SPEC file) * filename = optional parameter with filename to store the image """ # load the scan data, motors, counters, edfmats = id20read_object.readscan(scannumber) # the scan motor position = counters[motorname.lower()] # define a zoom ROI image = xrs_utilities.sumx(edfmats) roi_finder_obj = roifinder_and_gui.roi_finder() roi_finder_obj.get_zoom_rois(image) # construct the image roixinds = roi_finder_obj.roi_obj.x_indices[0] roiyinds = roi_finder_obj.roi_obj.y_indices[0] # go through all edf files of the scan, sum over the height of the roi and stack the resulting lines into a matrix axesrange = [0,roiyinds[-1],position[-1],position[0]] theimage = (np.sum(edfmats[:,np.amin(roixinds):np.amax(roixinds)+1,np.amin(roiyinds):np.amax(roiyinds)+1],axis=1)) plt.close() fig = plt.figure() ax = fig.add_subplot(111) ax.imshow(np.log(theimage),extent=axesrange) ax.set_aspect('auto') plt.xlabel('pixel along the beam') ylabelstr = motorname.lower() + ' position [mm]' plt.ylabel(ylabelstr) plt.show() # save the image, if a filename is provided if filename: from .xrs_imaging import LRimage f = open(filename, 'wb') yrange = np.arange(np.amin(roixinds),np.amax(roixinds)+1) theobject = LRimage(theimage, position, yrange) pickle.dump(theobject, f, protocol=-1) f.close() def alignment_image_old(so,scan_number,motorname): """ Loads a scan from a sample position scan (x-scan, y-scan, z-scan), lets you choose a zoomroi and constructs a 2D image from this INPUT: * scannumber = number of the scan * motorname = string that contains the motor name (must be the same as in the SPEC file) * filename = optional parameter with filename to store the image """ # load the scan scan = xrs_scans.Scan() scan.load(so.path, so.SPECfname, so.EDFprefix, so.EDFname, so.EDFpostfix, scan_number) motors = scan.motors counters = scan.counters edfmats = scan.edfmats # the scan motor position = counters[motorname.lower()] # define a zoom ROI image = xrs_utilities.sumx(edfmats) roi_finder_obj = roifinder_and_gui.roi_finder() roi_finder_obj.get_zoom_rois(image) # construct the image roixinds = roi_finder_obj.roi_obj.x_indices[0] roiyinds = roi_finder_obj.roi_obj.y_indices[0] # go through all edf files of the scan, sum over the height of the roi and stack the resulting lines into a matrix axesrange = [0,roiyinds[-1],position[-1],position[0]] theimage = (np.sum(edfmats[:,np.amin(roixinds):np.amax(roixinds)+1,np.amin(roiyinds):np.amax(roiyinds)+1],axis=1)) plt.close() fig = plt.figure() ax = fig.add_subplot(111) ax.imshow(np.log(theimage),extent=axesrange) ax.set_aspect('auto') plt.xlabel('pixel along the beam') ylabelstr = motorname.lower() + ' position [mm]' plt.ylabel(ylabelstr) plt.show() def alignment_image_new( so, scan_number, log_scaling=True, cmap='Blues', interpolation='nearest'): """ Loads a scan from a sample position scan (x-scan, y-scan, z-scan), lets you choose a zoomroi and constructs a 2D image from this INPUT: so = Hydra object scannumber = number of the scan """ # try import cursor try: from matplotlib.widgets import Cursor except: pass # load the scan scan = xrs_scans.Scan() scan.load(so.path, so.SPECfname, so.EDFprefix, so.EDFname, so.EDFpostfix, scan_number) motors = scan.motors counters = scan.counters edfmats = scan.edfmats # the scan motor position = scan.energy # define a zoom ROI image = xrs_utilities.sumx(edfmats) roi_finder = roifinder_and_gui.roi_finder() roi_finder.get_zoom_rois(image) # get the image scan.get_raw_signals( roi_finder.roi_obj, method='column' ) if log_scaling: im = np.log(scan.raw_signals['ROI00']) im_orig = scan.raw_signals['ROI00'] else: im = scan.raw_signals['ROI00'] im_shape = im.shape # plot the image axes_range = [0,im.shape[1], position[-1], position[0]] plt.close() fig = plt.figure( ) #figsize=(7, 20) ) # fig, ((ax0, ax1), (ax2, ax3)) = plt.subplot(2, 2) # main plot ax0 = plt.subplot2grid((16, 16), (0, 0), colspan=12, rowspan=12) ax0.imshow( im, extent=axes_range, cmap=cmap, interpolation=interpolation ) ax0.set_ylabel( '%s position [mm]'%scan.scan_motor.lower() ) ax0.set_aspect( 'auto' ) ax0.tick_params( left=True, labelleft=True, right=True, bottom=True, top=True, labelbottom=False, direction='in') # projection onto y ax1 = plt.subplot2grid((16, 16), (0, 12), colspan=4, rowspan=12) ax1.plot( np.sum(im_orig,axis=1), position, '-k' ) ax1.set_ylim(ax0.get_ylim()) ax1.yaxis.set_label_position("right") ax1.set_ylabel( '%s position [mm]'%scan.scan_motor.lower() ) ax1.tick_params( left=True, labelleft=False, right=True, labelright=True, bottom=True, top=True, labelbottom=False, direction='in') fwhm_lab = xrs_utilities.fwhm(position, np.sum(im_orig,axis=1) ) max_int_y = np.amax(np.sum(im_orig,axis=1)) max_int_ind = np.where( np.sum(im_orig,axis=1) == max_int_y )[0] ax1.annotate('Max. @ %0.3f \n FWHM @ %0.3f'%( position[max_int_ind], fwhm_lab[1] ), (0.1,0.8), xycoords='axes fraction') # ax1.legend(['FWHM %0.3f \n @%f'%(fwhm_lab[0], fwhm_lab[1])], frameon=False, loc='best', numpoints=0.1) # projection onto x ax2 = plt.subplot2grid((16, 16), (12, 0), colspan=12, rowspan=4) ax2.plot( np.sum(im_orig,axis=0), '-k' ) ax2.set_xlim([ 0, im.shape[1]]) ax2.tick_params( left=True, labelleft=False, right=True, labelright=False, bottom=True, top=True, labelbottom=True, direction='in') ax2.set_xlabel( 'direction along beam [pixel]' ) fwhm_lab = xrs_utilities.fwhm( np.arange(len(np.sum(im_orig,axis=0))) , np.sum(im_orig,axis=0) ) max_int_x = np.amax(np.sum(im_orig,axis=0)) max_int_ind = np.where( np.sum(im_orig,axis=0) == max_int_x )[0] ax2.annotate('Max. @ %0.3f \n FWHM @ %0.3f'%( np.arange(im_orig.shape[1])[max_int_ind], fwhm_lab[1] ), (0.05,0.7), xycoords='axes fraction') try: cursor1 = Cursor(ax0, useblit=True, color='black', linewidth=1) cursor2 = Cursor(ax1, useblit=True, color='black', linewidth=1, vertOn=False, linestyle='--' ) cursor3 = Cursor(ax2, useblit=True, color='black', linewidth=1, horizOn=False, linestyle='--' ) except: pass plt.show() def get_scans_pw(id20read_object,scannumbers): """ **get_scans_pw** Sums scans from pixelwise ROI integration for use in the PW roi refinement. """ if isinstance(scannumbers,list): scannums = scannumbers elif isinstance(scannumbers,int): scannums = [scannumbers] else: print('Please provide keyword \'scannumbers\' as integer or list of integers.') return if len(scannums)==1: scanname = 'Scan%03d' % scannums[0] pw_matrices = id20read_object.scans[scanname].signals_pw # normalize data pw_matrices_norm = [] for matrix in pw_matrices: for ii in range(matrix.shape[1]): matrix[:,ii] /= id20read_object.scans[scanname].monitor pw_matrices_norm.append(matrix) else: scanname = 'Scan%03d' % scannums[0] pw_matrices = id20read_object.scans[scanname].signals_pw # normalize data pw_matrices_norm = [] for matrix in pw_matrices: for ii in range(matrix.shape[1]): matrix[:,ii] /= id20read_object.scans[scanname].monitor pw_matrices_norm.append(matrix) for ii in scannums[1:]: scanname = 'Scan%03d' % ii for jj in range(len(pw_matrices)): pw_matrix = id20read_object.scans[scanname].signals_pw[jj] pw_matrices[jj] += pw_matrix for kk in range(pw_matrix.shape[1]): pw_matrix[:,kk] /= id20read_object.scans[scanname].monitor pw_matrices_norm[jj] += pw_matrix return pw_matrices_norm xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_resolution.py000066400000000000000000000344071412732462000233700ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range #!/usr/bin/python # Filename: xrs_utilities.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group and contains practical functions, # most of which are translated from Matlab functions from the University of # Helsinki Electronic Structure Laboratory. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import os import numpy as np import matplotlib.pyplot as plt from . import xrs_utilities data_installation_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), 'resources', 'data') # tabulated data / form factors Si_bragg_data = np.loadtxt(os.path.join(data_installation_dir, 'si_bragg_params.dat' )) Si_f2 = np.loadtxt(os.path.join(data_installation_dir, 'si_f2.dat' )) Ge_bragg_data = np.loadtxt(os.path.join(data_installation_dir, 'ge_bragg_params.dat' )) Ge_f2 = np.loadtxt(os.path.join(data_installation_dir, 'ge_f2.dat' )) # constants c = 299792458.0 # m/sec h = 4.13566727e-15 # eV/s e_mass = 9.10938291e-31 # kg e_charge = 1.60217657e-19 # C epsilon_0 = 8.854187817e-12 #F/m r_electron = 1.0/(4.0*np.pi*epsilon_0)*e_charge**2/e_mass/c**2 Si_latt = 5.43095 # Angstr. Ge_latt = 5.65734992136 # Angstr. def get_crystal_data(crystal): """ **get_crystal_data** Returns mass density, atomic mass number, atomic density, and poisson ratio for given crystal ('Si' and 'Ge' so far only). Args: ----- crystal (str): Keyword for crystal used (so far only 'Si' and 'Ge'). Returns: -------- mass_density, mass_number, atom_density, poisson_ratio (floats): mass density, atomic mass number, atomic density, and poisson ratio """ # Si params mass_density_si = 2329000 # g/m**3 mass_number_si = 28 atom_density_si = mass_density_si*6.022e23/mass_number_si poisson_ratio_si = 0.22 # Ge params mass_density_ge = 5323000 # g/m**3 mass_number_ge = 73 atom_density_ge = mass_density_ge*6.022e23/mass_number_ge poisson_ratio_ge = 0.27 if crystal=='Si': return mass_density_si, mass_number_si, atom_density_si, poisson_ratio_si elif crystal=='Ge': return mass_density_ge, mass_number_ge, atom_density_ge, poisson_ratio_ge else: print('No parameters for %s crystals, yet.'%crystal) return def get_bragg_data(crystal): """ **get_bragg_data** Returns an array of hkl-indices and delE/E_p data. Args: ----- crystal (str): Keyword for crystal used (so far only 'Si' and 'Ge'). Returns: -------- array (nd.array): hkl-indices and delE/E_p data. """ if crystal == 'Si': return Si_bragg_data elif crystal == 'Ge': return Ge_bragg_data else: print('No parameters for %s crystals, yet.'%crystal) return def get_lattice_constant(crystal): """ **get_lattice_constant** Returns an array of hkl-indices and delE/E_p data. Args: ----- crystal (str): Keyword for crystal used (so far only 'Si' and 'Ge'). Returns: -------- latt (float): Lattice constant of given crystal. """ latt = {'Si': 5.43095, 'Ge': 5.65734992136, 'SIXOP': 5.430919, 'SIKOH': 5.430707, 'LIF': 4.027, 'INSB': 6.4784, 'C': 6.708, 'DIA': 3.57, 'LI': 3.41, 'TCNE': 9.736, 'CU': 3.61, 'PB': 4.95, 'NA': 4.2906, 'AL': 4.0495 } try: return latt[crystal] except: print('No parameters for %s crystals, yet.'%crystal) return def get_f2(crystal, energy): """ **get_f2** Returns the anomalous form factor f" for given element and energy. Args: ----- crystal (str): String for element (so far only 'Ge', 'Si'). energy (float,array): Energy in [keV]. Returns: -------- f2 (float, array): form factor f2. """ if crystal == 'Si': return np.interp(energy*1e3,Si_f2[:,0], Si_f2[:,1]) elif crystal == 'Ge': return np.interp(energy*1e3,Ge_f2[:,0], Ge_f2[:,1]) else: print('No parameters for %s crystals, yet.'%crystal) return def find_column( crystal, hkl ): """ **find_column** Returns the row-index for a given crystal and hkl for the Bragg data file. Args: ----- crystal (str): Keyword describing the crystal (so far only 'Ge' and 'Si'). hkl (array): HKL-indices for wanted lattice plane. Returns: -------- ind (int): Row-index. """ data = get_bragg_data(crystal) return np.where( np.logical_and(np.logical_and( data[:,0]==hkl[0], data[:,1]==hkl[1]), data[:,2]==hkl[2] ) )[0] def get_delE_over_Ep( crystal, hkl ): data = get_bragg_data(crystal) ind = find_column( crystal, hkl ) return data[ind,4] def get_thetaB( energy, crystal, hkl ): latt = get_lattice_constant(crystal) lam = h*c/energy*1e7 sqrt_hkl = np.sqrt( hkl[0]**2 + hkl[1]**2 + hkl[2]**2 ) thetaB = 180./np.pi * np.arcsin((lam * sqrt_hkl)/ (2.0* latt)) return thetaB def get_ax(rowlandR, thetaB, asymmetry): return 2.0 * rowlandR * np.sin((thetaB-asymmetry)*np.pi/180.0 ) def get_delTh_darwin_microrad( energy, crystal, hkl ): delEOverEp = get_delE_over_Ep( crystal, hkl ) thetaB = get_thetaB( energy, crystal, hkl ) return delEOverEp*np.tan(thetaB*np.pi/180.0)*1.0e6 def get_delTh_darwin_meV( energy, crystal, hkl ): delEOverEp = get_delE_over_Ep( crystal, hkl ) return delEOverEp*energy*1e6 def get_source_contrib_x(energy, source_hx, crystal, hkl, rowlandR, asymmetry): thetaB = get_thetaB(energy, crystal, hkl) ax = get_ax(rowlandR, thetaB, asymmetry) return energy*source_hx/1000.0/ax/np.tan(thetaB*np.pi/180.0)**2*1000000 # in meV def get_source_contrib_y(energy, source_hy, crystal, hkl, rowlandR, asymmetry): thetaB = get_thetaB(energy, crystal, hkl) ax = get_ax(rowlandR, thetaB, asymmetry) return energy/8.0*(source_hy/1000/ax)**2*1000000 def get_source_contrib_z(energy, source_v, crystal, hkl, rowlandR, asymmetry): thetaB = get_thetaB(energy, crystal, hkl) ax = get_ax(rowlandR, thetaB, asymmetry) return source_v/1000/ax/np.tan(thetaB*np.pi/180.0)*energy*1000000 def get_pixel_contrib(energy, pixels, crystal, hkl, rowlandR, asymmetry): thetaB = get_thetaB(energy, crystal, hkl) ax = get_ax(rowlandR, thetaB, asymmetry) return pixels/1000/(2.0*rowlandR*np.sin((thetaB-asymmetry)*np.pi/180)+2.0*rowlandR*np.sin((thetaB+asymmetry)*np.pi/180))/np.tan(thetaB*np.pi/180)*energy*1000000 def get_Johann_contrib(energy, maskD, crystal, hkl, rowlandR, asymmetry): thetaB = get_thetaB(energy, crystal, hkl) ax = get_ax(rowlandR, thetaB, asymmetry) return energy*0.5*(maskD/2.0/ax)**2/np.tan(thetaB*np.pi/180.0)/np.tan((thetaB-asymmetry)*np.pi/180.0)*1000000 def get_offRowland_contrib(energy, z ,maskD, crystal, hkl, rowlandR): thetaB = get_thetaB(energy, crystal, hkl) return energy*z*maskD/(2*rowlandR*np.sin(np.radians(thetaB))+z)/(2*rowlandR*np.sin(np.radians(thetaB)))/2.0/np.tan(np.radians(thetaB))*1000000 def get_absorption_length(crystal, energy): mass_density, mass_number, atom_density, poisson_ratio = get_crystal_data(crystal) lam = h*c/energy*1e7 f2 = get_f2_data(crystal, energy) return (2.0*r_electron * atom_density*lam*0.0000000001*f2)**(-1)*1000000 def get_stress_contrib(energy, rowlandR, crystal, hkl): mass_density, mass_number, atom_density, poisson_ratio = get_crystal_data(crystal) a_length = get_absorption_length(crystal, energy) thetaB = get_thetaB(energy, crystal, hkl) f2 = get_f2_data(crystal, energy) return energy*a_length*np.sin(np.radians(thetaB))/1000.0/(2.0*rowlandR)*np.abs(1.0/np.tan(np.radians(thetaB))**2-2*poisson_ratio)*1000000 def get_diced_resolution(energy, crystal, hkl, source_hx, source_hy, source_z, pixel_s, rowlandR, maskD, asymmetry): delE_darwin = get_delTh_darwin_meV( energy, crystal, hkl ) delE_sx = get_source_contrib_x(energy, source_hx, crystal, hkl, rowlandR, asymmetry) delE_sy = get_source_contrib_y(energy, source_hy, crystal, hkl, rowlandR, asymmetry) delE_sz = get_source_contrib_z(energy, source_z, crystal, hkl, rowlandR, asymmetry) delE_pixel = get_pixel_contrib(energy, pixel_s, crystal, hkl, rowlandR, asymmetry) delE_Johann = get_Johann_contrib(energy, maskD, crystal, hkl, rowlandR, asymmetry) return np.sqrt( delE_darwin**2 + delE_sx**2 + delE_sy**2 + delE_sz**2 + delE_pixel**2 + delE_Johann**2 ) def get_asymmetry_factor(asymmetry, energy, crystal, hkl): thetaB = get_thetaB(energy, crystal, hkl) return np.sin(np.radians(thetaB + asymmetry))/( np.sin( np.radians(thetaB - asymmetry)) ) def get_resolution_4bounce(energy, crystal, hkl, asymmetry): delTh = get_delTh_darwin_microrad( energy, crystal, hkl ) asym = get_asymmetry_factor(asymmetry, energy, crystal, hkl) thetaB = get_thetaB(energy, crystal, hkl) return delTh/np.sqrt(asym)/asym*energy/np.tan(np.radians(thetaB)) def get_sigma_v(energy, crystal, hkl, asymmetry, source_dist): # additional contribution to the divergence of the X-ray beam # at distance souce_dist from the mono, due to the asymmetry delTh = get_delTh_darwin_microrad( energy, crystal, hkl ) asym = get_asymmetry_factor(asymmetry, energy, crystal, hkl) return delTh*(asym**2 -1.0)/np.sqrt(asym**3)*source_dist def get_deltaTh_max(energy, crystal, hkl, asymmetry): # 4-bounce delTh = get_delTh_darwin_microrad( energy, crystal, hkl ) asym = get_asymmetry_factor(asymmetry, energy, crystal, hkl) return delTh*(asym**2-1.0)/np.sqrt(asym) def find_angle(hkl1, hkl2, verbose=True): """ **find_angle** Find angle between two reflections HKL1 and HKL2. Args: ----- hkl1 (list, array): First reflection. hkl2 (list, array): Second reflection. Returns: angle (float): Angle between two reflection in degrees. """ angle = xrs_utilities.vangle(hkl1, hkl2) if verbose: print('The angle between ',hkl1,' and ',hkl2,' is: %f'%angle, 'degrees.') return angle def mean_stiff( e1, e2, e3 ): phi = np.arange(0.0, 360.0, 0.01) ME = np.array([e1,e2,e3]) res21 = [] res22 = [] for pp in phi: rotmat = xrs_utilities.Omega( pp ) MEp = np.dot(rotmat, ME) Sp = xrs_utilities.stiff_compl_matrix_Si( MEp[:,0], MEp[:,1], MEp[:,2] )[0] res21.append(Sp[1,0]) res22.append(Sp[1,1]) return np.mean(res21), np.mean(res22) def delta_compress_stress( r, phi, R, ThB, e1, e2, e3, dhkl ): c = 299792458.0 h = 4.13566727e-15 ME = np.array([e1,e2,e3]).T rotmat = xrs_utilities.Omega( phi, degrees=True ) MEp = np.dot(rotmat, ME) S = xrs_utilities.stiff_compl_matrix_Si( ME[:,0], ME[:,1], ME[:,2] )[0] Sp = xrs_utilities.stiff_compl_matrix_Si( MEp[:,0], MEp[:,1], MEp[:,2] )[0] alpha = r/R #eps_zz = -alpha**2/6.0 * (Sp[2,0] + 3.0*Sp[2,1])/(mean_stiff[0] + 3.0*mean_stiff[1]) beta = np.arctan(S[2,5]/(S[2,1]-S[2,0])) denom = 2*(S[2,0]+S[2,1]) + np.sqrt((S[2,1]-S[2,0])**2+S[2,5]**2)*np.cos(2.0*phi+beta) denum = (5.0*(S[0,0]+S[1,1])+6.0*S[1,0]+S[5,5])/4.0 eps_zz = -alpha**2/6.0 * denom/denum delE = -(h*c)/(2.0*dhkl*1e-10 * np.sin(np.radians(ThB))) * eps_zz return delE def delta_compress_stress2( r, phi, R, ThB, e1, e2, e3, dhkl ): c = 299792458.0 h = 4.13566727e-15 ME = np.array([e1,e2,e3]).T S = xrs_utilities.stiff_compl_matrix_Si( ME[:,0], ME[:,1], ME[:,2] )[0] C = np.arctan(S[2,5]/(S[2,1]-S[2,0])) B = 0.5 * np.sqrt((S[2,1]-S[2,0])**2+S[1,5]**2)/(S[2,0]+S[2,1]) A1 = 2.0/(3.0*R**2) A2 = (h*c)/(dhkl*1e-10*np.sin(np.radians(ThB))) A3 = (S[2,0]+S[2,1])/(5.0*(S[0,0]+S[1,1])+6.0*S[1,0]+S[5,5]) delE = A1*A2*A3*r**2*(1.0 + B*np.cos(2.0*np.radians(phi)+C)) return delE class analyzer: """ Class for estimating analyzer resolutions. """ def __init__(self, hkl, energy, crystal='Si',asymmetry=0.0, maskD=100.0, rowlandR=500 ): # constants self.c = 299792458.0 # m/sec self.h = 4.13566727e-15 # eV/s self.Si_latt = 5.43095 # Angstr. self.Ge_latt = 5.65734992136 # Angstr. # params def find_deltaE_over_Ep(crystal, hkl): pass def find_angle(self, hkl1, hkl2, verbose=True): """ **find_angle** Find angle between two reflections HKL1 and HKL2. Args: ----- hkl1 (list, array): First reflection. hkl2 (list, array): Second reflection. Returns: angle (float): Angle between two reflection in degrees. """ angle = find_angle(hkl1, hkl2, verbose=verbose) return angle xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_rois.py000066400000000000000000001042061412732462000221340ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import six from six.moves import range #!/usr/bin/python # Filename: xrs_rois.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import numpy as np import copy import h5py import os import matplotlib.pyplot as plt from collections import Iterable # commented the *import because otherwise sphinx documents all the symbol of other packages # from xrs_utilities import * # from math_functions import * from . import xrs_utilities from . import math_functions ########################################### from matplotlib.widgets import Cursor, Button from scipy.ndimage import measurements from scipy import signal def h5_assign_force(h5group, name, item): if name in h5group: del h5group[name] h5group[name] = item class container: """ Random container class to hold values """ def __init__(self): pass sl={} sl[0 ] = slice(0 ,256) sl[256] = slice(256,512) sl[512] = slice(512,768) sl[3*256] = slice(3*256 ,3*256 + 256) sl[4*256] = slice(4*256 ,4*256 + 256) V147 = [1,4,7,10,2,5,8,11,3,6,9,12] V1296 = [12,9,6,3,11,8,5,2,10,7,4,1] V1074 = [10,7,4,1,11,8,5,2,12,9,6,3 ] V369 = [3,6,9,12,2,5,8,11,1,4,7,10 ] V1234 = [1,2,3,4] def order_generator_ascending(a,b,c,d): return [ a,b,c,d, a+1,b+1,c+1,d+1, a+2,b+2,c+2,d+2] def order_generator_descending(a,b,c,d): return [ a,b,c,d, a-1,b-1,c-1,d-1, a-2,b-2,c-2,d-2] OG_inc = order_generator_ascending OG_dec = order_generator_descending geo_informations = {(256,768,None): { "DET_PIXEL_NUM":256, "geo":[256,768], "nofrois":36, "subnames": ["RD" ,"LU","B"], "subgeos" : [(sl[0] ,sl[0]), (sl[0],sl[256]), (sl[0],sl[512])] , "analyser_nIDs": {"LU":{"3x4":OG_inc(10,7,4,1),"Vertical": OG_inc(1,4,7,10)}, "RD":{"3x4":OG_inc(1,4,7,10),"Vertical": OG_inc(1,4,7,10)}, "B": {"3x4":OG_inc(1,4,7,10),"Vertical": OG_inc(1,4,7,10)} } }, (512,768,None) : { "DET_PIXEL_NUM":256, "geo":[512,768],"nofrois":72, "subnames":["VD" , "VU","VB","HR" ,"HL","HB", ] , "subgeos" :[(sl[0],sl[0] ), (sl[0],sl[256] ), (sl[0],sl[512] ), (sl[256],sl[0] ), (sl[256],sl[256] ), (sl[256],sl[512] )], "analyser_nIDs": {"VD":{"3x4": OG_dec(12,9,6,3) ,"Vertical": OG_dec(12,9,6,3) }, "VU":{"3x4": OG_dec(3,6,9,12) ,"Vertical": OG_dec(12,9,6,3) }, "VB":{"3x4": OG_dec(3,6,9,12) ,"Vertical": OG_dec(12,9,6,3) }, "HR":{"3x4": OG_inc(1,4,7,10) ,"Vertical": OG_inc(1,4,7,10) }, "HL":{"3x4": OG_inc(10,7,4,1) ,"Vertical": OG_inc(1,4,7,10) }, "HB":{"3x4": OG_inc(10,7,4,1) ,"Vertical": OG_inc(1,4,7,10) }, } }, (256,256,None):{"DET_PIXEL_NUM":256, "geo":[256,256],"nofrois":1,"subnames":["DETECTOR"],"subgeos" : [(sl[0] ,sl[0])], "analyser_nIDs": {"DETECTOR":{"3x4":V147,"Vertical": V147}} }, (256,256,"1X1-4"):{"DET_PIXEL_NUM":256, "geo":[256,256],"nofrois":4,"subnames":["DETECTOR"],"subgeos" : [(sl[0] ,sl[0])], "analyser_nIDs": {"DETECTOR":{ "Vertical": V1234} } } } geo_informations[(256,768,"1X3-12")]=geo_informations[(256,768,None)] geo_informations[(512,768,"2X3-12")]=geo_informations[(512,768,None)] geo_informations[(256,256,"1X1-12")]=geo_informations[(256,256,None)] for NBCU in [255,256]: geo_informations[(NBCU,5*259,"1X5-1")] = { "DET_PIXEL_NUM":NBCU, "geo":[NBCU,5*259],"nofrois":5, "subnames":["A" ,"B","C","D" , "E" ] , "subgeos" :[(slice(0,NBCU),slice(259*0,259*1) ), (slice(0,NBCU),slice(259*1,259*2) ), (slice(0,NBCU),slice(259*2,259*3) ), (slice(0,NBCU),slice(259*3,259*4) ), (slice(0,NBCU),slice(259*4,259*5) ), ], "analyser_nIDs": {"A":{"Vertical":[1]}, "B":{"Vertical":[1]}, "C":{"Vertical":[1]}, "D":{"Vertical":[1]}, "E":{"Vertical":[1]}, } } geo_informations[(NBCU,5*259+1,"1X5-1")] = geo_informations[(NBCU,5*259,"1X5-1")] def get_geo_informations(shape): return geo_informations[shape] class roi_object: """ Container class to hold all relevant information about given ROIs. """ def __init__( self ): self.roi_matrix = np.array([]) # single matrix of zeros, ones, twos, ... , # n's (where n is the number of ROIs defined) self.red_rois = {} # dictionary, one entry for each ROI, each ROI # has an origin and a rectangular box of ones # and zeros defining the ROI self.indices = [] # list of list of tuples (one list of tuples # for each ROI) self.number_of_rois = 0 # number of ROIs defined self.kind = [] # keyword (e.g. 'zoom', 'line', 'auto', etc.), # certain features (esp. in imaging) are only # available for certain kinds of ROIs self.x_indices = [] # list of numpy arrays of x-indices (for each ROI) self.y_indices = [] # list of numpy arrays of y-indices (for each ROI) self.masks = [] # 3D numpy array with slices of zeros and ones (same # size as detector image) for each roi self.input_image = [] # 2D imput image that was used to define the ROIs def __add__( self, roi_obj ): """ **__add__** Allows appending two ROI objects by using the + operator. """ assert ( type(roi_obj) == type(self) ) # create a new instance new_obj = roi_object() # copy the ROIs new_obj.red_rois = copy.deepcopy( self.red_rois ) # append the other ROIs self_len = len( new_obj.red_rois ) for ii,key in enumerate( sorted( roi_obj.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) ): new_key = 'ROI%02d'%( ii+self_len ) if not new_key in list( new_obj.red_rois.keys() ): new_obj.red_rois[new_key] = roi_obj.red_rois[key] new_obj.red_rois[new_key][1][ new_obj.red_rois[new_key][1]>0 ] += self_len else: 'something fishy happened, skipping %s.'%(key) return self # add the input images new_obj.input_image = self.input_image + roi_obj.input_image # convert summed ROIs to other ROI formats new_obj.roi_matrix = convert_redmatrix_to_matrix( new_obj.red_rois, np.zeros_like( new_obj.input_image ), offsetX=0, offsetY=0 ) new_obj.masks = convert_roi_matrix_to_masks( new_obj.roi_matrix) new_obj.indices = convert_matrix_rois_to_inds( new_obj.roi_matrix) new_obj.number_of_rois = int( np.amax( new_obj.roi_matrix ) ) new_obj.x_indices = convert_inds_to_xinds( new_obj.indices ) new_obj.y_indices = convert_inds_to_yinds( new_obj.indices ) return new_obj def load_rois_fromMasksDict( self, masksDict, newshape=None, kind="zoom" ): """ **load_rois_fromMasksDict** """ self.kind=kind self.red_rois = masksDict if newshape is not None: self.roi_matrix = np.zeros(newshape) self.roi_matrix = convert_redmatrix_to_matrix( masksDict, self.roi_matrix, offsetX=0, offsetY=0) self.masks = convert_roi_matrix_to_masks(self.roi_matrix) self.indices = convert_matrix_rois_to_inds(self.roi_matrix) self.number_of_rois = int(np.amax(self.roi_matrix)) self.x_indices = convert_inds_to_xinds(self.indices) self.y_indices = convert_inds_to_yinds(self.indices) def writeH5( self, fname ): """ **writeH5** Creates an HDF5 file and writes the ROIs into it. Args: * fname (str) : Full path and filename for the HDF5 file to be created. """ if self.indices: # check if file already exists if os.path.isfile(fname): os.remove(fname) f = h5py.File(fname, "w") f.require_group("rois_definition") f["rois_definition"]["image"] = self.input_image f["rois_definition"].require_group("rois_dict") write_rois_toh5(f["rois_definition"]["rois_dict"],self.red_rois) f.close() else: print('There are no ROIs to save.') def loadH5(self,fname): """ **loadH5** Loads ROIs from an HDF5 file written by the self.writeH5() method. Args: * fname (str) : Full path and filename for the HDF5 file to be read. """ groupname="" if ":" in fname: fname, groupname = xrs_utilities.split_hdf5_address(fname) groupname = groupname +"/" f = h5py.File(fname, "r") self.input_image = f[groupname + "rois_definition"]["image"][:] self.red_rois = {} if groupname == "" : shape = load_rois_fromh5(f,self.red_rois) else: shape = load_rois_fromh5(f[groupname],self.red_rois) if 1: self.load_rois_fromMasksDict(self.red_rois , newshape = shape, kind="zoom") else: self.roi_matrix = convert_redmatrix_to_matrix( self.red_rois, np.zeros_like(self.input_image), offsetX=0, offsetY=0) self.indices = convert_matrix_rois_to_inds(self.roi_matrix) self.number_of_rois = int(np.amax(self.roi_matrix)) self.x_indices = convert_inds_to_xinds(self.indices) self.y_indices = convert_inds_to_yinds(self.indices) self.masks = convert_roi_matrix_to_masks(self.roi_matrix) def load_shadok_h5( self, fname, group_name1, group_name2='ROI_AS_SELECTED' ): """ **load_shadok_h5** Load ROIs from a HDF5-file created by the Shadok/XRS_Swissknife. """ f = h5py.File(fname, "r") self.input_image = f[group_name1][group_name2]["rois_definition"]["image"][:] self.red_rois = {} load_rois_fromh5(f[group_name1][group_name2],self.red_rois) self.roi_matrix = convert_redmatrix_to_matrix( self.red_rois, np.zeros_like(self.input_image), offsetX=0, offsetY=0) self.indices = convert_matrix_rois_to_inds(self.roi_matrix) self.number_of_rois = int(np.amax(self.roi_matrix)) self.x_indices = convert_inds_to_xinds(self.indices) self.y_indices = convert_inds_to_yinds(self.indices) self.masks = convert_roi_matrix_to_masks(self.roi_matrix) def append( self, roi_object ): """ **append** Append other ROI definitions. Args: * roi_object (roi_obj) : Instance of the roi_object class. """ assert ( type(roi_object) == type(self) ) orig_length = len( self.red_rois ) for ii,key in enumerate(sorted(roi_object.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) )): new_key = 'ROI%02d'%(ii+orig_length) self.red_rois[new_key] = roi_object.red_rois[key] self.red_rois[new_key][1][ self.red_rois[new_key][1]>0 ] += orig_length # convert summed ROIs to other ROI formats self.roi_matrix = convert_redmatrix_to_matrix( self.red_rois, np.zeros_like( self.input_image ), offsetX=0, offsetY=0 ) self.masks = convert_roi_matrix_to_masks( self.roi_matrix) self.indices = convert_matrix_rois_to_inds( self.roi_matrix) self.number_of_rois = int( np.amax( self.roi_matrix ) ) self.x_indices = convert_inds_to_xinds( self.indices ) self.y_indices = convert_inds_to_yinds( self.indices ) def get_number_of_rois( self ): """ **get_number_of_rois** Returns the number of currently defined ROIs. """ return self.number_of_rois def get_indices(self): return self.indices def get_x_indices(self): return self.x_indices def get_y_indices(self): return self.y_indices def get_bounding_boxes(self): return self.bounding_boxes def get_masks(self): return self.masks def get_copy(self): """ **get_copy** Returns a deep copy of self. """ return copy.deepcopy(self) def strip( self ): """ **strip** Strips extra zeros from border of the ROIs. """ for key in self.red_rois: num = int("".join([c for c in key if c.isdigit()])) origin = self.red_rois[key][0] data = self.red_rois[key][1] inds1, inds2 = np.where(data>0) new_data = np.zeros((inds1.max()-inds1.min()+1, inds2.max()-inds2.min()+1)) new_data = data[inds1, inds2].reshape(new_data) new_origin = (origin[0]+inds1.min(), origin[1]+inds2.min()) self.red_rois[key][0] = new_origin self.red_rois[key][1] = new_data def delete_empty_rois( self ): """ **delete_empty_rois** Deletes ROI entries that are completely empty. """ for key in self.red_rois: if not np.any(self.red_rois[key][1]) > 0: self.pop(key) def shift( self, shiftVal, direction='horiz', roi_inds=None ): """ **shift** Displaces the defined ROIs by the provided value. Args * shiftVal (int) : Value by which the ROIs should be shifted. * direction (str) : Description of which direction to shift by (can be 'horiz' or 'vert'), default is 'horiz'. * roi_inds (int) or (sequence) : Index or Sequence (iterable) for which ROIs should be shifted. If None, all ROIs defined are shifted (default.) """ if not roi_inds: inds = list(range(len(self.red_rois))) else: inds = roi_inds if not isinstance( inds, Iterable ): inds = list([inds]) for ind in inds: key = 'ROI%02d'%ind if direction == 'horiz': self.red_rois[key][0][1] += shiftVal elif direction == 'vert': self.red_rois[key][0][0] += shiftVal # convert summed ROIs to other ROI formats self.roi_matrix = convert_redmatrix_to_matrix( self.red_rois, np.zeros_like( self.input_image ), offsetX=0, offsetY=0 ) self.masks = convert_roi_matrix_to_masks( self.roi_matrix) self.indices = convert_matrix_rois_to_inds( self.roi_matrix) self.number_of_rois = int( np.amax( self.roi_matrix ) ) self.x_indices = convert_inds_to_xinds( self.indices ) self.y_indices = convert_inds_to_yinds( self.indices ) def pop( self, roi_key=None ): """ **pop** Discards a ROI. Args * roi_key (str) : Dict key for ROI to delete. If None, the ROI with highest index (defined last) will be discarded (defalt). """ # delete last ROI if no key is specified if not roi_key: roi_key = sorted(list( self.red_rois.keys()) , key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) )[-1] # make sure the ROI exists assert(roi_key in list(self.red_rois.keys()) ) # delete the ROI from the maskDict self.red_rois.pop( roi_key ) # convert summed ROIs to other ROI formats self.roi_matrix = convert_redmatrix_to_matrix( self.red_rois, np.zeros_like( self.input_image ), offsetX=0, offsetY=0 ) self.masks = convert_roi_matrix_to_masks( self.roi_matrix) self.indices = convert_matrix_rois_to_inds( self.roi_matrix) self.number_of_rois = int( np.amax( self.roi_matrix ) ) self.x_indices = convert_inds_to_xinds( self.indices ) self.y_indices = convert_inds_to_yinds( self.indices ) def show(self, cmap='Blues', interpolation='nearest', logscaling=True): """ **show** Creates a figure showing the existing ROIs. Args: * colormap (str): Image colormape (matplotlib.colors.Colormap). * interpolation (str): see matplotlib.pyplot.imshow() * logscaling (bool): Use logarithmic scaling for image, default is True. """ # make sure ROIs are defined, image exists assert len(self.red_rois)>0, "Please select some rois first." assert np.any(self.input_image), "No 2D image found." if logscaling: inds = self.input_image == 0.0 image = copy.deepcopy(self.input_image) image[inds] = 1.0 image = np.log( image ) else: image = self.input_image # prepare figure plt.ioff() # unset interactive mode fig, ax = plt.subplots() ax.imshow( image, interpolation=interpolation, cmap=cmap ) #plt.subplots_adjust(bottom=0.2) # ticks and labels ax.set_xlabel( 'x [pixels]' ) ax.set_ylabel( 'y [pixels]' ) ax.set_xlim( [0, image.shape[1]] ) ax.set_ylim( [image.shape[0], 0] ) ax.tick_params( left=False, labelleft=True, right=False, labelright=False, bottom=False, top=False, labelbottom=True ) # draw ROIs and labels for ii, key in enumerate( sorted(self.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) ): # plot the ROI as frame corner = self.red_rois[key][0] inset = self.red_rois[key][1] shape = self.red_rois[key][1].shape contour_line_x = [corner[1], corner[1]+shape[1], corner[1]+shape[1], corner[1], corner[1] ] contour_line_y = [corner[0], corner[0], corner[0]+shape[0], corner[0]+shape[0], corner[0] ] ax.plot( contour_line_x, contour_line_y, '-k', lw=0.8 ) # find center of the ROI and write the label xcenter = corner[1] + int( inset.shape[1]/2) ycenter = corner[0] + int( inset.shape[0]/2) string = '%02d' % (ii+1) plt.text( xcenter, ycenter, string ) plt.show() def get_rois_as_mask(self, image_shape): result = np.zeros( (image_shape[0],image_shape[1]), dtype="f") result[:] = np.nan if len ( self.roi_matrix) : for ii in range(int(np.amax(self.roi_matrix))): result[ self.roi_matrix == ii+1] = ii return result def mask_into_rois(self, mask, labelformat= 'ROI%02d'): tmp_mask = np.copy(mask) tmp_mask[np.isnan(tmp_mask)] = -1 nids = int(tmp_mask.max()) + 1 redmatrix = {} for ide in range(nids): indices = np.where(mask == ide) if len(indices[0]): origin = (np.amin(indices[0]), np.amin(indices[1])) bound_box = mask[np.amin(indices[0]):np.amax(indices[0])+1,np.amin(indices[1]):np.amax(indices[1])+1]+1 thekey = labelformat % ide redmatrix[thekey] = [origin,bound_box] else:# handle empty ROIs indices = np.where(mask == ide+1) origin = (0, 0) bound_box = mask[0:0,0:0] thekey =labelformat % ide redmatrix[thekey] = [origin,bound_box] self.roi_matrix = convert_redmatrix_to_matrix( redmatrix, np.zeros_like( self.input_image ), offsetX=0, offsetY=0 ) self.red_rois = convert_matrix_to_redmatrix(self.roi_matrix) self.masks = convert_roi_matrix_to_masks( self.roi_matrix) self.indices = convert_matrix_rois_to_inds( self.roi_matrix) self.number_of_rois = int( np.amax( self.roi_matrix ) ) self.x_indices = convert_inds_to_xinds( self.indices ) self.y_indices = convert_inds_to_yinds( self.indices ) def convert_redmatrix_to_matrix( masksDict, mask, offsetX=0, offsetY=0 ): for key, (pos,M) in six.iteritems(masksDict): num=int("".join([c for c in key if c.isdigit()])) S = M.shape inset = (slice(offsetY+pos[0] , offsetY+pos[0]+S[0] ), slice( offsetX+pos[1] , offsetX+pos[1]+S[1] ) ) M=np.array(M) M[np.isnan(M)] = -1 mask[ inset ][M>0] = num+1 return mask def convert_redmatrix_to_matrix_my( masksDict, mask, offsetX=0, offsetY=0): for key in masksDict: num = int("".join([c for c in key if c.isdigit()])) origin = masksDict[key][0] data = masksDict[key][1] for xx in range(len(data[:,0])): for yy in range(len(data[0,:])): if data[xx,yy] >= 1.0: mask[origin[0]+xx,origin[1]+yy] = num+1 return mask def convert_inds_to_matrix(ind_rois,image_shape): """ Converts a ROI defined by a list of lists of tuples into a ROI that is defined by an array containing zeros, ones, twos, ..., n's, where n is the number of ROIs. ind_rois = list of lists with pairs of pixel indices image_shape = touple defining the shape of the matrix for which the ROIs are valid """ roi_matrix = np.zeros(image_shape) counter = 1 for pixel in ind_rois: for xyind in pixel: roi_matrix[int(xyind[0]),int(xyind[1])] = counter counter += 1 return roi_matrix def convert_matrix_to_redmatrix(matrix_rois, labelformat= 'ROI%02d'): """ Converts a ROI defined by an array containing zeros, ones, twos, ..., n's, where n is the number of ROIs, into a dictionary with keys 'ROI00', 'ROI01', ..., 'ROInn'. Each entry of the dictionary is a list containing a tuple with the origin and the reduced ROI. matrix_roi = numpy array """ redmatrix = {} for ind in range(int(np.amax(matrix_rois))): try: indices = np.where(matrix_rois == ind+1) origin = (np.amin(indices[0]), np.amin(indices[1])) bound_box = matrix_rois[np.amin(indices[0]):np.amax(indices[0])+1,np.amin(indices[1]):np.amax(indices[1])+1] thekey =labelformat % ind redmatrix[thekey] = [origin,bound_box] except: # handle empty ROIs indices = np.where(matrix_rois == ind+1) origin = (0, 0) bound_box = matrix_rois[0:0,0:0] thekey =labelformat % ind redmatrix[thekey] = [origin,bound_box] return redmatrix def convert_inds_to_xinds(roi_inds): """ Converts ROIs defined in lists of lists of x-y-coordinate tuples into a list of x-coordinates only. """ xind_rois = [] for roi in roi_inds: xinds = [] for pair in roi: xinds.append(pair[0]) xind_rois.append(xinds) return xind_rois def convert_inds_to_yinds(roi_inds): """ Converts ROIs defined in lists of lists of x-y-coordinate tuples into a list of y-coordinates only. """ yind_rois = [] for roi in roi_inds: yinds = [] for pair in roi: yinds.append(pair[1]) yind_rois.append(yinds) return yind_rois def convert_roi_matrix_to_masks(roi_matrix): """ Converts a 2D ROI matrix with zeros, ones, twos, ..., n's (where n is the number of ROIs) to a 3D matrix with one slice of zeros and ones per ROI. """ # get the shape roi_masks = np.zeros((int(np.amax(roi_matrix)),roi_matrix.shape[0],roi_matrix.shape[1])) for ii in range(int(np.amax(roi_matrix))): inds = np.where(roi_matrix[:,:] == ii+1) for jj in range(len(inds[0])): roi_masks[ii,inds[0][jj],inds[1][jj]] = ii+1 return roi_masks def convert_matrix_rois_to_inds(roi_matrix): """ Converts a 2D ROI matrix with zeros, ones, twos, ..., n's (where n is the number of ROIs) to a list of lists each of which has tuples with coordinates for each pixel in each roi. """ rois = [] number_of_rois = int(np.amax(roi_matrix)) for ii in range(int(number_of_rois)): inds = np.where(roi_matrix[:,:] == ii+1) oneroi = [] for i in range(len(inds[0])): oneroi.append( (inds[0][i],inds[1][i]) ) rois.append(oneroi) return rois def break_down_det_image(image,pixel_num): """ Desomposes a Detector image into subimages. Returns a 3D matrix. """ # check that there are integer multiples of pixel_num in the big image if not image.shape[0] % pixel_num == 0 or not image.shape[1] % pixel_num == 0: print( 'There must be an integer number of \'pixel_num\' in the large image.') return detnum_row = image.shape[0]//pixel_num detnum_col = image.shape[1]//pixel_num num_of_dets = detnum_row * detnum_col det_images = np.zeros((num_of_dets,pixel_num,pixel_num)) x_ranges = [] for ii in range(detnum_col): x_ranges.append((ii*pixel_num, (ii+1)*pixel_num)) y_ranges = [] for ii in range(detnum_row): y_ranges.append((ii*pixel_num, (ii+1)*pixel_num)) counter = 0 offsets = [] for i in x_ranges: for j in y_ranges: det_images[counter,:,:]=image[j[0]:j[1],i[0]:i[1]] offsets.append((j[0],i[0])) counter += 1 return det_images, offsets def shift_roi_indices(indices,shift): """ Applies a given shift (xshift,yshift) to given indices. \ indices = list of (x,y)-tuples shift = (xshift,yshift) tuple """ for ind in indices: ind[0] += shift[0] ind[1] += shift[1] return indices def merge_roi_objects_by_matrix(list_of_roi_objects,large_image_shape,offsets,pixel_num): """ Merges several roi_objects into one big one using the roi_matrices. """ # prepare a big roi matrix roi_matrix = np.zeros(large_image_shape) counter = 0 for roi_obj in list_of_roi_objects: max_number = int(np.amax(roi_matrix)) single_matrix = roi_obj.roi_obj.roi_matrix inds = single_matrix != 0 single_matrix[inds] += max_number roi_matrix[offsets[counter][0]:offsets[counter][0]+pixel_num,offsets[counter][1]:offsets[counter][1]+pixel_num] = single_matrix #roi_obj.roi_obj.roi_matrix + max_number counter += 1 # make a new object to return merged_obj = roi_object() merged_obj.roi_matrix = roi_matrix merged_obj.indices = convert_matrix_rois_to_inds(roi_matrix) merged_obj.red_rois = convert_matrix_to_redmatrix(roi_matrix) merged_obj.number_of_rois = int(np.amax(roi_matrix)) merged_obj.kind = list_of_roi_objects[0].roi_obj.kind merged_obj.x_indices = convert_inds_to_xinds(merged_obj.indices) merged_obj.y_indices = convert_inds_to_yinds(merged_obj.indices) merged_obj.masks = convert_roi_matrix_to_masks(roi_matrix) return merged_obj def swap_indices_old_rois(old_indices): """ Swappes x- and y-indices from indices ROIs. """ new_indices = [] for roi in old_indices: one_new_roi = [] for point in roi: one_new_roi.append((point[1],point[0])) new_indices.append(one_new_roi) return new_indices def load_rois_fromh5_address(address): filename, groupname = xrs_utilities.split_hdf5_address(address) h5file = h5py.File(filename, "r") h5group = h5file[groupname] masks={} newshape, imagesum = load_rois_fromh5(h5group,masks, retrieveImage=True) myroi = roi_object() myroi.load_rois_fromMasksDict(masks, newshape=newshape) return myroi def load_rois_fromh5(h5group_tot,md, retrieveImage=False, metadata = None): h5group = h5group_tot["rois_definition/rois_dict"] for key in h5group.keys(): md[key]=[] md[key].append(h5group[key]["origin"][:]) md[key].append(h5group[key]["mask"][:]) if metadata is not None: newmeta = {} print("metadata", h5group[key], key) if "metadata" in h5group[key]: for kk in h5group[key]["metadata"]: newmeta[kk] = h5group[key]["metadata"][kk].value metadata[key]=newmeta h5data = h5group_tot["rois_definition/image"] shape = h5data.shape if retrieveImage: return shape, np.array(h5data[:]) else: return shape def write_rois_toh5(h5group,md, filterMask=None, metadata=None): for key in md.keys(): if key in h5group: del h5group[key] h5group.require_group(key) README="""origin : the Y and X coordinates of the bottom left corner of the mask (putting the origin at the bottom left) mask : the mask , 1 for considered pixel, zero for discarded ones. """ h5_assign_force(h5group[key], "README" , README ) h5group[key]["origin"]=md[key][0] if filterMask is None: h5group[key]["mask"]=md[key][1] else: ori = md[key][0] sh = md[key][1].shape Mfilter = filterMask[ori[0]:ori[0]+sh[0], ori[1]:ori[1]+sh[1] ] h5group[key]["mask"]=md[key][1]*Mfilter if metadata is not None: if key in metadata: metagroup = h5group[key].require_group("metadata") for kk,mdata in metadata[key].items(): metagroup[kk] = mdata def write_rois_toh5_for_resynth(h5group,md, filterMask=None, metadata=None): for original_key in md.keys(): # key = str(int(original_key[len("ROI"):])) key = original_key h5group.require_group(key) h5group[key]["origin"]=md[original_key][0] if filterMask is None: h5group[key]["mask"]=md[original_key][1] else: ori = md[original_key][0] sh = md[original_key][1].shape Mfilter = filterMask[ori[0]:ori[0]+sh[0], ori[1]:ori[1]+sh[1] ] h5group[key]["mask"]=md[key][1]*Mfilter xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_scans.py000066400000000000000000003051521412732462000222720ustar00rootroot00000000000000#!/usr/bin/python # Filename: xrs_scans.py from __future__ import absolute_import from __future__ import division from __future__ import print_function #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import numpy as np from . import xrs_utilities, math_functions, xrs_fileIO, xrs_rois import h5py import os from itertools import groupby from scipy import optimize from scipy.interpolate import Rbf from matplotlib import pylab as plt from scipy import ndimage # try to import the fast PyMCA parsers try: import PyMca5.PyMcaIO.EdfFile as EdfIO import PyMca5.PyMcaIO.specfilewrapper as SpecIO use_PyMca = True except: use_PyMca = False print( ' >>>>>>>> use_PyMca ' , use_PyMca) __metaclass__ = type # new style classes class Scan: """ **Scan** Class for manipulating scan data from the Hydra and Fourc spectrometers. All relevant information from the SPEC- and EDF-files are organized in instances of this class. Attributes: edf_mats (np.array): Array containing all 2D images that belong to the scan. number (int): Scan number as in the SPEC file. scan_type (string): Keyword, used later to group scans (add similar scans, etc.). energy (np.array): Array containing the energy axis (1D). monitor (np.array): Array containing the monitor signal (1D). counters (dictionary): Counters with assiciated data from the SPEC file. motors (dictionary): Motor positions as found in the SPEC file header. eloss (np.array): Array of the energy loss scale. signals (np.array): Array of signals extracted from the ROIs. errors (np.array): Array of Poisson errors. cenom (list): Center of mass for each ROI (used if scan is an elastic line scan). signals_pw (list): Pixelwise (PW) data, one array of PW data per ROI. errors_pw (list): Pixelwise (PW) Poisson errors, one array of PW errors per ROI. cenom_pw (list): Center of mass for each pixel. signals_pw_interp (list): Interpolated signals for each pixel. Ein (float): Incident energy, used if energy2 is scanned. raw_signals (dict): Dictionary of raw data (summed, line-by-line, pixel-by-pixel). raw_errors (dict): Dictionary of raw erros (summed, line-by-line, pixel-by-pixel). """ normalizationDict={} def __init__( self ): self.edfmats = np.array([]) self.scan_number= None self.scan_type = None self.energy = np.array([]) self.monitor = np.array([]) self.counters = {} self.motors = {} self.eloss = np.array([]) self.signals = np.array([]) self.errors = np.array([]) self.cenom = [] self.signals_pw = [] self.errors_pw = [] self.cenom_pw = [] self.signals_pw_interp = [] self.Ein = None self.scan_motor = None self.raw_signals = {} self.raw_errors = {} self.__signals_normalized__ = False def load( self, path, SPECfname, EDFprefix, EDFname, EDFpostfix, scan_number, \ direct=False, roi_obj=None, scaling=None, scan_type='generic', \ en_column=None, moni_column='izero', method='sum', comp_factor=None,\ rot_angles=None, clean_edf_stack=False, cenom_dict=None, storeInsets=False ): """ **load** Parse SPEC-file and EDF-files for loading a scan. Note: If 'direct' is 'True' all EDF-files will be deleted after application of the ROIs. Args: path (str): Absolute path to directory in which the SPEC-file is located. SPECfname (str): SPEC-file name. EDFprefix (str): Prefix for the EDF-files. EDFpostfix (str): Postfix for the EDF-files. scan_number (int): Scan number of the scan to be loaded. direct (boolean): If 'True', all EDF-files will be deleted after loading the scan. method (str): Keyword specifying the selected choice of data treatment: can be 'sum', 'row', 'pixel', or 'column'. Default is 'sum'. """ print( 'Parsing EDF- and SPEC-files of scan No. %s.' % scan_number) self.scan_number = scan_number # load SPEC-file fname = os.path.join(path , SPECfname) if use_PyMca == True: spec_data, self.motors, self.counters, lables = xrs_fileIO.PyMcaSpecRead_my(fname,scan_number) else: spec_data, self.motors, self.counters = xrs_fileIO.SpecRead(fname,scan_number) # if isinstance(self.motors,dict): # print(" CONVERSIONE 1 ") # self.motors = list( self.motors.items() ) # assign values, energy only if en_column is specified, first counter in SPECfile otherwise if en_column: self.energy = np.array(self.counters[en_column.lower()]) self.scan_motor = en_column.lower() else: self.energy = np.array(self.counters[lables[0].lower()]) self.scan_motor = lables[0].lower() # normalization the_moni = np.array(self.counters[moni_column.lower()]) if moni_column.lower() == 'izero': the_moni *= np.mean(self.counters['seconds']) if moni_column not in self.normalizationDict: self.normalizationDict[moni_column.lower()] = np.mean(the_moni)/ np.mean(self.counters['seconds']) self.monitor = the_moni/self.normalizationDict[moni_column.lower()] # assign the scan type self.scan_type = scan_type # load EDF-files if use_PyMca == True: self.edfmats = xrs_fileIO.ReadEdfImages_PyMca( self.counters['ccdno'], path, EDFprefix, EDFname, EDFpostfix) else: self.edfmats = xrs_fileIO.ReadEdfImages_my( self.counters['ccdno'], path, EDFprefix, EDFname, EDFpostfix ) # remove totally saturated images if clean_edf_stack: self.edfmats = edf_cleaner(self.edfmats, 1.0e8) # apply ROIs (if applicable) if direct and isinstance( roi_obj, xrs_rois.roi_object ): self.get_raw_signals( roi_obj, method=method, scaling=scaling, rot_angles=rot_angles , storeInsets = storeInsets) if cenom_dict: if method == 'row': self.get_signals( method='row', comp_factor=comp_factor, scaling=scaling ) elif method == 'sum': self.get_signals( method='sum', scaling=scaling ) elif method == 'pixel': self.get_signals( method='pixel', cenom_dict=cenom_dict ) elif method == 'pixel2': self.get_signals( method='pixel2', cenom_dict=cenom_dict ) #elif method == 'column': # self.get_signals( method='column', cenom_dict=cenom_dict ) def assign( self, edf_arrays, scan_number, energy_scale, monitor_signal, counters, \ motor_positions, specfile_data, scan_type='generic' ): """ **assign** Method to group together existing data from a scan (for backward compatibility). Args: edf_arrays (np.array): Array of all 2D images that belong to the scan. scan_number (int): Number under which this scan can be found in the SPEC file. energy_scale (np.array): Array of the energy axis. monitor_signal (np.array): Array of the monitor signal. counters (dictionary): Counters with associated data from the SPEC file. motor_positions (list): Motor positions as found in the SPEC file header. specfile_data (np.array): Matrix with all data as found in the SPEC file. scantype (str): Keyword, used later to group scans (add similar scans, etc.). """ self.edfmats = np.array(edf_arrays) self.scan_number= scan_number self.energy = np.array(energy_scale) self.monitor = np.array(monitor_signal) self.counters = counters self.motors = motor_positions # if isinstance(self.motors,dict): # print(" CONVERSIONE 1 in assign") # self.motors = list( self.motors.items() ) self.spec_data = specfile_data self.scan_type = scan_type def save_hdf5( self, fname ): """ **save_hdf5** Save a scan in an HDF5 file. Note: HDF5 files are strange for overwriting files. Args: fname (str): Path and filename for the HDF5 file. """ if isinstance(fname, h5py.Group): f=fname else: f = h5py.File(fname, "w") h5_md = f.require_group("motorDict") for mn, mv in self.motors.items(): h5_md[mn] = mv h5_md = f.require_group("counters") for mn, mv in self.counters.items(): h5_md[mn] = mv for attr in ['edfmats', 'scan_number', 'energy', 'monitor', 'scan_type','signals','errors']: f[attr] = eval( 'self.' + attr ) for key in self.used_masks.keys(): hgroup = f.require_group(key) pos, mask = self.used_masks[key] hgroup["mask"] = mask hgroup["mask_pos"] = pos if hasattr( self, "insets") : hgroup["insets"] = self.insets [key] if not isinstance(fname, h5py.Group): f.close() def load_hdf5( self, fname ): """ **load_hdf5** Load a scan from an HDF5 file. Args: fname (str): Filename of the HDF5 file. """ doclose = False if isinstance(fname, h5py.Group): f=fname else: doclose = True f = h5py.File(fname, "r") for attr in ['edfmats', 'scan_number', 'energy', 'scan_type', 'signals','errors']: setattr( self , attr ,f[attr][()]) self.motors = {} if "motorDict" in f: subGroup = f["motorDict"] for mname in subGroup: self.motors[ mname] = subGroup[mname][()] self.counters = {} if "counters" in f: subGroup = f["counters"] for mname in subGroup: self.counters[mname] = subGroup[mname][()] self.used_masks = {} for key in f: if str(key)[:3] == "ROI" : mygroup = f[key] self.used_masks[key] = [mygroup["mask_pos"][:].tolist(), np.array(mygroup["mask"][:])] if doclose : f.close() def get_raw_signals( self, roi_obj, method='sum', scaling=None, rot_angles=None, storeInsets=False ): """ **get_raw_signals** Applies given ROIs to EDF-images. Applies the provided ROIs to the EDF-images in the specified mannar: summing, line-by-line, or pixel-by-pixel. Depending on the choice, the resulting data array is 2D (sum), 3D (line-by-line), or 4D (pixel-by-pixel). The scanned direction is always the first dimension of the resulting data matrix. Args: roi_obj (instance): Instance of the 'XRStools.xrs_rois.roi_object' class defining the ROIs. method (string): Keyword specifying the selected choice of data treatment: can be 'sum', 'row', 'pixel', or 'column'. Default is 'sum'. scaling (np.array): Array of float-type scaling factors (factor for each ROI). Returns: None if there are not EDF-files to apply the ROIs to. """ self.used_masks={} if storeInsets: self.insets={} for key, (pos, M) in roi_obj.red_rois.items(): S = M.shape self.insets[key] = np.zeros( [ len(self.edfmats), S[0] , S[1] ], self.edfmats.dtype ) for ii in range(len(self.edfmats)): for key, (pos, M) in roi_obj.red_rois.items(): S = M.shape inset = (slice( pos[0], pos[0]+(S[0]) ), slice( pos[1], pos[1]+(S[1]) )) self.used_masks[key] = ( pos, M ) if storeInsets: self.insets[key][ii] = self.edfmats[ii, inset[0], inset[1]] * (M/M.max()) # sum if method == 'sum': print('selected method is \'sum\': summing up pixels from each ROI.') signals = {} # dict (one entry per ROI, with vector (one entry per energy point)) errors = {} # sqrt of the sum of counts for key, (pos, M) in roi_obj.red_rois.items(): signals[key] = np.zeros((len(self.energy))) errors[key] = np.zeros((len(self.energy))) for ii in range(len(self.edfmats)): ind = 0 for key, (pos, M) in roi_obj.red_rois.items(): S = M.shape inset = (slice( pos[0], pos[0]+(S[0]) ), slice( pos[1], pos[1]+(S[1]) )) signals[key][ii] = np.sum( self.edfmats[ii, inset[0], inset[1]] * (M/M.max())) errors[key][ii] = np.sqrt(signals[key][ii]) signals[key][ii] /= self.monitor[ii] errors[key][ii] /= self.monitor[ii] ind += 1 # row elif method == 'row': print('selected method is \'row\': summing over non-dispersive direction for each ROI.') signals = {} # dict (one entry per ROI, with 2D matrix (energy vs row)) errors = {} # sqrt of the sum of counts rot_angles_dict = {} # put possible rotation angles into dict counter = 0 for key, (pos, M) in sorted(roi_obj.red_rois.items() ): signals[key] = np.zeros((len(self.energy), M.shape[0])) errors[key] = np.zeros((len(self.energy), M.shape[0])) if rot_angles is not None: rot_angles_dict[key] = rot_angles[counter] counter += 1 for ii in range(len(self.edfmats)): ind = 0 for key, (pos, M) in roi_obj.red_rois.items(): S = M.shape inset = (slice( pos[0], pos[0]+(S[0]) ), slice( pos[1], pos[1]+(S[1]) )) # rotate raw_signals and raw_errors if method is 'line' and angles are provided if rot_angles: if len(roi_obj.red_rois) is not len(rot_angles): print('Only %d rotation angles provided for %d ROIs. Will end here.'%(len(rot_angles), len(self.raw_signals))) return # rotate images before summation orig_slice = self.edfmats[ii, inset[0], inset[1]] * (M/M.max()) slice_for_sum = ndimage.interpolation.rotate( orig_slice, rot_angles_dict[key],\ reshape=False, order=0, mode='constant' ) else: slice_for_sum = self.edfmats[ii, inset[0], inset[1]] * (M/M.max()) signals[key][ii,:] = np.sum( slice_for_sum , axis=1) errors[key][ii,:] = np.sqrt(signals[key][ii,:]) signals[key][ii,:] /= self.monitor[ii] errors[key][ii,:] /= self.monitor[ii] ind += 1 # column elif method == 'column': print('selected method is \'column\': summing over dispersive direction for each ROI.') signals = {} # dict (one entry per ROI, with 2D matrix (energy vs row)) errors = {} # sqrt of the sum of counts for key, (pos, M) in roi_obj.red_rois.items(): signals[key] = np.zeros((len(self.energy), M.shape[1])) errors[key] = np.zeros((len(self.energy), M.shape[1])) for ii in range(len(self.edfmats)): ind = 0 for key, (pos, M) in roi_obj.red_rois.items(): S = M.shape inset = (slice( pos[0], pos[0]+(S[0]) ), slice( pos[1], pos[1]+(S[1]) )) signals[key][ii,:] = np.sum( self.edfmats[ii, inset[0], inset[1]] * (M/M.max()), axis=0) errors[key][ii,:] = np.sqrt( signals[key][ii,:] ) signals[key][ii,:] /= self.monitor[ii] errors[key][ii,:] /= self.monitor[ii] ind += 1 # pixel elif method == 'pixel' or method == 'pixel2': print('selected method is \'pixel\': returning ROI pixel-by-pixel.') signals = {} # dict (one entry per ROI, with 3D matrix (energy vs pixel_0 vs pixel_1)) errors = {} # sqrt of the sum of counts for key, (pos, M) in roi_obj.red_rois.items(): signals[key] = np.zeros((len(self.energy), M.shape[0], M.shape[1])) errors[key] = np.zeros((len(self.energy), M.shape[0], M.shape[1])) for ii in range(len(self.edfmats)): ind = 0 for key, (pos, M) in roi_obj.red_rois.items(): S = M.shape inset = (slice( pos[0], pos[0]+(S[0]) ), slice( pos[1], pos[1]+(S[1]) )) signals[key][ii,:,:] = self.edfmats[ii, inset[0], inset[1]] * (M/M.max()) errors[key][ii,:,:] = np.sqrt( signals[key][ii,:,:] ) signals[key][ii,:,:] /= self.monitor[ii] errors[key][ii,:,:] /= self.monitor[ii] ind += 1 # unknown method else: print( 'Unknown integration method. Use either \'sum\', \'row\', or \'pixel\'.' ) return # set normalization self.__signals_normalized__ = True # assign for key,ii in zip(sorted(roi_obj.red_rois, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ), range(len(roi_obj.red_rois))): if np.any(scaling): self.raw_signals[key] = signals[key] * scaling[ii] self.raw_errors[key] = errors[key] * scaling[ii] else: self.raw_signals[key] = signals[key] self.raw_errors[key] = errors[key] # delete EDF-files (these are obsolete after pixel-by-pixel integration) print( 'Deleting ++ EDF-files of scan No. %s.' % self.scan_number ) self.edfmats = np.array([]) def get_signals( self, method='sum', cenom_dict=None, comp_factor=None, scaling=None, PIXEL_SIZE=0.055 ): """ **get_signals** Turns pixel-, column- or sum-wise raw-data into data. Takes the raw-data after application of the ROIs and applies the chosen compensation scheme. Args: method (str): Keyword specifying the selected choice of data treatment: can be 'sum', 'row', or 'pixel'. Default is 'sum'. cenom_dict (dict): Dictionary with one entry per ROI holding information about the center of mass of the according elastic line. comp_factor (float): Factor used in the RIXS-style line-by-line compensation. scaling (np.array): Array of float-type scaling factors (factor for each ROI). """ if method == 'sum': self.signals = np.zeros(( len(self.energy), len(self.raw_signals) )) self.errors = np.zeros(( len(self.energy), len(self.raw_signals) )) for key,ii in zip(sorted(self.raw_signals, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ), range(len(self.raw_signals))): if not self.__signals_normalized__: self.signals[:,ii] = self.raw_signals[key]/self.monitor self.errors[:,ii] = self.raw_errors[key]/self.monitor else: self.signals[:,ii] = self.raw_signals[key] self.errors[:,ii] = self.raw_errors[key] elif method == 'pixel': # assign sign of compensation if self.scan_motor == 'energy': comp_direction = 1.0 elif self.scan_motor == 'anal energy': comp_direction = -1.0 else: print('Unknown energy motor, will break here.') return self.signals = np.zeros(( len(self.energy), len(self.raw_signals) )) self.errors = np.zeros(( len(self.energy), len(self.raw_signals) )) for key,ii in zip(sorted(self.raw_signals, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ), range(len(self.raw_signals))): S = cenom_dict[key].shape master_cenom = cenom_dict[key][int(S[0]/2.),int(S[1]/2.)] for dim1 in range(self.raw_signals[key].shape[1]): for dim2 in range(self.raw_signals[key].shape[2]): x = self.energy + ( comp_direction*cenom_dict[key][dim1,dim2] - comp_direction*master_cenom ) y = self.raw_signals[key][:,dim1,dim2] rbfi = Rbf( x, y, function='linear' ) self.signals[:,ii] += rbfi(self.energy) y = self.raw_errors[key][:,dim1,dim2] rbfi = Rbf( x, y, function='linear' ) self.errors[:,ii] += rbfi(self.energy)**2 if not self.__signals_normalized__: self.errors[:,ii] = np.sqrt( self.errors[:,ii] )/self.monitor self.signals[:,ii] /= self.monitor else: self.errors[:,ii] = np.sqrt( self.errors[:,ii] ) elif method == 'pixel2': # assign sign of compensation if self.scan_motor == 'energy': comp_direction = 1.0 elif self.scan_motor == 'anal energy': comp_direction = -1.0 else: print('Unknown energy motor, will break here.') return self.signals = np.zeros(( len(self.energy), len(self.raw_signals) )) self.errors = np.zeros(( len(self.energy), len(self.raw_signals) )) energy = self.energy #* 1e3 # energy in eV ## meanmon = np.mean(self.monitor) for key,ii in zip(sorted(self.raw_signals, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ), range(len(self.raw_signals))): S = cenom_dict[key].shape #the_hist = np.histogram(cenom_dict[key][cenom_dict[key]>0.0 ], bins=10) #master_cenom = np.average(the_hist[1][1:], weights= the_hist[0])#cenom_dict[key][int(S[0]/2.),int(S[1]/2.)] master_cenom = np.mean(cenom_dict[key][cenom_dict[key]>0.0 ]) y = self.raw_signals[key] yn = y# (y.T/self.monitor).T * meanmon yc = np.zeros(( len(energy),S[0]*S[1] )) for dim1 in range(S[0]): for dim2 in range(S[1]): sort = np.argsort(energy) if cenom_dict[key][dim1,dim2] > 0.0: yc[:,dim1*dim2] = np.interp( energy[sort] -(comp_direction*(cenom_dict[key][dim1,dim2] - \ master_cenom)), energy[sort], yn[sort,dim1,dim2], \ left=float('nan'), right=float('nan') ) else: yc[:,dim1*dim2] = yn[sort,dim1,dim2] self.signals[:,ii] = xrs_utilities.nonzeroavg(yc) self.errors[:,ii] = np.sqrt(self.signals[:,ii]) # and normalize to I0 if not self.__signals_normalized__: self.signals[:,ii] /= self.monitor self.errors[:,ii] /= self.monitor elif method == 'row': # assign sign of compensation !!! test this !!! if self.scan_motor == 'energy': comp_direction = 1.0 elif self.scan_motor == 'anal energy': comp_direction = -1.0 else: print('Unknown energy motor, will break here.') return self.signals = np.zeros(( len(self.energy), len(self.raw_signals) )) self.errors = np.zeros(( len(self.energy), len(self.raw_signals) )) energy = self.energy * 1e3 # energy in eV ### meanmon = np.mean(self.monitor) for key,ii in zip(sorted(self.raw_signals, key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ), range(len(self.raw_signals))): y = self.raw_signals[key] yn = (y.T/self.monitor).T #### * meanmon CHECK THIS REMOVAL meanii = len(range(yn.shape[1]))//2 yc = np.zeros_like(y) for jj in range(yn.shape[1]): sort = np.argsort(energy) yc[:,jj] = np.interp( energy[sort] + (jj-meanii)*comp_direction*comp_factor*PIXEL_SIZE, energy[sort], yn[sort,jj], left=float('nan'), right=float('nan') ) self.signals[:,ii] = xrs_utilities.nonzeroavg(yc) self.errors[:,ii] = np.sqrt(self.signals[:,ii]) # and normalize to I0 if not self.__signals_normalized__: self.signals[:,ii] /= self.monitor self.errors[:,ii] /= self.monitor else: print( 'Unknown integration method. Use either \'sum\', \'row\', or \'pixel\'.' ) return # set normalization self.__signals_normalized__ = True def apply_rois( self, roi_obj, scaling=None ): """ **apply_rois** Sums up intensities in each ROI. Note: Old function, keeping for backward compatibility. Args: roi_obj (instance): Instance of the 'XRStools.xrs_rois.roi_object' class defining the ROIs. scaling (np.array): Array of float-type scaling factors (factor for each ROI). Returns: None if there are not EDF-files to apply the ROIs to. """ # make sure there is data loaded if self.edfmats.size == 0: print( 'Please load some EDF-files first.' ) return # apply ROIs signals = np.zeros((len(self.energy), len(roi_obj.red_rois))) for ii in range(len(self.edfmats)): ind = 0 for key, (pos, M) in roi_obj.red_rois.items(): S = M.shape inset = (slice( pos[0], pos[0]+(S[0]) ), slice( pos[1], pos[1]+(S[1]) )) signals[ii,ind] = np.sum( self.edfmats[ii, inset[0], inset[1]] * (M/M.max())) ind += 1 # assign self.signals = signals self.errors = np.sqrt(signals) # apply scaling (if applicable) if np.any(scaling): # make sure, there is one scaling factor for each ROI assert len(scaling) == signals.shape[1] for ii in range(signals.shape[1]): self.signals[:,ii] *= scaling[ii] self.errors[:,ii] *= scaling[ii] def apply_rois_pw( self,roi_obj, scaling=None ): """ **apply_rois_pw** Pixel-wise reading of the ROIs' pixels into a list of arrays. I.e. each n-pixel ROI will have n Spectra, saved in a 2D array. Args: roi_obj (instance): Instance of the 'XRStools.xrs_rois.roi_object' class defining the ROIs. scaling (list) or (np.array): Array or list of float-type scaling factors (one factor for each ROI). """ data = [] # list of 2D arrays (energy vs. intensity for each pixel inside a single ROI) errors = [] for n in range(len(roi_obj.indices)): # each ROI roidata = np.zeros((len(self.edfmats),len(roi_obj.indices[n]))) # 2D np array energy vs pixels in current roi for m in range(len(self.edfmats)): # each energy point along the scan for l in range(len(roi_obj.indices[n])): # each pixel on the detector roidata[m,l] = self.edfmats[m,roi_obj.indices[n][l][0],roi_obj.indices[n][l][1]] data.append(roidata) # list which contains one array (energy point, pixel) per ROI errors.append(np.sqrt(roidata)) self.signals_pw = data self.errors_pw = errors # apply scaling (if applicable) if np.any(scaling): # make sure, there is one scaling factor for each ROI assert len(scaling) == len(roi_obj.indices) for ii in range(len(roi_obj.indices)): self.signals_pw[ii] *= scaling[ii] self.errors_pw[ii] *= scaling[ii] def append_scan( self, scan, method='sum', where='right' ): """ **append_scan** Appends scan to the current scan, either at higher energies (where='right') or at lower energies (where='left'). Args: scan (obj): Object of the Scan class. method (str): Keyword specifying the selected choice of data treatment: can be 'sum', 'row', or 'pixel'. Default is 'sum'. where (str): Keyword specifying if the scan should be appended at lower (where='left') or highger energies (where='righ'). Default is 'right'. """ if where == 'right': self.energy = np.append( self.energy , scan.energy ) self.monitor = np.append( self.monitor, scan.monitor ) for key in self.raw_signals: self.raw_signals[key] = np.append( self.raw_signals[key], scan.raw_signals[key], axis=0 ) self.raw_errors[key] = np.append( self.raw_errors[key], scan.raw_errors[key], axis=0 ) if where == 'left': self.energy = np.append( scan.energy, self.energy ) self.monitor = np.append( scan.monitor, self.monitor ) for key in self.raw_signals: self.raw_signals[key] = np.append( scan.raw_signals[key], self.raw_signals[key], axis=0 ) self.raw_errors[key] = np.append( scan.raw_errors[key], self.raw_errors[key], axis=0 ) def insert_scan( self, scan, method='sum', where=None ): """ **insert_scan** Inserts another scan into the current instance. Args: scan (obj): Object of the Scan class. method (str): Keyword specifying the selected choice of data treatment: can be 'sum', 'row', or 'pixel'. Default is 'sum'. where (list): Optional tuple of energy values (high and low (in keV)) for where to insert the scan. By default (None), the lowest and highest energy values of the given scan will be used. """ # find where given scan should be inserted if not where: low_inds = np.where( self.energy < scan.get_E_start() )[0] high_inds = np.where( self.energy > scan.get_E_end() )[0] else: low_inds = np.where( self.energy < where[0] )[0] high_inds = np.where( self.energy > where[1] )[0] # 'sum' if method == 'sum': self.energy = np.append( self.energy[low_inds], np.append( scan.energy, self.energy[high_inds] ) ) self.monitor = np.append( self.monitor[low_inds], np.append( scan.monitor, self.monitor[high_inds] ) ) for key in self.raw_signals: self.raw_signals[key] = np.append( self.raw_signals[key][low_inds], np.append( scan.raw_signals[key], self.raw_signals[key][high_inds], axis=0 ), axis=0 ) self.raw_errors[key] = np.append( self.raw_errors[key][low_inds], np.append( scan.raw_errors[key], self.raw_errors[key][high_inds], axis=0 ), axis=0 ) # 'pixel' elif method in [ 'pixel' , 'pixel2']: self.energy = np.append( self.energy[low_inds], np.append( scan.energy, self.energy[high_inds] ) ) self.monitor = np.append( self.monitor[low_inds], np.append( scan.monitor, self.monitor[high_inds] ) ) for key in self.raw_signals: self.raw_signals[key] = np.append( self.raw_signals[key][low_inds,:,:], np.append( scan.raw_signals[key], self.raw_signals[key][high_inds,:,:], axis=0 ), axis=0 ) self.raw_errors[key] = np.append( self.raw_errors[key][low_inds,:,:], np.append( scan.raw_errors[key], self.raw_errors[key][high_inds,:,:], axis=0 ), axis=0 ) # 'row' elif method == 'row': self.energy = np.append( self.energy[low_inds], np.append( scan.energy, self.energy[high_inds] ) ) self.monitor = np.append( self.monitor[low_inds], np.append( scan.monitor, self.monitor[high_inds] ) ) for key in self.raw_signals: self.raw_signals[key] = np.append( self.raw_signals[key][low_inds,:], np.append( scan.raw_signals[key], self.raw_signals[high_inds,:], axis=0 ), axis=0 ) self.raw_errors[key] = np.append( self.raw_errors[key][low_inds,:], np.append( scan.raw_errors[key], self.raw_errors[high_inds,:], axis=0 ), axis=0 ) else: print( 'Unknown method. Use either \'sum\', \'row\', or \'pixel\'.' ) return def add_scan(self, scan, method='sum', interp=False): """ **add_scan** Adds signals from a different scan. Args: scan (obj): Object of the Scan class. method (str): Keyword specifying the selected choice of data treatment: can be 'sum', 'row', or 'pixel'. Default is 'sum'. interp (boolean): Boolean specifying if norm-conserving linear wavelet interpolation should be used, False by default. """ # @@@@@@@@@@@@@@@@@@ DA VERIFICARE TUTTO L'ACCOUNTING # @@@@@@@@@@@@@@@@@@ changed by christoph: 13/07/2018 # @@@@@@@@@@@@@@@@@@ please double check if not interp: # sum monitors av_monitor = np.sum((self.monitor, scan.monitor), axis=0) for key in self.raw_signals: if method in [ 'sum', 'row', 'column']: # recover raw counts signals1 = self.raw_signals[key]*self.monitor signals2 = scan.raw_signals[key]*scan.monitor # sum signals av_signals = np.sum((signals1, signals2), axis=0) # errors of summed signals av_errors = np.sqrt(av_signals) # assign and renormalize self.raw_signals[key] = av_signals/av_monitor self.raw_errors[key] = av_errors/av_monitor if method in [ 'pixel', 'pixel2']: # recover raw counts signals1 = self.raw_signals[key]*self.monitor[:,None,None] signals2 = scan.raw_signals[key]*scan.monitor[:,None,None] # sum signals av_signals = np.sum((signals1, signals2), axis=0) # errors of summed signals av_errors = np.sqrt(av_signals) # assign and renormalize self.raw_signals[key] = av_signals/av_monitor[:,None,None] self.raw_errors[key] = av_errors/av_monitor[:,None,None] if interp: # find longest scan dimension dim0 = np.amax([len(self.energy), len(scan.energy)]) # sum the monitor signal (always 1D) mm = np.zeros((dim0, 2))*np.nan mm[0:len(self.monitor),0] = self.monitor mm[0:len(scan.monitor),1] = scan.monitor av_monitor = np.nansum( mm , axis=1) # add also unfinished scans for key in self.raw_signals: # construct a matrix for nanmean depending on the shape of the raw_signals if method in ['sum']: # recover raw counts signals1 = self.raw_signals[key]*self.monitor signals2 = scan.raw_signals[key]*scan.monitor # signals yy = np.zeros((dim0, 2))*np.nan yy[0:len(signals1),0] = signals1 yy[0:len(signals2),1] = signals2 av_signals = np.nansum( yy , axis=1) # errors of summed signals av_errors = np.sqrt(av_signals) # self.raw_signals[key] = av_signals/av_monitor self.raw_errors[key] = av_errors/av_monitor if method in ['pixel', 'pixel2']: # recover raw counts signals1 = self.raw_signals[key]*self.monitor[:,None,None] signals2 = scan.raw_signals[key]*scan.monitor[:,None,None] # signals y1 = np.zeros((dim0, signals1.shape[1], signals1.shape[2]))*np.nan y2 = np.zeros((dim0, signals1.shape[1], signals1.shape[2]))*np.nan y1[0:len(signals1),:,:] = signals1 y2[0:len(signals2),:,:] = signals2 av_signals = np.nansum( (y1,y2) , axis=0 ) # errors of summed signals av_errors = np.sqrt( av_signals ) # self.raw_signals[key] = av_signals/av_monitor[:,None,None] self.raw_errors[key] = av_errors/av_monitor[:,None,None] if method in ['row']: # recover raw counts signals1 = self.raw_signals[key]*self.monitor[:,None] signals2 = scan.raw_signals[key]*scan.monitor[:,None] # signals y1 = np.zeros((dim0, signals1.shape[1]))*np.nan y2 = np.zeros((dim0, signals1.shape[1]))*np.nan y1[0:len(signals1),:,:] = signals1 y2[0:len(signals2),:,:] = signals2 av_signals = np.nansum( (y1,y2) , axis=0 ) # errors of summed signals av_errors = np.sqrt( av_signals ) # self.raw_signals[key] = av_signals/av_monitor[:,None] self.raw_errors[key] = av_errors/av_monitor[:,None] if interp=='Rbf': #rbfi = Rbf( scan.energy, scan.monitor, function='linear' ) #self.monitor += rbgi( self.energy ) for key in self.raw_signals: # remove zeros in errors self.raw_errors[key][self.raw_errors[key] == 0 ] = 1.0 scan.raw_errors[key][scan.raw_errors[key] == 0 ] = 1.0 # recover raw counts signals1 = self.raw_signals[key]*self.monitor signals2 = scan.raw_signals[key]*scan.monitor # sum interpolated signals rbfi = Rbf( scan.energy, signals2, function='linear' ) av_signals = signals1 + rbfi( self.energy ) # sum interpolated monitors rbfi = Rbf( scan.energy, scan.monitor, function='linear' ) av_monitor = self.monitor + rbfi( self.energy ) # errors of summed signals av_errors = np.sqrt(av_signals) # assign and renormalize self.raw_signals[key] = av_signals/av_monitor self.raw_errors[key] = av_errors/av_monitor def get_type(self): """ **get_type** Returns the type of the scan. """ return self.scan_type def get_scan_number(self): """ **get_scan_number** Returns the number of the scan. """ return self.scan_number def get_shape(self): """ **get_shape** Returns the shape of the matrix holding the signals. """ if not np.any(self.signals): print( 'please apply the ROIs first.' ) return else: return self.signals.shape def get_num_of_rois(self): """ **get_num_of_rois** Returns the number of ROIs applied to the scan. """ if not self.signals.any(): print( 'please apply the ROIs first.' ) return else: return self.signals.shape[1] def get_E_start(self): """ **get_E_start** Returs the first energy value. """ return self.energy[0] def get_E_end(self): """ **get_E_end** Returs the last energy value. """ return self.energy[-1] def get_resolution( self, keV2eV=True ): """ **get_resolution** Returns the ROI-wise resolution based on the xrs_utilities fwhm method. """ resolutions = [] for ii in range(self.signals.shape[1]): x = self.energy y = self.signals[:,ii] if keV2eV: resolutions.append( xrs_utilities.fwhm(x,y)[0]*1.0e3 ) else: resolutions.append( xrs_utilities.fwhm(x,y)[0] ) return np.array(resolutions), np.mean(resolutions), np.std(resolutions) class scan: """ Container class, holding information of single scans performed with 2D detectors. """ def __init__(self,edf_arrays,scannumber,energy_scale,monitor_signal,counters,motor_positions,specfile_data,scantype='generic'): # rawdata self.edfmats = np.array(edf_arrays) # numpy array of all 2D images that belong to the scan self.number = scannumber # number under which this scan can be found in the SPEC file self.scantype = scantype # keyword, later used to group scans (add similar scans, etc.) self.energy = np.array(energy_scale) # numpy array of the energy axis self.monitor = np.array(monitor_signal) # numpy array of the monitor signal # some things maybe not imediately necessary self.counters = counters # names of all counters that appear in the SPEC file for this scan (maybe unnecessary) self.motors = motor_positions # all motor positions as found in the SPEC file header for this scan ( " ) self.specdata = np.array(specfile_data) # all data that is also in the SPEC file for this scan # data (to be filled after defining rois) self.eloss = [] # numpy array of the energy loss scale self.signals = [] # numpy array of signals extracted from the ROIs self.errors = [] # numpy array of all Poisson errors self.cenom = [] # list with center of masses (used if scan is an elastic line scan) # pixel-wise things self.signals_pw = [] self.cenom_pw = [] self.signals_pw_interp = [] def applyrois(self,indices,scaling=None): """ Sums up intensities found in the ROIs of each detector image and stores it into the self.signals attribute. roi_object = instance of the 'rois' class redining the ROIs scaling = numpy array of numerical scaling factors (has to be one for each ROIs) """ data = np.zeros((len(self.edfmats),len(indices))) for n in range(len(indices)): # each roi for m in range(len(self.edfmats)): # each energy point along the scan for l in range(len(indices[n])): # each pixel on the detector data[m,n] += self.edfmats[m,indices[n][l][0],indices[n][l][1]] self.signals = np.array(data) self.errors = np.sqrt(data) if np.any(scaling): assert len(scaling) == len(indices) # make sure, there is one scaling factor for each roi for ii in range(len(indices)): self.signals[:,ii] *= scaling[ii] self.errors[:,ii] *= scaling[ii] def applyrois_pw(self,indices,scaling=None): """ Pixel-wise reading of the ROI's pixels into a list of arrays. I.e. each n-pixel ROI will have n Spectra, saved in a 2D array. Parameters ---------- indices : list List of indices (attribute of the xrs_rois class). scaling : list of flaots, optional Python list of scaling factors (one per ROI defined) to be applied to all pixels of that ROI. """ data = [] # list of 2D arrays (energy vs. intensity for each pixel inside a single ROI) for n in range(len(indices)): # each ROI roidata = np.zeros((len(self.edfmats),len(indices[n]))) # 2D np array energy vs pixels in current roi for m in range(len(self.edfmats)): # each energy point along the scan for l in range(len(indices[n])): # each pixel on the detector roidata[m,l] = self.edfmats[m,indices[n][l][0],indices[n][l][1]] data.append(roidata) # list which contains one array (energy point, pixel) per ROI self.signals_pw = data def get_eloss_pw(self): """ Finds the center of mass for each pixel in each ROI, sets the energy loss scale in and interpolates the signals to a common energy loss scale. Finds the resolution (FWHM) for each pixel. """ if not self.scantype == 'elastic': # return, if scantype is not elastic print( 'This method is meant for elastic line scans only!' ) return if not self.signals_pw: # return, if there is no data print( 'Please use the applyrois_pw function first!' ) return else: cenom_pw = [] resolution_pw = [] for roiind in range(len(self.signals_pw)): # each ROI oneroi_cenom = [] oneroi_resolution = [] for pixelind in range(len(self.signals_pw[roiind])): # each pixel in the ROI oneroi_cenom.append(xrs_utilities.find_center_of_mass(self.energy,self.signals_pw[roiind][:,pixelind])) try: FWHM,x0 = xrs_utilities.fwhm((self.energy - oneroi_cenom[roiind][pixelind])*1e3,self.signals_pw[roiind][:,pixelind]) oneroi_resolution.append(FWHM) except: oneroi_resolution.append(0.0) cenom_pw.append(oneroi_cenom) resolution_pw.append(oneroi_resolution) # define mean of first ROI as 'master' energy loss scale for all Pixels self.eloss = (self.energy - np.mean(cenom_pw[0]))*1e3 # average energy loss in eV from first ROI # !!!! mayb it is better to just save the CENOM values and to the interpolation later with the stiched # !!!! whole spectrum... # define eloss-scale for each ROI and interpolate onto the 'master' eloss-scale #for roiind in range(len(self.signals_pw)): # each ROI # oneroi_signals = np.zeros_like(self.signals_pw[roiind]) # for pixelind in range(len(self.signals_pw[roiind])): # x = (self.energy-self.cenom_pw[roiind][pixelind])*1e3 # y = # f = interp1d(x,y, bounds_error=False,fill_value=0.0) # self.signals[:,n] = f(self.eloss) #self.signals_pw_interp = [] def get_type(self): return self.scantype def get_scannumber(self): return self.number def get_shape(self): if not np.any(self.signals): print( 'please apply the ROIs first.' ) return else: return np.shape(self.signals) def get_numofrois(self): if not self.signals.any(): print( 'please apply the ROIs first.' ) return else: return np.shape(self.signals)[1] class scangroup: """ Container class holding information from a group of scans. """ def __init__(self,energy,signals,errors,grouptype='generic'): self.energy = energy # common energy scale self.eloss = [] # common energy loss scale self.signals = signals # summed up signals self.errors = errors # Poisson errors self.grouptype = grouptype # keyword common to all scans self.signals_orig = signals # keep a copy of uninterpolated data def get_type(self): return self.grouptype def get_meanenergy(self): return np.mean(self.energy) def get_estart(self): return self.energy[0] def get_eend(self): return self.energy[-1] def get_meanegridspacing(self): return np.mean(np.diff(self.energy)) def get_maxediff(self): return (self.energy[-1]-self.energy[0]) class Scan_group: """ Container class holding information from a group of scans. """ def __init__(self, energy, signals, errors, group_type='generic'): self.energy = energy # common energy scale self.eloss = [] # common energy loss scale self.signals = signals # summed up signals self.errors = errors # Poisson errors self.grouptype = grouptype # keyword common to all scans self.raw_signals = {} self.raw_errors = {} def get_type(self): return self.grouptype def get_meanenergy(self): return np.mean(self.energy) def get_estart(self): return self.energy[0] def get_eend(self): return self.energy[-1] def get_meanegridspacing(self): return np.mean(np.diff(self.energy)) def get_maxediff(self): return (self.energy[-1]-self.energy[0]) class RC_interp_functor: def __init__(self, RC ) : self.RC = RC def __call__(self, X, shift, factor): return factor* np.interp ( (X-shift)*np.pi/180.0, self.RC[0], self.RC[1]) class offDiaDataSet: """ **offDiaDataSet** Class to hold information from an off-diagonal dataset. """ def __init__(self): self.RCmonitor = np.array([]) self.signalMatrix = np.array([]) self.errorMatrix = np.array([]) self.motorMatrix = np.array([]) self.I0Matrix = np.array([]) self.energy = np.array([]) self.eloss = np.array([]) self.ROI_number = 0 self.G_vector = np.array([]) self.q0 = np.array([]) self.qh = np.array([]) self.k0 = np.array([]) self.kh = np.array([]) self.kprime = np.array([]) self.alignedSignalMatrix = np.array([]) self.alignedErrorMatrix = np.array([]) self.alignedRCmonitor = np.array([]) self.masterRCmotor= np.array([]) def save_hdf5( self, fname ): if isinstance(fname, h5py.Group): f=fname else: f = h5py.File(fname, "w") attrs = ["RCmonitor","signalMatrix","errorMatrix","motorMatrix","I0Matrix","energy","eloss", "ROI_number","G_vector","q0","qh","k0","kh","kprime","alignedSignalMatrix","alignedErrorMatrix","alignedRCmonitor","masterRCmotor","RC_fit"] for attr in attrs: if hasattr(self, attr): f[attr] = eval( 'self.' + attr ) if not isinstance(fname, h5py.Group): f.flush() f.close() f=None def load_hdf5( self, fname ): doclose = False if isinstance(fname, h5py.Group): f=fname else: doclose = True f = h5py.File(fname, "r") attrs = ["RCmonitor","signalMatrix","errorMatrix","motorMatrix","I0Matrix","energy","eloss", "ROI_number","G_vector","q0","qh","k0","kh","kprime","alignedSignalMatrix","alignedErrorMatrix","alignedRCmonitor","masterRCmotor"] for attr in attrs : setattr( self , attr ,f[attr][()]) if doclose : f.close() def filterDetErrors(self,threshold=3000000): print( "threshold ", threshold) inds = np.where(self.signalMatrix >= threshold) print(" ne correggo ", inds) for ii in range(len(inds[0])): self.signalMatrix[inds[0][ii], inds[1][ii]] = 0.0 self.signalMatrix[inds[0][ii], inds[1][ii]] = np.interp(inds[0][ii], [inds[0][ii]-1,inds[0][ii]+1] , [self.signalMatrix[inds[0][ii], inds[1][ii]-1], self.signalMatrix[inds[0][ii], inds[1][ii]+1]] ) def normalizeSignals(self): raise Exception("Better to call normalizeRC because signalMatrix and errorMatrix come already normalized from the Scan class" ) def normalizeRC(self): self.signalMatrix /= 1.0 self.errorMatrix /= 1.0 self.RCmonitor /= self.I0Matrix self.signalMatrix *= np.mean(1.0) self.errorMatrix *= np.mean(1.0) self.RCmonitor *= np.mean(self.I0Matrix) def alignRCmonitor(self, RCcalc): # check if data exists if not np.any(self.RCmonitor): print('Please load some data first.') return #x = np.arange(len(self.motorMatrix.T[:,0])) #RCmonitor = self.RCmonitor#[:,diagonal_inds[0]:-diagonal_inds[1]] #motorMatrix = self.motorMatrix#[:,diagonal_inds[0]:-diagonal_inds[1]] #signalMatrix= self.signalMatrix#[:,diagonal_inds[0]:-diagonal_inds[1]] RCposition = [] RCmax = [] RC_fit = [] error_tot = 0.0 for ii in range(len(self.RCmonitor)): x = self.motorMatrix[ii,:] y = self.RCmonitor[ii,:] if(len(x)>2) : assert( RCcalc is not None) # guess = [x[np.where(y == np.amax(y))[0]][0], 0.01, 1.0, np.amax(y), 1.] # popt, pcov = optimize.curve_fit(math_functions.pearson7_forcurvefit, x, y,p0=guess) # RCposition.append(popt[0]) # RC_fit.append( math_functions.pearson7_forcurvefit( x, *popt ) ) # RCmax.append(np.amax(y)) # np.savetxt( "RC.txt", np.array([ RCcalc[0]*180.0/np.pi, RCcalc[1] ]).T) # np.savetxt( "xy.txt", np.array([ x, self.RCmonitor[10,:] ]).T) guess = [ x[np.argmax( self.RCmonitor[ii,:])] - RCcalc[0][np.argmax(RCcalc[1])]*180.0/np.pi , np.amax(y)/np.amax( RCcalc[1] ) ] myfunct = RC_interp_functor( RCcalc ) popt, pcov = optimize.curve_fit( myfunct , x, y,p0=guess) RCposition.append(popt[0]) simu = myfunct( x, *popt ) diff = y - simu error_tot += ( diff*diff ) .sum() RC_fit.append( simu ) RCmax.append(np.amax(y)) else: assert( RCcalc is None) RCposition.append(0.0) RCmax.append(y.mean()) RCmax = np.array(RCmax) RCmax /= np.mean(RCmax) # possibly correct for deviations from Bragg's law #RCfit = np.polyval(np.polyfit(self.energy,self.RCposition),self.energy) #for ii in range(len(RCfit)): master_phi = self.motorMatrix[10,:]-RCposition[10] # 10!!!!!! signalMatrix = np.zeros((len(self.energy),len(master_phi))) errorMatrix = np.zeros((len(self.energy),len(master_phi))) RCmonitor = np.zeros((len(self.energy),len(master_phi))) for ii in range(len(self.energy)): signalMatrix[ii,:] = np.interp(master_phi,self.motorMatrix[ii,:]-RCposition[ii],self.signalMatrix[ii,:])/RCmax[ii] errorMatrix[ii,:] = np.interp(master_phi,self.motorMatrix[ii,:]-RCposition[ii],self.errorMatrix[ii,:] )/RCmax[ii] RCmonitor[ii,:] = np.interp(master_phi,self.motorMatrix[ii,:]-RCposition[ii],self.RCmonitor[ii,:] )/RCmax[ii] self.alignedSignalMatrix = signalMatrix self.alignedErrorMatrix = errorMatrix self.masterRCmotor = master_phi self.alignedRCmonitor = RCmonitor self.RC_fit = RC_fit return error_tot def alignRCmonitor2(self): # check if data exists if not np.any(self.RCmonitor): print('Please load some data first.') return #x = np.arange(len(self.motorMatrix.T[:,0])) #RCmonitor = self.RCmonitor#[:,diagonal_inds[0]:-diagonal_inds[1]] #motorMatrix = self.motorMatrix#[:,diagonal_inds[0]:-diagonal_inds[1]] #signalMatrix= self.signalMatrix#[:,diagonal_inds[0]:-diagonal_inds[1]] RCposition = [] for ii in range(len(self.RCmonitor)): x = self.motorMatrix[ii,:] y = self.RCmonitor[ii,:] #try: # guess = [x[np.where(y == np.amax(y))[0]][0], 0.01, 1.0, np.amax(y), 1.] # popt, pcov = optimize.curve_fit(math_functions.pearson7_forcurvefit, x, y,p0=guess) # RCposition.append(popt[0]) #except: RCposition.append(xrs_utilities.find_center_of_mass(x,y)) # possibly correct for deviations from Bragg's law #RCfit = np.polyval(np.polyfit(self.energy,self.RCposition),self.energy) #for ii in range(len(RCfit)): master_phi = self.motorMatrix[10,:]-RCposition[10] signalMatrix = np.zeros((len(self.energy),len(master_phi))) errorMatrix = np.zeros((len(self.energy),len(master_phi))) RCmonitor = np.zeros((len(self.energy),len(master_phi))) for ii in range(len(self.energy)): signalMatrix[ii,:] = np.interp(master_phi,self.motorMatrix[ii,:]-RCposition[ii],self.signalMatrix[ii,:]) errorMatrix[ii,:] = np.interp(master_phi,self.motorMatrix[ii,:]-RCposition[ii],self.errorMatrix[ii,:]) RCmonitor[ii,:] = np.interp(master_phi,self.motorMatrix[ii,:]-RCposition[ii],self.RCmonitor[ii,:]) self.alignedSignalMatrix = signalMatrix self.alignedErrorMatrix = errorMatrix self.masterRCmotor = master_phi self.alignedRCmonitor = RCmonitor def alignRCmonitorCC(self,repeat=2): """ **alignRCmonitorCC** Use cross-correlation to align data matrix according to the Rockin-Curve monitor. """ # check if data exists if not np.any(self.RCmonitor): print('Please load some data first.') return signalMatrix = np.zeros((len(self.energy),len(self.motorMatrix[0,:]))) errorMatrix = np.zeros((len(self.energy),len(self.motorMatrix[0,:]))) RCmonitor = np.zeros((len(self.energy),len(self.motorMatrix[0,:]))) # first iteration for ii in range(len(self.RCmonitor)): x0 = self.RCmonitor[0,:] x = self.RCmonitor[ii,:] y = np.correlate(x0,x,mode='same') ind = np.where(y == np.amax(y))[0] if ii == 0: master_phi = self.motorMatrix[ii,:] - self.motorMatrix[ii,ind] signalMatrix[ii,:] = np.interp(master_phi,self.motorMatrix[ii,:]-self.motorMatrix[ii,ind],self.signalMatrix[ii,:]) errorMatrix[ii,:] = np.interp(master_phi,self.motorMatrix[ii,:]-self.motorMatrix[ii,ind],self.errorMatrix[ii,:]) RCmonitor[ii,:] = np.interp(master_phi,self.motorMatrix[ii,:]-self.motorMatrix[ii,ind],self.RCmonitor[ii,:]) # further iterations if repeat: for jj in range(repeat): for ii in range(len(RCmonitor)): x0 = RCmonitor[0,:] x = RCmonitor[ii,:] y = np.correlate(x0,x,mode='same') ind = np.where(y == np.amax(y))[0] signalMatrix[ii,:] = np.interp(master_phi,master_phi-master_phi[ind],self.signalMatrix[ii,:]) errorMatrix[ii,:] = np.interp(master_phi,master_phi-master_phi[ind],self.errorMatrix[ii,:]) RCmonitor[ii,:] = np.interp(master_phi,master_phi-master_phi[ind],self.RCmonitor[ii,:]) self.alignedSignalMatrix = signalMatrix self.alignedErrorMatrix = errorMatrix self.masterRCmotor = master_phi self.alignedRCmonitor = RCmonitor def deglitchSignalMatrix(self,startpoint,stoppoint,threshold): signalMatrix = self.alignedSignalMatrix for ii in range(signalMatrix.shape[1]): ind = np.where(signalMatrix[startpoint:stoppoint,ii] >= threshold)[0] if np.any(ind): signalMatrix[startpoint+ind, ii] = (signalMatrix[startpoint+ind-1,ii] + signalMatrix[startpoint+ind+1,ii] )/2.0 self.alignedSignalMatrix = signalMatrix def interpolateMatrix(self,master_matrix,master_energy,master_RCmotor): from scipy import interpolate signalMatrix = self.alignedSignalMatrix y=self.energy x=self.masterRCmotor xx, yy = np.meshgrid(x, y) z = signalMatrix f = interpolate.interp2d(x, y, z, kind='cubic') ynew = master_energy xnew = master_RCmotor znew = f(xnew, ynew) self.interpSignalMatrix = znew self.interpEnergy = ynew self.interpRCmotor = xnew def removeElastic(self,fitrange=[-6.0,2.0]): self.energy = np.array(self.energy) cenom = [] for ii in range(self.signalMatrix.shape[1]): inds = np.where(np.logical_and(np.array(self.energy) >= fitrange[0], np.array(self.energy)<= fitrange[1]))[0] x = self.energy[inds] y = self.alignedSignalMatrix[inds,ii] guess = [x[np.where(y==np.amax(y))[0]], 0.002, 100.0, np.amax(y) ,0.0] fitfct = lambda a: np.sum( (y - math_functions.pearson7(x,a) )**2.0 ) params = optimize.minimize(fitfct, guess, method='SLSQP').x cenom.append(params[0]) back = math_functions.pearson7(self.energy,params ) plt.ion() plt.cla() plt.plot(self.energy,self.alignedSignalMatrix[:,ii],self.energy,back,self.energy,self.alignedSignalMatrix[:,ii]-back) plt.waitforbuttonpress() self.alignedSignalMatrix[:,ii] = self.alignedSignalMatrix[:,ii]-back self.eloss = (self.energy - np.mean(cenom))*1.0e3 def removeElastic2(self, fitrange1, fitrange2, guess=None): """ **removeElastic2** Subtract Pearson7 plus linear. """ self.alignedSignalMatrixB = np.zeros_like(self.alignedSignalMatrix) self.energy = np.array(self.energy) cenom = [] for ii in range(self.signalMatrix.shape[1]): region1 = np.where(np.logical_and(np.array(self.energy) >= fitrange1[0], np.array(self.energy) <= fitrange1[1]))[0] region2 = np.where(np.logical_and(np.array(self.energy) >= fitrange2[0], np.array(self.energy) <= fitrange2[1]))[0] inds = np.append(region1,region2) x = self.energy[inds] y = self.alignedSignalMatrix[inds,ii] if not guess: guess = [x[np.where(y==np.amax(y))[0]], 0.001, 1.0, np.amax(y) ,0.0, -0.1, 0.01] fitfct = lambda a: np.sum( (y - math_functions.pearson7_linear(x,a) )**2.0 ) params = optimize.minimize(fitfct, guess, method='COBYLA',tol=1e-20).x cenom.append(params[0]) back = math_functions.pearson7_linear(self.energy,params ) plt.ion() plt.cla() plt.plot(self.energy,self.alignedSignalMatrix[:,ii],self.energy,back,self.energy,self.alignedSignalMatrix[:,ii]-back) plt.waitforbuttonpress() self.alignedSignalMatrixB[:,ii] = self.alignedSignalMatrix[:,ii]-back self.eloss = (self.energy - np.mean(cenom))*1.0e3 def windowSignalMatrix(self,estart,estop): inds = np.where(np.logical_and(self.eloss>=estart, self.eloss<= estop))[0] self.alignedSignalMatrix = self.alignedSignalMatrix[inds[0]:inds[-1],:] def removeLinearBack(self,fitrange1,fitrange2): self.energy = np.array(self.energy) for ii in range(self.signalMatrix.shape[1]): region1 = np.where(np.logical_and(np.array(self.energy) >= fitrange1[0], np.array(self.energy) <= fitrange1[1])) region2 = np.where(np.logical_and(np.array(self.energy) >= fitrange2[0], np.array(self.energy) <= fitrange2[1])) region = np.append(region1,region2) x = np.array(self.energy[region]) y = self.alignedSignalMatrix[region,ii] back = np.polyval(np.polyfit(x,y,1),self.energy) self.alignedSignalMatrix[:,ii] -= back def removeConstBack(self,fitrange1,fitrange2): self.energy = np.array(self.energy) for ii in range(self.signalMatrix.shape[1]): region1 = np.where(np.logical_and(np.array(self.energy) >= fitrange1[0], np.array(self.energy) <= fitrange1[1])) region2 = np.where(np.logical_and(np.array(self.energy) >= fitrange2[0], np.array(self.energy) <= fitrange2[1])) region = np.append(region1,region2) x = np.array(self.energy[region]) y = self.alignedSignalMatrix[region,ii] back = np.polyval(np.polyfit(x,y,0),self.energy) self.alignedSignalMatrix[:,ii] -= back def removePearsonBack(self,fitrange1,fitrange2): self.energy = np.array(self.energy) for ii in range(self.signalMatrix.shape[1]): region1 = np.where(np.logical_and(np.array(self.energy) >= fitrange1[0], np.array(self.energy) <= fitrange1[1])) region2 = np.where(np.logical_and(np.array(self.energy) >= fitrange2[0], np.array(self.energy) <= fitrange2[1])) region = np.append(region1,region2) x = np.array(self.energy[region]) y = self.alignedSignalMatrix[region,ii] guess = [x[np.where(y==np.amax(y))[0]], 0.002, 100.0, np.amax(y) ,0.0] fitfct = lambda a: np.sum( (y - math_functions.pearson7(x,a) )**2.0 ) params = optimize.minimize(fitfct, guess, method='SLSQP').x back = math_functions.pearson7(self.energy,params) self.alignedSignalMatrix[:,ii] -= back def replaceSignalByConstant(self,fitrange): self.energy = np.array(self.energy) for ii in range(self.signalMatrix.shape[1]): inds = np.where(np.logical_and(np.array(self.energy) >= fitrange[0], np.array(self.energy) <= fitrange[1]))[0] x = np.array(self.energy[inds]) y = self.alignedSignalMatrix[inds,ii] back = np.polyval(np.polyfit(x,y,0),self.energy) self.alignedSignalMatrix[:,ii] = back def findgroups(scans): """ this groups together instances of the scan class based on their "scantype" attribute and returns ordered scans """ allscannames = [] for scan in scans: print( scan ) allscannames.append(scan) allscannames.sort( key = lambda x: int(''.join(filter(str.isdigit, str(x) ))) ) # allscans = [] for scan in allscannames: allscans.append(scans[scan]) allscans = sorted(allscans,key=lambda x:x.get_type()) rawgroups = [] results = groupby(allscans,lambda x:x.get_type()) print( 'The following scangroups were found:' ) for key,thegroup in results: print( key ) ls = -1 thegroup = list(thegroup) for t in thegroup: if ls!=-1 and len(t.monitor)!=ls: print( " Scan Number :" +str( t.scan_number )+" is added to group of key " +key + " but has lenght "+ str(len(t.monitor)) + " versus " + str( ls) ) rawgroups.append(list(thegroup)) return rawgroups def makegroup(groupofscans,grouptype=None): """ takes a group of scans, sums up the signals and monitors, estimates poisson errors, and returns an instance of the scangroup class (turns several instances of the "scan" class into an instance of the "scangroup" class) """ if not grouptype: grouptype = groupofscans[0].get_type() # the type of the sum of scans will be the same as the first from the list theenergy = groupofscans[0].energy # all scans are splined onto energy grid of the first scan thesignals = np.zeros(groupofscans[0].get_shape()) theerrors = np.zeros(groupofscans[0].get_shape()) themonitors = np.zeros(np.shape(groupofscans[0].monitor)) for scan in groupofscans: f = interpolate.interp1d(scan.energy,scan.monitor, bounds_error=False, fill_value=0.0) moni = f(theenergy) themonitors += moni for n in range(thesignals.shape[-1]): f = interpolate.interp1d(scan.energy,scan.signals[:,n], bounds_error=False, fill_value=0.0) signal = f(theenergy) thesignals[:,n] += signal*moni for n in range(thesignals.shape[-1]): theerrors[:,n] = np.sqrt(thesignals[:,n]) # and normalize for n in range(thesignals.shape[-1]): thesignals[:,n] = thesignals[:,n]/themonitors theerrors[:,n] = theerrors[:,n]/themonitors group = scangroup(theenergy,thesignals,theerrors,grouptype) return group def makegroup_nointerp(groupofscans,grouptype=None,absCounts=False): """ takes a group of scans, sums up the signals and monitors, estimates poisson errors, and returns an instance of the scangroup class (turns several instances of the "scan" class into an instance of the "scangroup" class), same as makegroup but withouth interpolation to account for encoder differences... may need to add some "linspace" function in case the energy scale is not monotoneous... """ if not grouptype: grouptype = groupofscans[0].get_type() # the type of the sum of scans will be the same as the first from the list theenergy = groupofscans[0].energy thesignals = np.zeros(groupofscans[0].get_shape()) theerrors = np.zeros(groupofscans[0].get_shape()) themonitors = np.zeros(np.shape(groupofscans[0].monitor)) for scan in groupofscans: themonitors += scan.monitor for n in range(thesignals.shape[-1]): thesignals[:,n] += scan.signals[:,n]* scan.monitor for n in range(thesignals.shape[-1]): theerrors[:,n] = np.sqrt(thesignals[:,n]) # and normalize for n in range(thesignals.shape[-1]): thesignals[:,n] = thesignals[:,n]/themonitors theerrors[:,n] = theerrors[:,n]/themonitors group = scangroup(theenergy,thesignals,theerrors,grouptype) return group def make_scan_group_sum( group_of_scans, group_type=None, abs_counts=False): """ **make_scan_group_sum** Sums together a list of scans with equal scan_type and returns an instance of the Scan_group class. Args: group_of_scans (list): List containing instances of the Scan class to be summed up. group_type (str): Keyword defining the type of scans, if None (default) the group_type will be the type of the first scan in the group_of_scans list. abs_counts (boolean): Boolean defining if results should be returned in absolute count units (ct/s). time_counter (str): Counter name for the SPEC counting time mnemonic. Returns: group (obj): Instance of the Scan_group container class. """ # if not provided, type of returned group will be same as first in group_of_scans if not group_type: group_type = group_of_scans[0].get_type() # create the arrays for energy, signals, and errors theenergy = group_of_scans[0].energy thesignals = np.zeros( group_of_scans[0].get_shape() ) theerrors = np.zeros( group_of_scans[0].get_shape() ) themonitors = np.zeros( np.shape(group_of_scans[0].monitor) ) # sum up the different scans in the list for scan in group_of_scans: themonitors += scan.monitor for n in range(thesignals.shape[-1]): thesignals[:,n] += scan.signals[:,n]*scan.monitor for n in range(thesignals.shape[-1]): theerrors[:,n] = np.sqrt(thesignals[:,n]) # and normalize by the sum of the monitor signal for n in range(thesignals.shape[-1]): thesignals[:,n] = thesignals[:,n]/themonitors theerrors[:,n] = theerrors[:,n]/themonitors group = Scan_group( theenergy, thesignals, theerrors, group_type ) return group def make_scan_group_pixel( group_of_scans, group_type=None, abs_counts=False ): """ **make_scan_group_pixel** Pixel-by-pixel summation of a list of scans with equal scan_type and returns an instance of the Scan_group class. Args: group_of_scans (list): List containing instances of the Scan class to be summed up. group_type (str): Keyword defining the type of scans, if None (default) the group_type will be the type of the first scan in the group_of_scans list. abs_Counts (boolean): Boolean defining if results should be returned in absolute count units (ct/s). time_counter (str): Counter name for the SPEC counting time mnemonic. Returns: group (obj): Instance of the Scan_group container class. """ # if not provided, type of returned group will be same as first in group_of_scans if not group_type: group_type = group_of_scans[0].get_type() # create the arrays/dicts for energy, signals, and errors energy = group_of_scans[0].energy monitors = np.zeros(np.shape(groupofscans[0].monitor)) raw_signals = {} raw_errors = {} for key in group_of_scans[0].raw_signals: raw_signals[key] = np.zeros_like(group_of_scans[0].raw_signals[key]) raw_errors[key] = np.zeros_like(group_of_scans[0].raw_errors[key]) # sum up the different scans in the list (pixel-by-pixel) for scan in group_of_scans: monitors += scan.monitor for key in scan.raw_signals: print(scan.raw_signals[key].shape, scan.monitor.shape) raw_signals[key] += scan.raw_signals[key]*scan.monitor for key in raw_signals: raw_errors[key] = np.sqrt(raw_signals[key]) # and normalize by the sum of the monitor signal for key in raw_signals: raw_signals[key] /= monitors raw_errors[key] /= monitors # create the group group = Scan_group( energy, np.array([]), np.array([]), group_type ) group.raw_signals = raw_signals group.raw_errors = raw_errors return group def append2Scan_right(group1,group2,inds=None,grouptype='spectrum'): """ append two instancees of the scangroup class, return instance of scangroup append group2[inds] to the right (higher energies) of group1 if inds is not None, only append rows indicated by inds to the first group """ # assert isinstance(group1,scangroup) and isinstance(group2,scangroup) energy = group1.energy signals = group1.signals errors = group1.errors gtype = group1.grouptype if not inds: energy = np.append(energy,group2.energy) signals = np.append(signals,np.squeeze(group2.signals),0) errors = np.append(errors,np.squeeze(group2.errors),0) return scangroup(energy,signals,errors,grouptype=gtype) else: energy = np.append(energy,group2.energy[inds]) signals = np.append(signals,np.squeeze(group2.signals[inds,:]),0) errors = np.append(errors,np.squeeze(group2.errors[inds,:]),0) return scangroup(energy,signals,errors,grouptype=gtype) def append2Scan_left(group1,group2,inds=None,grouptype='spectrum'): """ append two instancees of the scangroup class, return instance of scangroup append group1[inds] to the left (lower energies) of group2 if inds is not None, only append rows indicated by inds to the first group """ assert isinstance(group1,scangroup) and isinstance(group2,scangroup) if not inds: energy = group1.energy signals = group1.signals errors = group1.errors gtype = group1.grouptype energy = np.append(energy,group2.energy) signals = np.append(signals,np.squeeze(group2.signals),0) errors = np.append(errors,np.squeeze(group2.errors),0) return scangroup(energy,signals,errors,grouptype=gtype) else: energy = group1.energy[inds] signals = group1.signals[inds,:] errors = group1.errors[inds,:] gtype = group1.grouptype energy = np.append(energy,group2.energy) signals = np.append(signals,np.squeeze(group2.signals),0) errors = np.append(errors,np.squeeze(group2.errors),0) return scangroup(energy,signals,errors,grouptype=gtype) def insertScan(group1,group2,grouptype='spectrum'): """ inserts group2 into group1 NOTE! there is a numpy insert function, maybe it would be better to use that one! """ # find indices for below and above group2 lowinds = np.where(group1.energygroup2.get_eend()) energy = np.append(group1.energy[lowinds],np.append(group2.energy,group1.energy[highinds])) signals = np.append(np.squeeze(group1.signals[lowinds,:]),np.append(group2.signals,np.squeeze(group1.signals[highinds,:]),0),0) errors = np.append(np.squeeze(group1.errors[lowinds,:]),np.append(group2.errors,np.squeeze(group1.errors[highinds,:]),0),0) return scangroup(energy,signals,errors,grouptype) def catScansLong(groups,include_elastic): """ takes a longscan and inserts other backgroundscans (scans that have 'long' in their name) and other scans and inserts them into the long scan. """ # the long scan spectrum = groups['long'] # groups that don't have 'long' in the grouptype allgroups = [] for group in groups: if not 'long' in group: allgroups.append(groups[group]) allgroups.sort(key = lambda x:x.get_estart()) # groups that have 'long' in the grouptype longgroups = [] for group in groups: if 'long' in group and group != 'long': longgroups.append(groups[group]) longgroups.sort(key = lambda x:x.get_estart()) # if there are other longscans: insert those first into the long scan for group in longgroups: spectrum = insertScan(spectrum,group) # insert other scans into the long scan for group in allgroups: spectrum = insertScan(spectrum,group) # cut off the elastic line if present in the groups if 'elastic' in groups and not include_elastic: inds = np.where(spectrum.energy > groups['elastic'].get_eend())[0] return spectrum.energy[inds], spectrum.signals[inds,:], spectrum.errors[inds,:] else: return spectrum.energy, spectrum.signals, spectrum.errors def catScans(groups,include_elastic): """ concatenate all scans in groups, return the appended energy, signals, and errors """ # sort the groups by their start-energy allgroups = [] for group in groups: allgroups.append(groups[group]) allgroups.sort(key = lambda x:x.get_estart()) # assign first group to the spectrum (which is an instance of the scangroup class as well) spectrum = scangroup(allgroups[0].energy,allgroups[0].signals,allgroups[0].errors,grouptype='spectrum') # go through all other groups and append them to the right of the spectrum if len(allgroups)>1: # check if there are groups to append for group in allgroups[1:]: spectrum = append2Scan_right(spectrum,group) # cut off the elastic line if present in the groups if 'elastic' in groups and not include_elastic: inds = np.where(spectrum.energy > groups['elastic'].get_eend())[0] return spectrum.energy[inds], spectrum.signals[inds,:], spectrum.errors[inds,:] else: return spectrum.energy, spectrum.signals, spectrum.errors def catScans_pixel( groups, include_elastic ): """ **catScans_pixel** Stitch together all scans in groups in a pixel-by-pixel fashion for the case of no available long (overview) scan. Args: groups (list): List of scan-groups to be stitched together. include_elastic (boolean): Boolean switch if the elastic line should be included in the final spectrum or not. Returns: energy (np.array): Array of energy loss values. raw_signals (dict): Dictionary (one entry per ROI) of pixel-by-pixel intensities. raw_errors (dict): Dictionary (one entry per ROI) of pixel-by-pixel Poisson errors. """ # sort the groups by their start-energy all_groups = [] for group in groups: all_groups.append(groups[group]) all_groups.sort(key = lambda x:x.get_estart()) # create a scan-group for the stitched spectrum spectrum = Scan_group( all_groups[0].energy, np.array([]), np.array([]), group_type='spectrum' ) spectrum.raw_signals = all_groups[0].raw_signals spectrum.raw_errors = all_groups[0].raw_errors # go through all other groups and append them to the right of the spectrum if len(all_groups)>1: # check if there are groups to append for group in all_groups[1:]: spectrum = append2Scan_right_pixel( spectrum, group ) # cut off the elastic line if present in the groups if 'elastic' in groups and not include_elastic: inds = np.where(spectrum.energy > groups['elastic'].get_eend())[0] return spectrum.energy[inds], spectrum.signals[inds,:], spectrum.errors[inds,:] else: return spectrum.energy, spectrum.signals, spectrum.errors def appendScans(groups,include_elastic): """ try including different background scans... append groups of scans ordered by their first energy value. long scans are inserted into gaps that at greater than two times the grid of the finer scans """ # find all types of groups grouptypes = [key for key in groups.keys()] if 'long' in grouptypes: print( " going to refine " ) return catScansLong(groups,include_elastic) else: return catScans(groups,include_elastic) def appendScans_pixel( groups, include_elastic ): """ **appendScans_pixel** Decides if there is a long scan available and selects accordingly which stitching method to use. Args: groups (list): List of scan-groups to be stitched together. include_elastic (boolean): Boolean switch if the elastic line should be included in the final spectrum or not. Returns: energy (np.array): Array of energy loss values. raw_signals (dict): Dictionary (one entry per ROI) of pixel-by-pixel intensities. raw_errors (dict): Dictionary (one entry per ROI) of pixel-by-pixel Poisson errors. """ # find all types of groups grouptypes = [key for key in groups.keys()] if 'long' in grouptypes: print( " going to refine " ) return catScansLong_pixel( groups, include_elastic ) else: return catScans_pixel( groups, include_elastic ) def stitch_groups_to_spectrum(groups, method='sum', include_elastic=False ): """ **stitch_groups_to_spectrum** Takes a dictionary of instances of the Scan class and stitches them together to produce a spectrum. Long scans and scans that have 'long' in ther scan_type attribute are treated specially. Args: groups (list): List of instances of the Scan class. method (str): Keyword describing the kind of integration scheme to be used (possible values are 'sum', 'pixel', or 'row'), default is 'sum'. include_elastic (boolean): Boolean flag deciding if the elastic should be included in the final spectrum. Returns: Scan class instance with the stitched spectrum. """ # create the final spectrum spectrum = Scan() # find all groups that are not long scans, sort by acending energy all_groups = [] for group in groups: if not 'long' in group: all_groups.append(groups[group]) all_groups.sort( key = lambda x:x.get_E_start() ) # groups that have 'long' in the grouptype long_groups = [] for group in groups: # if there is a long scan if group == 'long': spectrum.energy = groups[group].energy spectrum.monitor = groups[group].monitor #if method is 'sum': # spectrum.signals = groups[group].signals # spectrum.errors = groups[group].errors #elif method is 'pixel' or 'row': spectrum.raw_signals = groups[group].raw_signals spectrum.raw_errors = groups[group].raw_errors #else: # print('Method \''+method+'\' not supported, use either \'pixel\', \'column\', or \'row\'.') # return # if there is a scan that has 'long' in its name if 'long' in group and group != 'long': long_groups.append(groups[group]) long_groups.sort( key = lambda x:x.get_E_start() ) # if long exists, insert backround groups, then other scans if np.any(spectrum.energy): for group in long_groups: spectrum.insert_scan( group, method=method ) for group in all_groups: spectrum.insert_scan( group, method=method ) # if no long scan exists, just append all others else: spectrum.energy = all_groups[0].energy spectrum.monitor = all_groups[0].monitor #if method is 'sum': spectrum.raw_signals = all_groups[0].raw_signals spectrum.raw_errors = all_groups[0].raw_errors #elif method is 'pixel' or 'row': # spectrum.raw_signals = all_groups[0].raw_signals # spectrum.raw_errors = all_groups[0].raw_errors #else: # print('Method \''+method+'\' not supported, use either \'pixel\', \'column\', or \'row\'.') # return for group in all_groups[1::]: spectrum.append_scan(group, method=method) # cut elastic line if applicable if 'elastic' in groups and not include_elastic: inds = np.where(spectrum.energy > groups['elastic'].get_E_end())[0] spectrum.energy = spectrum.energy[inds] spectrum.monitor = spectrum.monitor[inds] for key in spectrum.raw_signals: if method == 'sum': spectrum.raw_signals[key] = spectrum.raw_signals[key][inds] spectrum.raw_errors[key] = spectrum.raw_errors[key][inds] if method in [ 'pixel' , 'pixel2']: spectrum.raw_signals[key] = spectrum.raw_signals[key][inds,:,:] spectrum.raw_errors[key] = spectrum.raw_errors[key][inds,:,:] if method == 'row': spectrum.raw_signals[key] = spectrum.raw_signals[key][inds,:] spectrum.raw_errors[key] = spectrum.raw_errors[key][inds,:] return spectrum def get_XES_spectrum( groups ): """ **get_XES_spectrum** Constructs a XES spectrum from the given scan groups (sums of separate emission scans). Args: groups (dict): Dictionary of groups with partial XES scans. """ # sort the groups by their end-energy (is the smallest in XES/energy2-scans) allgroups = [] for group in groups: if groups[group].energy[0] > groups[group].energy[-1]: groups[group].energy = np.flipud( groups[group].energy ) for ii in range(groups[group].signals.shape[1]): groups[group].signals[:,ii] = np.flipud( groups[group].signals[:,ii] ) allgroups.append(groups[group]) allgroups.sort(key = lambda x:x.get_E_end()) # find lowest energy, highest energy, smallest increment, define grid eStart = np.amin([ np.amin(allgroups[ii].energy) for ii in range(len(allgroups)) ]) eStop = np.amax([ np.amax(allgroups[ii].energy) for ii in range(len(allgroups)) ]) eStep = np.amin(np.abs( [np.diff(group.energy)[0] for group in allgroups] )) energy = np.arange(eStart,eStop,eStep) signals = np.zeros((len(energy),group.signals.shape[1],len(allgroups))) errors = np.zeros((len(energy),group.signals.shape[1],len(allgroups))) # interpolate all groups onto grid, put into a matrix for group,ii in zip(allgroups,range(len(allgroups))): for jj in range(group.signals.shape[1]): interp_signals = np.interp(energy, group.energy, group.signals[:,jj], left=0.0, right=0.0) signals[:,jj,ii] = interp_signals interp_errors = np.interp(energy, group.energy, group.errors[:,jj], left=0.0, right=0.0) errors[:,jj,ii] = interp_errors # sum up and weigh by number of available non-zero points sum_signals = np.zeros( (len(energy), group.signals.shape[1])) sum_errors = np.zeros( (len(energy), group.signals.shape[1])) for jj in range(group.signals.shape[1]): for ii in range(len(energy)): nParts = len(np.where(signals[ii,jj]>0.0)[0]) sum_signals[ii,jj] = np.sum(signals[ii,jj])/nParts sum_errors[ii,jj] = np.sqrt(np.sum(errors[ii,jj]**2.0))/nParts spectrum = scangroup(energy, sum_signals, sum_errors, grouptype='spectrum') return spectrum.energy, spectrum.signals, spectrum.errors def catXESScans(groups): """ Concatenate all scans in groups, return the appended energy, signals, and errors. This needs to be a bit smarter to also work for scans that are scanned from small to large energy... """ # sort the groups by their end-energy (is the smallest in XES/energy2-scans) allgroups = [] for group in groups: if groups[group].energy[0] > groups[group].energy[-1]: groups[group].energy = np.flipud( groups[group].energy ) for ii in range(groups[group].signals.shape[1]): groups[group].signals[:,ii] = np.flipud( groups[group].signals[:,ii] ) allgroups.append(groups[group]) allgroups.sort(key = lambda x:x.get_eend()) # find lowest energy, highest energy, smallest increment, define grid eStart = np.amin([ np.amin(allgroups[ii].energy) for ii in range(len(allgroups)) ]) eStop = np.amax([ np.amax(allgroups[ii].energy) for ii in range(len(allgroups)) ]) eStep = np.amin(np.abs( [np.diff(group.energy)[0] for group in allgroups] )) energy = np.arange(eStart,eStop,eStep) signals = np.zeros((len(energy),group.signals.shape[1],len(allgroups))) errors = np.zeros((len(energy),group.signals.shape[1],len(allgroups))) # interpolate all groups onto grid, put into a matrix for group,ii in zip(allgroups,range(len(allgroups))): for jj in range(group.signals.shape[1]): interp_signals = np.interp(energy, group.energy, group.signals[:,jj], left=0.0, right=0.0) signals[:,jj,ii] = interp_signals interp_errors = np.interp(energy, group.energy, group.errors[:,jj], left=0.0, right=0.0) errors[:,jj,ii] = interp_errors # sum up and weigh by number of available non-zero points sum_signals = np.zeros( (len(energy), group.signals.shape[1])) sum_errors = np.zeros( (len(energy), group.signals.shape[1])) for jj in range(group.signals.shape[1]): for ii in range(len(energy)): nParts = len(np.where(signals[ii,jj]>0.0)[0]) sum_signals[ii,jj] = np.sum(signals[ii,jj])/nParts sum_errors[ii,jj] = np.sqrt(np.sum(errors[ii,jj]**2.0))/nParts spectrum = scangroup(energy,sum_signals,sum_errors,grouptype='spectrum') return spectrum.energy, spectrum.signals, spectrum.errors def appendXESScans(groups): """ try including different background scans... append groups of scans ordered by their first energy value. long scans are inserted into gaps that at greater than two times the grid of the finer scans """ # find all types of groups grouptypes = [key for key in groups.keys()] return catXESScans(groups) def create_sum_image(scans,scannumbers): """ Returns a summed image from all scans with numbers 'scannumbers'. scans = dictionary of objects from the scan-class scannumbers = single scannumber, or list of scannumbers from which an image should be constructed """ # make 'scannumbers' iterable (even if it is just an integer) numbers = [] if not isinstance(scannumbers,list): numbers.append(scannumbers) else: numbers = scannumbers key = 'Scan%03d' % numbers[0] image = np.zeros_like(scans[key].edfmats[0,:,:]) for number in numbers: key = 'Scan%03d' % number for ii in range(scans[key].edfmats.shape[0]): image += scans[key].edfmats[ii,:,:] return image def create_diff_image(scans,scannumbers,energy_keV): """ Returns a summed image from all scans with numbers 'scannumbers'. scans = dictionary of objects from the scan-class scannumbers = single scannumber, or list of scannumbers from which an image should be constructed """ # make 'scannumbers' iterable (even if it is just an integer) numbers = [] if not isinstance(scannumbers,list): numbers.append(scannumbers) else: numbers = scannumbers key = 'Scan%03d' % numbers[0] below_image = np.zeros_like(scans[key].edfmats[0,:,:]) above_image = np.zeros_like(scans[key].edfmats[0,:,:]) # find indices below and above 'energy' below_inds = scans[key].energy < energy_keV above_inds = scans[key].energy > energy_keV for number in numbers: key = 'Scan%03d' % number for ii in below_inds: below_image += scans[key].edfmats[ii,:,:] for ii in above_inds: above_image += scans[key].edfmats[ii,:,:] return (above_image - below_image) def findScans_bytype(scans, tipo): """ **findRCscans** Returns a list of scans with name tipo. """ RCscans = [] for key in scans: if scans[key].get_type() == tipo: RCscans.append(scans[key]) return RCscans def sum_scans_to_group( group, method='sum', interp=False ): """ **sum_scans_to_group** Sums up all scans in the list group to form a scan-group. Args: group (list): List of scans to be added up. method (str): Keyword describing which data analysis method should be used. Possible values are 'sum', 'pixel', 'row'. Default is 'sum'. interp (boolean): Flag if interpolation onto the energy grid of the first scan should be applied (default is False). Returns: An instance of the Scan class containing the summed up results. """ # initialize result summed_group = Scan() summed_group.energy = group[0].energy summed_group.monitor = group[0].monitor # sum up summed_group.raw_signals = group[0].raw_signals summed_group.raw_errors = group[0].raw_errors for scan in group[1::]: summed_group.add_scan( scan, method=method, interp=interp ) return summed_group def edf_cleaner(edfmats, threshold, dim1_range=[60,190], dim2_range=[10,1286] ): """ **clean_edf_stack** Removes totally saturated detector images from the EDF-files. Args: edfmats (np.array): Three-dimensional array of EDF-matrices. Returns: edfmats (np.array): Cleaned stack of EDF-files. """ num_to_replace = [] for ii in range(edfmats.shape[0]): if np.sum(edfmats[ii,dim1_range[0]:dim1_range[1],dim2_range[0]:dim2_range[1]]) >= threshold: num_to_replace.append(ii) print('found corrupted images at: ', num_to_replace) # if two neighboring images are corrupted num_to_replace = np.array(num_to_replace) if np.where(np.diff(num_to_replace)==1)>0: double_bad_images = np.where(np.diff(num_to_replace)==1) print('found neighboring corrupted images!' , num_to_replace[double_bad_images]) for number in double_bad_images: if number > 0: # replace the first image by the previous one edfmats[num_to_replace[number],:,:] = edfmats[num_to_replace[number]-1,:,:] elif number == 0: # replace the second image by the next one edfmats[num_to_replace[number]+1,:,:] = edfmats[num_to_replace[number]+2,:,:] # search again for remaining single bad images num_to_replace = [] for ii in range(edfmats.shape[0]): if np.sum(edfmats[ii,dim1_range[0]:dim1_range[1],dim2_range[0]:dim2_range[1]]) >= threshold: num_to_replace.append(ii) print('found corrupted images at: ', num_to_replace) for number in num_to_replace: try: if number > 0 and number < (edfmats.shape[0]-1): # interpolate if image to replace is in the center of the scan edfmats[number,:,:] = (edfmats[number-1,:,:] + edfmats[number+1,:,:])/2.0 elif number == 0: # replace first image by second edfmats[number,:,:] = edfmats[number+1,:,:] elif number == edfmats.shape[0]: # replace last image by second to last edfmats[number,:,:] = edfmats[number-1,:,:] else: pass except: # if all fails print('WARNING: could not replace image number %d'%number) pass return edfmats xrstools-0.15.0+git20210910+c147919d/XRStools/xrs_utilities.py000066400000000000000000005014271412732462000232010ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range #!/usr/bin/python # Filename: xrs_utilities.py #/*########################################################################## # # The XRStools software package for XRS spectroscopy # # Copyright (c) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the XRStools XRS spectroscopy package developed at # the ESRF by the DEC and Software group and contains practical functions, # most of which are translated from Matlab functions from the University of # Helsinki Electronic Structure Laboratory. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "Christoph J. Sahle - ESRF" __contact__ = "christoph.sahle@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import os import math import copy import numpy as np import array as arr import matplotlib.pyplot as plt import pickle import traceback import sys from matplotlib.widgets import Cursor from itertools import groupby from scipy.integrate import trapz from scipy import interpolate, signal, integrate, constants, optimize from re import findall from scipy.ndimage import measurements from scipy.optimize import leastsq, fmin, fsolve, minimize, nnls from scipy.interpolate import Rbf, RectBivariateSpline from scipy.integrate import odeint # data_installation_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)),"..","..","..","..","share","xrstools","data") # data_installation_dir = os.path.abspath('.') # whne you test the file from its source directory you, /data sits on level above. In this case you can # work around it by creating a link in ./ to ../data data_installation_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)),"resources", 'data') # os.path.join(getattr(install_cmd, 'install_lib'),"xrstools"+version,"..","..","..","..","share","xrstools","data") def diode(current, energy, thickness=0.03): """ **diode** Calculates the number of photons incident for a Si PIPS diode. Args: * current (float): Diode current in [pA]. * energy (float): Photon energy in [keV]. * thickness (float): Thickness of Si active layer in [cm]. Returns: * flux (float): Number of photons per second. Function adapted from Matlab function by S. Huotari. """ t = thickness # thickness of diode in cm # Total cross-section of absorbed energy for Si (Storm & Israel) sx = np.array([2,3,4,5,6,8,10,15,20,30,40,50,60,80,100,150,200]) s = np.array([125000,44600,20600,11000,6600,2890,1510,448,186, \ 53.3,21.9,11.2, 6.61,3.18,2.06,1.41,1.34]) my = np.exp(np.interp(energy, sx , np.log(s))) my = (0.02144*2.32)*my n_ph= energy*(1.0-np.exp(-tauphoto(14,energy)*2.32*t)) n_ph=n_ph + (energy)*(1.0-np.exp(-sigmainc(14,energy)*2.32*t)) n_ph=n_ph/0.0036 n_ph = energy*(1.0-np.exp(-my*t))/0.0036 # number of electrons/photon n_pA = current/1.6022e-7 # number of electrons per pA print('The photon flux is: %E'%(n_pA/n_ph)) return n_pA/n_ph def cshift(w1, th): """ **cshift** Calculates Compton peak position. Args: * w1 (float, array): Incident energy in [keV]. * th (float): Scattering angle in [deg]. Returns: * w2 (foat, array): Energy of Compton peak in [keV]. Funktion adapted from Keijo Hamalainen. """ return w1/(1+w1/510.967*(1-np.cos(th/180*np.pi))) def tauphoto(Z, energy, logtablefile=os.path.join(data_installation_dir,'logtable.dat')): """ **tauphoto** Calculates Photoelectric Cross Section in cm^2/g using Log-Log Fit. Args: * z (int or string): Element number or elements symbol. * energy (float or array): Energy (can be number or vector) Returns: * tau (float or array): Photoelectric cross section in [cm**2/g] Adapted from original Matlab function of Keijo Hamalainen. """ en = np.array([]) en = np.append(en,energy) logtable = np.loadtxt(logtablefile) # find the right places in logtable if not isinstance(Z,int): Z = element(Z) try: ind = list(logtable[:,0]).index(Z) except: print( 'no such element in logtable.dat') c = np.array(logtable[ind:ind+5,:]) # 5 lines that corresponds to the element tau_i = np.zeros((4, len(en))) for ii in range(4): for jj in range(4): tau_i[ii,:] = tau_i[ii,:] + c[jj+1,ii]*np.log(en)**(jj) tau2 = np.zeros(len(en)) tau2 = (en=c[0,1] , en=c[0,2] , en=c[0,3])*1*tau_i[3,:] return np.exp(tau2)*0.6022/c[0,4] def sigmainc(Z, energy, logtablefile=os.path.join(data_installation_dir,'logtable.dat')): """ **sigmainc** Calculates the Incoherent Scattering Cross Section in cm^2/g using Log-Log Fit. Args: * z (int or string): Element number or elements symbol. * energy (float or array): Energy (can be number or vector) Returns: * tau (float or array): Photoelectric cross section in [cm**2/g] Adapted from original Matlab function of Keijo Hamalainen. """ en = np.array([]) en = np.append(en,energy) logtable = np.loadtxt(logtablefile) # find the right places in logtable if not isinstance(Z,int): Z = element(Z) try: ind = list(logtable[:,0]).index(Z) except: print( 'no such element in logtable.dat') c = np.array(logtable[ind:ind+5,:]) # 5 lines that corresponds to the element sigmai=0 for jj in range(4): sigmai = sigmai + c[jj+1,5]*np.log(energy)**(jj) return np.exp(sigmai)*0.6022/c[0,4] def Rx(chi, degrees=True): """ **Rx** Rotation matrix for vector rotations around the [1,0,0]-direction. Args: * chi (float) : Angle of rotation. * degrees(bool) : Angle given in radians or degrees. Returns: * 3x3 rotation matrix. """ if degrees: chi = np.radians(chi) return np.array([[1,0,0],[0, np.cos(chi), -np.sin(chi)], [0, np.sin(chi), np.cos(chi)]]) def Ry(phi, degrees=True): """ **Ry** Rotation matrix for vector rotations around the [0,1,0]-direction. Args: * phi (float) : Angle of rotation. * degrees(bool) : Angle given in radians or degrees. Returns: * 3x3 rotation matrix. """ if degrees: phi = np.radians(phi) return np.array([[np.cos(phi), 0, np.sin(phi)],[0, 1, 0],[-np.sin(phi), 0, np.cos(phi)]]) def Rz(omega, degrees=True): """ **Rz** Rotation matrix for vector rotations around the [0,0,1]-direction. Args: * omega (float) : Angle of rotation. * degrees(bool) : Angle given in radians or degrees. Returns: * 3x3 rotation matrix. """ if degrees: omega = np.radians(omega) return np.array([[np.cos(omega), -np.sin(omega), 0],[np.sin(omega), np.cos(omega), 0],[0,0,1]]) def Phi(phi, degrees=True): """ rotation around (0,1,0), neg sense """ if degrees: phi = np.radians(phi) return np.array([[np.cos(phi), 0, np.sin(phi)],[0, 1, 0],[-np.sin(phi), 0, np.cos(phi)]]) def Chi(chi, degrees=True): """ rotation around (1,0,0), pos sense """ if degrees: chi = np.radians(chi) return np.array([[1,0,0],[0, np.cos(chi), -np.sin(chi)], [0, np.sin(chi), np.cos(chi)]]) def Omega(omega, degrees=True): """ rotation around (0,0,1), pos sense """ if degrees: omega = np.radians(omega) return np.array([[np.cos(omega), -np.sin(omega), 0],[np.sin(omega), np.cos(omega), 0],[0,0,1]]) def get_UB_Q(tthv, tthh, phi, chi, omega, **kwargs): """ **get_UB_Q** Returns the momentum transfer and scattering vectors for given FOURC spectrometer and sample angles. U-, B-matrices and incident/scattered wavelength are passed as keyword-arguments. Args: * tthv (float): Spectrometer vertical 2Theta angle. * tthh (float): Spectrometer horizontal 2Theta angle. * chi (float): Sample rotation around x-direction. * phi (float): Sample rotation around y-direction. * omega (float): Sample rotation around z-direction. * kwargs (dict): Dictionary with key-word arguments: * kwargs['U'] (array): 3x3 U-matrix Lab-to-sample transformation. * kwargs['B'] (array): 3x3 B-matrix reciprocal lattice to absolute units transformation. * kwargs['lambdai'] (float): Incident x-ray wavelength in Angstrom. * kwargs['lambdao'] (float): Scattered x-ray wavelength in Angstrom. Returns: * Q_sample (array): Momentum transfer in sample coordinates. * Ki_sample (array): Incident beam direction in sample coordinates. * Ko_sample (array): Scattered beam direction in sample coordinates. """ U = kwargs['U'] B = kwargs['B'] Lab = kwargs['Lab'] beam_in = kwargs['beam_in'] lambdai = kwargs['lambdai'] lambdao = kwargs['lambdao'] # scattering vectors in laboratory frame Ki_test = 2.0*np.pi/lambdai * beam_in/np.linalg.norm(beam_in) Ko_test = 2.0*np.pi/lambdao * Rz(tthh).dot(Ry(tthv)).dot(beam_in/np.linalg.norm(beam_in)) # h_lab = Omega*Chi*Phi*U*B*h_cryst (Busing equ. 19) # invert # h_cryst = B_inv*U_inv*Phi_inv*Chi_inv*Omega_inv*h_lab Q_test = Ko_test - Ki_test Phi_inv = Phi(phi).T #np.linalg.inv(Phi(phi)) print(Phi_inv) Chi_inv = Chi(chi).T #np.linalg.inv(Chi(chi)) Omega_inv = Omega(omega).T #np.linalg.inv(Omega(omega)) U_inv = np.linalg.inv(U) B_inv = np.linalg.inv(B) Q_sample = np.matmul(B_inv ,np.matmul(U_inv , np.matmul( Phi_inv , np.matmul(Chi_inv, np.matmul( Omega_inv, Q_test))))) Ki_sample = np.matmul(B_inv ,np.matmul(U_inv , np.matmul( Phi_inv , np.matmul(Chi_inv, np.matmul( Omega_inv, Ki_test))))) Ko_sample = np.matmul(B_inv ,np.matmul(U_inv , np.matmul( Phi_inv , np.matmul(Chi_inv, np.matmul( Omega_inv, Ko_test))))) return Q_sample, Ki_sample, Ko_sample def find_diag_angles(q, x0, U, B, Lab, beam_in, lambdai, lambdao, tol=1e-8, method='BFGS'): """ **find_diag_angles** Finds the FOURC spectrometer and sample angles for a desired q. Args: * q (array): Desired momentum transfer in Lab coordinates. * x0 (list): Guesses for the angles (tthv, tthh, chi, phi, omega). * U (array): 3x3 U-matrix Lab-to-sample transformation. * B (array): 3x3 B-matrix reciprocal lattice to absolute units transformation. * lambdai (float): Incident x-ray wavelength in Angstrom. * lambdao (float): Scattered x-ray wavelength in Angstrom. * tol (float): Toleranz for minimization (see scipy.optimize.minimize) * method (str): Method for minimization (see scipy.optimize.minimize) Returns: * ans (array): tthv, tthh, phi, chi, omega """ # put UB matrix and energies into keyword argument for minimization kwargs = {'U': U, 'B': B, 'Lab': Lab, 'beam_in':beam_in, 'lambdai': lambdai, 'lambdao': lambdao} # least square minimization between wanted and guessed q fitfctn = lambda x: np.sum(( q - get_UB_Q(x[0], x[1], x[2], x[3], x[4], \ **kwargs)[0] )**2) ans=minimize(fitfctn, x0, bounds=((0.,0.),(-10.,110.),(-7.,7.),(-7.,7.),(None,None)), tol=tol, method=method) print( ans ) return ans.x def get_gnuplot_rgb( start=None, end=None, length=None ): """ **get_gnuplot_rgb** Prints out a progression of RGB hex-keys to use in Gnuplot. Args: * start (array): RGB code to start from (must be numbers out of [0,1]). * end (array): RGB code to end at (must be numbers out of [0,1]). * length (int): How many colors to print out. """ if start==None and end==None and length==None: rgb = [[0, 0, 1], [0, 0.2353, 0.7647], \ [0, 0.4706, 0.5294], [0, 0.7059, 0.2941], \ [0, 0.9412, 0.0588], [0.1765, 0.8235, 0 ], \ [0.4118, 0.5882, 0 ], [0.6471, 0.3529, 0 ], \ [1, 0, 0 ] ] else: rgb = np.zeros((length,3)) rgb[:,0] = np.linspace(start[0], end[0], length) rgb[:,1] = np.linspace(start[1], end[1], length) rgb[:,2] = np.linspace(start[2], end[2], length) # for ii in range(len(rgb)): print( 'set style line %d lt -1 lc rgb \'#%02x%02x%02x\' lw 1.5'%(ii+1, rgb[ii][0]*255., rgb[ii][1]*255., rgb[ii][2]*255.) ) def hex2rgb( hex_val ): return tuple( int(hex_val.lstrip('#')[i:i+2], 16) for i in (0, 2, 4) ) class maxipix_det: """ Class to store some useful values from the detectors used. To be used for arranging the ROIs. """ def __init__(self,name,spot_arrangement): self.name = name assert spot_arrangement in ['3x4','vertical'], 'unknown ROI arrangement, select \'3x4\' or \'vertical\'.' self.spot_arrangement = spot_arrangement self.roi_indices = [] self.roi_x_indices = [] self.roi_y_indices = [] self.roi_x_means = [] self.roi_y_means = [] self.pixel_size = [256,256] self.PIXEL_RANGE = {'VD': [0,256,0,256], 'VU': [0,256,256,512], 'VB': [0,256,512,768], 'HR': [256,512,0,256],'HL': [256,512,256,512],'HB': [256,512,512,768]} def get_pixel_range(self): return self.PIXEL_RANGE[self.name] def get_det_name(self): return self.name class bragg_refl: """ Dynamical theory of diffraction. """ def __init__(self, crystal, hkl, alpha=0.0 ): # constants self.hc = constants.h*constants.c self.C = 1.0 self.r0 = constants.e**2/ \ (4.0*np.pi*constants.epsilon_0* \ constants.m_e*constants.c**2)* \ 1.0e10 # classical electron radius expressed in Angstrom self.P = 1 # polarization factor self.alpha = alpha # params self.hkl = hkl self.crystal = crystal self.dspace = dspace( self.hkl, self.crystal ) self.ff_energy, self.f1_energy, self.f2_energy = \ self.get_nff() def get_reflectivity_bent(self, energy, delta_theta, R): # refl,e,dev,e0 = taupgen( energy, self.hkl, crystals=self.crystal, R=R, dev=delta_theta, alpha=self.alpha ) return refl, theta_B*180/np.pi def get_reflectivity(self, energy, delta_theta, case='sigma'): energy = energy*1e3 wavelength = self.hc/(energy*constants.e)*1e10 print(wavelength) theta_B = np.arcsin(wavelength/(2.0*self.dspace)) # Bragg angle theta_inc = theta_B + np.radians(self.alpha) # incidence angle theta_ref = theta_B - np.radians(self.alpha) # reflection angle b = -np.sin(theta_ref)/np.sin(theta_inc) # asymmetry factor # polarization factor P = self.get_polarization_factor(2.*theta_B, case=case) # chi chi_0, chi_h, chi_hbar = self.get_chi(energy, self.crystal, self.hkl) # index of refraction n = 1 + chi_0/2 n_delta = 1 - np.real(n) n_beta = -np.imag(n) # linear absorption coefficient mu = -2*np.pi*np.imag(chi_0)/wavelength # Bragg angle correction theta_B_correction = -chi_0*(1 - b)/(2*np.sin(2*theta_B)) # width of the total reflection domain delta = np.abs(P)*np.sqrt(np.abs(b)*chi_h*chi_hbar)/np.sin(2*theta_B) Darwin_width = 2*np.real(delta) lambda_B = wavelength*np.abs(np.sin(theta_inc))/ \ (2*np.pi*np.real(delta)*np.sin(2*theta_B)) delta_theta = delta_theta/1e6 # delta_theta is input in microrad eta = (delta_theta - theta_B_correction)/delta # deviation parameter # reflectivity curve reflectivity_curve = np.abs(eta - np.sign(np.real(eta))* \ np.sqrt(eta**2 - 1))**2 return reflectivity_curve, theta_B*180/np.pi, theta_B_correction def get_nff(self, nff_path = os.path.join(data_installation_dir,'atomic_form_factors')): fname = os.path.join(nff_path, self.crystal.lower()+'.nff') table = np.loadtxt(fname, unpack = True, skiprows = 1) table = np.transpose(table) ff_energy = table[:,0] f1_energy = table[:,1] f2_energy = table[:,2] return ff_energy, f1_energy, f2_energy def get_chi(self, energy, crystal=None, hkl=None): path = os.path.join(data_installation_dir,'chitable_') hkl_string = str(int(hkl[0])) + str(int(hkl[1])) + str(int(hkl[2])) filestring = path + crystal.lower() + hkl_string + '.dat' self.chi = np.loadtxt(filestring) self.chi_0 = complex(np.interp(energy, self.chi[:,0], self.chi[:,1]), \ np.interp(energy, self.chi[:,0],self.chi[:,2])) self.chi_h = complex(np.interp(energy, self.chi[:,0], self.chi[:,3]), \ np.interp(energy, self.chi[:,0], self.chi[:,4])) self.chi_hbar = np.conj(self.chi_h) return self.chi_0, self.chi_h, self.chi_hbar def get_polarization_factor(self, tth, case='sigma'): """ Calculate polarization factor. """ if case == 'sigma': P = 1.0 self.P = P return P elif case == 'pi': P = np.cos(tth)**2 self.P = P return P elif case == None: P = (1 + np.cos(tth)**2)/2.0 self.P = P return P class dtxrd: """ class to hold all things dynamic theory of diffraction. """ def __init__(self, hkl, energy, crystal='Si', asym_angle=0.0, angular_range=[-0.5e-3, 0.5e-3] , angular_step=1e-8 ): # constants self.hc = 12.3984191 self.C = 1.0 # params self.hkl = hkl self.energy = energy self.lam = self.hc/self.energy self.crystal = crystal self.dspace = dspace( self.hkl, self.crystal ) # load Chi from tables: path = os.path.join(data_installation_dir,'chitable_') hkl_string = str(int(hkl[0])) + str(int(hkl[1])) + str(int(hkl[2])) filestring = path + crystal.lower() + hkl_string + '.dat' self.chi = np.loadtxt(filestring) self.chi0 = complex(np.interp(self.energy, self.chi[:,0], self.chi[:,1]), \ np.interp(self.energy, self.chi[:,0],self.chi[:,2])) self.chih = complex(np.interp(self.energy, self.chi[:,0], self.chi[:,3]), \ np.interp(self.energy, self.chi[:,0], self.chi[:,4])) self.chihbar = np.conj(self.chih) # set Bragg angle: self.thetab = float( bragg( hkl, energy , xtal=crystal ) ) self.thetabd = float( braggd( hkl, energy , xtal=crystal ) ) # set asymmetry: self.set_asymmetry(asym_angle) # set reduced deviation parameter self.angular_range = angular_range self.angular_step = angular_step self.get_eta(angular_range, angular_step) self.lam_ext = None self.mu0 = None self.mus = None self.R = None self.omega_h = None self.omega_0 = None def set_asymmetry(self, alpha): """ negative alpha -> more grazing incidence """ self.alpha = alpha self.gammah = -np.sin(self.thetab + alpha) # Krisch et al. convention self.gamma0 = np.sin(self.thetab - alpha) # Krisch et al. convention self.gamma = self.gamma0/np.abs(self.gammah) self.beta = self.gammah/self.gamma0 def set_hkl(self, hkl): self.hkl = hkl def set_energy(self, energy): self.energy = energy def get_reflectivity(self, angular_range=None, angular_step=None): if not angular_range: angular_range = self.angular_range if not angular_step: angular_step = self.angular_step self.get_eta(angular_range, angular_step) pre_factor = (np.sqrt(self.chih*self.chihbar))/(self.chihbar) * \ 1.0/np.sqrt(np.abs(self.gamma)) * self.gammah/np.abs(self.gammah) * self.C/np.abs(self.C) self.R = np.abs(pre_factor * np.piecewise(self.eta, self.eta.real>0, \ [lambda x: x - np.sqrt( x**2 + self.gammah/np.abs(self.gammah) ) , \ lambda x: x + np.sqrt( x**2 + self.gammah/np.abs(self.gammah) ) ])) def get_eta(self, angular_range, angular_step=1e-8): self.theta = np.arange( self.thetab+angular_range[0], self.thetab+angular_range[1], angular_step ) self.eta = ( (self.theta-self.thetab)*np.sin(2.*self.thetab) + self.chi0/2.0*(1-self.gamma) ) / \ (np.abs(self.C)*np.sqrt(np.abs(self.gamma))* np.sqrt(self.chih*self.chihbar)) def get_anomalous_absorption(self, energy=None): if not energy: energy = self.energy # photoelectric absorption mu0 = myprho( energy, self.crystal )[0][0][0]*0.602252/ \ myprho( energy, self.crystal )[2] omega = np.arctan( (1.0 - np.abs(self.R)**2) / (1.0 + np.abs(self.R)**2) * np.tan(self.thetab) ) mus = mu0 * np.cos(omega)/np.cos(self.thetab) * ( 1.0 + 2.0*np.abs(self.C) * \ self.chih.imag/self.chi0.imag * self.R.real/(1.0 + np.abs(self.R)**2) ) self.mus = mus def get_extinction_length(self, energy=None): if energy: lam = energy/self.hc else: lam = self.lam kappa = -np.abs( (self.chih.imag)/(self.chih.real) ) self.lam_ext = (lam*np.sqrt(self.gamma0 * np.abs(self.gammah))* np.cos(kappa) ) / \ (2.0*np.pi*np.abs(self.C) * np.sqrt( np.abs(self.chih.real*self.chihbar.real) )) def get_reflection_width(self): if not self.lam_ext: self.get_extinction_length() self.omega_h = self.lam*np.abs(self.gammah)/( np.pi*self.lam_ext*np.sin(2.0*self.thetab) ) self.omega_0 = self.lam*self.gamma0/( np.pi*self.lam_ext*np.sin(2.0*self.thetab) ) def dtxrd_reflectivity( energy, hkl, alpha=0.0, crystal='Si', angular_range=np.arange(-0.5e-3, 0.5e-3) ): # constants hc, lam, dsp, thetab, alpha, C = \ get_dtxrd_constants( energy, hkl, crystal=crystal, alpha=alpha ) # theta scale theta = np.arange( thetab+angular_range[0], \ thetab+angular_range[1], 1e-8 ) # load chi chi0, chih, chihbar = get_dtxrd_chi( energy, hkl, crystal=crystal ) # asymmetry parameter gamma, gamma0, gammah, beta = get_dtxrd_assymmetry_params(thetab, alpha) # calculate eta eta = get_dtxrd_eta(theta, thetab, chi0, chih, chihbar) # calculate reflectivity pre_factor = (np.sqrt(chih*chihbar))/(chihbar) * \ 1.0/np.sqrt(np.abs(gamma)) * gammah/np.abs(gammah) * C/np.abs(C) r = pre_factor * np.piecewise(eta, eta.real>0, \ [lambda x: x - np.sqrt( x**2 + gammah/np.abs(gammah) ) , \ lambda x: x + np.sqrt( x**2 + gammah/np.abs(gammah) ) ]) return theta, r def dtxrd_anomalous_absorption( energy, hkl, alpha=0.0, crystal='Si', angular_range=np.arange(-0.5e-3, 0.5e-3) ): # constants hc = 12.3984191 lam = hc/energy * 1e-10 dsp = dspace(hkl, crystal) thetab = float( bragg( hkl, energy , xtal=crystal ) ) alpha = np.radians(alpha) C = 1.0 # theta scale theta = np.arange( thetab+angular_range[0], \ thetab+angular_range[1], 1e-8 ) # load chi path = os.path.join(data_installation_dir,'chitable_') hkl_string = str(int(hkl[0])) + str(int(hkl[1])) + str(int(hkl[2])) filestring = path + crystal.lower() + hkl_string + '.dat' chi = np.loadtxt(filestring) chi0 = complex(np.interp(energy,chi[:,0],chi[:,1]), np.interp(energy,chi[:,0],chi[:,2])) chih = complex(np.interp(energy,chi[:,0],chi[:,3]), np.interp(energy,chi[:,0],chi[:,4])) chihbar = np.conj(chih) # asymmetry parameter gammah = -np.sin(thetab + alpha) # Krisch et al. convention gamma0 = np.sin(thetab - alpha) # Krisch et al. convention beta = gamma0/np.abs(gammah) gamma = gammah/gamma0 # calculate eta eta = ( (theta-thetab)*np.sin(2.*thetab) + chi0/2.0*(1-gamma) ) / \ (np.abs(C)*np.sqrt(np.abs(gamma))* np.sqrt(chih*chihbar)) # calculate reflectivity pre_factor = (np.sqrt(chih*chihbar))/(chihbar) * \ 1.0/np.sqrt(np.abs(gamma)) * gammah/np.abs(gammah) * C/np.abs(C) r = pre_factor * np.piecewise(eta, eta.real>0, \ [lambda x: x - np.sqrt( x**2 + gammah/np.abs(gammah) ) , \ lambda x: x + np.sqrt( x**2 + gammah/np.abs(gammah) ) ]) # photoelectric absorption mu0 = myprho( energy, crystal )[0][0][0]*0.602252/ \ myprho( energy, crystal )[2] mus = mu0 * np.cos(omega)/np.cos(thetab) * ( 1.0 + 2.0*np.abs(C) * chih.imag/chi0.imag * r.real/(1.0 + np.abs(r)**2) ) return theta, mus def dtxrd_extinction_length( energy, hkl, alpha=0.0, crystal='Si' ): pass def delE_dicedAnalyzerIntrinsic(E, Dw, Theta): """Calculates the intrinsic energy resolution of a diced crystal analyzer. Args: E (float): Working energy in [eV]. Dw (float): Darwin width of the used reflection [microRad]. Theta (float): Analyzer Bragg angle [degree]. Returns: Intrinsic energy resolution of a perfect analyzer crystal. """ Dw = Dw/1000000.0 # conversion to radians return E * (Dw)/(np.tan(np.radians(Theta))) def delE_JohannAberration(E, A, R, Theta): """Calculates the Johann aberration of a spherical analyzer crystal. Args: E (float): Working energy in [eV]. A (float): Analyzer aperture [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree]. Returns: Johann abberation in [eV]. """ return E/2.0 * ((A)/(2.0*R*np.tan(np.radians(Theta))))**2 def delE_pixelSize(E, p, R, Theta): """Calculates the pixel size contribution to the resolution function of a diced analyzer crystal. Args: E (float): Working energy in [eV]. p (float): Pixel size in [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree]. Returns: Pixel size contribution in [eV] to the energy resolution for a diced analyzer crystal. """ return E * (p/(2.0*R*np.sin(np.radians(Theta))))/(np.tan(np.radians(Theta))) def delE_sourceSize(E, s, R, Theta): """Calculates the source size contribution to the resolution function. Args: E (float): Working energy in [eV]. s (float): Source size in [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree]. Returns: Source size contribution in [eV] to the energy resolution. """ return E * (s/(R*np.sin(np.radians(Theta))))/(np.tan(np.radians(Theta))) def delE_offRowland(E, z, A, R, Theta): """Calculates the off-Rowland contribution of a spherical analyzer crystal. Args: E (float): Working energy in [eV]. z (float): Off-Rowland distance [mm]. A (float): Analyzer aperture [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree]. Returns: Off-Rowland contribution in [eV] to the energy resolution. """ return E * (z*A)/( (R*np.sin(np.radians(Theta)) + z)**2 ) * (1.0)/(np.tan(np.radians(Theta))) def delE_stressedCrystal(E, t, v, R, Theta): """Calculates the stress induced contribution to the resulution function of a spherically bent crystal analyzer. Args: E (float): Working energy in [eV]. t (float): Absorption length in the analyzer material [mm]. v (float): Poisson ratio of the analyzer material. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree]. Returns: Stress-induced contribution in [eV] to the energy resolution. """ return E * t/R * np.absolute((1.0)/(np.tan(np.radians(Theta))**2) - 2.0*v) def get_num_of_MD_steps(time_ps,time_step): """Calculates the number of steps in an MD simulation for a desired time (in ps) and given step size (in a.u.) Args: time_ps (float): Desired time span (ps). time_step (float): Chosen time step (a.u.). Returns: The number of steps required to span the desired time span. """ return time_ps / time_step /1.0e12 / 2.418884326505e-17 def nonzeroavg(y=None): dim1 = y.shape[0] yavg = np.zeros(dim1,float) for ii in range(dim1): length = 0 rowsum = 0. for ij in range(y.shape[1]): if (not np.isnan(y[ii, ij])): length += 1 rowsum += y[ii, ij] yavg[ii] = rowsum / float(length) yavg = yavg * y.shape[1] return(yavg) def fermi(rs): """ **fermi** Calculates the plasmon energy (in eV), Fermi energy (in eV), Fermi momentum (in a.u.), and critical plasmon cut-off vector (in a.u.). Args: * rs (float): electron separation parameter Returns: * wp (float): plasmon energy (in eV) * ef (float): Fermi energy (in eV) * kf (float): Fermi momentum (in a.u.) * kc (float): critical plasmon cut-off vector (in a.u.) Based on Matlab function from A. Soininen. """ au = 27.212 alfa = (9.0*np.pi/4.0)**(1.0/3.0) kf = alfa/rs ef = kf*kf/2.0 wp = np.sqrt(3.0/rs/rs/rs) kc = kf * (np.sqrt(1.0+wp/ef)-1.0) wp = wp*au ef = ef*au return wp, ef, kf, kcem def lindhard_pol(q,w,rs=3.93,use_corr=False, lifetime=0.28): """ **lindhard_pol** Calculates the Lindhard polarizability function (RPA) for certain q (a.u.), w (a.u.) and rs (a.u.). Args: * q (float): momentum transfer (in a.u.) * w (float): energy (in a.u.) * rs (float): electron parameter * use_corr (boolean): if True, uses Bernardo's calculation for n(k) instead of the Fermi function. * lifetime (float): life time (default is 0.28 eV for Na). Based on Matlab function by S. Huotari. """ if type(w) in [float, int]: w = np.array([w]) wp, ef, kf, kc = fermi(rs) ef = ef/27.212 gammal = lifetime/27.212 # lifetime (0.28 eV for Na) th = np.arange( 0.0, np.pi, np.pi/700.0 ) k = np.arange( 0.0, 2.0*kf, kf/1000.0 ) [K,TH] = np.meshgrid(k,th) ek = K**2/2.0 ekq = ( K**2+q**2+2*q*K*np.cos(TH) )/2.0 if not use_corr: fek = np.zeros(np.shape(ek)) fek[ek<=ef]=1.0 fekq=np.zeros(np.shape(ekq)) fekq[ekq<=ef] = 1.0 if use_corr: print('Not implemented yet!') x = np.zeros_like(w, dtype='complex') for ii in range(len(w)): y=np.sin(TH)*(fek-fekq)/(w[ii]+ek-ekq+np.complex(0,1)*gammal) y=np.trapz( y, th, axis=0 ) y=np.trapz( k**2.0*y, k, axis=0 ) x[ii]=y x = 4.0*np.pi*x x = x/(2.0*np.pi)**3 return x def energy(d,ba): """ % ENERGY Calculates energy corrresponing to Bragg angle for given d-spacing % function e=energy(dspace,bragg_angle) % % dspace for reflection % bragg_angle in DEG % % KH 28.09.93 """ hc = 12.3984191 # CODATA 2002 physics.nist.gov/constants return (2.0*d*np.sin(ba/180.0*np.pi)/hc)**(-1) def dspace(hkl=[6,6,0],xtal='Si'): """ % DSPACE Gives d-spacing for given xtal % d=dspace(hkl,xtal) % hkl can be a matrix i.e. hkl=[1,0,0 ; 1,1,1]; % xtal='Si','Ge','LiF','InSb','C','Dia','Li' (case insensitive) % if xtal is number this is user as a d0 % % KH 28.09.93 % SH 2005 % """ # create a database of lattice constants (could be a shelf) xtable = {} xtable['SI'] = 5.43102088 xtable['GE'] = 5.657 xtable['SIXOP'] = 5.430919 xtable['SIKOH'] = 5.430707 xtable['LIF'] = 4.027 xtable['INSB'] = 6.4784 xtable['C'] = 6.708 xtable['DIA'] = 3.57 xtable['LI'] = 3.41 xtable['TCNE'] = 9.736 xtable['CU'] = 3.61 xtable['PB'] = 4.95 xtable['NA'] = 4.2906 xtable['AL'] = 4.0495 if isinstance(xtal,str): try: a0 = xtable[xtal.upper()] except KeyError: print( 'Lattice constant is not in database') return else: a0 = xtal # if number is provided, it's taken as lattice constant return a0/np.sqrt(np.sum(np.array(hkl)**2.0)) def bragg(hkl,e,xtal='Si'): """ % BRAGG Calculates Bragg angle for given reflection in RAD % output=bangle(hkl,e,xtal) % hkl can be a matrix i.e. hkl=[1,0,0 ; 1,1,1]; % e=energy in keV % xtal='Si', 'Ge', etc. (check dspace.m) or d0 (Si default) % % KH 28.09.93 % """ hc = 12.3984191 # CODATA 2002 recommended value, physics.nist.gov/constants return np.real(np.arcsin((2.0*dspace(hkl,xtal)*e/hc)**(-1.0))) def braggd(hkl,e,xtal='Si'): """ # BRAGGD Calculates Bragg angle for given reflection in deg # Call BRAGG.M # output=bangle(hkl,e,xtal) # hkl can be a matrix i.e. hkl=[1,0,0 ; 1,1,1]; # e=energy in keV # xtal='Si', 'Ge', etc. (check dspace.m) or d0 (Si default) # # KH 28.09.93 """ return bragg(hkl,e,xtal)/np.pi*180.0 def addch(xold,yold,n,n0=0,errors=None): """ # ADDCH Adds contents of given adjacent channels together # # [x2,y2] = addch(x,y,n,n0) # x = original x-scale (row or column vector) # y = original y-values (row or column vector) # n = number of channels to be summed up # n0 = offset for adding, default is 0 # x2 = new x-scale # y2 = new y-values # # KH 17.09.1990 # Modified 29.05.1995 to include offset """ n0=int(n0-np.fix(n0/n)*n) if n0<0: n0 = (n + n0) datalen = np.floor( (len(xold) - n0) / n) xnew = np.zeros(int(np.min([datalen,len(xold)]))) ynew = np.zeros(int(np.min([datalen,len(xold)]))) errnew = np.zeros(int(np.min([datalen,len(xold)]))) for i in range(int(datalen)): xnew[i] = np.sum(xold[i*n+n0:i*n+n+n0])/n ynew[i] = np.sum(yold[i*n+n0:i*n+n+n0])/n if np.any(errors): errnew[i] = np.sqrt(np.sum(errors[i*n+n0:i*n+n+n0]**2.0)) return xnew, ynew, errnew return xnew, ynew def fwhm(x,y): """ finds full width at half maximum of the curve y vs. x returns f = FWHM x0 = position of the maximum """ if x[-1] < x[0]: x = np.flipud(x) y = np.flipud(y) y0 = np.amax(y) i0 = np.where(y == y0)[0] if len(i0)>1: i0 = i0[0] x0 = x[i0] i1 = np.where(np.logical_and(y>y/3.0, xy/3.0, x>x0))[0] if len(y[i1])==0 or len(y[i2])==0: return 0,0 #f = interpolate.interp1d(y[i1],x[i1], bounds_error=False, fill_value=0.0) #x1 = f(y0/2.0) #f = interpolate.interp1d(y[i2],x[i2], bounds_error=False, fill_value=0.0) #x2 = f(y0/2.0) x1 = np.interp(y0/2.0,y[i1],x[i1]) x2 = np.interp(y0/2.0,np.flipud(y[i2]),np.flipud(x[i2])) fwhm = x2 - x1 x0 = np.mean([x2, x1]) return fwhm, x0 def gauss(x,x0,fwhm): # area-normalized gaussian sigma = fwhm/(2*np.sqrt(2*np.log(2))); y = np.exp(-(x-x0)**2/2/sigma**2)/sigma/np.sqrt(2*np.pi) return y def convg(x,y,fwhm): """ Convolution with Gaussian x = x-vector y = y-vector fwhm = fulll width at half maximum of the gaussian with which y is convoluted """ dx = np.min(np.absolute(np.diff(x))) x2 = np.arange(np.min(x)-1.5*fwhm, np.max(x)+1.5*fwhm, dx) xg = np.arange(-np.floor(2.0*fwhm/dx)*dx, np.floor(2.0*fwhm/dx)*dx, dx) yg = gauss(xg,0,fwhm) yg = yg/np.sum(yg) y2 = spline2(x,y,x2) c = np.convolve(y2,yg, mode='full') n = int( np.floor(np.max(np.shape(xg))/2)) c = c[n:len(c)-n+1] # not sure about the +- 1 here f = interpolate.interp1d(x2,c) return f(x) def interpolate_M(xc, xi, yi, i0): """ Linear interpolation scheme after Martin Sundermann that conserves the absolute number of counts. ONLY WORKS FOR EQUALLY/EVENLY SPACED XC, XI! Args: xc (np.array): The x-coordinates of the interpolated values. xi (np.array): The x-coordinates of the data points, must be increasing. yi (np.array): The y-coordinates of the data points, same length as `xp`. i0 (np.array): Normalization values for the data points, same length as `xp`. Returns: ic (np.array): The interpolated and normalized data points. from scipy.interpolate import Rbf x = arange(20) d = zeros(len(x)) d[10] = 1 xc = arange(0.5,19.5) rbfi = Rbf(x, d) di = rbfi(xc) """ assert len(xi)==len(yi) and len(xi)==len(i0), "xi, yi, and i0 must have the same length." xc = np.array(xc) xi = np.array(xi) yi = np.array(yi) i0 = np.array(i0) dx = (xi-xc[:len(xi)])*(len(xc)-1)/(xc[-1]-xc[0]) yc = 0.0*xc ic = 0.0*xc for i in np.unique(np.floor(np.sort(dx))): dxi = [x-i if(((x-i)>0) and ((x-i)<1)) else -1 for x in dx] if((i>=0) and (i+i+len(xi)+1)<=len(xc)): # if yi and i0 lay inside the grid range add them yc[i:i+len(xi)] = yc[i:i+len(xi)] + yi*(1-np.absolute(dxi)) ic[i:i+len(xi)] = ic[i:i+len(xi)] + i0*(1-np.absolute(dxi)) yc[i+1:i+len(xi)+1] = yc[i+1:i+len(xi)+1] + yi*np.maximum(dxi,0) ic[i+1:i+len(xi)+1] = ic[i+1:i+len(xi)+1] + i0*np.maximum(dxi,0) else: if (min(i+len(xi),len(xc))-max(i,0)) >= 0: # if yi and i0 lay only partial inside the grid range add only the overlapping region yc[max(i,0):min(i+len(xi),len(xc))] = yc[max(i,0):min(i+len(xi),len(xc))] + (yi*(1-np.absolute(dxi)))[max(-i,0):len(xi)+min(len(xc)-i-len(xi),0)] ic[max(i,0):min(i+len(xi),len(xc))] = ic[max(i,0):min(i+len(xi),len(xc))] + (i0*(1-np.absolute(dxi)))[max(-i,0):len(xi)+min(len(xc)-i-len(xi),0)] if (min(i+len(xi)+1,len(xc))-max(i+1,0)) >= 0: yc[max(i+1,0):min(i+len(xi)+1,len(xc))] = yc[max(i+1,0):min(i+len(xi)+1,len(xc))] + (yi*np.maximum(dxi,0))[max(-i-1,0):len(xi)+min(len(xc)-i-len(xi)-1,0)] ic[max(i+1,0):min(i+len(xi)+1,len(xc))] = ic[max(i+1,0):min(i+len(xi)+1,len(xc))] + (i0*np.maximum(dxi,0))[max(-i-1,0):len(xi)+min(len(xc)-i-len(xi)-1,0)] return yc, ic def spline2(x,y,x2): """ Extrapolates the smaller and larger valuea as a constant """ xmin = np.min(x) xmax = np.max(x) imin = x == xmin imax = x == xmax f = interpolate.interp1d(x,y, bounds_error=False, fill_value=0.0) y2 = f(x2) i = np.where(x2xmax) y2[i] = y[imax] return y2 def pz2e1(w2,pz,th): """Calculates the incident energy for a specific scattered photon and momentum value. Returns the incident energy for a given photon energy and scattering angle. This function is translated from Keijo Hamalainen's Matlab implementation (KH 29.05.96). Args: * w2 (float): scattered photon energy in [keV] * pz (np.array): pz scale in [a.u.] * th (float): scattering angle two theta in [deg] Returns: * w1 (np.array): incident energy in [keV] """ pz = np.array(pz) w = np.array(np.arange(np.array(w2)/4.0,4.0*np.array(w2),np.array(w2)/5000.0)) p = e2pz(w,w2,th)[0] if ( p[1]-p[0] <0) : tck = interpolate.UnivariateSpline(p[::-1],w[::-1]) else: tck = interpolate.UnivariateSpline(p,w) w1 = tck(pz) return w1 def e2pz(w1,w2,th): """Calculates the momentum scale and the relativistic Compton cross section correction according to P. Holm, PRA 37, 3706 (1988). This function is translated from Keijo Hamalainen's Matlab implementation (KH 29.05.96). Args: * w1 (float or np.array): incident energy in [keV] * w2 (float or np.array): scattered energy in [keV] * th (float): scattering angle two theta in [deg] returns: * pz (float or np.array): momentum scale in [a.u.] * cf (float or np.array): cross section correction factor such that: J(pz) = cf * d^2(sigma)/d(w2)*d(Omega) [barn/atom/keV/srad] """ w1 = np.array(w1) # make sure arrays are used w2 = np.array(w2) m = constants.value('electron mass energy equivalent in MeV')*1e3 #511.003 # Mass in natural units th = math.radians(th) # th/180.0*np.pi # Angle to radians alp = constants.value('fine-structure constant') #1.0/137.036 # Fine structure constant r0 = constants.value('classical electron radius') #2.8179e-15 # Electron radius q = np.sqrt(w1**2.0 + w2**2.0-2.0*w1*w2*np.cos(th)) # Momentum transfer pz = q/2.0 - (w1-w2) * np.sqrt(1.0/4.0 + m**2.0/(2.0*w1*w2*(1.0-np.cos(th)))) # In natural units E = np.sqrt(m**2.0+pz**2.0) A = ((w1-w2)*E-w1*w2*(1.0-np.cos(th)))/q D = (w1-w2*np.cos(th))*A/q R = w1*(E-D) R2 = R-w1*w2*(1-np.cos(th)) chi = R/R2 + R2/R + 2.0*m**2.0 * (1.0/R-1.0/R2) + m**4.0 * (1.0/R-1.0/R2)**2.0 cf = 2.0*w1*q*E/(m**2.0*r0**2.0*w2*chi) cf = cf*(1.0e-28*(m*alp)) # Cross section now in barns/atom/keV/srad pz = pz/(m*alp) # pz to atomic units (a.u.) return pz, cf def momtrans_au(e1,e2,tth): """ Returns the momentum transfer (in a.u.). Calculates the momentum transfer in atomic units for two given energies e1 and e1 (in keV) and the scattering angle tth (two theta). Args: *e1 (float or np.array): incident energy in [keV], can be a single value or a vector *e2 (float or np.array): scattered energy in [keV], can be a single value or a vector *tth (float): scattering angle two theta in [deg] Returns: * q (float or np.array): momentum transfer [a.u.], single value or vector depending on input """ e1 = np.array(e1*1.0e3/13.60569172/2.0) e2 = np.array(e2*1.0e3/13.60569172/2.0) th = math.radians(tth)#tth/180.0*np.pi hbarc = 137.03599976 q = 1/hbarc*np.sqrt(e1**2.0+e2**2.0-2.0*e1*e2*np.cos(th)); return q def vrot(v,vaxis,phi): """ **vrot** Rotates a vector around a given axis. Args: * v (np.array): vector to be rotated * vaxis (np.array): rotation axis * phi (float): angle [deg] respecting the right-hand rule Returns: * v2 (np.array): new rotated vector Function by S. Huotari (2007) adopted to Python. """ h = vaxis[0] k = vaxis[1] l = vaxis[2] alpha = np.arctan2(k,h) if np.absolute(alpha)>np.finfo(float).eps: h2 = np.cos(alpha)*(h+k*np.tan(alpha)) else: h2 = h v2 = np.array([h2, 0.0, l]) ca = np.cos(alpha) sa = np.sin(alpha) R1 = np.array([[ca, sa, 0.0], [-sa, ca, 0.0], [0.0, 0.0, 1.0]]) beta = np.radians(vangle(v2,np.array([0.0, 0.0, 1.0]))) cb = np.cos(beta) sb = np.sin(beta) R2 = np.array([[cb, 0.0, -sb], [0.0, 1.0, 0.0], [sb, 0.0, cb]]) phi = np.radians(phi) cp = np.cos(phi) sp = np.sin(phi) R3 = np.array([[cp, -sp, 0.0], [sp, cp, 0.0], [0.0, 0.0, 1.0]]) v2 = np.dot(R3,np.dot(R2,np.dot(R1,v))) v2 = np.dot(np.linalg.inv(R1),np.dot(np.linalg.inv(R2),v2)) return v2 def vrot2(vector1,vector2,angle): """ **rotMatrix** Rotate vector1 around vector2 by an angle. """ theta = np.radians(angle) R=np.array([[vector2[0]**2+(1.0-vector2[0]**2)*np.cos(theta), (1.0-np.cos(theta))*vector2[0]*vector2[1]-np.sin(theta)*vector2[2], (1.0-np.cos(theta))*vector2[0]*vector2[2]+np.sin(theta)*vector2[1]], [(1.0-np.cos(theta))*vector2[0]*vector2[1]+np.sin(theta)*vector2[2], vector2[1]**2+(1.0-vector2[1]**2)*np.cos(theta), (1.0-np.cos(theta))*vector2[1]*vector2[2]-np.sin(theta)*vector2[0]],[(1.0-np.cos(theta))*vector2[0]*vector2[2]-np.sin(theta)*vector2[1], (1.0-np.cos(theta))*vector2[1]*vector2[2]+np.sin(theta)*vector2[0], vector2[2]**2+(1.0-vector2[2]**2)*np.cos(theta)]]) return np.dot(R,vector1) def vangle(v1, v2): """ **vangle** Calculates the angle between two cartesian vectors v1 and v2 in degrees. Args: * v1 (np.array): first vector. * v2 (np.array): second vector. Returns: * th (float): angle between first and second vector. Function by S. Huotari, adopted for Python. """ return np.arccos(np.dot(v1,v2)/np.linalg.norm(v1)/np.linalg.norm(v2))/np.pi*180.0; def convtoprim(hklconv): """ **convtoprim** converts diamond structure reciprocal lattice expressed in conventional lattice vectors to primitive one (Helsinki -> Palaiseau conversion) from S. Huotari """ return hklconv[2]*np.array([0.5,0.5,0.0]) + hklconv[1]*np.array([0.5,0.0,0.5]) + hklconv[0]*np.array([0.0,0.5,0.5]) def primtoconv(hklprim): """ **primtoconv** converts diamond structure reciprocal lattice expressed in primitive basis to the conventional basis (Palaiseau -> Helsinki conversion) from S. Huotari """ a = np.array([0.0, 0.5, 0.5]) b = np.array([0.5, 0.0, 0.5]) c = np.array([0.5, 0.5, 0.0]) Gp = np.linalg.inv([a,b,c]).T ap = Gp[0,:] bp = Gp[1,:] cp = Gp[2,:] return hklprim[0]*ap + hklprim[1]*bp + hklprim[2]*cp def householder(b,k): """ function H = householder(b, k) % H = householder(b, k) % Atkinson, Section 9.3, p. 611 % b is a column vector, k an index < length(b) % Constructs a matrix H that annihilates entries % in the product H*b below index k % $Id: householder.m,v 1.1 2008-01-16 15:33:30 mike Exp $ % M. M. Sussman """ n = len(b) d = b[k:n] if d[0] >= 0.0: alpha = -np.linalg.norm(d) else: alpha = np.linalg.norm(d) if alpha == 0.0: H = np.eye(n) return lenD = len(d) v = np.zeros(lenD) v[0] = np.sqrt(0.5*(1.0-d[0]/alpha)) p = -alpha*v[0] v[1:lenD] = d[1:lenD]/(2.0*p) w = np.append( np.zeros((k,1)) ,v).reshape(n,1) H = np.eye(n)-2.0 * np.dot(w,w.T) return H def svd_my(M,maxiter=100,eta=0.1): sind = 0 import copy import scipy as sp # initialize U,S,V X = copy.deepcopy(M) m,n = np.shape(X) k = np.amin([m,n]) U = np.random.rand(m,k) V = np.random.rand(n,k) S = np.random.rand(k,k) # orthogonalize U,V #U = sp.linalg.orth(U) #V = sp.linalg.orth(V) # compute S #S = np.dot(np.dot(U.T,X),V) # compute cost J0 J0 = 0.5*np.linalg.norm(X - np.dot(np.dot(U,S),V.T) )**2 J = J0 dJ = J while sind <= maxiter: sind += 1 # update U and V U = U + eta*(np.dot(X,V) + U.dot(V.T).dot(X.T).dot(U) ).dot(S) V = V + eta*(np.dot(X.T,U) + V.dot(U.T).dot(X).dot(V) ).dot(S) # compute S S = U.T.dot(X).dot(V) # make S_ii positive V = np.dot(V,np.sign(S)) S = np.abs(S) Jnew = 0.5*np.linalg.norm(X - np.dot(np.dot(U,S),V.T) )**2 dJ = Jnew - J J = Jnew print( Jnew) return U,S,V def bidiag_reduction(A): """ function [U,B,V]=bidiag_reduction(A) % [U B V]=bidiag_reduction(A) % Algorithm 6.5-1 in Golub & Van Loan, Matrix Computations % Johns Hopkins University Press % Finds an upper bidiagonal matrix B so that A=U*B*V' % with U,V orthogonal. A is an m x n matrix """ import copy m,n = np.shape(A) B = copy.deepcopy(A) U = np.eye(m) V = np.eye(n) for k in range(n): # eliminate non-zeros below the diagonal H = householder(B[:,k],k) B = np.dot(H,B) U = np.dot(U,H) # eliminate non-zeros to the right of the # superdiagonal by working with the transpose if k= 360.0: # psi -= 360.0 return tthv, tthh, psi def cixsUBgetQ_secondo(tthv, tthh, psi): G = np.array([-2.,-2.,0.0]) # incoming/outgoing energy/wavelength hc = 12.3984191 bragg_ang = 86.5 wo = energy(dspace([4., 4., 4.]),bragg_ang) lambdao = hc/wo wi = wo lambdai = hc/wi # lattice parameters lattice = np.array([5.43095, 5.43095, 5.43095]) angles = np.radians(np.array([90.0, 90.0, 90.0])) # in radians !!! a = np.array([lattice[0], 0, 0]) b = np.array([lattice[0]*np.cos(angles[2]), lattice[1]*np.sin(angles[2]), 0]) c = np.array([lattice[2]*np.cos(angles[1]), lattice[2]*(-np.cos(angles[1])*np.arctan(angles[2])+np.cos(angles[0])*(1.0/np.sin(angles[2]))), lattice[2]/np.sqrt(2.0)*np.sqrt((1.0/np.sin(angles[2]))*((4.0*np.cos(angles[0])*np.cos(angles[1])*np.arctan(angles[2])-(1.0 + np.cos(2.0*angles[0])+np.cos(2.0*angles[1])+np.cos(2.0*angles[2]))*(1.0/np.sin(angles[2])))))]) # lab-to-sample reference system transformation matrix U th = braggd(G,wo) xxx = vrot(np.array([1.0,-1.0, 0.0]),np.array([0.0,0.0,1.0]),th) yyy = vrot(np.array([0.0, 0.0, 1.0]),np.array([0.0,0.0,1.0]),th) zzz = vrot(G,np.array([0.0,0.0,1.0]),th) U = np.zeros((3,3)) U[:,0] = xxx/np.linalg.norm(xxx) U[:,1] = yyy/np.linalg.norm(yyy) U[:,2] = zzz/np.linalg.norm(zzz) # reciprocal lattice to absolute units transformation matrix a_star = 2.0*np.pi*np.cross(b,c)/np.dot(a,np.cross(b,c)) b_star = 2.0*np.pi*np.cross(c,a)/np.dot(a,np.cross(b,c)) c_star = 2.0*np.pi*np.cross(a,b)/np.dot(a,np.cross(b,c)) angles_star = np.array([np.arccos(np.dot(b_star,c_star)/np.linalg.norm(b_star)/np.linalg.norm(c_star)), np.arccos(np.dot(c_star,a_star)/np.linalg.norm(c_star)/np.linalg.norm(a_star)), np.arccos(np.dot(a_star,b_star)/np.linalg.norm(a_star)/np.linalg.norm(b_star))]) B = np.zeros((3,3)) B[:,0] = np.array([np.linalg.norm(a_star), np.linalg.norm(b_star)*np.cos(angles_star[2]), np.linalg.norm(c_star)*np.cos(angles_star[1])]) B[:,1] = np.array([0.0, np.linalg.norm(b_star)*np.sin(angles_star[2]), -np.linalg.norm(c_star)*np.sin(angles_star[1])*np.cos(angles[0])]) B[:,2] = np.array([0.0, 0.0, 2.0*np.pi/np.linalg.norm(c)]) # laboratory reference frame X = np.array([1.0, 0.0, 0.0]) Y = np.array([0.0, 1.0, 0.0]) Z = np.array([0.0, 0.0, 1.0]) # axis of rotation of psi v = np.array([-np.sin(np.radians(th)), 0.0, np.cos(np.radians(th))]) Ki_test = 2.0*np.pi/lambdai*X Ko_test = 2.0*np.pi/lambdao*vrot(vrot(X,Y,-tthv) ,Z, tthh) Q_test = np.dot(np.linalg.lstsq(B,U)[0],vrot(Ki_test-Ko_test,v,-psi)) return Q_test def cixsUBgetAngles_terzo(Q): G = np.array([-1.0,-1.0,-1.0]) # incoming/outgoing energy/wavelength hc = 12.3984191 bragg_ang = 86.5 wo = energy(dspace([4., 4., 4.]),bragg_ang) lambdao = hc/wo wi = wo lambdai = hc/wi # lattice parameters lattice = np.array([5.43095, 5.43095, 5.43095]) angles = np.radians(np.array([90.0, 90.0, 90.0])) # in radians !!! a = np.array([lattice[0], 0, 0]) b = np.array([lattice[0]*np.cos(angles[2]), lattice[1]*np.sin(angles[2]), 0]) c = np.array([lattice[2]*np.cos(angles[1]), lattice[2]*(-np.cos(angles[1])*np.arctan(angles[2])+np.cos(angles[0])*(1.0/np.sin(angles[2]))), lattice[2]/np.sqrt(2.0)*np.sqrt((1.0/np.sin(angles[2]))*((4.0*np.cos(angles[0])*np.cos(angles[1])*np.arctan(angles[2])-(1.0 + np.cos(2.0*angles[0])+np.cos(2.0*angles[1])+np.cos(2.0*angles[2]))*(1.0/np.sin(angles[2])))))]) # lab-to-sample reference system transformation matrix U for Si220-crystal th = braggd(G,wo) #xxx = vrot(np.array([0.0,-1.0,1.0]),np.array([-2.0,1.0,1.0]),th) #yyy = vrot(np.array([-2.0,1.0,1.0]),np.array([-2.0,1.0,1.0]),th) #zzz = vrot(G,np.array([-2.0,1.0,1.0]),th) xxx = vrot(np.array([0.0,1.0,-1.0]),np.array([2.0,-1.0,-1.0]),th) yyy = vrot(np.array([2.0,-1.0,-1.0]),np.array([2.0,-1.0,-1.0]),th) zzz = vrot(G,np.array([2.0,-1.0,-1.0]),th) U = np.zeros((3,3)) U[:,0] = xxx/np.linalg.norm(xxx) U[:,1] = yyy/np.linalg.norm(yyy) U[:,2] = zzz/np.linalg.norm(zzz) # reciprocal lattice to absolute units transformation matrix a_star = 2.0*np.pi*np.cross(b,c)/np.dot(a,np.cross(b,c)) b_star = 2.0*np.pi*np.cross(c,a)/np.dot(a,np.cross(b,c)) c_star = 2.0*np.pi*np.cross(a,b)/np.dot(a,np.cross(b,c)) angles_star = np.array([np.arccos(np.dot(b_star,c_star)/np.linalg.norm(b_star)/np.linalg.norm(c_star)), np.arccos(np.dot(c_star,a_star)/np.linalg.norm(c_star)/np.linalg.norm(a_star)), np.arccos(np.dot(a_star,b_star)/np.linalg.norm(a_star)/np.linalg.norm(b_star))]) B = np.zeros((3,3)) B[:,0] = np.array([np.linalg.norm(a_star), np.linalg.norm(b_star)*np.cos(angles_star[2]), np.linalg.norm(c_star)*np.cos(angles_star[1])]) B[:,1] = np.array([0.0, np.linalg.norm(b_star)*np.sin(angles_star[2]), -np.linalg.norm(c_star)*np.sin(angles_star[1])*np.cos(angles[0])]) B[:,2] = np.array([0.0, 0.0, 2.0*np.pi/np.linalg.norm(c)]) # laboratory reference frame X = np.array([1.0, 0.0, 0.0]) Y = np.array([0.0, 1.0, 0.0]) Z = np.array([0.0, 0.0, 1.0]) # desired momentum in the laboratory reference system before any rotation is applied v_c = np.dot(B,Q) Q_lab = np.linalg.lstsq(U,v_c)[0] #$[angles,FVAL,EXITFLAG,OUTPUT] = fsolve(@(x) UBfind(x, G, Q_lab), [0 45 0]); lab_angles = optimize.fsolve(cixsUBfind, [55., 20.0, 0.0], args=(G,Q_lab,wi,wo,lambdai,lambdao), xtol=1.49012e-12,maxfev=1000000) tthv = lab_angles[1] tthh = lab_angles[0] psi = lab_angles[2] #if psi <= -360.0: # psi += 360.0 #if psi >= 360.0: # psi -= 360.0 return tthv, tthh, psi def cixsUBgetQ_terzo(tthv, tthh, psi): G = np.array([-1.0,-1.0,-1.0]) # incoming/outgoing energy/wavelength hc = 12.3984191 bragg_ang = 86.5 wo = energy(dspace([4., 4., 4.]),bragg_ang) lambdao = hc/wo wi = wo lambdai = hc/wi # lattice parameters lattice = np.array([5.43095, 5.43095, 5.43095]) angles = np.radians(np.array([90.0, 90.0, 90.0])) # in radians !!! a = np.array([lattice[0], 0, 0]) b = np.array([lattice[0]*np.cos(angles[2]), lattice[1]*np.sin(angles[2]), 0]) c = np.array([lattice[2]*np.cos(angles[1]), lattice[2]*(-np.cos(angles[1])*np.arctan(angles[2])+np.cos(angles[0])*(1.0/np.sin(angles[2]))), lattice[2]/np.sqrt(2.0)*np.sqrt((1.0/np.sin(angles[2]))*((4.0*np.cos(angles[0])*np.cos(angles[1])*np.arctan(angles[2])-(1.0 + np.cos(2.0*angles[0])+np.cos(2.0*angles[1])+np.cos(2.0*angles[2]))*(1.0/np.sin(angles[2])))))]) # lab-to-sample reference system transformation matrix U th = braggd(G,wo) #xxx = vrot(np.array([0.0,-1.0,1.0]),np.array([-2.0,1.0,1.0]),th) #yyy = vrot(np.array([-2.0,1.0,1.0]),np.array([-2.0,1.0,1.0]),th) #zzz = vrot(G,np.array([-2.0,1.0,1.0]),th) xxx = vrot(np.array([0.0,1.0,-1.0]),np.array([2.0,-1.0,-1.0]),th) yyy = vrot(np.array([2.0,-1.0,-1.0]),np.array([2.0,-1.0,-1.0]),th) zzz = vrot(G,np.array([2.0,-1.0,-1.0]),th) U = np.zeros((3,3)) U[:,0] = xxx/np.linalg.norm(xxx) U[:,1] = yyy/np.linalg.norm(yyy) U[:,2] = zzz/np.linalg.norm(zzz) # reciprocal lattice to absolute units transformation matrix a_star = 2.0*np.pi*np.cross(b,c)/np.dot(a,np.cross(b,c)) b_star = 2.0*np.pi*np.cross(c,a)/np.dot(a,np.cross(b,c)) c_star = 2.0*np.pi*np.cross(a,b)/np.dot(a,np.cross(b,c)) angles_star = np.array([np.arccos(np.dot(b_star,c_star)/np.linalg.norm(b_star)/np.linalg.norm(c_star)), np.arccos(np.dot(c_star,a_star)/np.linalg.norm(c_star)/np.linalg.norm(a_star)), np.arccos(np.dot(a_star,b_star)/np.linalg.norm(a_star)/np.linalg.norm(b_star))]) B = np.zeros((3,3)) B[:,0] = np.array([np.linalg.norm(a_star), np.linalg.norm(b_star)*np.cos(angles_star[2]), np.linalg.norm(c_star)*np.cos(angles_star[1])]) B[:,1] = np.array([0.0, np.linalg.norm(b_star)*np.sin(angles_star[2]), -np.linalg.norm(c_star)*np.sin(angles_star[1])*np.cos(angles[0])]) B[:,2] = np.array([0.0, 0.0, 2.0*np.pi/np.linalg.norm(c)]) # laboratory reference frame X = np.array([1.0, 0.0, 0.0]) Y = np.array([0.0, 1.0, 0.0]) Z = np.array([0.0, 0.0, 1.0]) # axis of rotation of psi v = np.array([-np.sin(np.radians(th)), 0.0, np.cos(np.radians(th))]) Ki_test = 2.0*np.pi/lambdai*X Ko_test = 2.0*np.pi/lambdao*vrot(vrot(X,Y,-tthv) ,Z, tthh) Q_test = np.dot(np.linalg.lstsq(B,U)[0],vrot(Ki_test-Ko_test,v,-psi)) return Q_test def cixsUBfind(x,G,Q_sample,wi,wo,lambdai,lambdao): """ **cixsUBfind** """ tthh = x[0] tthv = x[1] psi = x[2] X = np.array([1, 0, 0]) Y = np.array([0, 1, 0]) Z = np.array([0, 0, 1]) Ki = 2.0*np.pi/lambdai*X Ko = 2.0*np.pi/lambdao* vrot(vrot(X,Y,-tthv ),Z,tthh) Q = Ki-Ko th = braggd(G,wo) v = np.array([-np.sin(np.radians(th)), 0.0, np.cos(np.radians(th))]) y = Q - vrot(Q_sample, v, psi) tthh = y[0] tthv = y[1] psi = y[2] return tthh, tthv, psi def cixs_primo(tthv,tthh,psi,anal_braggd=86.5): """ **cixs_primo** """ import copy lattice_a = dspace([1., 0., 0.]) # Si lattice constant # crystal vectors crystVec1 = np.array([-1.,-1.,-1.])/np.linalg.norm(np.array([-1.,-1.,-1.])) # "z-axis" crystVec2 = np.array([ 0.,-1., 1.])/np.linalg.norm(np.array([ 0.,-1., 1.])) # "x-axis" crystVec3 = np.array([-2., 1., 1.])/np.linalg.norm(np.array([-2., 1., 1.])) # "y-axis" # rotate x- and y-vectors about G by the miscut of PRIMO crystVec2 = vrot(crystVec2,crystVec1,-39.8) crystVec3 = vrot(crystVec3,crystVec1,-39.8) # calculate energies and wavelengths hc = 12.3984191 # CODATA 2002 recommended value, physics.nist.gov/constants E_out = energy(dspace(np.array([4., 4., 4.])),anal_braggd) lam_out = hc/E_out E_in = E_out #+0.02; % if want to be precise, E=Eout-20 eV @ plasmon peak lam_in = hc/E_in # initially k0 is along crystVec2, # then rotate k0 about crystVec3 by the Bragg angle k0 = vrot(crystVec2,crystVec3,braggd(np.array([1., 1., 1.]),E_in)) k0 = k0/np.linalg.norm(k0)*2.0*np.pi/lam_in # define lab coordinates hutch_x = copy.deepcopy(k0) # k0 is along the beam hutch_y = copy.deepcopy(crystVec2) # perpendicular to beam/untouched so far hutch_z = vrot(crystVec1,crystVec3,braggd(np.array([1., 1., 1.]),E_in)) # toward hutch ceiling (if k0 rotates, z has to rotate with it) # rotate the crystal abouts its G vector k0 = vrot(k0,crystVec1,psi) hutch_x = copy.deepcopy(k0) # hutch_x is always along k0 hutch_y = vrot(hutch_y,crystVec1,psi) # perpendicular to beam hutch_z = vrot(hutch_z,crystVec1,psi) # toward hutch ceiling # calculate kh using G-vector kh = k0 + np.array([-1.,-1.,-1.])/np.linalg.norm(np.array([-1.,-1.,-1.])) # rotate vertical kprime = vrot(k0,hutch_y,-tthv) # we can rotate vertical tth from 0 to 90 (eta from 0 to 90) kprime = vrot(kprime,hutch_z,tthh) # we can rotate horizontal tth from 0 to 90 kprime = kprime/np.linalg.norm(kprime)*2.0*np.pi/lam_out # calculate momentum transfer qh = kh-kprime q0 = k0-kprime return q0, qh, kprime #hutch_x, hutch_y, hutch_z def cixs_secondo(tthv,tthh,psi,anal_braggd=86.5): """ **cixs_secondo** """ import copy lattice_a = dspace([1., 0., 0.]) # Si lattice constant # crystal vectors crystVec1 = np.array([-2.,-2., 0.])/np.linalg.norm(np.array([-2.,-2., 0.])) # "z-axis" crystVec2 = np.array([ 1.,-1., 0.])/np.linalg.norm(np.array([ 1.,-1., 0.])) # "x-axis" crystVec3 = np.array([ 0., 0., 1.])/np.linalg.norm(np.array([ 0., 0., 1.])) # "y-axis" # rotate x- and y-vectors about G by the miscut of PRIMO crystVec2 = vrot(crystVec2,crystVec1,0.0) crystVec3 = vrot(crystVec3,crystVec1,0.0) # calculate energies and wavelengths hc = 12.3984191 # CODATA 2002 recommended value, physics.nist.gov/constants E_out = energy(dspace(np.array([4., 4., 4.])),anal_braggd) lam_out = hc/E_out E_in = E_out #+0.02; % if want to be precise, E=Eout-20 eV @ plasmon peak lam_in = hc/E_in # initially k0 is along crystVec2, # then rotate k0 about crystVec3 by the Bragg angle k0 = vrot(crystVec2,crystVec3,braggd(np.array([1., 1., 1.]),E_in)) k0 = k0/np.linalg.norm(k0)*2.0*np.pi/lam_in # define lab coordinates hutch_x = copy.deepcopy(k0) # k0 is along the beam hutch_y = copy.deepcopy(crystVec2) # perpendicular to beam/untouched so far hutch_z = vrot(crystVec1,crystVec3,braggd(np.array([2., 2., 0.]),E_in)) # toward hutch ceiling (if k0 rotates, z has to rotate with it) # rotate the crystal abouts its G vector k0 = vrot(k0,crystVec1,psi) hutch_x = copy.deepcopy(k0) # hutch_x is always along k0 hutch_y = vrot(hutch_y,crystVec1,psi) # perpendicular to beam hutch_z = vrot(hutch_z,crystVec1,psi) # toward hutch ceiling # calculate kh using G-vector kh = k0 + np.array([-2.,-2.,0.])/np.linalg.norm(np.array([-2.,-2.,0.])) # rotate vertical kprime = vrot(k0,hutch_y,-tthv) # we can rotate vertical tth from 0 to 90 (eta from 0 to 90) kprime = vrot(kprime,hutch_z,tthh) # we can rotate horizontal tth from 0 to 90 kprime = kprime/np.linalg.norm(kprime)*2.0*np.pi/lam_out # calculate momentum transfer qh = kh-kprime q0 = k0-kprime return q0, qh, hutch_x, hutch_y, hutch_z def cixs_terzo(tthv,tthh,psi,anal_braggd=86.5): """ **cixs_terzo** """ hc = 12.3984191 # CODATA 2002 recommended value, physics.nist.gov/constants zz = np.array([-1., -1., -1.]) G = 2.0*np.pi*zz/dspace(np.array([1., 0., 0.])) xx = vrot(np.array([0., 1., -1.,]),np.array([-1., -1., -1.]),90-81.1) xx = vrot(xx,G,psi) yy = vrot(xx,zz,90.0) a = dspace(np.array([1., 0., 0.])) Eout = energy(dspace(np.array([4., 4., 4.])),anal_braggd) lambdaout = hc/Eout E = Eout #+0.02; lambdain = hc/E k0 = vrot(xx,yy,braggd(zz,E)) k0 = k0/np.linalg.norm(k0)*2.0*np.pi/lambdain nn = vrot(zz,yy,braggd(zz,E)) # nn is our spectrometer (hutch) vertical coordinate kh = k0 + G kprime = vrot(k0,yy,-tthv) # we can rotate vertical tth from 0 to 90 (eta from 0 to 90) kprime = kprime/np.linalg.norm(kprime)*2.0*np.pi/lambdaout kprime = vrot(kprime,nn,tthh) # we can rotate horizontal tth from 0 q0 = k0-kprime qh = kh-kprime return q0, qh # def constrained_nnmf(A,W_ini,H_ini,W_up,H_up,max_iter=10000,verbose=False): # """ **constrained_nnmf** # Approximate non-negative matrix factorization with constrains. # function [W H]=johannes_nnmf_ALS(A,W_ini,H_ini,W_up,H_up) # % ***************************************************************** # % ***************************************************************** # % ** [W H]=johannes_nnmf(A,W_ini,H_ini,W_up,H_up) ** # % ** performs A=WH approximate matrix factorization, ** # % ** where A(n*m), W(n*k), and H(k*m) are non-negative matrices, ** # % ** and k0: x = F[idxs] else: x = np.array([]) idxs=np.where(C_up == 1) nC=len(idxs[0]) if nC>0: x = np.hstack([x, C[idxs]]) return x def vec2mat(x,F,C,F_up,C_up,n,k,m): idxs=np.where(F_up == 1) nF=len(idxs[0]) if idxs: F[idxs] = x[:nF] idxs=np.where(C_up == 1) nC=len(idxs[0]) if idxs: C[idxs] = x[nF:] F=F.reshape(n,k) C=C.reshape(k,m) return F, C def NNMFcost_old(x,A,W,H,W_up,H_up): """ **NNMFcost** Returns cost and gradient for NNMF with constraints. """ # calculate W, H W, H = con2mat(x,W,H,W_up,H_up) # calculate cost and gradient J = np.sum(np.sum(0.5*(A-np.dot(W,H))*(A-np.dot(W,H)))) gradW = -(np.dot((A-np.dot(W,H)),H.T)) gradH = -(np.dot((A-np.dot(W,H)).T,W)).T # return constraint only for updates xgrad = mat2con(gradW,gradH,W_up,H_up) return J, xgrad def NNMFcost(x,A,F,C,F_up,C_up,n,k,m): """ **NNMFcost** Returns cost and gradient for NNMF with constraints. """ # calculate W, H F, C = vec2mat(x,F,C,F_up,C_up,n,k,m) # calculate cost and gradient J = np.sum(np.sum(0.5*(A-np.dot(F,C))*(A-np.dot(F,C)))) #gradF = -2.0*(np.dot((A-np.dot(F,C)),C.T)) #gradC = -2.0*(np.dot((A-np.dot(F,C)).T,F)).T # return gradient only for updates #gradF=gradF.reshape(1,n*k)[0] #gradC=gradC.reshape(1,k*m)[0] #xgrad = mat2vec(gradF,gradC,F_up,C_up,n,k,m) return J def NNMFcost_der(x,A,F,C,F_up,C_up,n,k,m): F, C = vec2mat(x,F,C,F_up,C_up,n,k,m) gradF = -2.0*(np.dot((A-np.dot(F,C)),C.T)) gradC = -2.0*(np.dot((A-np.dot(F,C)).T,F)).T # return gradient only for updates gradF=gradF.reshape(1,n*k)[0] gradC=gradC.reshape(1,k*m)[0] xgrad = mat2vec(gradF,gradC,F_up,C_up,n,k,m) return xgrad def bootstrapCNNMF(A,F_ini, C_ini, F_up, C_up, Niter): """ **bootstrapCNNMF** Constrained non-negative matrix factorization with bootstrapping for error estimates. """ n,m = A.shape k = np.shape(F_ini)[1] print( 'NNMF problem of dimension: ' + str(n) + 'x' + str(k) + 'x' + str(m) ) F1s = [] C1s = [] A1 = copy.deepcopy(A) # make sure A is non-negative A1[A1<0.0] = 0.0 # normalize A1 = A1/np.matmul(np.ones((A1.shape[0],1)),np.sum(A1,axis=0,keepdims=True)) for ii in range(Niter): #A1 = copy.deepcopy(A) # make non-negative #A1[A1<0.0] = 0.0 # normalize #A1 = A1/np.matmul(np.ones((A1.shape[0],1)),np.sum(A1,axis=0,keepdims=True)) F_ini = F_ini/np.matmul(np.ones((F_ini.shape[0],1)),np.sum(F_ini,axis=0,keepdims=True)) C_ini = C_ini/np.matmul(np.ones((C_ini.shape[0],1)),np.sum(C_ini,axis=0,keepdims=True)) # add random noise #A1 += np.random.random((n,m))*Aerr F1 = F_ini.reshape(1,n*k)[0] C1 = C_ini.reshape(1,k*m)[0] F1up = F_up.reshape(1,n*k)[0] C1up = C_up.reshape(1,k*m)[0] #print( len(np.where(F1up==1)[0])) #print( len(np.where(C1up==1)[0])) # get starting values x0 = mat2vec(F1, C1, F1up, C1up, n, k, m) # set limits bnds = [(0.0,1.0) for ii in x0] # minimize cost res=optimize.minimize(NNMFcost,x0,args=(A1,F1,C1,F1up,C1up,n,k,m), jac=NNMFcost_der, bounds=bnds, method='SLSQP', options={'maxiter': 100000, 'disp': True}, tol=1e-14) #translate vector of parameters bacj to matrices Fbs1, Cbs1 = vec2mat(res.x,F1,C1,F1up,C1up,n,k,m) # Normalize, translated to python by Risto & Johannes Fbs1_help = Fbs1/(np.dot(np.ones((n,1)),np.sum(Fbs1,axis=0).reshape(1,k))) Cbs1 = Cbs1*(np.dot(np.sum(Fbs1,axis=0).reshape(k,1), np.ones((1,m)))) Fbs1 = Fbs1_help # store meaningful data F1s.append(Fbs1.copy()) C1s.append(Cbs1.copy()) if Niter>1: # stantard deviation Cerr=np.squeeze(np.std(np.array(C1s),axis=0)) Ferr=np.squeeze(np.std(np.array(F1s),axis=0)) # average C=np.squeeze(np.mean(np.array(C1s),axis=0)) F=np.squeeze(np.mean(np.array(F1s),axis=0)) else: # only one of each matrix F=np.array(F1s[0]) C=np.array(C1s[0]) Ferr=np.zeros(np.shape(F)) Cerr=np.zeros(np.shape(C)) return F, C, Ferr, Cerr def bootstrapCNNMF_old(A,k,Aerr, F_ini, C_ini, F_up, C_up, Niter=100): """ **bootstrapCNNMF** Constrained non-negative matrix factorization with bootstrapping for error estimates. """ n,m = A.shape import copy F1s = np.zeros((Niter,n,k)) C1s = np.zeros((Niter,C_ini.shape[0],C_ini.shape[1])) for ii in range(Niter): A1 = copy.deepcopy(A) # add random noise A1 += np.random.random((n,m))*Aerr F1 = F_ini*(1.0-F_up) + F_up*np.random.random((n,k)) C1 = C_ini*(1.0-C_up) + C_up*np.random.random((k,m)) F1[F1<0.0]=0.0 C1[C1<0.0]=0.0 # minimize with trust-region-algorithm # get starting values x0 = mat2con(F1,C1,F_up,C_up) cons = ({'args': (A1,F1,C1,F_up,C_up)}) bnds = [(0.0,1.0) for ii in x0] costfun = lambda x:NNMFcost(x,A1,F1,C1,F_up,C_up) #[0] #gradfun = lambda x:NNMFcost(x,A1,F1,C1,F_up,C_up)[1] x=minimize(NNMFcost_old,x0,args=(A1,F1,C1,F_up,C_up), method='Newton-CG', tol=1e-5, jac=True, bounds=bnds,options={'maxiter' : 1e6, 'disp': True} ).x Fbs1, Cbs1 = con2mat(x,F1,C1,F_up,C_up) # store meaningful data print( Fbs1.shape) print( Cbs1.shape) F1s[ii,:,:] = Fbs1/(np.dot(np.ones((np.shape(Fbs1)[0],1)), np.sum(Fbs1,axis=0).reshape(1,len(np.sum(Fbs1,axis=0))) )) print( ) C1s[ii,:,:] = Cbs1 * ( np.dot(np.sum(Fbs1,axis=0).reshape(k,1), np.ones(( 1, A1.shape[1] )) ) ) # do RMS print( F1s.shape, C1s.shape) Cerr=np.squeeze(np.std(C1s,axis=0)) Ferr=np.squeeze(np.std(F1s,axis=0)) # average C=np.squeeze(np.mean(C1s,axis=0)) F=np.squeeze(np.mean(F1s,axis=0)) return F, C, Ferr, Cerr def cNNMF_chris( A, W_fixed, W_free, maxIter=100, verbose=True ): # set up a matrix of guesses and free columns (W_free can be None) if W_free is not None: W = np.zeros(( W_fixed.shape[0] , (W_fixed.shape[1]+W_free.shape[1])) ) for ii in range(W_free.shape[-1]): W_free[:,ii] /= np.linalg.norm(W_free[:,ii]) else: W = np.zeros( (W_fixed.shape[0] , (W_fixed.shape[1])) ) # fill the W matrix W[:, 0: W_fixed.shape[1] ] = W_fixed if W_free is not None: W[:, W_fixed.shape[1]: ] = W_free # make W non-zero everywhere W[ W<0.0 ] = 0.0 # iterate for ii in range(maxIter): # W * coeffs = A # find first set of coefficients # coeffs = (np.linalg.lstsq( W, A, rcond=None))[0] # coeffs[coeffs<0] = 0.0 coeffs = np.zeros( (W.shape[1], A.shape[1]) ) for kk in range(A.shape[1]): coeffs[:,kk] = nnls(W, A[:,kk])[0] # if there is no spectra to vary, just return the coeffs if W_free is None: return W, coeffs diff = A - np.dot( W , coeffs) error = np.linalg.norm(diff) if verbose: print( "Error at iteration %d of MF is %f" %(ii, error*error)) # update the free components reduced_A = A - np.dot( W[:,: W_fixed.shape[1] ], coeffs[0:W_fixed.shape[1],:] ) W_free = (np.linalg.lstsq( (coeffs[W_fixed.shape[1]:,: ] ).T, reduced_A.T, rcond=None ))[0].T W[:, W_fixed.shape[1]:] = W_free # make sure W is non-negative everywhere W[ W<0.0 ] = 0.0 # normalize W = W/np.dot( np.ones((A.shape[0],1)), np.sum(W,axis=0).reshape(1,W.shape[1]) ) coeffs = coeffs * ( np.dot(np.sum(W,axis=0).reshape(W.shape[1],1), np.ones(( 1, A.shape[1] )) ) ) #for jj in range( W.shape[-1] ): # W[:,jj] = W[:,jj]/np.linalg.norm(W[:,jj]) return W, coeffs def constrained_svd(M,U_ini,S_ini,VT_ini,U_up,max_iter=10000,verbose=False): """ **constrained_nnmf** Approximate singular value decomposition with constraints. function [U, S, V] = constrained_svd(M,U_ini,S_ini,V_ini,U_up,max_iter=10000,verbose=False) """ # initialize matrices # M = [n x m] U = U_ini # [n x n] (unitary) S = S_ini # [n x m] (diagonal matrix) VT = VT_ini # [m x m] (unitary) n,m = np.shape(M) # initial cost J = np.sum(np.sum(0.5 * (M-np.dot(np.dot(U,S),VT))*(M-np.dot(np.dot(U,S),VT)))) print('Initial cost J = %1.4f at step 0') % J dJ = -0.1 sind = 0 while sind <= max_iter: sind += 1 # solve S from: U*S = M*(VT)^-1 S = np.linalg.lstsq( U, np.dot(M, np.linalg.pinv(VT)))[0] # make S diagonal for ii in range(S.shape[0]): for jj in range(S.shape[1]): if ii != jj: S[ii,jj] = 0.0 # solve VT from: U*S*V=M VT = np.linalg.lstsq( np.dot(U,S),M )[0] # solve U from: VT.T*S.T*U.T = M.T U = np.linalg.lstsq( np.dot(VT.T,S.T) , M.T )[0].T # restore fixed components inds = U_up==0.0 U[inds] = U_ini[inds] # formalize spectra and coefficients U = U/(np.dot(np.ones((np.shape(U)[0],1)),np.sum(U,axis=0).reshape(1,len(np.sum(U,axis=0))) )) VT = VT/(np.dot(np.ones((np.shape(VT)[0],1)),np.sum(VT,axis=0).reshape(1,len(np.sum(VT,axis=0))) )) # print some progression if sind % 100 == 0 and verbose: Jnew = np.sum(np.sum(0.5 * (M-np.dot(np.dot(U,S),VT))*(M-np.dot(np.dot(U,S),VT)))) dJ = Jnew-J J = Jnew print('Iteration %1d J = %1.4f') %(sind,J) print('dJ = %5.3f') % dJ return U, S, VT def unconstrained_mf(A,numComp=3, maxIter=1000, tol=1.0e-8): """ **unconstrained_mf** Returns main components from an off-diagonal Matrix (energy-loss x angular-departure), using the power method iteratively on the different main components. """ # initialize random coefficient matrix coeff = np.random.random((A.shape[1],numComp)) W = np.random.random((numComp,A.shape[0])) # normalize W for ii in range(numComp): W[ii,:] /= np.linalg.norm(W[ii,:]) ind = 0 err = 1.0e8 # start looping: while ind <= maxIter or dJ <= tol: # update coefficient matrix abc = np.linalg.lstsq( W.T,A)[0].T coeff = np.copy(abc) for comp in range(numComp): # updatea coefficients and # set one of the coefficient vectors to zero coeff[:,comp] = np.zeros_like(abc[:,comp]) # calculate error matrix errM = A - np.dot(coeff,W).T # initialize power method V = np.random.random((len(W[comp,:]),1)) V /= np.linalg.norm(V) for jj in range(1000): vnew = np.dot(errM, errM.T).dot(V) vnew /= np.linalg.norm(vnew) V = vnew V /= np.linalg.norm(V) W[comp,:] = V.reshape(W[comp,:].shape) # set the zeroed coefficients back to orig coeff[:,comp] = abc[:,comp] # calculate error newerr = np.linalg.norm(A - np.dot(coeff,W).T) dJ = err - newerr err = newerr ind += 1 return W, coeff, err def constrained_mf(A, W_ini, W_up, coeff_ini, coeff_up, maxIter=1000, tol=1.0e-8, maxIter_power=1000): """ **cfactorizeOffDiaMatrix** constrained version of factorizeOffDiaMatrix Returns main components from an off-diagonal Matrix (energy-loss x angular-departure). """ numComp = coeff_ini.shape[1] # initialize random coefficient matrix coeff = np.copy(coeff_ini) W = np.copy(W_ini) # normalize W for ii in range(numComp): W[:,ii] /= np.linalg.norm(W[:,ii]) # looping index ind = 0 err = 1.0e8 # find columns to be updated W_up_cols = [] coeff_up_cols = [] for ii in range(numComp): if np.all(W_up[:,ii] == 1): W_up_cols.append(ii) if np.all(coeff_up[:,ii] == 1): coeff_up_cols.append(ii) # start looping: while ind <= maxIter: # update coefficient matrix where desired abc = np.linalg.lstsq( W,A)[0].T coeff = np.copy(abc) coeff[:,coeff_up_cols] = abc[:,coeff_up_cols] for col in W_up_cols: # set one of the coefficient vectors to zero coeff[:,col] = np.zeros_like(coeff[:,col]) # calculate error matrix errM = A - np.dot(coeff,W.T).T # initialize power method V = np.random.random((len(W[:,col]),1)) V /= np.linalg.norm(V) for jj in range(maxIter_power): vnew = np.dot(errM, errM.T).dot(V) vnew /= np.linalg.norm(vnew) V = vnew V /= np.linalg.norm(V) W[:,col] = V.reshape(W[:,col].shape) # set the zeroed coefficients back to orig coeff[:,col] = abc[:,col] # calculate error newerr = np.linalg.norm(A - np.dot(coeff,W.T).T) dJ = err - newerr err = newerr ind += 1 return W, coeff, err def readbiggsdata(filename,element): """ Reads Hartree-Fock Profile of element 'element' from values tabulated by Biggs et al. (Atomic Data and Nuclear Data Tables 16, 201-309 (1975)) as provided by the DABAX library (http://ftp.esrf.eu/pub/scisoft/xop2.3/DabaxFiles/ComptonProfiles.dat). input: filename = path to the ComptonProfiles.dat file (the file should be distributed with this package) element = string of element name returns: * data = the data for the according element as in the file: * #UD Columns: * #UD col1: pz in atomic units * #UD col2: Total compton profile (sum over the atomic electrons * #UD col3,...coln: Compton profile for the individual sub-shells * occupation = occupation number of the according shells * bindingen = binding energies of the accorting shells * colnames = strings of column names as used in the file """ elementid = '#S' sizeid = '#N' occid = '#UOCCUP' bindingid = '#UBIND' colnameid = '#L' data = [] f = open(filename,'r') istrue = True while istrue: line = f.readline() if line[0:2] == elementid: if line.split()[-1] == element: line = f.readline() while line[0:2] != elementid: if line[0:2] == sizeid: arraysize = int(line.split()[-1]) line = f.readline() if line[0:7] == occid: occupation = line.split()[1:] line = f.readline() if line[0:6] == bindingid: bindingen = line.split()[1:] line = f.readline() if line[0:2] == colnameid: colnames = line.split()[1:] line = f.readline() if line[0]== ' ': data.append([float(n) for n in line.strip().split()]) #data = np.zeros((31,arraysize)) line = f.readline() break length = len(data) data = (np.reshape(np.array(data),(length,arraysize))) return data, occupation, bindingen, colnames def makepzprofile(element,filename=os.path.join(data_installation_dir,'ComptonProfiles.dat')): """ constructs compton profiles of element 'element' on pz-scale (-100:100 a.u.) from the Biggs tables provided in 'filename' input: * element = element symbol (e.g. 'Si', 'Al', etc.) * filename = path and filename to tabulated profiles returns: * pzprofile = numpy array of the CP: * 1. column: pz-scale * 2. ... n. columns: compton profile of nth shell * binden = binding energies of shells * occupation = number of electrons in the according shells """ theory,occupation,binden,colnames = readbiggsdata(filename,element) # first spline onto a rough grid: roughpz = np.logspace(0.01,2,65)-1 roughtheory = np.zeros((len(roughpz),len(binden)+2)) roughtheory[:,0] = roughpz for n in range(len(binden)+1): intf = interpolate.pchip(theory[:,0], theory[:,2]) # interpolate.interp1d(theory[:,0],theory[:,n+1]) roughtheory[:,n+1] = intf(roughpz) pzscale = np.linspace(-100,100,num=4000) pzprofile = np.zeros((len(pzscale),len(binden)+1)) pzprofile[:,0] = pzscale # mirror, spline onto fine grid for n in range(len(binden)): intf = interpolate.splrep(roughtheory[:,0],roughtheory[:,n+2],s=0.000000001,k=2) # skip the column with the total J for now #try interp1d with bounds_error=False and fill_value=0.0 pzprofile[:,n+1] = interpolate.splev(abs(pzscale),intf,der=0) # normalize to one electron, multiply by number of electrons for n in range(len(binden)): normval = integrate.trapz(pzprofile[:,n+1],pzprofile[:,0]) pzprofile[:,n+1] = pzprofile[:,n+1]/normval*int(occupation[n]) binden = [float(en) for en in binden] occupation = [float(val) for val in occupation] return pzprofile, binden, occupation def makeprofile(element,filename=os.path.join(data_installation_dir,'ComptonProfiles.dat'),E0=9.69,tth=35.0,correctasym=None): """ takes the profiles from 'makepzprofile()', converts them onto eloss scale and normalizes them to S(q,w) [1/eV] input: element = element symbol (e.g. 'Si', 'Al', etc.) filename = path and filename to tabulated profiles E0 = scattering energy [keV] tth = scattering angle [deg] returns: enscale = energy loss scale J = total CP C = only core contribution to CP V = only valence contribution to CP q = momentum transfer [a.u.] """ pzprofile,binden,occ = makepzprofile(element,filename) # convert to eloss scale enscale = ((np.flipud(pz2e1(E0,pzprofile[:,0],tth))-E0)*1e3) q = momtrans_au(enscale/1000.0+E0,E0,tth) # add asymmetry if needed (2p1/2 and 2p3/2 for Z > 35 (Br)) asymmetry = np.flipud(HRcorrect(pzprofile,occ,q)); # asymmetry flipped for conversion to e-loss scale (???) if correctasym: pzprofile[:,1:4] = pzprofile[:,1:4] + asymmetry*correctasym # discard profiles below zero hfprofile = pzprofile[np.nonzero(enscale.T>=0)[0],:] q = q[np.nonzero(enscale.T>=0)[0]] #q[:,np.nonzero(enscale.T>=0)[0]] enscale = enscale[np.nonzero(enscale.T>=0)[0]] #enscale[:,np.nonzero(enscale.T>=0)[0]] hfprofile[:,0] = enscale # cut at edges for n in range(len(binden)): hfprofile[np.where(enscale1: J = J*concentrations[0] C = C*concentrations[0] V = V*concentrations[0] for n in range(len(formulas[1:])): eloss,j,c,v,q = makeprofile_comp(formulas[n+1],filename,E0,tth,correctasym[n+1]) J += j*concentrations[n+1] C += c*concentrations[n+1] V += v*concentrations[n+1] return eloss,J,C,V,q def HRcorrect(pzprofile,occupation,q): """ Returns the first order correction to filled 1s, 2s, and 2p Compton profiles. Implementation after Holm and Ribberfors (citation ...). Args: * pzprofile (np.array): Compton profile (e.g. tabulated from Biggs) to be corrected (2D matrix). * occupation (list): electron configuration. * q (float or np.array): momentum transfer in [a.u.]. Returns: asymmetry (np.array): asymmetries to be added to the raw profiles (normalized to the number of electrons on pz scale) """ # prepare output matrix if len(occupation) == 1: asymmetry = np.zeros((len(pzprofile[:,0]),1)) elif len(occupation) == 2: asymmetry = np.zeros((len(pzprofile[:,0]),2)) elif len(occupation) >= 3: asymmetry = np.zeros((len(pzprofile[:,0]),3)) # take care for the cases where 2p levels have spin-orbit split taken into account in the Biggs table if len(occupation)>3 and occupation[2]==2 and occupation[3]==4: pzprofile[:,3] = pzprofile[:,3] + pzprofile[:,4] occupation[2] = 6 # 1s if occupation[0] < 2: pass else: # find gamma1s lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2 fitfct = lambda a: (np.absolute(np.max(pzprofile[:,1])-np.max(occupation[0]*8.0*a**5.0/3.0/np.pi/(a**2.0+pzprofile[:,0]**2.0)**3.0))) res = optimize.leastsq(fitfct,np.sum(occupation)) gamma1s = res[0][0] # calculate j0 and j1 j0 = occupation[0]*8.0*gamma1s**5.0/3.0/np.pi/((gamma1s**2.0+pzprofile[:,0]**2.0)**3.0) j1 = 2.0*gamma1s*np.arctan2(pzprofile[:,0],gamma1s)-3.0/2.0*pzprofile[:,0] j1 = j1/q*j0 asymmetry[:,0] = j1 # 2s if len(occupation)>1: if occupation[1] < 2: pass else: # find gamma2s fitfct = lambda a: (np.absolute(np.max(pzprofile[:,2])-np.max(occupation[1]*((a**4.0-10.0*a**2.0*pzprofile[:,0]**2 + 40.0*pzprofile[:,0]**4.0)*128.0*a**5.0/15.0/np.pi/(a**2.0 + 4.0*pzprofile[:,0]**2.0)**5.0)))) res = optimize.leastsq(fitfct,np.sum(occupation)*2.0/3.0) gamma2s = res[0][0] # calculate j0 and j1 j0 = occupation[1]*(gamma2s**4.0-10.0*gamma2s**2.0*pzprofile[:,0]**2.0+40.0*pzprofile[:,0]**4.0)*128.0*gamma2s**5.0/15.0/np.pi/(gamma2s**2.0 + 4.0*pzprofile[:,0]**2.0)**5.0 j1 = 2.0*gamma2s*np.arctan2(2.0*pzprofile[:,0],gamma2s)-5.0/4.0*(gamma2s**4.0+48.0*pzprofile[:,0]**4.0)/(gamma2s**4.0-10.0*gamma2s**2.0*pzprofile[:,0]**2.0+40.0*pzprofile[:,0]**4.0)*pzprofile[:,0] j1 = j1/q*j0 asymmetry[:,1] = j1 # 2p if len(occupation)>2: if occupation[2] < 6: pass else: forgamma = 3.0*pzprofile[:,3]/np.trapz(pzprofile[:,3],pzprofile[:,0]) # 2p correction is defined for 3 electrons in the 2p shell # find gamma2p fitfct = lambda a: (np.absolute(np.max(forgamma)-np.max(((a**2.0+20.0*pzprofile[:,0]**2.0)*64.0*a**7.0/5.0/np.pi/(a**2.0+4.0*pzprofile[:,0]**2.0)**5.0)))) res = optimize.leastsq(fitfct,np.sum(occupation)*1.0/3.0) gamma2p = res[0][0] # calculate j0 and j1 j0 = 2.0*(gamma2p**2.0+20.0*pzprofile[:,0]**2.0)*64.0*gamma2p**7.0/5.0/np.pi/(gamma2p**2.0+4.0*pzprofile[:,0]**2.0)**5.0 j1 = 2.0*gamma2p*np.arctan2(2.0*pzprofile[:,0],gamma2p)-2.0/3.0*pzprofile[:,0]*(10.0*gamma2p**2.0+60.0*pzprofile[:,0]**2.0)/(gamma2p**2.0+20.0*pzprofile[:,0]**2.0) j1 = j1/q*j0 asymmetry[:,2] = j1 return asymmetry def parseformula(formula): """Parses a chemical sum formula. Parses the constituing elements and stoichiometries from a given chemical sum formula. Args: * formula (string): string of a chemical formula (e.g. 'SiO2', 'Ba8Si46', etc.) Returns: * elements (list): list of strings of constituting elemental symbols. * stoichiometries (list): list of according stoichiometries in the same order as 'elements'. """ elements = [] stoichiometries = [] splitted = findall(r'([A-Z][a-z]*)(\d*)',formula) elements.extend([element[0] for element in splitted]) stoichiometries.extend([(int(element[1]) if element[1] else 1) for element in splitted]) return elements,stoichiometries def element(z): """Converts atomic number into string of the element symbol and vice versa. Returns atomic number of given element, if z is a string of the element symbol or string of element symbol of given atomic number z. Args: * z (string or int): string of the element symbol or atomic number. Returns: * Z (string or int): string of the element symbol or atomic number. """ zs = ['H','He','Li','Be','B','C','N','O','F','Ne','Na','Mg','Al', 'Si','P','S','Cl','Ar','K','Ca','Sc','Ti','V','Cr','Mn','Fe','Co','Ni', 'Cu','Zn','Ga','Ge','As','Se','Br','Kr','Rb','Sr','Y','Zr','Nb','Mo', 'Tc','Ru','Rh','Pd','Ag','Cd','In','Sn','Sb','Te','I','Xe','Cs','Ba', 'La','Ce','Pr','Nd','Pm','Sm','Eu','Gd','Tb','Dy','Ho','Er','Tm','Yb', 'Lu','Hf','Ta','W','Re','Os','Ir','Pt','Au','Hg','Tl','Pb','Bi','Po', 'At','Rn','Fr','Ra','Ac','Th','Pa','U','Np','Pu','Am','Cm','Bk','Cf', 'Es','Fm','Md','No','Lr','Ku'] if isinstance(z,str): try: Z = zs.index(z)+1 except: Z = None print( 'Given element ' + z + ' unknown.') elif isinstance(z,int): if z > 0 and z < 105: Z = zs[z-1] else: print( 'Element Z = '+ str(z) +' unknown.') else: print( 'type '+ str(type(z)) + 'not supported.' ) return Z #os.path.join(data_installation_dir,'data/logtable.dat') def myprho(energy,Z,logtablefile=os.path.join(data_installation_dir,'logtable.dat') ): """Calculates the photoelectric, elastic, and inelastic absorption of an element Z Calculates the photelectric , elastic, and inelastic absorption of an element Z. Z can be atomic number or element symbol. Args: * energy (np.array): energy scale in [keV]. * Z (string or int): atomic number or string of element symbol. Returns: * murho (np.array): absorption coefficient normalized by the density. * rho (float): density in UNITS? * m (float): atomic mass in UNITS? """ en = np.array([]) en = np.append(en,energy) logtable = np.loadtxt(logtablefile) # find the right places in logtable if not isinstance(Z,int): Z = element(Z) try: ind = list(logtable[:,0]).index(Z) except: print( 'no such element in logtable.dat') c = np.array(logtable[ind:ind+5,:]) # 5 lines that corresponds to the element le = np.log(en) # logarithm of the energy mr = np.exp(c[1,3]+le*(c[2,3]+le*(c[3,3]+le*c[4,3]))) # extract mu from loglog table i = np.where(en<=c[0,3]) l = le[i] mr[i] = np.exp(c[1,2]+l*(c[2,2]+l*(c[3,2]+l*c[4,2]))) i = np.where(en= 0: # reflection geometry ac = cosa*(mu1/cosa + mu2/cosb)/(1.0 - np.exp(-mu1*samthick/cosa - mu2*samthick/cosb)) elif np.absolute(mu1/cosa - mu2/cosb).any() > np.spacing(1): # transmission geometry ac = -cosa*(mu1/cosa - mu2/cosb)/(np.exp(-mu1*samthick/cosa) - np.exp(-mu2*samthick/cosb)) else: ac = cosa/(samthick*np.exp(-mu1*samthick/cosa)) return ac def absCorrection(mu1,mu2,alpha,beta,samthick,geometry='transmission'): """ **absCorrection** Calculates absorption correction for given mu1 and mu2. Multiply the measured spectrum with this correction factor. This is a translation of Keijo Hamalainen's Matlab function (KH 30.05.96). Args * mu1 : np.array Absorption coefficient for the incident energy in [1/cm]. * mu2 : np.array Absorption coefficient for the scattered energy in [1/cm]. * alpha : float Incident angle relative to plane normal in [deg]. * beta : float Exit angle relative to plane normal [deg]. * samthick : float Sample thickness in [cm]. * geometry : string, optional Key word for different sample geometries ('transmission', 'reflection', 'sphere'). If *geometry* is set to 'sphere', no angular dependence is assumed. Returns * ac : np.array Absorption correction factor. Multiply this with your measured spectrum. """ cosa = np.cos(math.radians(alpha)) cosb = np.cos(math.radians(beta)) # reflection geometry if geometry == 'reflection': if beta >= 90.0: print('WARNING: are you sure about the beta angle?') ac = cosa*(mu1/cosa + mu2/cosb)/(1.0 - np.exp(-mu1*samthick/cosa - mu2*samthick/cosb)) # transmission geometry elif geometry == 'transmission' and np.absolute(mu1/cosa - mu2/cosb).any() > np.spacing(1): ac = -cosa*(mu1/cosa - mu2/cosb)/(np.exp(-mu1*samthick/cosa) - np.exp(-mu2*samthick/cosb)) elif geometry == 'transmission' and np.absolute(mu1/cosa - mu2/cosb).any() <= np.spacing(1): ac = cosa/(samthick*np.exp(-mu1*samthick/cosa)) # spherical sample elif geometry == 'sphere': ac = (mu1 + mu2)/(1.0 - np.exp(-mu1*samthick -mu2*samthick)) return ac def gettransmission(energy,formulas,concentrations,densities,thickness): """ returns the transmission through a sample composed of chemical formulas with certain densities mixed to certain concentrations, and a thickness """ en = np.array([]) # turn energy into an iterable array en = np.append(en,energy) if not isinstance(formulas,list): theformulas = [] theformulas.append(formulas) else: theformulas = formulas if not isinstance(concentrations,list): theconcentrations = [] theconcentrations.append(concentrations) else: theconcentrations = concentrations if not isinstance(densities,list): thedensities = [] thedensities.append(densities) else: thedensities = densities # get mu mu_tot = np.zeros((len(en))) for n in range(len(theformulas)): mu_tot += mpr(en,theformulas[n])[0]*theconcentrations[n]*thedensities[n] return np.exp(-mu_tot*thickness) def plottransmission(energy,formulas,concentrations,densities,thickness): """ opens a plot with the transmission plotted along the given energy vector """ if not isinstance(formulas,list): theformulas = [] theformulas.append(formulas) else: theformulas = formulas if not isinstance(concentrations,list): theconcentrations = [] theconcentrations.append(concentrations) else: theconcentrations = concentrations if not isinstance(densities,list): thedensities = [] thedensities.append(densities) else: thedensities = densities transmission = gettransmission(energy,formulas,concentrations,densities,thickness) plt.plot(energy,transmission) titlestring = 'transmission of: ' + ' '.join(formulas) plt.title(titlestring) plt.xlabel('energy [keV]') plt.ylabel('transmission [%]') plt.grid(False) plt.show() def getpenetrationdepth(energy,formulas,concentrations,densities): """ returns the penetration depth of a mixture of chemical formulas with certain concentrations and densities """ en = np.array([]) # turn energy into an iterable array en = np.append(en,energy) if not isinstance(formulas,list): theformulas = [] theformulas.append(formulas) else: theformulas = formulas if not isinstance(concentrations,list): theconcentrations = [] theconcentrations.append(concentrations) else: theconcentrations = concentrations if not isinstance(densities,list): thedensities = [] thedensities.append(densities) else: thedensities = densities # get mu mu_tot = np.zeros((len(en))) for n in range(len(theformulas)): mu_tot += mpr(en,theformulas[n])[0]*theconcentrations[n]*thedensities[n] return 1.0/mu_tot def plotpenetrationdepth(energy,formulas,concentrations,densities): """ opens a plot window of the penetration depth of a mixture of chemical formulas with certain concentrations and densities plotted along the given energy vector """ if not isinstance(formulas,list): theformulas = [] theformulas.append(formulas) else: theformulas = formulas if not isinstance(concentrations,list): theconcentrations = [] theconcentrations.append(concentrations) else: theconcentrations = concentrations if not isinstance(densities,list): thedensities = [] thedensities.append(densities) else: thedensities = densities pendepth = getpenetrationdepth(energy,formulas,concentrations,densities) plt.plot(energy,pendepth) titlestring = 'penetration depth of: ' + ' '.join(formulas) plt.title(titlestring) plt.xlabel('energy [keV]') plt.ylabel('penetration depth [cm]') plt.grid(False) plt.show() def sumx(A): """ Short-hand command to sum over 1st dimension of a N-D matrix (N>2) and to squeeze it to N-1-D matrix. """ return np.squeeze(np.sum(A,axis=0)) def specread(filename,nscan): """ reads scan "nscan" from SPEC-file "filename" INPUT: * filename = string with the SPEC-file name * nscan = number (int) of desired scan OUTPUT: * data = * motors = * counters = dictionary """ scannid = '#S' countid = '#L' motorid = '#P' data = [] motors = [] counterss = [] f = open(filename,'r') while True: line = f.readline() if not line: break if line[0:2] == scannid: if int(line.split()[1]) == nscan: line = '##'+line while line and line[0:2]!='#S': line = f.readline() if not line: break if line[0:2] == countid: cline = ' '+line[2:] counterss = [n.strip() for n in [_f for _f in cline.split(' ')[1:] if _f]] if line[0:2] == motorid: motors.append([float(n) for n in line.strip().split()[1:]]) if line[0] != '#': data.append([float(n) for n in line.strip().split()]) data.pop(-1) # the trailing empty line f.close() # put the data into a dictionary with entries from the counterss counters = {} for n in range(len(counterss)): counters[counterss[n].lower()] = [row[n] for row in data] # data[:,n] return data, motors, counters def edfread(filename): """ reads edf-file with filename "filename" OUTPUT: data = 256x256 numpy array """ # get some info from header f = open(filename,'rb').readlines() counter = 0 predata = [] for entry in f: counter += 1 if entry.strip().split()[0] == '}': break for entry in f[:counter]: if entry.strip().split()[0] == 'Dim_1': dim1 = int(entry.strip().split()[2]) if entry.strip().split()[0] == 'Dim_2': dim2 = int(entry.strip().split()[2]) if entry.strip().split()[0] == 'Size': size = int(entry.strip().split()[2]) if entry.strip().split()[0] == 'UnsignedShort': type_code = 'H' if entry.strip().split()[0] == 'SignedInteger': type_code = 'i' length = 0 for line in f: length += len(line) headerlength = (length-size)//2 # get the data f = open(filename,'rb') predata = arr.array(type_code) predata.fromfile(f,(headerlength+dim1*dim2)) # this prevents the header (1024 characters long) to end up in the 256x256 picture data = np.reshape(predata[headerlength:],(dim1,dim2)) # this prevents the header (1024 characters long) to end up in the 256x256 picture f.close() return data def edfread_test(filename): """ reads edf-file with filename "filename" OUTPUT: data = 256x256 numpy array here is how i opened the HH data: data = np.fromfile(f,np.int32) image = np.reshape(data,(dim,dim)) """ # get some info from header f = open(filename,'rb').readlines() counter = 0 predata = [] for entry in f: counter += 1 if entry.strip().split()[0] == '}': break for entry in f[:counter]: if entry.strip().split()[0] == 'Dim_1': dim1 = int(entry.strip().split()[2]) if entry.strip().split()[0] == 'Dim_2': dim2 = int(entry.strip().split()[2]) if entry.strip().split()[0] == 'Size': size = int(entry.strip().split()[2]) length = 0 for line in f: length += len(line) headerlength = (length-size)//2 # get the data f = open(filename,'rb') predata = arr.array('H') predata.fromfile(f,(headerlength+dim1*dim2)) # this prevents the header (1024 characters long) to end up in the 256x256 picture data = np.reshape(predata[headerlength:],(dim2,dim1)) # this prevents the header (1024 characters long) to end up in the 256x256 picture f.close() return data def momtrans_au(e1,e2,tth): """ Calculates the momentum transfer in atomic units input: e1 = incident energy [keV] e2 = scattered energy [keV] tth = scattering angle [deg] returns: q = momentum transfer [a.u.] (corresponding to sin(th)/lambda) """ e1 = np.array(e1*1e3/13.60569172/2) e2 = np.array(e2*1e3/13.60569172/2) th = np.radians(tth)#tth/180.0*numpy.pi hbarc = 137.03599976 q = 1/hbarc*np.sqrt(e1**2.0+e2**2.0-2.0*e1*e2*np.cos(th)); return q def momtrans_inva(e1,e2,tth): """ Calculates the momentum transfer in inverse angstrom input: e1 = incident energy [keV] e2 = scattered energy [keV] tth = scattering angle [deg] returns: q = momentum transfer [a.u.] (corresponding to sin(th)/lambda) """ e = 1.602e-19 c = 2.9979e8 hbar = 6.626e-34/2/np.pi e1 = np.array(e1*1e3*e/c/hbar) e2 = np.array(e2*1e3*e/c/hbar) th = np.radians(tth) q = np.sqrt(e1**2+e2**2-2*e1*e2*np.cos(th))/1e10 return q def energy_monoangle(angle,d=5.4307/np.sqrt(11)): """ % ENERGY Calculates energy corrresponing to Bragg angle for given d-spacing % function e=energy(dspace,bragg_angle) % % dspace for reflection (defaulf for Si(311) reflection) % bragg_angle in DEG % % KH 28.09.93 % """ hc = 12.3984191 # CODATA 2002 physics.nist.gov/constants e = (2.0*d*np.sin(angle/180.0*np.pi)/hc)**(-1.0) return e def find_center_of_mass(x,y): """ Returns the center of mass (first moment) for the given curve y(x) """ deno = np.trapz(y,x) if deno==0.0: return 0.0 # print "*** print_tb:" # traceback.print_stack() # print " DENO==0!" # return 0.0 return np.trapz(y*x,x)/deno def is_allowed_refl_fcc(H): """ **is_allowed_refl_fcc** Check if given reflection is allowed for a FCC lattice. Args: * H (array, list, tuple): H=[h,k,l] Returns: * boolean """ h = H[0] k = H[1] l = H[2] if h%2==0.0 and k%2==0.0 and l%2==0.0: answer = True elif (h+k+l)/4.0%1==0.0: answer = True elif h%2==1.0 and k%2==1.0 and l%2==1.0: answer = True else: answer = False return answer def TTsolver1D(el_energy, hkl=[6,6,0], crystal='Si', R=1.0, dev=np.arange(-50.0,150.0,1.0), alpha=0.0, chitable_prefix='/home/christoph/sources/XRStools/data/chitables/chitable_'): """ **TTsolver** Solves the Takagi-Taupin equation for a bent crystal. This function is based on a Matlab implementation by S. Huotari of M. Krisch's Fortran programs. Args: * el_energy (float): Fixed nominal (working) energy in keV. * hkl (array): Reflection order vector, e.g. [6, 6, 0] * crystal (str): Crystal used (can be silicon 'Si' or 'Ge') * R (float): Crystal bending radius in m. * dev (np.array): Deviation parameter (in arc. seconds) for which the reflectivity curve should be calculated. * alpha (float): Crystal assymetry angle. Returns: * refl (np.array): Reflectivity curve. * e (np.array): Deviation from Bragg angle in meV. * dev (np.array): Deviation from Bragg angle in microrad. """ # load dielectric susceptibility data (tabulated) chi = np.loadtxt(chitable_prefix + crystal.lower() + str(int(hkl[0])) + str(int(hkl[1])) + str(int(hkl[2])) + '.dat') if len(chi[:,0]) == 1: print( 'Will only calculate for the following energy: ' + '%.4f' % chi[0,0] + ' keV!!!') else: if el_energy < np.min(chi[:,0]) or el_energy > np.max(chi[:,0]): print( 'Energy outside of values defined in Chi-table.') return # interpolate chi0 = complex(np.interp(el_energy,chi[:,0],chi[:,1]),np.interp(el_energy,chi[:,0],chi[:,2])) chih = complex(np.interp(el_energy,chi[:,0],chi[:,3]),np.interp(el_energy,chi[:,0],chi[:,4])) # set the stress tensor values depending on crystal used if crystal.upper() == 'SI': s13 = -0.278 elif crystal.upper() == 'GE': s13 = -0.273 else: print( 'Poisson ratio for this crystal not defined') return s15 = -0.0 # s15/s11 # scattering angle in degree th = braggd(hkl,el_energy,crystal) # dspace in m dsp = dspace(hkl,crystal)/10.0*1e-9 # wavelength in m lam = 12.3984191/el_energy/10.0*1e-9 # debye-waller factor dwf = 1.0 # dwf = 0.899577 # meridional bending radius radius = R # sagittal bending radius rsag = R*np.sin(np.radians(th))**2.0 # thickness in m thick = 500.0*1e-6 # asymmetry in radians alpha = np.radians(alpha) # deviation parameter in arcsec dev = dev/3600.0/180.0*np.pi # gamma0,gammah = cos , n = inward normal of crystal surface, K_0,h = wave vector gammah = -np.sin(np.arcsin(lam/(2.0*dsp)) + alpha) # Krisch et al. convention gamma0 = np.sin(np.arcsin(lam/(2.0*dsp)) - alpha) # Krisch et al. convention gamma = gammah/gamma0 a0 = np.sqrt(1-gamma0**2.0) ah = np.sqrt(1-gammah**2.0) beta = gamma0/np.abs(gammah) # polarization factor cpol = 1.0 # penetration depth mu = -2.0*np.pi/lam*chi0.imag tdepth = 1.0/mu/(1.0/np.abs(gamma0)+1.0/np.abs(gammah)) lex = lam*np.sqrt(gamma0*np.abs(gammah))/(np.pi*chih.real) y0 = chi0.imag*(1.0+beta)/(2.0*np.sqrt(beta)*chih.real) c1 = cpol*dwf* complex(1.0,-chih.imag/chih.real) #abbreviation concerning the deviation parameter y abb0 = -np.sqrt(beta)/2.0/chih.real abb1 = chi0.real*(1.0+beta)/(2.0*np.sqrt(beta)*chih.real) #abbreviations concerning the deformation field abb2 = gamma0*gammah*(gamma0-gammah) abb3 = 1.0 + 1.0/(gamma0*gammah) abb4 = s13*(1.0 + radius/rsag) abb5 = (ah - a0)/(gamma0 - gammah)*s15 abb6 = 1.0/(np.abs(cpol)*chih.real*np.cos(np.arcsin(lam/(2.0*dsp)))*radius) abb7 = 2.0*np.abs(cpol)*chih.real*np.cos(np.arcsin(lam/(2.0*dsp)))/gamma0 # spherical diced crystal, 1-m bending radius, nearly backscattering conditions, strain gradient sgbeta = abb6*(abb2*(abb3 - abb4 + abb5)) # number of steps along reflectivity curve nstep=len(dev) # reflectivity curve refl = np.zeros_like(dev) OLDMETHOD = 0 if OLDMETHOD: # loop over all steps along the reflectivity curve for l in range(nstep): # deviation parameter abb8 = -2.0*np.sin(2.0*np.arcsin(lam/(2.0*dsp)))*dev[l] T = np.arange(np.max([-10.0*tdepth, -thick]),0.0,1e-8) Y = odeint(odefctn,np.array([0.0, 0.0]),T,args=(abb0,abb1,abb7,abb8,lex,sgbeta,y0,c1)) # normalized reflectivity at this point refl[l] = np.sum(Y[-1,:]**2.0) else: # deviation parameter abb8 = -2.0*np.sin(2.0*np.arcsin(lam/(2.0*dsp)))*dev # dev axis (complex) YY = np.zeros([nstep],"D") # small step size ministep = tdepth/10000.0 ssrk = ministep/2 xpoints = np.arange(np.max([-10.0*tdepth, -thick]),0, ministep) for xpos in xpoints[:-1]: Yp0 = odefctn_CN( YY, xpos+0*ssrk, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1) Yp1 = odefctn_CN( YY+1.0*ssrk*Yp0, xpos+1*ssrk, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1) Yp2 = odefctn_CN( YY+1.0*ssrk*Yp1, xpos+1*ssrk, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1) Ypb = odefctn_CN( YY+2.0*ssrk*Yp2, xpos+2*ssrk, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1) YY = YY + ministep*( Yp0 + 2.0*(Yp1+Yp2) + Ypb )/6.0 refl = YY.real*YY.real+YY.imag*YY.imag # deviation in degree dev = dev/4.848136811e-06/3600.0 # deviation in meV e_meV = (energy(dspace(hkl,crystal),th+dev)-el_energy)*1.0e6 # deviation in microrad dev = dev*1.e3 return refl, e_meV, dev def odefctn_CN(yCN,t,abb0,abb1,abb7,abb8N,lex,sgbeta,y0,c1): fcomp = 1.0/(complex(0,-lex)) * (-2.0*((abb0*(abb8N + abb7*sgbeta*t) + abb1) + complex(0,y0))*(yCN) + c1*(1.0 + yCN* yCN) ) return fcomp def odefctn(y,t,abb0,abb1,abb7,abb8,lex,sgbeta,y0,c1): """ #% [T,Y] = ODE23(ODEFUN,TSPAN,Y0,OPTIONS,P1,P2,...) passes the additional #% parameters P1,P2,... to the ODE function as ODEFUN(T,Y,P1,P2...), and to #% all functions specified in OPTIONS. Use OPTIONS = [] as a place holder if #% no options are set. """ #print 'shape of y is ' , np.shape(y), np.shape(t) fcomp = 1.0/(complex(0,-lex)) * (-2.0*((abb0*(abb8 + abb7*sgbeta*t) + abb1) + complex(0,y0))*(y[0] + complex(0,y[1])) + c1*(1.0 + (y[0] + complex(0,y[1]))**2.0)) return fcomp.real,fcomp.imag def odefctn_CN(yCN,t,abb0,abb1,abb7,abb8N,lex,sgbeta,y0,c1): fcomp = 1.0/(complex(0,-lex)) * (-2.0*((abb0*(abb8N + abb7*sgbeta*t) + abb1) + complex(0,y0))*(yCN) + c1*(1.0 + yCN* yCN) ) return fcomp def taupgen(e, hkl = [6,6,0], crystals = 'Si', R = 1.0, dev = np.arange(-50.0,150.0,1.0), alpha = 0.0): """ % TAUPGEN Calculates the reflectivity curves of bent crystals % % function [refl,e,dev]=taupgen_new(e,hkl,crystals,R,dev,alpha); % % e = fixed nominal energy in keV % hkl = reflection order vector, e.g. [1 1 1] % crystals = crystal string, e.g. 'si' or 'ge' % R = bending radius in meters % dev = deviation parameter for which the % curve will be calculated (vector) (optional) % alpha = asymmetry angle % based on a FORTRAN program of Michael Krisch % Translitterated to Matlab by Simo Huotari 2006, 2007 % Is far away from being good matlab writing - mostly copy&paste from % the fortran routines. Frankly, my dear, I don't give a damn. % Complaints -> /dev/null """ path = os.path.join(data_installation_dir,'chitable_') # prefix + 'data/chitables/chitable_' # path to chitables # load the according chitable (tabulated) hkl_string = str(int(hkl[0])) + str(int(hkl[1])) + str(int(hkl[2])) filestring = path + crystals.lower() + hkl_string + '.dat' chi = np.loadtxt(filestring) # good for 1 m bent crystals in backscattering ystart = -50.0 # start value of angular range in arcsecs yend = 150.0 # end value of angular range in arcsecs ystep = 1.0 # step width in arcsecs if len(chi[:,0]) == 1: print( ' I will only calculate for the following energy: ' + '%.4f' % chi[0,0] + ' keV!!!') else: if e < np.min(chi[:,0]) or e > np.max(chi[:,0]): print( 'Energy outside of the range in ' + filestring) return chi0r = np.interp(e,chi[:,0],chi[:,1]) chi0i = np.interp(e,chi[:,0],chi[:,2]) chihr = np.interp(e,chi[:,0],chi[:,3]) chihi = np.interp(e,chi[:,0],chi[:,4]) th = braggd(hkl,e,crystals) lam = 12.3984191/e/10.0 # wavelength in nm reflcorr = 0.0 chi0 = complex(chi0r,chi0i) chih = complex(chihr,chihi) if crystals.upper() == 'SI': s13 = -0.278 elif crystals.upper() == 'GE': s13 = -0.273 else: print( 'Poisson ratio for this crystal not defined') return s15 = -0.0 # s15/s11 dsp = dspace(hkl,crystals)/10.0 # dspace dwf = 1.0 # dwf = 0.899577 # debye-waller factor radius = R # meridional bending radius rsag = R*np.sin(np.radians(th))**2.0 # sagittal bending radius thick = 500.0 # thickness in micrometers #rsag = R lam = lam*1e-9 dsp = dsp*1e-9 alpha = np.radians(alpha) # alpha in rad thick = thick*1e-6 ystart = ystart/3600.0/180.0*np.pi yend = yend/3600.0/180.0*np.pi ystep = ystep/3600.0/180*np.pi dev = dev/3600.0/180.0*np.pi reflcorr = reflcorr/3600.0/180.0*np.pi thetab = np.arcsin(lam/(2.0*dsp)) cpol = 1.0 # cpol=0.5*(1+cos(2*thetab).^2) # cpol=cos(2*thetab).^2 # gamma0 = sin(thetab+alpha) # normal convention # gammah = -sin(thetab-alpha) # normal convention gammah = -np.sin(thetab + alpha) # Krisch et al. convention (really!) gamma0 = np.sin(thetab - alpha) # Krisch et al. convention (I'm not kidding!!) beta = gamma0/np.abs(gammah) gamma = gammah/gamma0 a0 = np.sqrt(1-gamma0**2.0) ah = np.sqrt(1-gammah**2.0) mu = -2.0*np.pi/lam*chi0i tdepth = 1.0/mu/(1.0/np.abs(gamma0)+1.0/np.abs(gammah)) lex = lam*np.sqrt(gamma0*np.abs(gammah))/(np.pi*chihr) y0 = chi0i*(1.0+beta)/(2.0*np.sqrt(beta)*chihr) pfried = -chihi/chihr c1 = cpol*dwf* complex(1.0,pfried) #abbreviation concerning the deviation parameter y abb0 = -np.sqrt(beta)/2.0/chihr abb1 = chi0r*(1.0+beta)/(2.0*np.sqrt(beta)*chihr) #abbreviations concerning the deformation field abb2 = gamma0*gammah*(gamma0-gammah) abb3 = 1.0 + 1.0/(gamma0*gammah) abb4 = s13*(1.0 + radius/rsag) abb5 = (ah - a0)/(gamma0 - gammah)*s15 abb6 = 1.0/(np.abs(cpol)*chihr*np.cos(thetab)*radius) abb7 = 2.0*np.abs(cpol)*chihr*np.cos(thetab)/gamma0 # a spectrometer based on a spherical diced analyzer crystal with a 1-m bending radius in nearly backscattering conditions utilizing a strain gradient beta sgbeta = abb6*(abb2*(abb3 - abb4 + abb5)) nstep=len(dev) eta = np.zeros_like(dev) abb8z = np.zeros_like(dev) refl = np.zeros_like(dev) refl1 = np.zeros_like(dev) refl2 = np.zeros_like(dev) OLDMETHOD = 1 if OLDMETHOD : for l in range(nstep): # actual value of the deviation angle # dev[l] = ystart + (l - 1)*ystep # deviation parameter abb8 = -2.0*np.sin(2.0*thetab)*dev[l] eta[l] = (dev[l]*np.sin(2.0*thetab)+np.abs(chi0.real)/2.0*(1.0-gamma))/(np.abs(cpol)*np.sqrt(np.abs(gamma))*np.sqrt(chih*chih)) eta[l] = eta[l].real ndiff = 2 xend = 0 x = np.max([-10.0*tdepth, -thick]) y = np.array([0.0, 0.0]) h = xend abb8z[l] = abb8 # in this point call the subroutine # [T,Y] = ODE23(ODEFUN,TSPAN,Y0,OPTIONS,P1,P2,...) passes the additional # parameters P1,P2,... to the ODE function as ODEFUN(T,Y,P1,P2...), and to # all functions specified in OPTIONS. Use OPTIONS = [] as a place holder if # no options are set. #print 'the fucking shape of y is ', np.shape(y) T = np.arange(x,xend,1e-8) Y = odeint(odefctn,y,T,args=(abb0,abb1,abb7,abb8,lex,sgbeta,y0,c1)) # normalized reflectivity at this point refl[l] = np.sum(Y[-1,:]**2.0) refl1[l] = Y[-1,0] refl2[l] = Y[-1,1] else: YY = np.zeros([nstep],"D") for l in range(nstep): abb8 = -2.0*np.sin(2.0*thetab)*dev[l] eta[l] = ((dev[l]*np.sin(2.0*thetab)+np.abs(chi0.real)/2.0*(1.0-gamma))/ (np.abs(cpol)*np.sqrt(np.abs(gamma))*np.sqrt(chih*chih))) eta[l] = eta[l].real abb8z[l] = abb8 xend = 0 x = np.max([-10.0*tdepth, -thick]) ministep = tdepth/1000.0 ## when delta/beta is 100 we have still 10 xpoints = np.arange(x,0, ministep) substep_RungeKutta = ministep/2 ssrk = substep_RungeKutta for xpos in xpoints[:-1]: Yp0 = odefctn_CN( YY, xpos+0*ssrk , abb0,abb1,abb7,abb8z,lex,sgbeta,y0,c1) Yp1 = odefctn_CN( YY+1*ssrk*Yp0, xpos+1*ssrk , abb0,abb1,abb7,abb8z,lex,sgbeta,y0,c1) Yp2 = odefctn_CN( YY+1*ssrk*Yp1, xpos+1*ssrk , abb0,abb1,abb7,abb8z,lex,sgbeta,y0,c1) Ypb = odefctn_CN( YY+2*ssrk*Yp2, xpos+2*ssrk , abb0,abb1,abb7,abb8z,lex,sgbeta,y0,c1) YY = YY + ministep*( Yp0 + 2*(Yp1+Yp2) + Ypb )/6.0 refl1 = YY.real refl2 = YY.imag refl = refl1*refl1+refl2*refl2 de = dev * e * 1.0e6 /np.tan(thetab) lam = lam *1.0e+09 dsp = dsp*1.0e+09 alpha = alpha/np.pi*180.0 ystart = ystart/4.848136811e-06 yend = yend/4.848136811e-06 ystep = ystep/4.848136811e-06 dev = dev/4.848136811e-06 # dev in arcsecs dev = dev/3600.0 # in degrees thb = th th = thb + dev e0 = e e = energy(dspace(hkl,crystals),th)-e0 e = e*1e6 dev = dev*3600.0 # back to arcsecs return refl,e,dev,e0 def taupgen_amplitude(e, hkl = [6,6,0], crystals = 'Si', R = 1.0, dev = np.arange(-50.0,150.0,1.0), alpha = 0.0): """ % TAUPGEN Calculates the reflectivity curves of bent crystals % % function [refl,e,dev]=taupgen_new(e,hkl,crystals,R,dev,alpha); % % e = fixed nominal energy in keV % hkl = reflection order vector, e.g. [1 1 1] % crystals = crystal string, e.g. 'si' or 'ge' % R = bending radius in meters % dev = deviation parameter for which the % curve will be calculated (vector) (optional) % alpha = asymmetry angle % based on a FORTRAN program of Michael Krisch % Translitterated to Matlab by Simo Huotari 2006, 2007 % Is far away from being good matlab writing - mostly copy&paste from % the fortran routines. Frankly, my dear, I don't give a damn. % Complaints -> /dev/null """ path = os.path.join(data_installation_dir,'chitable_') # prefix + 'data/chitables/chitable_' # path to chitables # load the according chitable (tabulated) hkl_string = str(int(hkl[0])) + str(int(hkl[1])) + str(int(hkl[2])) filestring = path + crystals.lower() + hkl_string + '.dat' chi = np.loadtxt(filestring) # good for 1 m bent crystals in backscattering ystart = -50.0 # start value of angular range in arcsecs yend = 150.0 # end value of angular range in arcsecs ystep = 1.0 # step width in arcsecs if len(chi[:,0]) == 1: print( ' I will only calculate for the following energy: ' + '%.4f' % chi[0,0] + ' keV!!!') else: if e < np.min(chi[:,0]) or e > np.max(chi[:,0]): print( 'Energy outside of the range in ' + filestring) return chi0r = np.interp(e,chi[:,0],chi[:,1]) chi0i = np.interp(e,chi[:,0],chi[:,2]) chihr = np.interp(e,chi[:,0],chi[:,3]) chihi = np.interp(e,chi[:,0],chi[:,4]) th = braggd(hkl,e,crystals) lam = 12.3984191/e/10.0 # wavelength in nm reflcorr = 0.0 chi0 = complex(chi0r,chi0i) chih = complex(chihr,chihi) if crystals.upper() == 'SI': s13 = -0.278 elif crystals.upper() == 'GE': s13 = -0.273 else: print( 'Poisson ratio for this crystal not defined') return s15 = -0.0 # s15/s11 dsp = dspace(hkl,crystals)/10.0 # dspace dwf = 1.0 # dwf = 0.899577 # debye-waller factor radius = R # meridional bending radius rsag = R*np.sin(np.radians(th))**2.0 # sagittal bending radius thick = 500.0 # thickness in micrometers #rsag = R lam = lam*1e-9 dsp = dsp*1e-9 alpha = np.radians(alpha) # alpha in rad thick = thick*1e-6 ystart = ystart/3600.0/180.0*np.pi yend = yend/3600.0/180.0*np.pi ystep = ystep/3600.0/180*np.pi dev = dev/3600.0/180.0*np.pi reflcorr = reflcorr/3600.0/180.0*np.pi thetab = np.arcsin(lam/(2.0*dsp)) cpol = 1.0 # cpol=0.5*(1+cos(2*thetab).^2) # cpol=cos(2*thetab).^2 # gamma0 = sin(thetab+alpha) # normal convention # gammah = -sin(thetab-alpha) # normal convention gammah = -np.sin(thetab + alpha) # Krisch et al. convention (really!) gamma0 = np.sin(thetab - alpha) # Krisch et al. convention (I'm not kidding!!) beta = gamma0/np.abs(gammah) gamma = gammah/gamma0 a0 = np.sqrt(1-gamma0**2.0) ah = np.sqrt(1-gammah**2.0) mu = -2.0*np.pi/lam*chi0i tdepth = 1.0/mu/(1.0/np.abs(gamma0)+1.0/np.abs(gammah)) lex = lam*np.sqrt(gamma0*np.abs(gammah))/(np.pi*chihr) y0 = chi0i*(1.0+beta)/(2.0*np.sqrt(beta)*chihr) pfried = -chihi/chihr c1 = cpol*dwf* complex(1.0,pfried) #abbreviation concerning the deviation parameter y abb0 = -np.sqrt(beta)/2.0/chihr abb1 = chi0r*(1.0+beta)/(2.0*np.sqrt(beta)*chihr) #abbreviations concerning the deformation field abb2 = gamma0*gammah*(gamma0-gammah) abb3 = 1.0 + 1.0/(gamma0*gammah) abb4 = s13*(1.0 + radius/rsag) abb5 = (ah - a0)/(gamma0 - gammah)*s15 abb6 = 1.0/(np.abs(cpol)*chihr*np.cos(thetab)*radius) abb7 = 2.0*np.abs(cpol)*chihr*np.cos(thetab)/gamma0 # a spectrometer based on a spherical diced analyzer crystal with a 1-m bending radius in nearly backscattering conditions utilizing a strain gradient beta sgbeta = abb6*(abb2*(abb3 - abb4 + abb5)) nstep=len(dev) eta = np.zeros_like(dev) abb8z = np.zeros_like(dev) refl = np.zeros_like(dev) refl1 = np.zeros_like(dev) refl2 = np.zeros_like(dev) OLDMETHOD = 0 if OLDMETHOD : for l in range(nstep): # actual value of the deviation angle # dev[l] = ystart + (l - 1)*ystep # deviation parameter abb8 = -2.0*np.sin(2.0*thetab)*dev[l] eta[l] = (dev[l]*np.sin(2.0*thetab)+np.abs(chi0.real)/2.0*(1.0-gamma))/(np.abs(cpol)*np.sqrt(np.abs(gamma))*np.sqrt(chih*chih)) eta[l] = eta[l].real ndiff = 2 xend = 0 x = np.max([-10.0*tdepth, -thick]) y = np.array([0.0, 0.0]) h = xend abb8z[l] = abb8 # in this point call the subroutine # [T,Y] = ODE23(ODEFUN,TSPAN,Y0,OPTIONS,P1,P2,...) passes the additional # parameters P1,P2,... to the ODE function as ODEFUN(T,Y,P1,P2...), and to # all functions specified in OPTIONS. Use OPTIONS = [] as a place holder if # no options are set. #print 'the fucking shape of y is ', np.shape(y) T = np.arange(x,xend,1e-8) Y = odeint(odefctn,y,T,args=(abb0,abb1,abb7,abb8,lex,sgbeta,y0,c1)) # normalized reflectivity at this point refl[l] = np.sum(Y[-1,:]**2.0) refl1[l] = Y[-1,0] refl2[l] = Y[-1,1] else: YY = np.zeros([nstep],"D") for l in range(nstep): abb8 = -2.0*np.sin(2.0*thetab)*dev[l] eta[l] = ((dev[l]*np.sin(2.0*thetab)+np.abs(chi0.real)/2.0*(1.0-gamma))/ (np.abs(cpol)*np.sqrt(np.abs(gamma))*np.sqrt(chih*chih))) eta[l] = eta[l].real abb8z[l] = abb8 xend = 0 x = np.max([-10.0*tdepth, -thick]) ministep = tdepth/1000.0 ## when delta/beta is 100 we have still 10 xpoints = np.arange(x,0, ministep) substep_RungeKutta = ministep/2 ssrk = substep_RungeKutta for xpos in xpoints[:-1]: Yp0 = odefctn_CN( YY, xpos+0*ssrk , abb0,abb1,abb7,abb8z,lex,sgbeta,y0,c1) Yp1 = odefctn_CN( YY+1*ssrk*Yp0, xpos+1*ssrk , abb0,abb1,abb7,abb8z,lex,sgbeta,y0,c1) Yp2 = odefctn_CN( YY+1*ssrk*Yp1, xpos+1*ssrk , abb0,abb1,abb7,abb8z,lex,sgbeta,y0,c1) Ypb = odefctn_CN( YY+2*ssrk*Yp2, xpos+2*ssrk , abb0,abb1,abb7,abb8z,lex,sgbeta,y0,c1) YY = YY + ministep*( Yp0 + 2*(Yp1+Yp2) + Ypb )/6.0 refl1 = YY.real refl2 = YY.imag refl = refl1*refl1+refl2*refl2 de = dev * e * 1.0e6 /np.tan(thetab) lam = lam *1.0e+09 dsp = dsp*1.0e+09 alpha = alpha/np.pi*180.0 ystart = ystart/4.848136811e-06 yend = yend/4.848136811e-06 ystep = ystep/4.848136811e-06 dev = dev/4.848136811e-06 # dev in arcsecs dev = dev/3600.0 # in degrees thb = th th = thb + dev e0 = e e = energy(dspace(hkl,crystals),th)-e0 e = e*1e6 dev = dev*3600.0 # back to arcsecs return refl,e,dev,e0, refl1, refl2 def hlike_Rwfn(n,l,r,Z): """ **hlike_Rwfn** Returns an array with the radial part of a hydrogen-like wave function. Args: * n (integer): main quantum number n * l (integer): orbitalquantum number l * r (array): vector of radii on which the function should be evaluated * Z (float): effective nuclear charge """ import math from scipy import special a0 = 0.52917721092 factor1 = np.sqrt( (2.0*Z/(n*a0))**3 * math.factorial((n-l-1.0))/(2.0*n*math.factorial((n+1.0)) ) ) factor2 = (2.0*Z*r/(n*a0))**(l) factor3 = np.exp(-(Z*r/(n*a0))) lag = special.eval_genlaguerre(n-l-1.0,2.0*l+1.0,2.0*Z*r/(n*a0)) return factor1*factor2*factor3*lag#*np.sqrt(n+1.0) def compute_matrix_elements(R1,R2,k,r): # for ii=1:length(q); # fun=y3d.^2.*besselj(4,q(ii)*r); # int4(ii)=simpson(r,fun); # end from scipy import special q = np.linspace(0,60,len(r)) r2RsphBR = np.linspace(0,60,len(r)) for ii in range(len(q)): sphB = np.zeros_like(q) for jj in range(len(sphB)): # special.sph_jn returns the function in [0] and the derivative in [1] sphB[jj] = special.sph_jn(k,q[ii]*r[jj])[0][-1] fun = r**2*R1*sphB*R2 r2RsphBR[ii] = np.trapz(fun,r) return q/2.0,r2RsphBR def read_dft_wfn(element, n, l, spin=None, directory=data_installation_dir): """ **read_dft_wfn** Parses radial parts of wavefunctions. Args: * element (str): Element symbol. * n (int): Main quantum number. * l (int): Orbital quantum number. * spin (str): Which spin channel, default is average over up and down. * directory (str): Path to directory where the wavefunctions can be found. Returns: * r (np.array): radius * wfn (np.array): """ element_name = element.lower() subfolder = 'ae' l_name = ['s', 'p', 'd', 'f', 'g'][l] prefix = 'wf-%d%s_'%(n, l_name) postfix1 = 'up' postfix2 = 'dn' path1 = os.path.join(directory, 'wave_functions', element_name, subfolder, prefix+postfix1) path2 = os.path.join(directory, 'wave_functions', element_name, subfolder, prefix+postfix2) # load au2a = constants.physical_constants['atomic unit of length'][0]*1e10 wfn1 = np.loadtxt(path1) wfn2 = np.loadtxt(path2) r = wfn1[:,0] wfn = np.zeros_like(r) if not spin: wfn = wfn1[:,1]/2. + wfn2[:,1]/2. elif spin == 'up': wfn = wfn1[:,1] elif spin == 'dn' or spin == 'down': wfn = wfn2[:,1] else: print('unknown keyword for spin') # raise proper error return # normalize norm = np.trapz( r**2 * wfn*np.conj(wfn), r ) wfn /= norm return r, wfn def readfio(prefix, scannumber, repnumber=0): """ if repnumber = 0: reads a spectra-file (name: prefix_scannumber.fio) if repnumber > 1: reads a spectra-file (name: prefix_scannumber_rrepnumber.fio) """ suffix = '.fio' filename = prefix + '%05d' % scannumber + suffix if repnumber > 0: filename = prefix + '%05d' % scannumber + 'r' + '%d' % repnumber + suffix # analyze structure of file fid = open(filename,'r') colnameflag = ' Col' colstartflag = '%d' colnames = [] linenum = 0 for line in fid: linenum +=1 if colnameflag in line: colnames.append(line.strip()) if colnameflag in line: startline = linenum fid.close() thefile = open(filename,'r').readlines() data = [] for line in thefile[(len(colnames)+startline):]: data.append([float(x) for x in line.strip().split()]) return np.array(data), colnames def energy_monoangle(angle,d=5.4307/np.sqrt(11)): """ % ENERGY Calculates energy corrresponing to Bragg angle for given d-spacing % function e=energy(dspace,bragg_angle) % % dspace for reflection (defaulf for Si(311) reflection) % bragg_angle in DEG % % KH 28.09.93 % """ hc = 12.3984191 # CODATA 2002 physics.nist.gov/constants e = (2.0*d*sin(angle/180.0*np.pi)/hc)**(-1.0) return e def convertSplitEDF2EDF(foldername): """ converts the old style EDF files (one image for horizontal and one image for vertical chambers) to the new style EDF (one single image). Arg: foldername (str): Path to folder with all the EDF-files to be converted. """ allfiles = os.listdir(foldername) vfiles = [] hfiles = [] for f in allfiles: if 'v_' in f: vfiles.append(f) elif 'h_' in f: hfiles.append(f) else: print( 'WHAT?' ) for vfile in vfiles: if vfile[0:2] == 'ra': pass elif vfile[0:2] == 'v_': imv = fabio.open(foldername + vfile) imh = fabio.open(foldername + 'h_' + vfile[2::]) im = imv im.data = np.append(im.data,imh.data,axis=0) im.write(foldername+vfile[2::]) def savitzky_golay(y, window_size, order, deriv=0, rate=1): """Smooth (and optionally differentiate) data with a Savitzky-Golay filter. The Savitzky-Golay filter removes high frequency noise from data. It has the advantage of preserving the original shape and features of the signal better than other types of filtering approaches, such as moving averages techniques. Parameters: * y : array_like, shape (N,) the values of the time history of the signal. * window_size : int the length of the window. Must be an odd integer number. * order : int the order of the polynomial used in the filtering. Must be less then `window_size` - 1. * deriv: int the order of the derivative to compute (default = 0 means only smoothing) Returns * ys : ndarray, shape (N) the smoothed signal (or it's n-th derivative). Notes: The Savitzky-Golay is a type of low-pass filter, particularly suited for smoothing noisy data. The main idea behind this approach is to make for each point a least-square fit with a polynomial of high order over a odd-sized window centered at the point. Examples :: t = np.linspace(-4, 4, 500) y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape) ysg = savitzky_golay(y, window_size=31, order=4) import matplotlib.pyplot as plt plt.plot(t, y, label='Noisy signal') plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal') plt.plot(t, ysg, 'r', label='Filtered signal') plt.legend() plt.show() References :: .. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8), pp 1627-1639. .. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery Cambridge University Press ISBN-13: 9780521880688 """ import numpy as np from math import factorial try: window_size = np.abs(np.int(window_size)) order = np.abs(np.int(order)) except ValueError : raise ValueError("window_size and order have to be of type int") if window_size % 2 != 1 or window_size < 1: raise TypeError("window_size size must be a positive odd number") if window_size < order + 2: raise TypeError("window_size is too small for the polynomials order") order_range = range(order+1) half_window = (window_size -1) // 2 # precompute coefficients b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)]) m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv) # pad the signal at the extremes with # values taken from the signal itself firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] ) lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1]) y = np.concatenate((firstvals, y, lastvals)) return np.convolve( m[::-1], y, mode='valid') def sgolay2d ( z, window_size, order, derivative=None): """ """ # number of terms in the polynomial expression n_terms = ( order + 1 ) * ( order + 2) / 2.0 if window_size % 2 == 0: raise ValueError('window_size must be odd') if window_size**2 < n_terms: raise ValueError('order is too high for the window size') half_size = window_size // 2 # exponents of the polynomial. # p(x,y) = a0 + a1*x + a2*y + a3*x^2 + a4*y^2 + a5*x*y + ... # this line gives a list of two item tuple. Each tuple contains # the exponents of the k-th term. First element of tuple is for x # second element for y. # Ex. exps = [(0,0), (1,0), (0,1), (2,0), (1,1), (0,2), ...] exps = [ (k-n, n) for k in range(order+1) for n in range(k+1) ] # coordinates of points ind = np.arange(-half_size, half_size+1, dtype=np.float64) dx = np.repeat( ind, window_size ) dy = np.tile( ind, [window_size, 1]).reshape(window_size**2, ) # build matrix of system of equation A = np.empty( (window_size**2, len(exps)) ) for i, exp in enumerate( exps ): A[:,i] = (dx**exp[0]) * (dy**exp[1]) # pad input array with appropriate values at the four borders new_shape = z.shape[0] + 2*half_size, z.shape[1] + 2*half_size Z = np.zeros( (new_shape) ) # top band band = z[0, :] Z[:half_size, half_size:-half_size] = band - np.abs( np.flipud( z[1:half_size+1, :] ) - band ) # bottom band band = z[-1, :] Z[-half_size:, half_size:-half_size] = band + np.abs( np.flipud( z[-half_size-1:-1, :] ) -band ) # left band band = np.tile( z[:,0].reshape(-1,1), [1,half_size]) Z[half_size:-half_size, :half_size] = band - np.abs( np.fliplr( z[:, 1:half_size+1] ) - band ) # right band band = np.tile( z[:,-1].reshape(-1,1), [1,half_size] ) Z[half_size:-half_size, -half_size:] = band + np.abs( np.fliplr( z[:, -half_size-1:-1] ) - band ) # central band Z[half_size:-half_size, half_size:-half_size] = z # top left corner band = z[0,0] Z[:half_size,:half_size] = band - np.abs( np.flipud(np.fliplr(z[1:half_size+1,1:half_size+1]) ) - band ) # bottom right corner band = z[-1,-1] Z[-half_size:,-half_size:] = band + np.abs( np.flipud(np.fliplr(z[-half_size-1:-1,-half_size-1:-1]) ) - band ) # top right corner band = Z[half_size,-half_size:] Z[:half_size,-half_size:] = band - np.abs( np.flipud(Z[half_size+1:2*half_size+1,-half_size:]) - band ) # bottom left corner band = Z[-half_size:,half_size].reshape(-1,1) Z[-half_size:,:half_size] = band - np.abs( np.fliplr(Z[-half_size:, half_size+1:2*half_size+1]) - band ) # solve system and convolve if derivative == None: m = np.linalg.pinv(A)[0].reshape((window_size, -1)) return scipy.signal.fftconvolve(Z, m, mode='valid') elif derivative == 'col': c = np.linalg.pinv(A)[1].reshape((window_size, -1)) return scipy.signal.fftconvolve(Z, -c, mode='valid') elif derivative == 'row': r = np.linalg.pinv(A)[2].reshape((window_size, -1)) return scipy.signal.fftconvolve(Z, -r, mode='valid') elif derivative == 'both': c = np.linalg.pinv(A)[1].reshape((window_size, -1)) r = np.linalg.pinv(A)[2].reshape((window_size, -1)) return scipy.signal.fftconvolve(Z, -r, mode='valid'), scipy.signal.fftconvolve(Z, -c, mode='valid') def readp01image(filename): """ reads a detector file from PetraIII beamline P01 """ dim = 256 f = open(filename,'rb') data = np.fromfile(f,np.int32) # predata = arr.array('H') # predata.fromfile(f,(dim*dim)) image = np.reshape(data,(dim,dim)) f.close() return image def readp01scan(prefix,scannumber): """ reads a whole scan from PetraIII beamline P01 (experimental) """ print ("parsing files of scan No. %s" % scannumber) #fioname = prefix + 'online/hasylab_' + "%05d" % scannumber + '.fio' fioprefix = prefix + 'online/ixs_scan_' fiodata = readfio(fioprefix,scannumber)[0] mats1 = np.zeros((np.shape(fiodata)[0],256,256)) mats2 = np.zeros((np.shape(fiodata)[0],256,256)) mats = np.zeros((np.shape(fiodata)[0],256,256*2)) for n in range(np.shape(fiodata)[0]): matname1 = prefix + 'ixs_scan_' + "%05d" % scannumber + '/mdpxa/ixs_scan_' + "%05d" % scannumber + '_a_' + "%05d" % (n+1) matname2 = prefix + 'ixs_scan_' + "%05d" % scannumber + '/mdpxa/ixs_scan_' + "%05d" % scannumber + '_b_' + "%05d" % (n+1) mats1[n,:,:] = readp01image(matname1) mats2[n,:,:] = readp01image(matname2) mats[n,:,0:256] = mats1[n,:,:] mats[n,:,256:] = mats2[n,:,:] return fiodata, mats, mats1, mats2 def readp01scan_rep(prefix,scannumber,repetition): """ reads a whole scan with repititions from PetraIII beamline P01 (experimental) """ print ("parsing files of scan No. %s" % scannumber) #fioname = prefix + 'online/hasylab_' + "%05d" % scannumber + 'r' + "%1d" % repetition + '.fio' fioprefix = prefix + 'online/ixs_scan_' fiodata = readfio(fioprefix,scannumber,repetition)[0] mats1 = np.zeros((np.shape(fiodata)[0],256,256)) mats2 = np.zeros((np.shape(fiodata)[0],256,256)) mats = np.zeros((np.shape(fiodata)[0],256,256*2)) for n in range(np.shape(fiodata)[0]): matname1 = prefix + 'ixs_scan_' + "%05d" % scannumber + 'r' + "%1d" % repetition + '/mdpxa/ixs_scan_' + "%05d" % scannumber + 'r' + "%1d" % repetition + '_a_' + "%05d" % (n+1) matname2 = prefix + 'ixs_scan_' + "%05d" % scannumber + 'r' + "%1d" % repetition + '/mdpxa/ixs_scan_' + "%05d" % scannumber + 'r' + "%1d" % repetition + '_b_' + "%05d" % (n+1) mats1[n,:,:] = readp01image(matname1) mats2[n,:,:] = readp01image(matname2) mats[n,:,0:256] = mats1[n,:,:] mats[n,:,256:] = mats2[n,:,:] return fiodata, mats, mats1, mats2 def split_hdf5_address(dataadress): pos = dataadress.rfind(":") if ( pos==-1): raise Exception( """ roiaddress must be given in the form roiaddress : "myfile.hdf5:/path/to/hdf5/group" but : was not found """) filename, groupname = dataadress[:pos], dataadress[pos+1:] return filename, groupname def stiff_compl_matrix_Si( e1, e2, e3, ansys=False ): """ **stiff_compl_matrix_Si** Returns stiffnes and compliance tensor of Si for a given orientation. Args: * e1 (np.array): unit vector normal to crystal surface * e2 (np.array): unit vector crystal surface * e3 (np.array): unit vector orthogonal to e2 Returns: * S (np.array): compliance tensor in new coordinate system * C (np.array): stiffnes tensor in new coordinate system * E (np.array): Young's modulus in [GPa] * G (np.array): shear modulus in [GPa] * nu (np.array): Poisson ratio Copied from S.I. of L. Zhang et al. "Anisotropic elasticity of silicon and its application to the modelling of X-ray optics." J. Synchrotron Rad. 21, no. 3 (2014): 507-517. """ c11 = 165.7 # GPa c12 = 63.9 # GPa c44 = 79.6 # GPa C100 = np.array( [[c11, c12, c12, 0, 0, 0], [c12, c11, c12, 0, 0, 0], [c12, c12, c11, 0, 0, 0], [ 0, 0, 0, c44, 0, 0], [ 0, 0, 0, 0, c44, 0], [ 0, 0, 0, 0, 0, c44]]) mu = c11 - c12 - 2*c44 Ce = np.zeros((6,6)) Ce[0,0] = mu Ce[1,1] = mu Ce[2,2] = mu if ansys: M0 = np.array( [e1*e1, e2*e2, e3*e3, e1*e2, e2*e3, e3*e1] ) # ANSYS definition else: M0 = np.array( [e1*e1, e2*e2, e3*e3, e2*e3, e1*e3, e1*e2] ) # Common definition M = np.dot( M0,M0.transpose() ) C = C100+mu*M-Ce # Stiffness matrix (GPa) S = np.linalg.inv(C) # Compliance tensor E = [1/S[0,0], 1/S[1,1], 1/S[2,2]] # Young's modulus (GPa) G = [1/S[3,3], 1/S[4,4], 1/S[5,5]] # shear modulus (GPa) nu = -S[ 0:3,0:3 ] / np.array( [[S[0,0],S[0,0],S[0,0]], [S[1,1],S[1,1],S[1,1]], [S[2,2],S[2,2],S[2,2]] ] ) return S, C, np.array(E), np.array(G), np.array(nu) xrstools-0.15.0+git20210910+c147919d/batch_extraction.py000066400000000000000000000402521412732462000220500ustar00rootroot00000000000000# Hi Alessandro, # here's a file with 1. column ROI# and 2. column E0 (in keV). # There were in total two samples we tried to measure at many energies (I also attached the logbook for clearity): # 1) this is the little insect trapped in amber # Scans: #440 - #589, the scheme was as follows: # - scans 440 - 464 were around the elastic line, 2 scans per energy (i.e. 2 sty scans for two different values of stz at each energy point), the energy grid was 0.5 eV # - scans 464 - 589 were around the C K-edge, 2 scans per energy (same as for elastic) # 2) this was a little piece of burnt prehistoric wood # Scans: #1047 - #1141, the scheme is the same as above, but 0.25 eV energy steps: # - scans 1047 - 1195: scans around the elastic line (2 sty scans per energy at 2 different stz positions) # - scans 1095 - 1141, scans around the C K-edge (2 sty scans per energy at 2 different stz positions) # the reference scans (25 micron Kapton foil) are: # # 591 E = 9.686 # # 592 E = 9.9712 # # 593 E = 9.9745 # # 594 E = 9.9776 # # 595 E = 9.986 # # 596 E = 9.956 # # 597 E = 9.946 # # 598 E = 10.006 # # 599 E = 10.026 # let me know if all this makes sense (or not). Thanks a lot! # Christoph import numpy as np import h5py import glob cenom = np.array( [ 0, 9.68715366 , 1, 9.68646557 , 2, 9.68662118 , 3, 9.68626895 , 4, 9.68712335 , 5, 9.68650054 , 6, 9.68603224 , 7, 9.68585970 , 8, 9.68648548 , 9, 9.68587371 , 10, 9.68528659 , 11, 9.68522053 , 12, 9.68575133 , 13, 9.68453831 , 14, 9.68495099 , 15, 9.68573933 , 16, 9.68612397 , 17, 9.68537876 , 18, 9.68550170 , 19, 9.68629621 , 20, 9.68686062 , 21, 9.68620125 , 22, 9.68611576 , 23, 9.68710568 , 24, 9.68663625 , 25, 9.68615702 , 26, 9.68634820 , 27, 9.68703000 , 28, 9.68593704 , 29, 9.68544516 , 30, 9.68526687 , 31, 9.68596309 , 32, 9.68507243 , 33, 9.68492916 , 34, 9.68469521 , 35, 9.68589879 , 36, 9.68620847 , 37, 9.68638990 , 38, 9.68647063 , 39, 9.68743229 , 40, 9.68610182 , 41, 9.68520195 , 42, 9.68558591 , 43, 9.68649882 , 44, 9.68551274 , 45, 9.68487196 , 46, 9.68511143 , 47, 9.68583333 , 48, 9.68539694 , 49, 9.68503707 , 50, 9.68508553 , 51, 9.68637998 , 52, 9.68646747 , 53, 9.68580797 , 54, 9.68568058 , 55, 9.68629252 , 56, 9.68729703 , 57, 9.68647016 , 58, 9.68634066 , 59, 9.68693394 , 60, 9.68626225 , 61, 9.68617870 , 62, 9.68654891 , 63, 9.68712219 , 64, 9.68598538 , 65, 9.68545723 , 66, 9.68553384 , 67, 9.68648591 , 68, 9.68543486 , 69, 9.68492219 , 70, 9.68508252 , 71, 9.68554590 ]) cenom=np.reshape(cenom,[-1,2]) Enominal = np.median(cenom[:,1]) cenom[:,1] -= Enominal import os def process_input(s, go=0): open("input_tmp_%d.par"%go, "w").write(s) if not(go%12): os.system("XRS_swissknife input_tmp_%d.par "%go) else: os.system("XRS_swissknife input_tmp_%d.par &"%go) def select_rois(roi_scan_num=592): inputstring = """ create_rois: expdata : /data/id20/inhouse/data/run5_17/run7_ihr/hydra scans : [{roi_scan_num}] roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED filter_path : mask.h5:/FILTER_MASK/filter """ s=inputstring.format(roi_scan_num = roi_scan_num ) process_input(s) def extract_sample_givenrois(roi_scan_num=592, nick_name="insect_ck", target_file ="signals.h5" , Start = 464, End = 589, Thickness = 2 ): inputstring = """ loadscan_2Dimages : expdata : /data/id20/inhouse/data/run5_17/run7_ihr/hydra roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED monitor_column : izero/0.000001 scan_interval : [{start}, {end}] energy_column : sty signaladdress : {target_file}:/{where}/_{start}_{end} sumto1D : 0 monitorcolumn : izero/0.000001 """ for start in range(Start,End, Thickness): s=inputstring.format(start=str(start), end=str(start+2) , where= nick_name ,roi_scan_num = roi_scan_num, target_file =target_file ) process_input(s) class InterpInfo: def __init__(self, cenom, interp_file, source, target): volum_list = list(interp_file[source].keys()) scan_num_list = np.array([ int( t.split("_") [1]) for t in volum_list]) ene_list = np.array([ interp_file[source][vn]["scans"]["Scan%d"%sn ]["motorDict"]["energy"].value for vn,sn in zip(volum_list, scan_num_list ) ]) print ( " ecco la scannumlist " , scan_num_list) print (" ecco ene_list", ene_list) self.volum_list = volum_list self.scan_num_list = scan_num_list self.ene_list = ene_list order = np.argsort( self.ene_list ) self.ene_list = self.ene_list [order] self.scan_num_list = self.scan_num_list [order] self.volum_list = [ self.volum_list [ii] for ii in order ] self.interp_file=interp_file self.source= source self.target = target self.cenom=cenom def interpola(self): # print ( " ECCO I DATI ") # print ( self.ene_list ) # print ( self.cenom ) # raise for t_vn, t_sn, t_ene in zip(self.volum_list, self.scan_num_list, self.ene_list ): rois_coeffs={} for roi_num, de in enumerate( self.cenom ): if t_ene+de < self.ene_list .min() or t_ene+de > self.ene_list .max(): continue print ( t_ene+de, self.ene_list .min() ,self.ene_list .max() ) i0 = np.searchsorted( self.ene_list , t_ene+de )-1 assert(i0>=0) i1=i0+1 print (i0, i1, len(self.ene_list)) print (self.ene_list) assert(i1/dev/null 2>&1; echo $$?), 1) $(error The '$(SPHINXBUILD)' command was not found. 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If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath('../')) sys.path.insert(0, os.path.abspath('../XRStools')) # sys.path.insert(0, os.path.abspath('../XRStools/XRStools')) sys.path.insert(0, os.path.abspath('../nonregressions')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.imgmath', 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode' ] # , 'breathe' ] # ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'XRStools' copyright = u'2015, Christoph Sahle, Alessandro Mirone' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '1' # The full version, including alpha/beta/rc tags. release = '1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. 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Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'XRStoolsdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ('index', 'XRStools.tex', u'XRStools Documentation', u'Christoph Sahle, Alessandro Mirone', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'xrstools', u'XRStools Documentation', [u'Christoph Sahle, Alessandro Mirone'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'XRStools', u'XRStools Documentation', u'Christoph Sahle, Alessandro Mirone', 'XRStools', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False def maybe_skip_member(app, what, name, obj, skip, options): # if what == "module": return False print( " /////////////////////////////// " ) print( app) print( what) print( name) # print obj # print obj.__module__ # print skip print( options) print( " ///////////////////////////////////// " ) return True #def setup(app): # app.connect('autodoc-skip-member', maybe_skip_member) xrstools-0.15.0+git20210910+c147919d/doc/developer_corner.rst000066400000000000000000000046151412732462000230140ustar00rootroot00000000000000Developers Corner ================= :mod:`XRStools.roifinder_and_gui` Module ----------------------------------------- .. automodule:: XRStools.roifinder_and_gui :members: :undoc-members: :show-inheritance: :mod:`XRStools.xrs_utilities` Module ------------------------------------- .. automodule:: XRStools.xrs_utilities :members: :undoc-members: :show-inheritance: :mod:`XRStools.XRStool` Package ------------------------------- .. automodule:: XRStools.__init__ :members: :undoc-members: :show-inheritance: :mod:`XRStools.xrs_calctools` Module ------------------------------------ .. automodule:: XRStools.xrs_calctools :members: :undoc-members: :show-inheritance: :mod:`XRStools.xrs_extraction` Module ------------------------------------- .. automodule:: XRStools.xrs_extraction :members: :undoc-members: :show-inheritance: :mod:`XRStools.xrs_imaging` Module ---------------------------------- .. automodule:: XRStools.xrs_imaging :members: :undoc-members: :show-inheritance: :mod:`XRStools.xrs_read` Module ------------------------------- .. automodule:: XRStools.xrs_read :members: :undoc-members: :show-inheritance: :mod:`XRStools.xrs_scans` Module -------------------------------- .. automodule:: XRStools.xrs_scans :members: :undoc-members: :show-inheritance: :mod:`XRStools.xrs_ComptonProfiles` Module ------------------------------------------ .. automodule:: XRStools.xrs_ComptonProfiles :members: :undoc-members: :show-inheritance: :mod:`XRStools.xrs_fileIO` Module --------------------------------- .. automodule:: XRStools.xrs_fileIO :members: :undoc-members: :show-inheritance: :mod:`XRStools.xrs_prediction` Module ------------------------------------- .. automodule:: XRStools.xrs_prediction :members: :undoc-members: :show-inheritance: :mod:`XRStools.xrs_rois` Module ------------------------------- .. automodule:: XRStools.xrs_rois :members: :undoc-members: :show-inheritance: :mod:`XRStools.xrs_utilities` Module ------------------------------------ .. automodule:: XRStools.xrs_utilities :members: :undoc-members: :show-inheritance: :mod:`XRStools.roifinder_and_gui` Module ---------------------------------------- .. automodule:: XRStools.roifinder_and_gui :members: :undoc-members: :show-inheritance: ` xrstools-0.15.0+git20210910+c147919d/doc/examples_en_vrac.rst000066400000000000000000000002711412732462000227640ustar00rootroot00000000000000Examples En Vrac ================ xrstools imaging example ------------------------ .. automodule:: xrstools_imaging_example :members: :undoc-members: :show-inheritance: xrstools-0.15.0+git20210910+c147919d/doc/index.rst000066400000000000000000000010561412732462000205620ustar00rootroot00000000000000.. pyhst2 documentation master file, created by sphinx-quickstart on Tue Dec 18 18:13:53 2012. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Welcome to XRStools's documentation! ==================================== Contents: .. toctree:: :maxdepth: 3 installation invocation examples_en_vrac videos input_format output_format how_to_use_rixs developer_corner Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` xrstools-0.15.0+git20210910+c147919d/doc/installation.rst000066400000000000000000000022711412732462000221540ustar00rootroot00000000000000Installation ============ * If you install from a Debian package you can skip the following points, install it , and then go directly to the code invocation section * Using Git, sources can be retrived with the following command :: git clone https://gitlab.esrf.fr/ixstools/xrstools * for a local installation you can use :: python setup.py install --prefix=~/packages then to run the code you must do beforehand :: export PYTHONPATH=/home/yourname/packages/lib/python2.7/site-packages export PATH=/home/yourname/bin:$PATH * To install by creating a virtual environment :: export MYPREFIX=/REPLACE/WITH/YOUR/TARGET cd ${MYPREFIX} python3 -m venv myenv source ${MYPREFIX}/myenv/bin/activate pip install pip --upgrade pip install setuptools --upgrade git clone https://gitlab.esrf.fr/ixstools/xrstools cd ${MYPREFIX}/xrstools/ pip install -r requirements.txt python setup.py install * Examples can be found in the nonregression directory. * For the roi selection tool you need a recent version of pymca installed on your sistem * Usage examples can be found in the non regression directory. xrstools-0.15.0+git20210910+c147919d/doc/invocation.rst000066400000000000000000000042201412732462000216200ustar00rootroot00000000000000Code Invocation =============== * Some of the XRStools capabilities can be accessed by invocation of the *XRS_swissknife* script, providing as input a file in the *yaml* format. * To use the wizard the suggested instruction is :: XRS_wizard --wroot ~/software/XRStoolsSuperResolution/XRStools/WIZARD/methods/ the wroot argument tells where extra workflow can be found. In the above instruction we give workflows in the home source directory. This is practical because the wizard allows to edit them online and the modification will remain in the sources. or to access extra workflows that are not coming with the main disribution. * Depending on the details of your installation, you have the *XRS_swissknife* script sitting somewhere in a directory. Check the *Installation* page to see how to set PYTHONPATH and PATH in case of a local installation. *The following documentation has been generated automatically from the comments found in the code*. GENERALITIES about XRS_swissknife --------------------------------- .. automodule:: XRS_swissknife :members: generality_doc Super Resolution ---------------- to fit optical responses of all the analysers (you selected a ROI for) and the pixel response based on a foil scan ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ embedded doc : .. automodule:: XRS_swissknife :members: superR_fit_responses to extrapolate to a larger extent the ROIS and the foils scan, thus to cover a larger sample ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ embedded doc : .. automodule:: XRS_swissknife :members: superR_recreate_rois to calculate the scalar product between a foil scan and a sample, for futher use in the inversion problem ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ embedded doc : .. automodule:: XRS_swissknife :members: superR_scal_deltaXimages Other features -------------- .. automodule:: XRS_swissknife :members: help, create_rois,load_scans,Extraction, HFspectrum e_rois xrstools-0.15.0+git20210910+c147919d/doc/make.bat000066400000000000000000000150611412732462000203270ustar00rootroot00000000000000@ECHO OFF REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set BUILDDIR=_build set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . set I18NSPHINXOPTS=%SPHINXOPTS% . if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. singlehtml to make a single large HTML file echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and a HTML help project echo. qthelp to make HTML files and a qthelp project echo. devhelp to make HTML files and a Devhelp project echo. epub to make an epub echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. text to make text files echo. man to make manual pages echo. texinfo to make Texinfo files echo. gettext to make PO message catalogs echo. changes to make an overview over all changed/added/deprecated items echo. xml to make Docutils-native XML files echo. pseudoxml to make pseudoxml-XML files for display purposes echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) %SPHINXBUILD% 2> nul if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) if "%1" == "html" ( %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. goto end ) if "%1" == "singlehtml" ( %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in %BUILDDIR%/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in %BUILDDIR%/qthelp, like this: echo.^> qcollectiongenerator %BUILDDIR%\qthelp\XRStools.qhcp echo.To view the help file: echo.^> assistant -collectionFile %BUILDDIR%\qthelp\XRStools.ghc goto end ) if "%1" == "devhelp" ( %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp if errorlevel 1 exit /b 1 echo. echo.Build finished. goto end ) if "%1" == "epub" ( %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub if errorlevel 1 exit /b 1 echo. echo.Build finished. The epub file is in %BUILDDIR%/epub. goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex if errorlevel 1 exit /b 1 echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "latexpdf" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex cd %BUILDDIR%/latex make all-pdf cd %BUILDDIR%/.. echo. echo.Build finished; the PDF files are in %BUILDDIR%/latex. goto end ) if "%1" == "latexpdfja" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex cd %BUILDDIR%/latex make all-pdf-ja cd %BUILDDIR%/.. echo. echo.Build finished; the PDF files are in %BUILDDIR%/latex. goto end ) if "%1" == "text" ( %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text if errorlevel 1 exit /b 1 echo. echo.Build finished. The text files are in %BUILDDIR%/text. goto end ) if "%1" == "man" ( %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man if errorlevel 1 exit /b 1 echo. echo.Build finished. The manual pages are in %BUILDDIR%/man. goto end ) if "%1" == "texinfo" ( %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo if errorlevel 1 exit /b 1 echo. echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. goto end ) if "%1" == "gettext" ( %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale if errorlevel 1 exit /b 1 echo. echo.Build finished. The message catalogs are in %BUILDDIR%/locale. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes if errorlevel 1 exit /b 1 echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck if errorlevel 1 exit /b 1 echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest if errorlevel 1 exit /b 1 echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) if "%1" == "xml" ( %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml if errorlevel 1 exit /b 1 echo. echo.Build finished. The XML files are in %BUILDDIR%/xml. goto end ) if "%1" == "pseudoxml" ( %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml if errorlevel 1 exit /b 1 echo. echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. goto end ) :end xrstools-0.15.0+git20210910+c147919d/doc/videos.rst000066400000000000000000000010361412732462000207420ustar00rootroot00000000000000VIDEOS ====== * A Tool to clean the spectra from Compton profile and absorption edge .. raw:: html * A Tool to define ROI by using NNMF in spectral and spatial domain .. raw:: html xrstools-0.15.0+git20210910+c147919d/fitcc/000077500000000000000000000000001412732462000172425ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/fitcc/CMakeLists.txt000066400000000000000000000105561412732462000220110ustar00rootroot00000000000000cmake_minimum_required (VERSION 3.8) option (USE_STATIC_LIBRARIES "Link with static libraries if available" OFF) option (TRY_CUDA "compile Cuda files" OFF) # c++ as a basis project (FRSV LANGUAGES CXX C) # optionally we can use cuda # to compile conditionally include(CheckLanguage) find_package(HighFivee ) if (HighFive_FOUND) message(STATUS "found high five") else() ## manual fix set( hfive_include_dir ${PROJECT_SOURCE_DIR}/HighFive/include/ "/usr/include/hdf5/serial/" ) set( hfive_libs /usr/lib/x86_64-linux-gnu/hdf5/serial/libhdf5.so ) endif() find_package(MPI ) if(MPI_C_FOUND) message(STATUS "YES : MPI support: configured variable MPI_C_COMPILER = ${MPI_C_COMPILER} " ) message(STATUS " MPI_C_COMPILE_FLAGS = ${MPI_C_COMPILEFLAGS} " ) message(STATUS " MPI_C_INCLUDE_PATH = ${MPI_C_INCLUDE_PATH} " ) message(STATUS " MPI_C_LINK_FLAGS = ${MPI_C_LINK_FLAGS} " ) message(STATUS " MPI_C_ LIBRARIES = ${MPI_C_LIBRARIES} " ) message(STATUS " MPI_CXX_COMPILER = ${MPI_CXX_COMPILER} " ) message(STATUS " MPI_CXX_COMPILE_FLAGS = ${MPI_CXX_COMPILEFLAGS} " ) message(STATUS " MPI_CXX_INCLUDE_PATH = ${MPI_CXX_INCLUDE_PATH} " ) message(STATUS " MPI_CXX_LINK_FLAGS = ${MPI_CXX_LINK_FLAGS} " ) message(STATUS " MPI_CXX_ LIBRARIES = ${MPI_CXX_LIBRARIES} " ) endif() find_package(Boost 1.50 REQUIRED COMPONENTS filesystem system) message(STATUS "Boost version: ${Boost_VERSION}") if(Boost_FOUND) message(" FOUND Boost") message( Boost_INCLUDEDIR "${BOOST_INCLUDE_DIR}" ) message( Boost_LIBRARY_DIR_RELEASE "${BOOST_LIBRARY_DIR_RELEASE}" ) message( "${}" ) message( "${}" ) endif() find_package(Yaml-cpp) if(Yaml-cpp_FOUND) message(" FOUND YAML-CPP") message(STATUS "YAML_CPP_INCLUDE_DIR ${YAML_CPP_INCLUDE_DIR} " ) message(STATUS "YAML_CPP_LIBRARIES ${YAML_CPP_LIBRARIES} " ) message(STATUS " YAML_CPP_VERSION = ${YAML_CPP_VERSION} " ) message(STATUS " YAML_CPP_LIBRARY_DIR = ${YAML_CPP_LIBRARY_DIR} " ) endif() include(CheckCXXCompilerFlag) # The version number. set (FRSV_VERSION_MAJOR 1) set (FRSV_VERSION_MINOR 0) # Set minimum C++ to 2011 standards set(CMAKE_CXX_STANDARD 11) set(CMAKE_CXX_STANDARD_REQUIRED ON) # configure a header file to pass some of the CMake settings # to the source code configure_file ( "${PROJECT_SOURCE_DIR}/FRSVConfig.h.in" "${PROJECT_BINARY_DIR}/FRSVConfig.h" ) # add the binary tree to the search path for include files # so that we will find FRSVConfig.h include_directories("${PROJECT_BINARY_DIR}") set(CMAKE_BUILD_TYPE RELEASE) if(NOT CMAKE_BUILD_TYPE) set(CMAKE_BUILD_TYPE DEBUG) endif() find_package(OpenMP) if (OPENMP_FOUND) set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}") endif() ## set(CMAKE_CXX_FLAGS "-Wextra -Wall -mfpmath=sse -Ofast -flto -mcpu=native ${CMAKE_CXX_FLAGS} " ) set(CMAKE_CXX_FLAGS_DEBUG "-g -Wextra -Wall -fopenmp ${CMAKE_CXX_FLAGS} " ) set(CMAKE_CXX_FLAGS_RELEASE " -g -Wextra -Wall -fopenmp -Ofast -mtune=native${CMAKE_CXX_FLAGS} " ) ## set(CMAKE_CXX_FLAGS_PIPPO "-g -Wextra -Wall -Ofast -flto -mtune=native ${CMAKE_CXX_FLAGS}" ) set(CMAKE_CXX_FLAGS_PIPPO "-g -Wextra -Wall ${CMAKE_CXX_FLAGS}" ) # set(CMAKE_CXX_FLAGS "-g -Wextra -Wall -mfpmath=sse -Ofast -mtune=native ${CMAKE_CXX_FLAGS}" ) add_executable (frsv frsv.cc) if (HighFivee_FOUND) target_link_libraries( frsv PRIVATE yaml-cpp Boost::filesystem Boost::system ) target_include_directories ( frsv PRIVATE "${PROJECT_SOURCE_DIR}" ) else() target_link_libraries( frsv PRIVATE yaml-cpp Boost::filesystem Boost::system ${hfive_libs} ) target_include_directories ( frsv PRIVATE "${PROJECT_SOURCE_DIR}" ${hfive_include_dir} ) endif() xrstools-0.15.0+git20210910+c147919d/fitcc/FRSVConfig.h.in000066400000000000000000000003631412732462000217300ustar00rootroot00000000000000// the configured options and settings for Tutorial #define FRSV_VERSION_MAJOR @FRSV_VERSION_MAJOR@ #define FRSV_VERSION_MINOR @FRSV_VERSION_MINOR@ /* #define HASCUDA @HASCUDA@ */ /* #define HASAF @HASAF@ */ /* #cmakedefine USE_MYMATH */ xrstools-0.15.0+git20210910+c147919d/fitcc/batch_extraction.py000066400000000000000000000462341412732462000231460ustar00rootroot00000000000000import numpy as np import h5py import glob import json BATCH_PARALLELISM = 4 cenom = np.array( [ 0, 12.91684471961497 , 1, 12.91601322225362 , 2, 12.915539860755496 , 3, 12.916480273530496 , 4, 12.915731392088265 , 5, 12.914869749579399 , 6, 12.916264935543595 , 7, 12.91573965859977 , 8, 12.914987140018741 , 9, 12.917537037788902 , 10, 12.916944061630726 , 11, 12.917218345706756 , 12, 12.915891035359477 , 13, 12.916352608181027 , 14, 12.917177018098796 , 15, 12.914005418194852 , 16, 12.915154657961107 , 17, 12.916191453728914 , 18, 12.914335816165174 , 19, 12.915077949987177 , 20, 12.915914957907148 , 21, 12.915288356811502 , 22, 12.91598889195904 , 23, 12.91695899376287 , 24, 12.916900296092969 , 25, 12.91604539858451 , 26, 12.914871297189173 , 27, 12.916231311661814 , 28, 12.915245568912306 , 29, 12.914471665531154 , 30, 12.916385293003266 , 31, 12.91497576912035 , 32, 12.914105451420639 , 33, 12.917157692493532 , 34, 12.915718809628475 , 35, 12.915646376430596 , 36, 12.91406510719366 , 37, 12.914584874090105 , 38, 12.914253022577238 , 39, 12.914765752067101 , 40, 12.913952660702538 , 41, 12.914056540248373 , 42, 12.914686992985423 , 43, 12.914358771568466 , 44, 12.914322364160563 , 45, 12.914834493680436 , 46, 12.91468849314193 , 47, 12.914392397943747 , 48, 12.91526519846475 , 49, 12.916546273857113 , 50, 12.91754238416663 , 51, 12.91462504324549 , 52, 12.915558554542379 , 53, 12.916489766393653 , 54, 12.914555294751848 , 55, 12.91532484033084 , 56, 12.916251843583483 , 57, 12.916091870082658 , 58, 12.91604809986116 , 59, 12.917105964460655 , 60, 12.916403610900606 , 61, 12.916009142769497 , 62, 12.915288063762233 , 63, 12.916156971954502 , 64, 12.91517887482311 , 65, 12.914460401828784 , 66, 12.916150384998303 , 67, 12.915275296447305 , 68, 12.914661911784687 , 69, 12.917387263528335 , 70, 12.916532490728155 , 71, 12.915658283253318 ]) cenom=np.reshape(cenom,[-1,2]) Enominal = np.median(cenom[:,1]) cenom[:,1] -= Enominal first_scann = 651 # 891 last_scann = 720 # 1015 thickness = 10 ### 25 roi_scann = 246 # 245 247 #### other_rois_for_ref = [245,247] #first_scann = 24 #last_scann = 26 n_energies = 7 # 6 import os def process_input(s, go=0, exploit_slurm_mpi = 0, stop_omp = False): open("input_tmp_%d.par"%go, "w").write(s) background_activator = "" if (go % BATCH_PARALLELISM ): background_activator = "&" prefix="" if stop_omp: prefix = prefix +"export OMP_NUM_THREADS=1 ;" if ( exploit_slurm_mpi==0 ): os.system(prefix +"mpirun -n 1 XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) elif ( exploit_slurm_mpi>0 ): os.system(prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: os.system(prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) def select_rois(roi_scan_num=6): inputstring = """ create_rois: expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' scans : [{roi_scan_num}] roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED filter_path : mask.h5:/FILTER_MASK/filter """ s=inputstring.format(roi_scan_num = roi_scan_num ) process_input(s, exploit_slurm_mpi = 0 ) def extract_sample_givenrois(roi_scan_num=roi_scann, nick_name="org", Start=first_scann, End=(first_scann+thickness*n_energies), Thickness = thickness ): inputstring = """ loadscan_2Dimages : expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED monitor_column : izero/0.000001 scan_interval : [{start}, {end}] energy_column : sty signaladdress : signals.h5:/{where}/_{start}_{end} sumto1D : 0 monitorcolumn : izero/0.000001 """ for start in range(Start,End, Thickness): s=inputstring.format(start=str(start), end=str(start+Thickness) , where= nick_name ,roi_scan_num = roi_scan_num ) process_input(s, exploit_slurm_mpi = 1) class InterpInfo: def __init__(self, cenom, interp_file, source, custom_ene_list = None): volum_list = list(interp_file[source].keys()) scan_num_list = np.array([ int( t.split("_") [1]) for t in volum_list]) ene_list = np.array([ interp_file[source][vn]["scans"]["Scan%03d"%sn ]["motorDict"]["energy"][()] for vn,sn in zip(volum_list, scan_num_list ) ]) print ( " ecco la scannumlist " , scan_num_list) print (" ecco ene_list", ene_list) self.volum_list = volum_list self.scan_num_list = scan_num_list self.ene_list = ene_list order = np.argsort( self.ene_list ) self.ene_list = self.ene_list [order] if custom_ene_list is None: self.custom_ene_list = self.ene_list else: self.custom_ene_list = custom_ene_list self.scan_num_list = self.scan_num_list [order] self.volum_list = [ self.volum_list [ii] for ii in order ] self.interp_file=interp_file self.source= source self.cenom=cenom def interpola(self): print ( " ECCO I DATI ") print ( self.ene_list ) print ( self.cenom ) info_dict = {} for i_intervallo in range(len(self.custom_ene_list)): info_dict[str(i_intervallo)] = {} info_dict[str(i_intervallo)]["E"] = self.custom_ene_list[ i_intervallo ] info_dict[str(i_intervallo)]["coefficients"]={} for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list )): info_dict[str(i_intervallo)]["coefficients" ][ t_vn ]={} for i_intervallo in range(len(self.custom_ene_list)-1): cE1 = self.custom_ene_list[ i_intervallo ] cE2 = self.custom_ene_list[ i_intervallo+1 ] for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list ))[0:]: for roi_num, de in enumerate( self.cenom ): if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2-cE1) info_dict[str(i_intervallo)]["coefficients" ][ str(t_vn) ][ str(roi_num) ] = alpha info_dict[str(i_intervallo+1)]["coefficients"][ str(t_vn) ][ str(roi_num) ] = 1-alpha print( info_dict) return info_dict def get_reference(roi_scan_num=roi_scann): inputstring = """ loadscan_2Dimages : expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED monitor_column : izero/0.000001 scan_interval : [{roi_scan_num},{roi_scan_num_plus1} ] signaladdress : calibration_scan isolateSpot : 6 save_also_roi : True sumto1D : 0 energycolumn : 'stx' monitorcolumn : izero/0.000001 """ s=inputstring.format( roi_scan_num= roi_scan_num, roi_scan_num_plus1= roi_scan_num+1 ) process_input( s , exploit_slurm_mpi = 1) def resynthetise_scan( old_scan_address="roi_%d.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"%(roi_scann, roi_scann), response_file = "reponse.h5" , target_filename = "newrois.h5:/ROIS/" ): inputstring = """ superR_recreate_rois : ### we have calculated the responses in responsefilename ### and we want to enlarge the scan by a margin of 3 times ### the original scan on the right and on the left ### ( so for a total of a 7 expansion factor ) responsefilename : {response_file} nex : 0 ## the old scan covered by the old rois old_scan_address : {old_scan_address} ## where new rois and bnew scan are written target_filename : {target_filename} filter_rois : 0 """ s=inputstring.format( response_file = response_file , target_filename = target_filename, old_scan_address=old_scan_address ) process_input( s , exploit_slurm_mpi = 0, stop_omp = True) def synthetise_response(scan_address=None, target_file=None): inputstring = """ superR_fit_responses : foil_scan_address : "{scan_address}" nref : 7 # the number of subdivision per pixel dimension used to # represent the optical response function at higher resolution niter_optical : 100 # the number of iterations used in the optimisation of the optical # response beta_optical : 0.1 # The L1 norm factor in the regularisation # term for the optical functions pixel_dim : 1 # The pixel response function is represented with a # pixel_dim**2 array niter_pixel : 10 # The number of iterations in the pixel response optimisation # phase. A negative number stands for ISTA, positive for FISTA beta_pixel : 0.0 # L1 factor for the pixel response regularisation ## The used trajectories are always written whith the calculated response ## They can be reloaded and used as initialization(and freezed with do_refine_trajectory : 0 ) ## Uncomment the following line if you want to reload a set of trajectories ## without this options trajectories are initialised from the spots drifts ## # reload_trajectories_file : "response.h5" filter_rois : 0 ###### ## The method first find an estimation of the foil scan trajectory on each roi ## then, based on this, obtain a fit of the optical response function ## assuming a flat pixel response. Finally the pixel response is optimised ## ## There is a final phase where a global optimisation ## is done in niter_global steps. ## ## Each step is composed of optical response fit, followed by a pixel response fit. ## If do_refine_trajectory is different from zero, the trajectory is reoptimised at each step ## niter_global : 3 ## if do_refine_trajectory=1 the start and end point of the trajectory are free ## if =2 then the start and end point are forced to a trajectory which is obtained ## from a reference scan : the foil scan may be short, then one can use the scan of ## an object to get another one : key *trajectory_reference_scan_address* ## do_refine_trajectory : 1 ## optional: only if do_refine_trajectory = 2 trajectory_reference_scansequence_address : "demo_newrois.h5:/ROI_FOIL/images/scans/" trajectory_threshold : 0.1 ## if the pixel response function is forced to be symmetrical simmetrizza : 1 ## where the found responses are written target_file : {target_file} # target_file : "fitted_responses.h5" """ s=inputstring.format( scan_address=scan_address , target_file=target_file ) process_input( s , exploit_slurm_mpi = 1, stop_omp = True) def get_scalars( Start = first_scann, Thickness = thickness , roi_scan_num=roi_scann , nick = None, ref_file=None): inputstring = """ superR_scal_deltaXimages_Esynt : # sample_address : interpolated_signals.h5:/{nick}/_{start}_{end}/scans sample_address : signals.h5:/{nick}/_{start}_{end}/scans delta_address : {ref_file}/scans/Scan{roi_scan_num} # delta_address : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan{roi_scan_num} nbin : 1 optional_solution : target_address : volumes.h5:/{nick}/_{start}_{end}/scal_prods # roi_keys : "7" """ # volumes.h5:/{nick}/_{start}_{end}/volume # volumes.h5:/{nick}/_{start}_{end}/volume # inputstring = """ # superR_scal_deltaXimages : # sample_address : {signals_file}:/{nick}/_{start}_{end}/scans # delta_address : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan00{roi_scan_num} # nbin : 2 # optional_solution : volumes.h5:/{nick}/_{start}_{end}/volume # target_address : volumes.h5:/{nick}/_{start}_{end}/scal_prods # """ s=inputstring.format(start=Start, end=Start+Thickness , roi_scan_num = roi_scan_num, nick=nick , ref_file=ref_file ) process_input(s, exploit_slurm_mpi = 1) def get_volume(nick = "reinterp_org" ): inputstring = """ superR_getVolume_Esynt : scalprods_address : volumes.h5:/{nick} target_address : volumes.h5:/{nick}/volumes dict_interp : interpolation.json debin : [1, 1] output_prefix : volumes/test0_ """ s=inputstring.format( nick=nick ) print ( " INPUT ", s) process_input(s, exploit_slurm_mpi = 1) def myOrder(tok): if("volume" not in tok): tokens = tok.split("_") print( tokens) return int(tokens[1])*10000+ int(tokens[2]) else: return 0 def reshuffle( volumefile = "volumes.h5", nick = None ): h5file_root = h5py.File( volumefile ,"r+" ) h5file = h5file_root[nick] scankeys = list( h5file.keys()) scankeys.sort(key=myOrder) print( scankeys) volumes = [] for k in scankeys: if k[:1]!="_": continue print( k) if "volume" in h5file[k]: volumes.append( h5file[k]["volume"] ) # volume = np.concatenate(volumes,axis=0) volume = np.array(volumes) if "concatenated_volume" in h5file: del h5file["concatenated_volume"] h5file["concatenated_volume"]=volume h5file_root.close() ## THE FOLLOWING PART IS THE RELEVANT ONE if(0): # ROI selection and reference scan select_rois(roi_scan_num=roi_scann) if(0): # SAMPLE extraction extract_sample_givenrois(roi_scan_num=roi_scann, nick_name="org", Start = first_scann, End = (first_scann+thickness*n_energies), Thickness = thickness ) if(0): # INTERPOLATION interp_file_source = h5py.File("signals.h5","r+") # i_info = InterpInfo( cenom[:,1] , interp_file_source, "org", custom_ene_list = [ 12.913005 + 2.0e-4 , 13.253006, 13.25551, 13.258008, 13.260506 , 13.263004, 13.265505 -2.0e-4 ] ) i_info = InterpInfo( cenom[:,1] , interp_file_source, "org", custom_ene_list = np.arange(13.253006, 13.265505 , 0.01/10) ) info_dict = i_info.interpola() interp_file_source.close() json.dump(info_dict,open("interpolation.json","w")) if(0): # of course we need the REFERENCE SCAN clip1= 90 clip2= 180 ## get_reference(roi_scan_num=247) get_reference(roi_scan_num=roi_scann) for other in other_rois_for_ref: os.system("cp roi_%d.h5 roi_%d.h5"%(roi_scann, other) ) if clip1 is not None: ftarget = h5py.File( "roi_%d.h5" % roi_scann ,"r+") target_group = ftarget["extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"% roi_scann ] for k in target_group.keys(): if k != "motorDict": print(" SHRINKING scan for ROI %s in file roi_%d.h5 " %( k, roi_scann )) for dsn in ["matrix", "monitor", "xscale"]: mat = target_group[k][dsn][()] del target_group[k][dsn] target_group[k][dsn] = mat[clip1:clip2] ftarget.close() for other in other_rois_for_ref: get_reference(roi_scan_num=other) ftarget = h5py.File( "roi_%d.h5" % roi_scann ,"r+") fsource = h5py.File( "roi_%d.h5" % other , "r") source_group = fsource["extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"% other ] target_group = ftarget["extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"% roi_scann ] for k in target_group.keys(): if k != "motorDict": print(" ADDING data for ROI %s from file roi_%d.h5 " %( k, other )) mat = source_group[k]["matrix"][()] if clip1 is not None: mat = mat[clip1:clip2] target_group[k]["matrix"][:] += mat print( " SUCCESS ") if(0): ## resintesi ; fit of the reponse synthetise_response( scan_address="roi_%d.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"%(roi_scann, roi_scann), target_file = "reponse.h5:/FIT" ) if(0): ## resintesi : scan rerynsthesis resynthetise_scan( old_scan_address="roi_%d.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"%(roi_scann, roi_scann), response_file = "reponse.h5:/FIT" , target_filename = "newrois.h5:/ROIS" ) if(0): ## The scala products, which define the equation to invert for start in range(first_scann,(first_scann+n_energies*thickness),thickness): get_scalars( Start = start, Thickness = thickness , roi_scan_num=roi_scann ,nick="org" , ref_file = "newrois.h5:/ROIS" ) if(1): # inversion of the equations get_volume(nick = "org") if(0): fl = glob.glob("volumes_*.h5") target = h5py.File("volumes.h5","r+" ) for fn in fl: source = h5py.File( fn ,"r" ) keylist = list( source.keys() ) for k in keylist: keylist2 = list( source[k].keys() ) for k2 in keylist2: print(" copiando ", k,k2, " da ", fn) if k2 +"/volume" in target[k]: del target[k][k2 +"/volume" ] source[k].copy( k2+"/volume" , target[k], name = k2 +"/volume" ) if(0): # putting everything in a 4D volume Dimensions : ispectra,z,y,x reshuffle( volumefile = "volumes.h5", nick = "org" ) xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/000077500000000000000000000000001412732462000205065ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/ESYNT/000077500000000000000000000000001412732462000214105ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/ESYNT/2020/000077500000000000000000000000001412732462000217735ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/ESYNT/2020/org/000077500000000000000000000000001412732462000225625ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/ESYNT/2020/org/batch_extraction_esynth1.py000066400000000000000000000454701412732462000301420ustar00rootroot00000000000000import numpy as np import h5py import glob import json BATCH_PARALLELISM = 4 cenom = np.array( [ 0, 12.91684471961497 , 1, 12.91601322225362 , 2, 12.915539860755496 , 3, 12.916480273530496 , 4, 12.915731392088265 , 5, 12.914869749579399 , 6, 12.916264935543595 , 7, 12.91573965859977 , 8, 12.914987140018741 , 9, 12.917537037788902 , 10, 12.916944061630726 , 11, 12.917218345706756 , 12, 12.915891035359477 , 13, 12.916352608181027 , 14, 12.917177018098796 , 15, 12.914005418194852 , 16, 12.915154657961107 , 17, 12.916191453728914 , 18, 12.914335816165174 , 19, 12.915077949987177 , 20, 12.915914957907148 , 21, 12.915288356811502 , 22, 12.91598889195904 , 23, 12.91695899376287 , 24, 12.916900296092969 , 25, 12.91604539858451 , 26, 12.914871297189173 , 27, 12.916231311661814 , 28, 12.915245568912306 , 29, 12.914471665531154 , 30, 12.916385293003266 , 31, 12.91497576912035 , 32, 12.914105451420639 , 33, 12.917157692493532 , 34, 12.915718809628475 , 35, 12.915646376430596 , 36, 12.91406510719366 , 37, 12.914584874090105 , 38, 12.914253022577238 , 39, 12.914765752067101 , 40, 12.913952660702538 , 41, 12.914056540248373 , 42, 12.914686992985423 , 43, 12.914358771568466 , 44, 12.914322364160563 , 45, 12.914834493680436 , 46, 12.91468849314193 , 47, 12.914392397943747 , 48, 12.91526519846475 , 49, 12.916546273857113 , 50, 12.91754238416663 , 51, 12.91462504324549 , 52, 12.915558554542379 , 53, 12.916489766393653 , 54, 12.914555294751848 , 55, 12.91532484033084 , 56, 12.916251843583483 , 57, 12.916091870082658 , 58, 12.91604809986116 , 59, 12.917105964460655 , 60, 12.916403610900606 , 61, 12.916009142769497 , 62, 12.915288063762233 , 63, 12.916156971954502 , 64, 12.91517887482311 , 65, 12.914460401828784 , 66, 12.916150384998303 , 67, 12.915275296447305 , 68, 12.914661911784687 , 69, 12.917387263528335 , 70, 12.916532490728155 , 71, 12.915658283253318 ]) cenom=np.reshape(cenom,[-1,2]) Enominal = np.median(cenom[:,1]) cenom[:,1] -= Enominal first_scann = 651 # 891 last_scann = 720 # 1015 thickness = 10 ### 25 roi_scann = 246 # 245 247 #### other_rois_for_ref = [245,247] #first_scann = 24 #last_scann = 26 n_energies = 7 # 6 import os def process_input(s, go=0, exploit_slurm_mpi = 0, stop_omp = False): open("input_tmp_%d.par"%go, "w").write(s) background_activator = "" if (go % BATCH_PARALLELISM ): background_activator = "&" prefix="" if stop_omp: prefix = prefix +"export OMP_NUM_THREADS=1 ;" if ( exploit_slurm_mpi==0 ): os.system(prefix +"mpirun -n 1 XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) elif ( exploit_slurm_mpi>0 ): os.system(prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: os.system(prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) def select_rois(roi_scan_num=6): inputstring = """ create_rois: expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' scans : [{roi_scan_num}] roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED filter_path : mask.h5:/FILTER_MASK/filter """ s=inputstring.format(roi_scan_num = roi_scan_num ) process_input(s, exploit_slurm_mpi = 0 ) def extract_sample_givenrois(roi_scan_num=roi_scann, nick_name="org", Start=first_scann, End=(first_scann+thickness*n_energies), Thickness = thickness ): inputstring = """ loadscan_2Dimages : expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED monitor_column : izero/0.000001 scan_interval : [{start}, {end}] energy_column : sty signaladdress : signals.h5:/{where}/_{start}_{end} sumto1D : 0 monitorcolumn : izero/0.000001 """ for start in range(Start,End, Thickness): s=inputstring.format(start=str(start), end=str(start+Thickness) , where= nick_name ,roi_scan_num = roi_scan_num ) process_input(s, exploit_slurm_mpi = 1) class InterpInfo_Esynt: def __init__(self, cenom, interp_file, source, custom_ene_list = None): volum_list = list(interp_file[source].keys()) scan_num_list = np.array([ int( t.split("_") [1]) for t in volum_list]) ene_list = np.array([ interp_file[source][vn]["scans"]["Scan%03d"%sn ]["motorDict"]["energy"][()] for vn,sn in zip(volum_list, scan_num_list ) ]) print ( " ecco la scannumlist " , scan_num_list) print (" ecco ene_list", ene_list) self.volum_list = volum_list self.scan_num_list = scan_num_list self.ene_list = ene_list order = np.argsort( self.ene_list ) self.ene_list = self.ene_list [order] if custom_ene_list is None: self.custom_ene_list = self.ene_list else: self.custom_ene_list = custom_ene_list self.scan_num_list = self.scan_num_list [order] self.volum_list = [ self.volum_list [ii] for ii in order ] self.interp_file=interp_file self.source= source self.cenom=cenom def interpola_Esynt(self): print ( " ECCO I DATI ") print ( self.ene_list ) print ( self.cenom ) info_dict = {} for i_intervallo in range(len(self.custom_ene_list)): info_dict[str(i_intervallo)] = {} info_dict[str(i_intervallo)]["E"] = self.custom_ene_list[ i_intervallo ] info_dict[str(i_intervallo)]["coefficients"]={} for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list )): info_dict[str(i_intervallo)]["coefficients" ][ t_vn ]={} for i_intervallo in range(len(self.custom_ene_list)-1): cE1 = self.custom_ene_list[ i_intervallo ] cE2 = self.custom_ene_list[ i_intervallo+1 ] for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list ))[0:]: for roi_num, de in enumerate( self.cenom ): if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2-cE1) info_dict[str(i_intervallo)]["coefficients" ][ str(t_vn) ][ str(roi_num) ] = alpha info_dict[str(i_intervallo+1)]["coefficients"][ str(t_vn) ][ str(roi_num) ] = 1-alpha print( info_dict) return info_dict def get_reference(roi_scan_num=roi_scann): inputstring = """ loadscan_2Dimages : expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED monitor_column : izero/0.000001 scan_interval : [{roi_scan_num},{roi_scan_num_plus1} ] signaladdress : calibration_scan isolateSpot : 6 save_also_roi : True sumto1D : 0 energycolumn : 'stx' monitorcolumn : izero/0.000001 """ s=inputstring.format( roi_scan_num= roi_scan_num, roi_scan_num_plus1= roi_scan_num+1 ) process_input( s , exploit_slurm_mpi = 1) def resynthetise_scan( old_scan_address="roi_%d.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"%(roi_scann, roi_scann), response_file = "reponse.h5" , target_filename = "newrois.h5:/ROIS/" ): inputstring = """ superR_recreate_rois : ### we have calculated the responses in responsefilename ### and we want to enlarge the scan by a margin of 3 times ### the original scan on the right and on the left ### ( so for a total of a 7 expansion factor ) responsefilename : {response_file} nex : 0 ## the old scan covered by the old rois old_scan_address : {old_scan_address} ## where new rois and bnew scan are written target_filename : {target_filename} filter_rois : 0 """ s=inputstring.format( response_file = response_file , target_filename = target_filename, old_scan_address=old_scan_address ) process_input( s , exploit_slurm_mpi = 0, stop_omp = True) def synthetise_response(scan_address=None, target_file=None): inputstring = """ superR_fit_responses : foil_scan_address : "{scan_address}" nref : 7 # the number of subdivision per pixel dimension used to # represent the optical response function at higher resolution niter_optical : 100 # the number of iterations used in the optimisation of the optical # response beta_optical : 0.1 # The L1 norm factor in the regularisation # term for the optical functions pixel_dim : 1 # The pixel response function is represented with a # pixel_dim**2 array niter_pixel : 10 # The number of iterations in the pixel response optimisation # phase. A negative number stands for ISTA, positive for FISTA beta_pixel : 0.0 # L1 factor for the pixel response regularisation ## The used trajectories are always written whith the calculated response ## They can be reloaded and used as initialization(and freezed with do_refine_trajectory : 0 ) ## Uncomment the following line if you want to reload a set of trajectories ## without this options trajectories are initialised from the spots drifts ## # reload_trajectories_file : "response.h5" filter_rois : 0 ###### ## The method first find an estimation of the foil scan trajectory on each roi ## then, based on this, obtain a fit of the optical response function ## assuming a flat pixel response. Finally the pixel response is optimised ## ## There is a final phase where a global optimisation ## is done in niter_global steps. ## ## Each step is composed of optical response fit, followed by a pixel response fit. ## If do_refine_trajectory is different from zero, the trajectory is reoptimised at each step ## niter_global : 3 ## if do_refine_trajectory=1 the start and end point of the trajectory are free ## if =2 then the start and end point are forced to a trajectory which is obtained ## from a reference scan : the foil scan may be short, then one can use the scan of ## an object to get another one : key *trajectory_reference_scan_address* ## do_refine_trajectory : 1 ## optional: only if do_refine_trajectory = 2 trajectory_reference_scansequence_address : "demo_newrois.h5:/ROI_FOIL/images/scans/" trajectory_threshold : 0.1 ## if the pixel response function is forced to be symmetrical simmetrizza : 1 ## where the found responses are written target_file : {target_file} # target_file : "fitted_responses.h5" """ s=inputstring.format( scan_address=scan_address , target_file=target_file ) process_input( s , exploit_slurm_mpi = 1, stop_omp = True) def get_scalars( Start = first_scann, Thickness = thickness , roi_scan_num=roi_scann , nick = None, ref_file=None, target_file = "volumes.h5", signals_file = "interpolated_signals.h5"): inputstring = """ superR_scal_deltaXimages_Esynt : sample_address : {signals_file}:/{nick}/_{start}_{end}/scans delta_address : {ref_file}/scans/Scan{roi_scan_num} # delta_address : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan{roi_scan_num} nbin : 1 optional_solution : target_address : {target_file}:/{nick}/_{start}_{end}/scal_prods # roi_keys : "7" """ s=inputstring.format(start=Start, end=Start+Thickness , roi_scan_num = roi_scan_num, nick=nick , ref_file = ref_file, target_file = target_file, signals_file = signals_file ) process_input(s, exploit_slurm_mpi = 0) def get_volume_Esynt(nick = "reinterp_org" , volumes_file = "volumes_esynt.h5"): inputstring = """ superR_getVolume_Esynt : scalprods_address : {volumes_file}:/{nick} target_address : volumes.h5:/{nick}/volumes dict_interp : interpolation.json debin : [1, 1] output_prefix : DATASFORCC/test0_ """ os.system("mkdir DATASFORCC") s=inputstring.format( nick=nick , volumes_file=volumes_file) print ( " INPUT ", s) process_input(s, exploit_slurm_mpi = 1) def myOrder(tok): if("volume" not in tok): tokens = tok.split("_") print( tokens) return int(tokens[1])*10000+ int(tokens[2]) else: return 0 def reshuffle( volumefile = "volumes.h5", nick = None ): h5file_root = h5py.File( volumefile ,"r+" ) h5file = h5file_root[nick] scankeys = list( h5file.keys()) scankeys.sort(key=myOrder) print( scankeys) volumes = [] for k in scankeys: if k[:1]!="_": continue print( k) if "volume" in h5file[k]: volumes.append( h5file[k]["volume"] ) # volume = np.concatenate(volumes,axis=0) volume = np.array(volumes) if "concatenated_volume" in h5file: del h5file["concatenated_volume"] h5file["concatenated_volume"]=volume h5file_root.close() ## THE FOLLOWING PART IS THE RELEVANT ONE if(0): # ROI selection and reference scan select_rois(roi_scan_num=roi_scann) if(0): # SAMPLE extraction extract_sample_givenrois(roi_scan_num=roi_scann, nick_name="org", Start = first_scann, End = (first_scann+thickness*n_energies), Thickness = thickness ) if(0): # INTERPOLATION ESYNTH interp_file_source = h5py.File("signals.h5","r+") i_info = InterpInfo_Esynt( cenom[:,1] , interp_file_source, "org", custom_ene_list = [ 2*13.253006- 13.25551 , 13.253006, 13.25551, 13.258008, 13.260506 , 13.263004, 13.265505 ] ) # i_info = InterpInfo_Esynt( cenom[:,1] , interp_file_source, "org", custom_ene_list = [ 12.913005 , 13.253006, 13.25551, 13.258008, 13.260506 , 13.263004, 13.265505 ] ) #i_info = InterpInfo_Esynt( cenom[:,1] , interp_file_source, "org", custom_ene_list = np.arange(13.253006, 13.265505 , 0.01/10) ) info_dict = i_info.interpola_Esynt() interp_file_source.close() d = info_dict for ke in d.keys(): for kv in d[ke]["coefficients"].keys(): d[ke]["coefficients"][kv]["11"]=0 json.dump(info_dict,open("interpolation.json","w")) if(0): # of course we need the REFERENCE SCAN clip1= 90 clip2= 180 ## get_reference(roi_scan_num=247) get_reference(roi_scan_num=roi_scann) for other in other_rois_for_ref: os.system("cp roi_%d.h5 roi_%d.h5"%(roi_scann, other) ) if clip1 is not None: ftarget = h5py.File( "roi_%d.h5" % roi_scann ,"r+") target_group = ftarget["extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"% roi_scann ] for k in target_group.keys(): if k != "motorDict": print(" SHRINKING scan for ROI %s in file roi_%d.h5 " %( k, roi_scann )) for dsn in ["matrix", "monitor", "xscale"]: mat = target_group[k][dsn][()] del target_group[k][dsn] target_group[k][dsn] = mat[clip1:clip2] ftarget.close() for other in other_rois_for_ref: get_reference(roi_scan_num=other) ftarget = h5py.File( "roi_%d.h5" % roi_scann ,"r+") fsource = h5py.File( "roi_%d.h5" % other , "r") source_group = fsource["extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"% other ] target_group = ftarget["extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"% roi_scann ] for k in target_group.keys(): if k != "motorDict": print(" ADDING data for ROI %s from file roi_%d.h5 " %( k, other )) mat = source_group[k]["matrix"][()] if clip1 is not None: mat = mat[clip1:clip2] target_group[k]["matrix"][:] += mat print( " SUCCESS ") if(0): ## resintesi ; fit of the reponse synthetise_response( scan_address="roi_%d.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"%(roi_scann, roi_scann), target_file = "reponse.h5:/FIT" ) if(0): ## resintesi : scan rerynthesis resynthetise_scan( old_scan_address="roi_%d.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"%(roi_scann, roi_scann), response_file = "reponse.h5:/FIT" , target_filename = "newrois.h5:/ROIS" ) ### ESYNTH if(0): ## The scala products, which define the equation to invert for start in range(first_scann,(first_scann+n_energies*thickness),thickness): get_scalars( Start = start, Thickness = thickness , roi_scan_num=roi_scann , nick="org" , ref_file = "newrois.h5:/ROIS" , signals_file = "signals.h5", target_file="volumes.h5") ### ESYNTH if(1): # inversion of the equations with E synthesis across the spectra get_volume_Esynt(nick = "org", volumes_file="volumes.h5") xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/ESYNT/2020/org/extract_volperroi.py000066400000000000000000000004051412732462000267060ustar00rootroot00000000000000 from h5py import * from numpy import * f=File("volumes.h5","r")["org"] keys=list(f.keys()) keys.sort() print( keys) res=[] for k in keys[1:]: res.append(f[k]["scal_prods"]["scalDS"][()]) res=swapaxes(res,0,1) File("scalpro_by_roi.h5","w")["vol"] = res xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/ESYNT/2020/org/setta_to_0.py000066400000000000000000000003211412732462000251710ustar00rootroot00000000000000import json d = json.load(open("interpolation.json","r")) for ke in d.keys(): for kv in d[ke]["coefficients"].keys(): d[ke]["coefficients"][kv]["11"]=0 json.dump(d,open( "newinterp.json" ,"w")) xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/ESYNT/2020/org/toh5.py000066400000000000000000000001751412732462000240160ustar00rootroot00000000000000from numpy import * from h5py import * d=fromfile("solution.raw","f") d.shape = 7, 10,41,90 File("solution.h5","w")["vol"]=d xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/ESYNT/2020/org/toh5_1.py000066400000000000000000000001761412732462000242370ustar00rootroot00000000000000from numpy import * from h5py import * d=fromfile("solution.raw","f") d.shape = 25, 1,41,241 File("solution.h5","w")["vol"]=d xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/INTERP/000077500000000000000000000000001412732462000215075ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/INTERP/2020/000077500000000000000000000000001412732462000220725ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/INTERP/2020/org/000077500000000000000000000000001412732462000226615ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/fitcc/batchs/INTERP/2020/org/batch_extraction_interp.py000066400000000000000000000602641412732462000301450ustar00rootroot00000000000000import numpy as np import h5py import glob import json import numpy as np import math BATCH_PARALLELISM = 1 cenom = np.array( [ 0, 12.91684471961497 , 1, 12.91601322225362 , 2, 12.915539860755496 , 3, 12.916480273530496 , 4, 12.915731392088265 , 5, 12.914869749579399 , 6, 12.916264935543595 , 7, 12.91573965859977 , 8, 12.914987140018741 , 9, 12.917537037788902 , 10, 12.916944061630726 , 11, 12.917218345706756 , 12, 12.915891035359477 , 13, 12.916352608181027 , 14, 12.917177018098796 , 15, 12.914005418194852 , 16, 12.915154657961107 , 17, 12.916191453728914 , 18, 12.914335816165174 , 19, 12.915077949987177 , 20, 12.915914957907148 , 21, 12.915288356811502 , 22, 12.91598889195904 , 23, 12.91695899376287 , 24, 12.916900296092969 , 25, 12.91604539858451 , 26, 12.914871297189173 , 27, 12.916231311661814 , 28, 12.915245568912306 , 29, 12.914471665531154 , 30, 12.916385293003266 , 31, 12.91497576912035 , 32, 12.914105451420639 , 33, 12.917157692493532 , 34, 12.915718809628475 , 35, 12.915646376430596 , 36, 12.91406510719366 , 37, 12.914584874090105 , 38, 12.914253022577238 , 39, 12.914765752067101 , 40, 12.913952660702538 , 41, 12.914056540248373 , 42, 12.914686992985423 , 43, 12.914358771568466 , 44, 12.914322364160563 , 45, 12.914834493680436 , 46, 12.91468849314193 , 47, 12.914392397943747 , 48, 12.91526519846475 , 49, 12.916546273857113 , 50, 12.91754238416663 , 51, 12.91462504324549 , 52, 12.915558554542379 , 53, 12.916489766393653 , 54, 12.914555294751848 , 55, 12.91532484033084 , 56, 12.916251843583483 , 57, 12.916091870082658 , 58, 12.91604809986116 , 59, 12.917105964460655 , 60, 12.916403610900606 , 61, 12.916009142769497 , 62, 12.915288063762233 , 63, 12.916156971954502 , 64, 12.91517887482311 , 65, 12.914460401828784 , 66, 12.916150384998303 , 67, 12.915275296447305 , 68, 12.914661911784687 , 69, 12.917387263528335 , 70, 12.916532490728155 , 71, 12.915658283253318 ]) cenom=np.reshape(cenom,[-1,2]) Enominal = np.median(cenom[:,1]) cenom[:,1] -= Enominal first_scann = 651 # 891 last_scann = 720 # 1015 thickness = 10 ### 25 roi_scann = 246 # 245 247 #### other_rois_for_ref = [245,247] #first_scann = 24 #last_scann = 26 n_energies = 7 # 6 import os def process_input(s, go=0, exploit_slurm_mpi = 0, stop_omp = False): open("input_tmp_%d.par"%go, "w").write(s) background_activator = "" if (go % BATCH_PARALLELISM ): background_activator = "&" prefix="" if stop_omp: prefix = prefix +"export OMP_NUM_THREADS=1 ;" if ( exploit_slurm_mpi==0 ): os.system(prefix +"mpirun -n 1 XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) elif ( exploit_slurm_mpi>0 ): os.system(prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: os.system(prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) def collect_factors(pattern="factors_*_*", newfile="newfactors.json"): files = glob.glob(pattern) indexes = [ int(s.split("_")[1]) for s in files ] order = np.argsort(indexes) files = [files[i] for i in order] print( files) files = files[1:-1] result = {} result2 = {} Nkeys = None for f in files: factors = json.load(open(f,"r")) if Nkeys is None: Nkeys = len(list(factors.keys())) assert(Nkeys == len(list(factors.keys())) ) for k,val in factors.items(): if k not in result: result[k] = 0.0 result2[k] = 0.0 result[k] += factors[k]/ len(files) result2[k] += (factors[k]*factors[k])/ len(files) json.dump(result,open(newfile,"w") ) keys = list(result.keys() ) keys.sort(key=int) for k in keys: print( k , " ", result[k] , " " , math.sqrt( result2[k] - result[k]*result[k] ) / result[k] ) def select_rois(roi_scan_num=6): inputstring = """ create_rois: expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' scans : [{roi_scan_num}] roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED filter_path : mask.h5:/FILTER_MASK/filter """ s=inputstring.format(roi_scan_num = roi_scan_num ) process_input(s, exploit_slurm_mpi = 0 ) def extract_sample_givenrois(roi_scan_num=roi_scann, nick_name="org", Start=first_scann, End=(first_scann+thickness*n_energies), Thickness = thickness ): inputstring = """ loadscan_2Dimages : expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' # roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED roiaddress : "newrois.h5:/ROIS" monitor_column : izero/0.000001 scan_interval : [{start}, {end}] energy_column : sty signaladdress : signals.h5:/{where}/_{start}_{end} sumto1D : 0 monitorcolumn : izero/0.000001 """ for start in range(Start,End, Thickness): s=inputstring.format(start=str(start), end=str(start+Thickness) , where= nick_name ,roi_scan_num = roi_scan_num ) process_input(s, exploit_slurm_mpi = 0) class InterpInfo: def __init__(self, cenom, interp_file, source, interp_file_target): volum_list = list(interp_file[source].keys()) scan_num_list = np.array([ int( t.split("_") [1]) for t in volum_list]) ene_list = np.array([ interp_file[source][vn]["scans"]["Scan%03d"%sn ]["motorDict"]["energy"].value for vn,sn in zip(volum_list, scan_num_list ) ]) print ( " ecco la scannumlist " , scan_num_list) print (" ecco ene_list", ene_list) self.volum_list = volum_list self.scan_num_list = scan_num_list self.ene_list = ene_list order = np.argsort( self.ene_list ) self.ene_list = self.ene_list [order] self.scan_num_list = self.scan_num_list [order] self.volum_list = [ self.volum_list [ii] for ii in order ] self.interp_file=interp_file self.interp_file_target=interp_file_target self.source= source self.target = source self.cenom=cenom def interpola(self): print ( " ECCO I DATI ") print ( self.ene_list ) print ( self.cenom ) # raise for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list ))[0:]: rois_coeffs={} for roi_num, de in enumerate( self.cenom ): print ( roi_num, "===== " , t_ene+de , self.ene_list .min() , t_ene+de , self.ene_list .max() ) if t_ene+de < self.ene_list .min() or t_ene+de > self.ene_list .max(): continue print ( " CONTINUO ", t_ene+de, self.ene_list .min() ,self.ene_list .max() ) i0 = np.searchsorted( self.ene_list , t_ene+de )-1 assert(i0>=0) i1=i0+1 print (i0, i1, len(self.ene_list)) print (self.ene_list) assert(i1 #include #include #include #include #include #include #include #include #include #include #define assertm(exp, msg) assert(((void)msg, exp)) namespace h5 = HighFive; struct DimPars_struct { size_t NE ; size_t NV ; size_t NROI; size_t DIMZ; size_t DIMY; size_t DIMX; int ZSTART ; int ZEND ; }; typedef DimPars_struct DimPars; struct InputFileNames_struct { std::string DSname; std::string DDname; std::string SSname; std::string COEFFSname; }; typedef InputFileNames_struct InputFileNames; float * read_volume(std::string fn, std::vector &dims) { float * result; std::string testh5_name(fn) ; h5::File test_file( testh5_name , h5::File::ReadOnly); h5::DataSet test_dataset = test_file.getDataSet("data"); dims = test_dataset.getDimensions(); size_t tot = 1; for(size_t i=0; i< dims.size(); i++) tot *= dims[i]; result = (float*) malloc( tot * sizeof(float)); test_dataset.read( result , h5::AtomicType() ) ; return result; } void save_volume(std::string fn, float * X , std::vector dims) { std::string testh5_name(fn) ; h5::File test_file( testh5_name , h5::File::ReadWrite | h5::File::Create | h5::File::Truncate ); h5::DataSet dataset = test_file.createDataSet("data", h5::DataSpace(dims)); dataset.write_raw(X); } class SolVol { public: float *__restrict__ ptr; }; class ProjectionVol { public: float *__restrict__ ptr; }; class CoefficientsVol { public: float * __restrict__ ptr; }; class FreeFactsVol { public: float * __restrict__ ptr; }; // DS SHAPE (7, 72, 10, 41, 241) // DD SHAPE (7, 72) // SS SHAPE (7, 72, 241, 241) class Problem { public: Problem(InputFileNames &if_names_a, DimPars & dimpars_a ){ this->if_names = if_names_a; this->dimpars = dimpars_a; { std::vector dims ; this->DS = read_volume( if_names.DSname, dims) ; dimpars.NV = dims[0] ; dimpars.NROI = dims[1] ; dimpars.DIMZ = dims[2] ; dimpars.DIMY = dims[3] ; dimpars.DIMX = dims[4] ; } if(dimpars.ZSTART>=0) { int dimz = 1+ dimpars.ZEND - dimpars.ZSTART ; float *ds = (float*) malloc(size_t(dimpars.NV)*size_t(dimpars.NROI)*size_t(dimz)* size_t(dimpars.DIMY)* size_t(dimpars.DIMX) * sizeof(float) ) ; size_t BLOCK = size_t(dimpars.DIMZ)* size_t(dimpars.DIMY)* size_t(dimpars.DIMX ) ; size_t block = size_t( dimz)* size_t(dimpars.DIMY)* size_t(dimpars.DIMX ) ; size_t offset = size_t(dimpars.ZSTART)* size_t(dimpars.DIMY)* size_t(dimpars.DIMX) ; for(int iv=0; iv< (int) dimpars.NV; iv++) { for(int ir=0; ir< (int) dimpars.NROI; ir++) { memcpy( ds + (iv*dimpars.NROI+ir)*block , DS + (iv*dimpars.NROI+ir)*BLOCK+offset, block*sizeof(float) ); } } dimpars.DIMZ = dimz ; free( DS ) ; this->DS = ds ; } { std::vector dims ; this->DD = read_volume( if_names.DDname, dims) ; assert( dims[0] == dimpars.NV ) ; assert( dims[1] == dimpars.NROI ) ; } { std::vector dims ; this->SS = read_volume( if_names.SSname, dims) ; assert( dims[0] == dimpars.NROI ) ; assert( dims[1] == dimpars.DIMX ) ; assert( dims[2] == dimpars.DIMX ) ; } { std::vector dims ; this->coefficients = read_volume( if_names.COEFFSname, dims) ; dimpars.NE = dims[0] ; assert( dims[1] == dimpars.NV ) ; assert( dims[2] == dimpars.NROI ) ; } /*{ int cnt=0; for(int iE=0; iE< dimpars.NE; iE++) { for(int iV=0; iV< dimpars.NV; iV++) { for(int iR=0; iR< dimpars.NROI; iR++) { this->coefficients[cnt] = ( iE==iV ); if(iR!=1) this->coefficients[cnt] = 0; cnt++; } } } } */ DimPars *d = & dimpars; this->Matff = (float*) malloc( d->NE * d->NE * d->NROI * sizeof(float) ) ; this->MatSffF2 = (float*) malloc( d->NE*d->NE* d->DIMX*d->DIMX * sizeof(float) ) ; this->setMatff(); this->VectA = (float*) malloc( d->NE * d->DIMZ * d->DIMY * d->DIMX * sizeof(float) ) ; this->MatR = (float*) malloc( d->NROI * sizeof(float) ) ; this->VectR = (float*) malloc( d->NROI * sizeof(float) ) ; } #define SOL_addr( iE, iz, iy , ix ) ((( (iE)*DIMZ + (iz))*DIMY+ (iy))*DIMX+ (ix)) #define SS_addr( iroi, ix1, ix2 ) (( (iroi)*DIMX+ (ix1))*DIMX+ (ix2)) #define PRO_addr( iV, iroi, iz, iy, ix ) ((( ( (iV)*NROI +iroi )*DIMZ + (iz))*DIMY+ (iy))*DIMX+ (ix)) void setVectA_and_Mat(SolVol target, FreeFactsVol F ) { setMatSffF2(F) ; setVectA( target, F ) ; }; void setVectA(SolVol target, FreeFactsVol F ) { int NE = dimpars.NE; int NV = dimpars.NV; int NROI = dimpars.NROI; int DIMZ = dimpars.DIMZ; int DIMY = dimpars.DIMY; int DIMX = dimpars.DIMX; #pragma omp parallel for for(int ie=0 ; ie dims = {size_t(dimpars.NE),size_t(dimpars.DIMZ), size_t(dimpars.DIMY), size_t(dimpars.DIMX) }; save_volume( name,X.ptr, dims); }; template size_t size() { return 0; }; void gradReg_2_Add(SolVol target, SolVol source , float betaE, float betaV) { int NE = dimpars.NE; int DIMZ = dimpars.DIMZ; int DIMY = dimpars.DIMY; int DIMX = dimpars.DIMX; if( betaE==0 && betaV ==0 ) return; for( int iE=0; iE0) { add += betaE*( source.ptr[SOL_addr( iE,iz,iy,ix )]-source.ptr[ SOL_addr( iE-1,iz,iy,ix ) ] ) ; } if(iz0) { add += betaV*( source.ptr[SOL_addr( iE,iz,iy,ix )]-source.ptr[ SOL_addr( iE,iz-1,iy,ix ) ] ) ; } if(iy0) { add += betaV*( source.ptr[SOL_addr( iE,iz,iy,ix )]-source.ptr[ SOL_addr( iE,iz,iy-1,ix ) ] ) ; } if(ix0) { add += betaV*( source.ptr[SOL_addr( iE,iz,iy,ix )]-source.ptr[ SOL_addr( iE,iz,iy,ix-1 ) ] ) ; } target.ptr[SOL_addr( iE,iz,iy,ix )] += add; } } } } } void gradReg_1_Add(SolVol target, SolVol source , float betaE, float betaV) { int NE = dimpars.NE; int DIMZ = dimpars.DIMZ; int DIMY = dimpars.DIMY; int DIMX = dimpars.DIMX; if( betaE==0 && betaV ==0 ) return; for( int iE=0; iE0) { add += copysign( betaE,( source.ptr[SOL_addr( iE,iz,iy,ix )]-source.ptr[ SOL_addr( iE-1,iz,iy,ix ) ] ) ); } if(iz0) { add += copysign( betaV,( source.ptr[SOL_addr( iE,iz,iy,ix )]-source.ptr[ SOL_addr( iE,iz-1,iy,ix ) ] ) ); } if(iy0) { add += copysign( betaV,( source.ptr[SOL_addr( iE,iz,iy,ix )]-source.ptr[ SOL_addr( iE,iz,iy-1,ix ) ] ) ); } if(ix0) { add += copysign( betaV,( source.ptr[SOL_addr( iE,iz,iy,ix )]-source.ptr[ SOL_addr( iE,iz,iy,ix-1 ) ] ) ); } target.ptr[SOL_addr( iE,iz,iy,ix )] += add; } } } } } template void allocate(T &vol) { vol.ptr = (float *) malloc( size() * sizeof(float) ); }; template void settozero(T vol) { memset( vol.ptr, 0, size() * sizeof(float) ); }; template void settoVal(T __restrict__ vol, float val) { size_t numels = this->size(); for(size_t i = 0; i void copy(T __restrict__ target, T __restrict__ source ) { size_t numels = this->size(); for(size_t i = 0; i void Scal( T source , float alpha) { size_t numels = this->size(); for(size_t i = 0; i void copyScal(T target, T source , float alpha) { size_t numels = this->size(); for(size_t i = 0; i void copyScal(T target, float * source , float alpha) { size_t numels = this->size(); for(size_t i = 0; i double scalar(T target, T source ) { size_t numels = this->size(); double res=0.0; for(size_t i = 0; i void AXPBYCR(float a , T vol_a, float b, T vol_b , float c, T __restrict__ vol_res ){ size_t numels = this->size(); for(size_t i = 0; i size_t Problem::size () { return size_t(dimpars.NE)*size_t(dimpars.DIMZ)*size_t(dimpars.DIMY)*size_t(dimpars.DIMX) ; }; template<> size_t Problem::size() { return size_t(dimpars.NV)*size_t(dimpars.NROI)*size_t(dimpars.DIMZ)* size_t(dimpars.DIMY)* size_t(dimpars.DIMX) ; }; template<> size_t Problem::size() { return size_t(dimpars.NROI) ; }; int main(int argc, char ** argv) { int index_input; { const char * usage= "frsv [-h] input_file \n" " options :\n" " -h prints this help \n" " arguments :\n" " input_file : a yaml file containing \n" " DSname : test0_DS.h5 \n" " DDname : test0_DD.h5 \n" " SSname : test0_SS.h5 \n" " COEFFSname : coefficients.h5 \n"; int hflag = 0; // char *cvalue = NULL; int c; opterr = 0; // while ((c = getopt (argc, argv, "hc:")) != -1) { while ((c = getopt (argc, argv, "h")) != -1) { switch (c) { case 'h': hflag = 1; break; // case 'c': // cvalue = optarg; // break; case '?': if (optopt == 'c') fprintf (stderr, "Option -%c requires an argument.\n", optopt); else if (isprint (optopt)) fprintf (stderr, "Unknown option `-%c'.\n", optopt); else fprintf (stderr, "Unknown option character `\\x%x'.\n", optopt); return 1; default: abort (); } } index_input = optind ; if (hflag || index_input != (argc-1) ) { std::cout << " USAGE \n" << usage ; } } YAML::Node mockup_config; mockup_config = YAML::LoadFile(argv[index_input]); assert( mockup_config["DSname"] ) ; assert( mockup_config["DDname"] ) ; assert( mockup_config["SSname"] ) ; assert( mockup_config["COEFFSname"] ) ; DimPars dimpars ; dimpars.ZSTART = -1; dimpars.ZEND = 100000 ; if( mockup_config["ZSTART"] ) { dimpars.ZSTART = mockup_config["ZSTART"].as(); dimpars.ZEND = mockup_config["ZEND" ].as(); } ; float betaE = 0.0, betaV = 0.0 ; if( mockup_config["betaE"] ) { betaE = mockup_config["betaE"].as(); } ; if( mockup_config["betaV"] ) { betaV = mockup_config["betaV"].as(); } ; boost::filesystem::path p(argv[index_input]); std::string dirname = p.parent_path().string()+"/"; InputFileNames if_names = { dirname + mockup_config["DSname"].as() , dirname + mockup_config["DDname"].as() , dirname + mockup_config["SSname"].as(), dirname + mockup_config["COEFFSname"].as() }; Problem pb( if_names, dimpars ); SolVol X, grad , XvectA, Xtmp; pb.allocate(X); pb.allocate(Xtmp); pb.allocate(grad); pb.allocate(XvectA); ProjectionVol Perror; pb.allocate(Perror); FreeFactsVol ffacts,fftmp; pb.allocate(ffacts); pb.allocate(fftmp); pb.settoVal(ffacts ,1.0f); /* ffacts.ptr[0]=0; ffacts.ptr[2]=0; ffacts.ptr[3]=0; */ { double norma = sqrt(pb.scalar(ffacts, ffacts)); pb.Scal( ffacts,1.0/norma ) ; } pb.settoVal(X ,0.0f); for(int iter_c = 0; iter_c<10; iter_c++) { pb.setVectA_and_Mat( XvectA, ffacts ) ; float Lip=1.0; pb.settoVal(Xtmp ,1.0f); for(int i=0; i< 10; i++) { if(0) { pb.copyScal(fftmp, ffacts, 1.0); pb.setFreeFacts( fftmp , X) ; } pb.applyMatA(grad , Xtmp); pb.gradReg_2_Add(grad, Xtmp , betaE, betaV) ; double norma = sqrt(pb.scalar(grad, grad )); pb.copyScal( Xtmp, grad,1.0/norma ) ; printf("Norma Lipschitz %e\n", norma); Lip = norma*2; } for(int iter = 0; iter<40; iter++) { pb.settozero(grad); pb.applyMatA(grad , X); pb.gradReg_2_Add(grad, X , betaE, betaV ) ; pb.AXPBYCR( -1.0/Lip, grad, +1.0/Lip, XvectA, 1.0, X); // pb.project_solution(X); double merit = pb.scalar(grad, grad) + pb.scalar( XvectA, XvectA) - 2*pb.scalar(grad, XvectA); printf(" iter %d %e\n", iter, merit); } pb.save(X, "solution.h5"); pb.setFreeFacts( ffacts , X) ; { double norma = sqrt(pb.scalar(ffacts, ffacts)); pb.Scal( ffacts,1.0/norma ) ; } printf(" set ok \n"); } pb.save(X, "solution.h5"); } xrstools-0.15.0+git20210910+c147919d/fitcc/scripts/000077500000000000000000000000001412732462000207315ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/fitcc/scripts/ESYNT/000077500000000000000000000000001412732462000216335ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/fitcc/scripts/ESYNT/batch_extraction_esynth1.py000066400000000000000000000454701412732462000272130ustar00rootroot00000000000000import numpy as np import h5py import glob import json BATCH_PARALLELISM = 4 cenom = np.array( [ 0, 12.91684471961497 , 1, 12.91601322225362 , 2, 12.915539860755496 , 3, 12.916480273530496 , 4, 12.915731392088265 , 5, 12.914869749579399 , 6, 12.916264935543595 , 7, 12.91573965859977 , 8, 12.914987140018741 , 9, 12.917537037788902 , 10, 12.916944061630726 , 11, 12.917218345706756 , 12, 12.915891035359477 , 13, 12.916352608181027 , 14, 12.917177018098796 , 15, 12.914005418194852 , 16, 12.915154657961107 , 17, 12.916191453728914 , 18, 12.914335816165174 , 19, 12.915077949987177 , 20, 12.915914957907148 , 21, 12.915288356811502 , 22, 12.91598889195904 , 23, 12.91695899376287 , 24, 12.916900296092969 , 25, 12.91604539858451 , 26, 12.914871297189173 , 27, 12.916231311661814 , 28, 12.915245568912306 , 29, 12.914471665531154 , 30, 12.916385293003266 , 31, 12.91497576912035 , 32, 12.914105451420639 , 33, 12.917157692493532 , 34, 12.915718809628475 , 35, 12.915646376430596 , 36, 12.91406510719366 , 37, 12.914584874090105 , 38, 12.914253022577238 , 39, 12.914765752067101 , 40, 12.913952660702538 , 41, 12.914056540248373 , 42, 12.914686992985423 , 43, 12.914358771568466 , 44, 12.914322364160563 , 45, 12.914834493680436 , 46, 12.91468849314193 , 47, 12.914392397943747 , 48, 12.91526519846475 , 49, 12.916546273857113 , 50, 12.91754238416663 , 51, 12.91462504324549 , 52, 12.915558554542379 , 53, 12.916489766393653 , 54, 12.914555294751848 , 55, 12.91532484033084 , 56, 12.916251843583483 , 57, 12.916091870082658 , 58, 12.91604809986116 , 59, 12.917105964460655 , 60, 12.916403610900606 , 61, 12.916009142769497 , 62, 12.915288063762233 , 63, 12.916156971954502 , 64, 12.91517887482311 , 65, 12.914460401828784 , 66, 12.916150384998303 , 67, 12.915275296447305 , 68, 12.914661911784687 , 69, 12.917387263528335 , 70, 12.916532490728155 , 71, 12.915658283253318 ]) cenom=np.reshape(cenom,[-1,2]) Enominal = np.median(cenom[:,1]) cenom[:,1] -= Enominal first_scann = 651 # 891 last_scann = 720 # 1015 thickness = 10 ### 25 roi_scann = 246 # 245 247 #### other_rois_for_ref = [245,247] #first_scann = 24 #last_scann = 26 n_energies = 7 # 6 import os def process_input(s, go=0, exploit_slurm_mpi = 0, stop_omp = False): open("input_tmp_%d.par"%go, "w").write(s) background_activator = "" if (go % BATCH_PARALLELISM ): background_activator = "&" prefix="" if stop_omp: prefix = prefix +"export OMP_NUM_THREADS=1 ;" if ( exploit_slurm_mpi==0 ): os.system(prefix +"mpirun -n 1 XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) elif ( exploit_slurm_mpi>0 ): os.system(prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: os.system(prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) def select_rois(roi_scan_num=6): inputstring = """ create_rois: expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' scans : [{roi_scan_num}] roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED filter_path : mask.h5:/FILTER_MASK/filter """ s=inputstring.format(roi_scan_num = roi_scan_num ) process_input(s, exploit_slurm_mpi = 0 ) def extract_sample_givenrois(roi_scan_num=roi_scann, nick_name="org", Start=first_scann, End=(first_scann+thickness*n_energies), Thickness = thickness ): inputstring = """ loadscan_2Dimages : expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED monitor_column : izero/0.000001 scan_interval : [{start}, {end}] energy_column : sty signaladdress : signals.h5:/{where}/_{start}_{end} sumto1D : 0 monitorcolumn : izero/0.000001 """ for start in range(Start,End, Thickness): s=inputstring.format(start=str(start), end=str(start+Thickness) , where= nick_name ,roi_scan_num = roi_scan_num ) process_input(s, exploit_slurm_mpi = 1) class InterpInfo_Esynt: def __init__(self, cenom, interp_file, source, custom_ene_list = None): volum_list = list(interp_file[source].keys()) scan_num_list = np.array([ int( t.split("_") [1]) for t in volum_list]) ene_list = np.array([ interp_file[source][vn]["scans"]["Scan%03d"%sn ]["motorDict"]["energy"][()] for vn,sn in zip(volum_list, scan_num_list ) ]) print ( " ecco la scannumlist " , scan_num_list) print (" ecco ene_list", ene_list) self.volum_list = volum_list self.scan_num_list = scan_num_list self.ene_list = ene_list order = np.argsort( self.ene_list ) self.ene_list = self.ene_list [order] if custom_ene_list is None: self.custom_ene_list = self.ene_list else: self.custom_ene_list = custom_ene_list self.scan_num_list = self.scan_num_list [order] self.volum_list = [ self.volum_list [ii] for ii in order ] self.interp_file=interp_file self.source= source self.cenom=cenom def interpola_Esynt(self): print ( " ECCO I DATI ") print ( self.ene_list ) print ( self.cenom ) info_dict = {} for i_intervallo in range(len(self.custom_ene_list)): info_dict[str(i_intervallo)] = {} info_dict[str(i_intervallo)]["E"] = self.custom_ene_list[ i_intervallo ] info_dict[str(i_intervallo)]["coefficients"]={} for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list )): info_dict[str(i_intervallo)]["coefficients" ][ t_vn ]={} for i_intervallo in range(len(self.custom_ene_list)-1): cE1 = self.custom_ene_list[ i_intervallo ] cE2 = self.custom_ene_list[ i_intervallo+1 ] for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list ))[0:]: for roi_num, de in enumerate( self.cenom ): if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2-cE1) info_dict[str(i_intervallo)]["coefficients" ][ str(t_vn) ][ str(roi_num) ] = alpha info_dict[str(i_intervallo+1)]["coefficients"][ str(t_vn) ][ str(roi_num) ] = 1-alpha print( info_dict) return info_dict def get_reference(roi_scan_num=roi_scann): inputstring = """ loadscan_2Dimages : expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED monitor_column : izero/0.000001 scan_interval : [{roi_scan_num},{roi_scan_num_plus1} ] signaladdress : calibration_scan isolateSpot : 6 save_also_roi : True sumto1D : 0 energycolumn : 'stx' monitorcolumn : izero/0.000001 """ s=inputstring.format( roi_scan_num= roi_scan_num, roi_scan_num_plus1= roi_scan_num+1 ) process_input( s , exploit_slurm_mpi = 1) def resynthetise_scan( old_scan_address="roi_%d.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"%(roi_scann, roi_scann), response_file = "reponse.h5" , target_filename = "newrois.h5:/ROIS/" ): inputstring = """ superR_recreate_rois : ### we have calculated the responses in responsefilename ### and we want to enlarge the scan by a margin of 3 times ### the original scan on the right and on the left ### ( so for a total of a 7 expansion factor ) responsefilename : {response_file} nex : 0 ## the old scan covered by the old rois old_scan_address : {old_scan_address} ## where new rois and bnew scan are written target_filename : {target_filename} filter_rois : 0 """ s=inputstring.format( response_file = response_file , target_filename = target_filename, old_scan_address=old_scan_address ) process_input( s , exploit_slurm_mpi = 0, stop_omp = True) def synthetise_response(scan_address=None, target_file=None): inputstring = """ superR_fit_responses : foil_scan_address : "{scan_address}" nref : 7 # the number of subdivision per pixel dimension used to # represent the optical response function at higher resolution niter_optical : 100 # the number of iterations used in the optimisation of the optical # response beta_optical : 0.1 # The L1 norm factor in the regularisation # term for the optical functions pixel_dim : 1 # The pixel response function is represented with a # pixel_dim**2 array niter_pixel : 10 # The number of iterations in the pixel response optimisation # phase. A negative number stands for ISTA, positive for FISTA beta_pixel : 0.0 # L1 factor for the pixel response regularisation ## The used trajectories are always written whith the calculated response ## They can be reloaded and used as initialization(and freezed with do_refine_trajectory : 0 ) ## Uncomment the following line if you want to reload a set of trajectories ## without this options trajectories are initialised from the spots drifts ## # reload_trajectories_file : "response.h5" filter_rois : 0 ###### ## The method first find an estimation of the foil scan trajectory on each roi ## then, based on this, obtain a fit of the optical response function ## assuming a flat pixel response. Finally the pixel response is optimised ## ## There is a final phase where a global optimisation ## is done in niter_global steps. ## ## Each step is composed of optical response fit, followed by a pixel response fit. ## If do_refine_trajectory is different from zero, the trajectory is reoptimised at each step ## niter_global : 3 ## if do_refine_trajectory=1 the start and end point of the trajectory are free ## if =2 then the start and end point are forced to a trajectory which is obtained ## from a reference scan : the foil scan may be short, then one can use the scan of ## an object to get another one : key *trajectory_reference_scan_address* ## do_refine_trajectory : 1 ## optional: only if do_refine_trajectory = 2 trajectory_reference_scansequence_address : "demo_newrois.h5:/ROI_FOIL/images/scans/" trajectory_threshold : 0.1 ## if the pixel response function is forced to be symmetrical simmetrizza : 1 ## where the found responses are written target_file : {target_file} # target_file : "fitted_responses.h5" """ s=inputstring.format( scan_address=scan_address , target_file=target_file ) process_input( s , exploit_slurm_mpi = 1, stop_omp = True) def get_scalars( Start = first_scann, Thickness = thickness , roi_scan_num=roi_scann , nick = None, ref_file=None, target_file = "volumes.h5", signals_file = "interpolated_signals.h5"): inputstring = """ superR_scal_deltaXimages_Esynt : sample_address : {signals_file}:/{nick}/_{start}_{end}/scans delta_address : {ref_file}/scans/Scan{roi_scan_num} # delta_address : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan{roi_scan_num} nbin : 1 optional_solution : target_address : {target_file}:/{nick}/_{start}_{end}/scal_prods # roi_keys : "7" """ s=inputstring.format(start=Start, end=Start+Thickness , roi_scan_num = roi_scan_num, nick=nick , ref_file = ref_file, target_file = target_file, signals_file = signals_file ) process_input(s, exploit_slurm_mpi = 0) def get_volume_Esynt(nick = "reinterp_org" , volumes_file = "volumes_esynt.h5"): inputstring = """ superR_getVolume_Esynt : scalprods_address : {volumes_file}:/{nick} target_address : volumes.h5:/{nick}/volumes dict_interp : interpolation.json debin : [1, 1] output_prefix : DATASFORCC/test0_ """ os.system("mkdir DATASFORCC") s=inputstring.format( nick=nick , volumes_file=volumes_file) print ( " INPUT ", s) process_input(s, exploit_slurm_mpi = 1) def myOrder(tok): if("volume" not in tok): tokens = tok.split("_") print( tokens) return int(tokens[1])*10000+ int(tokens[2]) else: return 0 def reshuffle( volumefile = "volumes.h5", nick = None ): h5file_root = h5py.File( volumefile ,"r+" ) h5file = h5file_root[nick] scankeys = list( h5file.keys()) scankeys.sort(key=myOrder) print( scankeys) volumes = [] for k in scankeys: if k[:1]!="_": continue print( k) if "volume" in h5file[k]: volumes.append( h5file[k]["volume"] ) # volume = np.concatenate(volumes,axis=0) volume = np.array(volumes) if "concatenated_volume" in h5file: del h5file["concatenated_volume"] h5file["concatenated_volume"]=volume h5file_root.close() ## THE FOLLOWING PART IS THE RELEVANT ONE if(0): # ROI selection and reference scan select_rois(roi_scan_num=roi_scann) if(0): # SAMPLE extraction extract_sample_givenrois(roi_scan_num=roi_scann, nick_name="org", Start = first_scann, End = (first_scann+thickness*n_energies), Thickness = thickness ) if(0): # INTERPOLATION ESYNTH interp_file_source = h5py.File("signals.h5","r+") i_info = InterpInfo_Esynt( cenom[:,1] , interp_file_source, "org", custom_ene_list = [ 2*13.253006- 13.25551 , 13.253006, 13.25551, 13.258008, 13.260506 , 13.263004, 13.265505 ] ) # i_info = InterpInfo_Esynt( cenom[:,1] , interp_file_source, "org", custom_ene_list = [ 12.913005 , 13.253006, 13.25551, 13.258008, 13.260506 , 13.263004, 13.265505 ] ) #i_info = InterpInfo_Esynt( cenom[:,1] , interp_file_source, "org", custom_ene_list = np.arange(13.253006, 13.265505 , 0.01/10) ) info_dict = i_info.interpola_Esynt() interp_file_source.close() d = info_dict for ke in d.keys(): for kv in d[ke]["coefficients"].keys(): d[ke]["coefficients"][kv]["11"]=0 json.dump(info_dict,open("interpolation.json","w")) if(0): # of course we need the REFERENCE SCAN clip1= 90 clip2= 180 ## get_reference(roi_scan_num=247) get_reference(roi_scan_num=roi_scann) for other in other_rois_for_ref: os.system("cp roi_%d.h5 roi_%d.h5"%(roi_scann, other) ) if clip1 is not None: ftarget = h5py.File( "roi_%d.h5" % roi_scann ,"r+") target_group = ftarget["extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"% roi_scann ] for k in target_group.keys(): if k != "motorDict": print(" SHRINKING scan for ROI %s in file roi_%d.h5 " %( k, roi_scann )) for dsn in ["matrix", "monitor", "xscale"]: mat = target_group[k][dsn][()] del target_group[k][dsn] target_group[k][dsn] = mat[clip1:clip2] ftarget.close() for other in other_rois_for_ref: get_reference(roi_scan_num=other) ftarget = h5py.File( "roi_%d.h5" % roi_scann ,"r+") fsource = h5py.File( "roi_%d.h5" % other , "r") source_group = fsource["extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"% other ] target_group = ftarget["extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"% roi_scann ] for k in target_group.keys(): if k != "motorDict": print(" ADDING data for ROI %s from file roi_%d.h5 " %( k, other )) mat = source_group[k]["matrix"][()] if clip1 is not None: mat = mat[clip1:clip2] target_group[k]["matrix"][:] += mat print( " SUCCESS ") if(0): ## resintesi ; fit of the reponse synthetise_response( scan_address="roi_%d.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"%(roi_scann, roi_scann), target_file = "reponse.h5:/FIT" ) if(0): ## resintesi : scan rerynthesis resynthetise_scan( old_scan_address="roi_%d.h5:/extracted/ROI_AS_SELECTED/calibration_scan/scans/Scan%03d"%(roi_scann, roi_scann), response_file = "reponse.h5:/FIT" , target_filename = "newrois.h5:/ROIS" ) ### ESYNTH if(0): ## The scala products, which define the equation to invert for start in range(first_scann,(first_scann+n_energies*thickness),thickness): get_scalars( Start = start, Thickness = thickness , roi_scan_num=roi_scann , nick="org" , ref_file = "newrois.h5:/ROIS" , signals_file = "signals.h5", target_file="volumes.h5") ### ESYNTH if(1): # inversion of the equations with E synthesis across the spectra get_volume_Esynt(nick = "org", volumes_file="volumes.h5") xrstools-0.15.0+git20210910+c147919d/fitcc/scripts/ESYNT/extract_volperroi.py000066400000000000000000000004051412732462000257570ustar00rootroot00000000000000 from h5py import * from numpy import * f=File("volumes.h5","r")["org"] keys=list(f.keys()) keys.sort() print( keys) res=[] for k in keys[1:]: res.append(f[k]["scal_prods"]["scalDS"][()]) res=swapaxes(res,0,1) File("scalpro_by_roi.h5","w")["vol"] = res xrstools-0.15.0+git20210910+c147919d/fitcc/scripts/ESYNT/setta_to_0.py000066400000000000000000000003211412732462000242420ustar00rootroot00000000000000import json d = json.load(open("interpolation.json","r")) for ke in d.keys(): for kv in d[ke]["coefficients"].keys(): d[ke]["coefficients"][kv]["11"]=0 json.dump(d,open( "newinterp.json" ,"w")) xrstools-0.15.0+git20210910+c147919d/fitcc/scripts/ESYNT/toh5.py000066400000000000000000000001751412732462000230670ustar00rootroot00000000000000from numpy import * from h5py import * d=fromfile("solution.raw","f") d.shape = 7, 10,41,90 File("solution.h5","w")["vol"]=d xrstools-0.15.0+git20210910+c147919d/fitcc/scripts/ESYNT/toh5_1.py000066400000000000000000000001761412732462000233100ustar00rootroot00000000000000from numpy import * from h5py import * d=fromfile("solution.raw","f") d.shape = 25, 1,41,241 File("solution.h5","w")["vol"]=d xrstools-0.15.0+git20210910+c147919d/fitcc/scripts/INTERP/000077500000000000000000000000001412732462000217325ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/fitcc/scripts/INTERP/batch_extraction_interp.py000066400000000000000000000540071412732462000272140ustar00rootroot00000000000000import numpy as np import h5py import glob import json BATCH_PARALLELISM = 1 cenom = np.array( [ 0, 12.91684471961497 , 1, 12.91601322225362 , 2, 12.915539860755496 , 3, 12.916480273530496 , 4, 12.915731392088265 , 5, 12.914869749579399 , 6, 12.916264935543595 , 7, 12.91573965859977 , 8, 12.914987140018741 , 9, 12.917537037788902 , 10, 12.916944061630726 , 11, 12.917218345706756 , 12, 12.915891035359477 , 13, 12.916352608181027 , 14, 12.917177018098796 , 15, 12.914005418194852 , 16, 12.915154657961107 , 17, 12.916191453728914 , 18, 12.914335816165174 , 19, 12.915077949987177 , 20, 12.915914957907148 , 21, 12.915288356811502 , 22, 12.91598889195904 , 23, 12.91695899376287 , 24, 12.916900296092969 , 25, 12.91604539858451 , 26, 12.914871297189173 , 27, 12.916231311661814 , 28, 12.915245568912306 , 29, 12.914471665531154 , 30, 12.916385293003266 , 31, 12.91497576912035 , 32, 12.914105451420639 , 33, 12.917157692493532 , 34, 12.915718809628475 , 35, 12.915646376430596 , 36, 12.91406510719366 , 37, 12.914584874090105 , 38, 12.914253022577238 , 39, 12.914765752067101 , 40, 12.913952660702538 , 41, 12.914056540248373 , 42, 12.914686992985423 , 43, 12.914358771568466 , 44, 12.914322364160563 , 45, 12.914834493680436 , 46, 12.91468849314193 , 47, 12.914392397943747 , 48, 12.91526519846475 , 49, 12.916546273857113 , 50, 12.91754238416663 , 51, 12.91462504324549 , 52, 12.915558554542379 , 53, 12.916489766393653 , 54, 12.914555294751848 , 55, 12.91532484033084 , 56, 12.916251843583483 , 57, 12.916091870082658 , 58, 12.91604809986116 , 59, 12.917105964460655 , 60, 12.916403610900606 , 61, 12.916009142769497 , 62, 12.915288063762233 , 63, 12.916156971954502 , 64, 12.91517887482311 , 65, 12.914460401828784 , 66, 12.916150384998303 , 67, 12.915275296447305 , 68, 12.914661911784687 , 69, 12.917387263528335 , 70, 12.916532490728155 , 71, 12.915658283253318 ]) cenom=np.reshape(cenom,[-1,2]) Enominal = np.median(cenom[:,1]) cenom[:,1] -= Enominal first_scann = 258 # 891 last_scann = 437 # 1015 thickness = 30 ### 25 roi_scann = 246 # 245 247 #### other_rois_for_ref = [245,247] #first_scann = 24 #last_scann = 26 n_energies = 5 # 6 import os def process_input(s, go=0, exploit_slurm_mpi = 0, stop_omp = False): open("input_tmp_%d.par"%go, "w").write(s) background_activator = "" if (go % BATCH_PARALLELISM ): background_activator = "&" prefix="" if stop_omp: prefix = prefix +"export OMP_NUM_THREADS=1 ;" if ( exploit_slurm_mpi==0 ): os.system(prefix +"mpirun -n 1 XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) elif ( exploit_slurm_mpi>0 ): os.system(prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: os.system(prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) def select_rois(roi_scan_num=6): inputstring = """ create_rois: expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' scans : [{roi_scan_num}] roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED filter_path : mask.h5:/FILTER_MASK/filter """ s=inputstring.format(roi_scan_num = roi_scan_num ) process_input(s, exploit_slurm_mpi = 0 ) def extract_sample_givenrois(roi_scan_num=roi_scann, nick_name="org", Start=first_scann, End=(first_scann+thickness*n_energies), Thickness = thickness ): inputstring = """ loadscan_2Dimages : expdata : '/data/id20/inhouse/data/run3_20/run3_es949/hydra' roiaddress : roi_{roi_scan_num}.h5:/extracted/ROI_AS_SELECTED monitor_column : izero/0.000001 scan_interval : [{start}, {end}] energy_column : sty signaladdress : signals.h5:/{where}/_{start}_{end} sumto1D : 0 monitorcolumn : izero/0.000001 """ for start in range(Start,End, Thickness): s=inputstring.format(start=str(start), end=str(start+Thickness) , where= nick_name ,roi_scan_num = roi_scan_num ) process_input(s, exploit_slurm_mpi = 1) class InterpInfo: def __init__(self, cenom, interp_file, source, interp_file_target): volum_list = list(interp_file[source].keys()) scan_num_list = np.array([ int( t.split("_") [1]) for t in volum_list]) ene_list = np.array([ interp_file[source][vn]["scans"]["Scan%03d"%sn ]["motorDict"]["energy"].value for vn,sn in zip(volum_list, scan_num_list ) ]) print ( " ecco la scannumlist " , scan_num_list) print (" ecco ene_list", ene_list) self.volum_list = volum_list self.scan_num_list = scan_num_list self.ene_list = ene_list order = np.argsort( self.ene_list ) self.ene_list = self.ene_list [order] self.scan_num_list = self.scan_num_list [order] self.volum_list = [ self.volum_list [ii] for ii in order ] self.interp_file=interp_file self.interp_file_target=interp_file_target self.source= source self.target = 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It is the energy of the analyser image4roi = rixs.SumDirect( [237] ) roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(image4roi) # rixs.set_roiObj(roifinder.roi_obj) rixs.loadscan(237,'elastic') xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/XES_no_compensation.py000066400000000000000000000022461412732462000273460ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function ################################################### # generic example for extraction of a XES spectrum # using the Fourc spectrometer at ID20 with bent # crystal analyzers ################################################### from .XRStools import xrs_read, roifinder_and_gui import numpy as np import pylab as pl path_to_data = '/data/id20/inhouse/XRStools_nonregression_data/xes_no_compensation/' path_to_data = '/media/christoph/Seagate Expansion Drive/data/hc2270_xes/' xes = xrs_read.Fourc( path_to_data,SPECfname='rixs', moni_column='kaprixs', EinCoor='energy' ) # ROI image4roi = xes.SumDirect( [71] ) roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(image4roi) # pass ROI object to main class xes.set_roiObj(roifinder.roi_obj) # load scans xes.load_scan(70, direct=True, scan_type='part1', method='sum') xes.load_scan(71, direct=True, scan_type='part2', method='sum') # construct emission spectrum xes.get_XES_spectrum() # plot results pl.plot(xes.energy, xes.signals) pl.xlabel('energy [keV]') pl.ylabel('intensity [arb. units]') pl.show() xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/XRStools_Raman_minimum_example_hBN.py000066400000000000000000000053761412732462000323130ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .XRStools import xrs_read, xrs_extraction, xrs_rois, roifinder_and_gui, xrs_utilities, math_functions from pylab import * from six.moves import range ion() import numpy as np # set some input variables # except for the 'path', all are default (see xrs_read.Hydra class) # and the user should not need to set them for a standard experiment path = '/data/id20/inhouse/data/run4_16/run1_ihr/' path = '/media/christoph/Seagate Expansion Drive/data/run4_16/run1_ihr/' SPECfname = 'hydra' EDFprefix = '/edf/' EDFname = 'hydra_' EDFpostfix = '.edf' en_column = 'energy' moni_column = 'izero' # in case the ROI should be saved: #ROIpath = '/media/christoph/Seagate Expansion Drive/data/run4_16/run1_ihr/rois/hbn_1_72_rois_ordered_mini-example.h5' ROIpath = None # file name for ASCII output / HDF5 file ASCIIpath = None HDF4path = None ################ # hexagonal BN # ################ hbn = xrs_read.Hydra(path, SPECfname=SPECfname, EDFprefix=EDFprefix, EDFname=EDFname, \ EDFpostfix=EDFpostfix, en_column=en_column, moni_column=en_column) # # here the ROI-GUI could/should be used # image4roi = hbn.SumDirect( [475] ) roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(image4roi) if ROIpath: roifinder.roi_obj.writeH5(ROIpath) # set the ROI obj and load scans hbn.set_roiObj(roifinder.roi_obj) hbn.load_scan( 475, scan_type='elastic', direct=True, scaling=None ) # the long scan needs some scaling (unfortunately a different factor for # each ROI (here ROIs in groups of three)) scaling = np.zeros(72) scaling[list(range(3))] = 126.439/2.2 scaling[list(range(3,6))] = 126.439/1.8 scaling[list(range(6,9))] = 126.439/2.9 scaling[list(range(9,12))] = 126.439/8.8 scaling[list(range(12,15))] = 251. scaling[list(range(15,18))] = 241. scaling[list(range(18,21))] = 238.1 scaling[list(range(21,24))] = 201.0 scaling[list(range(24,27))] = 180.1 scaling[list(range(27,30))] = 149.7 scaling[list(range(30,33))] = 115.2 scaling[list(range(33,36))] = 109.31 hbn.load_scan(438, scan_type='long', direct=True, scaling=scaling ) hbn.load_scan([476,479], scan_type='B', direct=True, scaling=None ) hbn.load_scan([477,480], scan_type='N', direct=True, scaling=None ) # stitch the spectrum together hbn.get_spectrum(include_elastic=False, abs_counts=False) # check/visualize the result ion() plot(hbn.eloss, np.sum(hbn.signals[:,list(range(33,36))],axis=1)) # save the spectrum as ASCII-file if ASCIIpath: hbn.dump_signals(ASCIIpath) # or alternatively/additionally, the whole instance could be saved # in HDF5 (BUT I could not get it working yet) if HDF4path: hbn.save_state_hdf5( filename, groupname, comment="" ) xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/compara.py000066400000000000000000000233671412732462000250650ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import h5py import fabio import os from matplotlib.colors import LogNorm from matplotlib.colors import LinearSegmentedColormap, LogNorm, Normalize from matplotlib import patches from six.moves import range from six.moves import zip SHOW = 0 # Temperature as defined in spslut cdict = {'red': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (0.75, 1.0, 1.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25, 1.0, 1.0), (0.75, 1.0, 1.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 1.0, 1.0), (0.25, 1.0, 1.0), (0.5, 0.0, 0.0), (1.0, 0.0, 0.0))} #Do I really need as many colors? temperature = LinearSegmentedColormap('temperature',cdict, 65536) filea = "demo_foilroi.h5" fileb = "demo_newrois_scan.h5" filer = "demo_responses_0.0_0.2_Tfreezed.h5" filer = "demo_responses_0.0_2.0_Tfreezed.h5" totf = fabio.open("FIGS/cumulata.edf","r").data h5resp = h5py.File(filer, "r") pixelr = h5resp["response"][:] group_scan = "/ROI_FOIL/foil_scan/scans/Scan273/" group_rois = "/ROI_FOIL/rois_definition/rois_dict/" group_scanb = "/ROI_FOIL/scan_foil/scans/Scan273/" from pylab import * def get_data(filea, group_scan,chiave ): h5f = h5py.File(filea, "r") h5 = h5f[group_scan] data = h5[str(chiave)]["matrix"][:] h5f.close() return data def get_rois_list(filea,group_rois ): h5f = h5py.File(filea, "r") h5 = h5f[group_rois] chiavi = list(h5.keys()) chiavi.sort() origins = [] sezioni = [] for c in chiavi: h5b = h5[c] origins.append(h5b["origin"][:]) if "sezioneold" in h5b: sezioni.append(h5b["sezioneold"][:]) else: sezioni.append(None) chiavi = [ int( c [-2:]) for c in chiavi ] h5f.close() return chiavi, origins, sezioni roiskeys_list_a,origins_list_a, tmp = get_rois_list(filea,group_rois ) roiskeys_list_b,origins_list_b,sezione_list = get_rois_list(fileb,group_rois ) assert(roiskeys_list_a==roiskeys_list_b) imshow(pixelr, interpolation='nearest') if SHOW: show() else: savefig('FIGS/PR.png', dpi=200, bbox_inches='tight') def mostra(chiave,data_a, data_b , data_b_ori, sez, pointer, totf, shor, inset_b): statsa = (data_a.sum(axis=-1)).sum(axis=-1) statsb = (data_b.sum(axis=-1)).sum(axis=-1) statsbb = (data_b_ori.sum(axis=-1)).sum(axis=-1) d = "FIGS/"+str(chiave)+"/" if not os.path.exists(d): os.makedirs(d) fig = plt.figure() # ax = fig.add_subplot(111, autoscale_on=False, xlim=(-1, 5), ylim=(-4, 3)) ax = fig.add_subplot(111) ax.imshow(totf, interpolation='nearest', cmap=temperature, norm=LogNorm(vmin=1 ) ) # vmin=0.01, vmax=1 dim1,dim2 = totf.shape print( dim1,dim2) ax.annotate('Analyser '+str(chiave), xy=(ob[1], ob[0]) , xycoords='data', color = "red", xytext=(0,0*dim1//2), textcoords ='data', # textcoords='offset points', size=20, # bbox=dict(boxstyle="round", fc="0.8"), arrowprops=dict(arrowstyle="->",# arrowstyle="fancy", # fc="0.6", ec="none", # patchB=el, connectionstyle="angle,angleA=-120,angleB=0,rad=10", color="red") # connectionstyle="angle3,angleA=180,angleB=-90", color = "red") ) for thick in range(4) : ax.add_patch( patches.Rectangle( (ob[1]-thick, ob[0]-thick), shor[2]+2*thick, shor[1]+2*thick, fill=False , color = "red" # remove background ) ) if SHOW : show() else: fig.savefig(d+'roi.png', dpi=200, bbox_inches='tight') if (chiave == 10000) : for chiave_, oa_, ob_, sez_ in zip( roiskeys_list_a, origins_list_a, origins_list_b , sezione_list ) : shor_ = get_data(fileb, group_scanb,chiave_ ).shape for thick in range(4) : ax.add_patch( patches.Rectangle( (ob_[1]-thick, ob_[0]-thick), shor_[2]+2*thick, shor_[1]+2*thick, fill=False , color = "red" # remove background ) ) fig.savefig('FIGS/tutteroi.png', dpi=200, bbox_inches='tight') fig = plt.figure() inset = totf[ ob[0]-3*0: ob[0]+shor[1]+3*0, ob[1]-3*0: ob[1]+shor[2]+3*0 ] ax = fig.add_subplot(111) ax.imshow( inset, interpolation='nearest', cmap=temperature, norm=LogNorm(vmin=1 ) ) # vmin=0.01, vmax=1 # for thick in range(4) : # ax.add_patch( patches.Rectangle((3-thick, 3-thick),shor[2]+2*thick,shor[1]+2*thick,fill=False,color = "red" ) ) if SHOW : show() else: fig.savefig(d+'insetbig.png', dpi=200, bbox_inches='tight') fig = plt.figure() inset = zeros( [ shor[1]+6 , shor[2]+6 ], "f") inset[ 3*0: inset_b.shape[0]+3*0, 3*0: inset_b.shape[1]+3*0 ] = inset_b ax = fig.add_subplot(111) ax.imshow(inset, interpolation='nearest', cmap=temperature, norm=LogNorm(vmin=1 ) ) # vmin=0.01, vmax=1 # for thick in range(4) : # ax.add_patch( patches.Rectangle((3-thick, 3-thick),shor[2]+2*thick,shor[1]+2*thick,fill=False,color = "red" ) ) if SHOW : show() else: fig.savefig(d+'insetsmall.png', dpi=200, bbox_inches='tight') fig = plt.figure() inset = totf[ ob[0]-3*0: ob[0]+shor[1]+3*0, ob[1]-3*0: ob[1]+shor[2]+3*0 ] spia = inset_b.sum(axis=0) dove = (spia>0).astype("f") # dove = roll(dove,1) + roll(dove,-1)+roll(dove,2) + roll(dove,-2)+roll(dove,3) + roll(dove,-3) #inset[:, dove>0 ] = inset_b[:,dove>0] inset[ inset_b>0 ] = inset_b[inset_b>0] ax = fig.add_subplot(111) ax.imshow(inset, interpolation='nearest', cmap=temperature, norm=LogNorm(vmin=1 ) ) # vmin=0.01, vmax=1 # for thick in range(4) : # ax.add_patch( patches.Rectangle((3-thick, 3-thick),shor[2]+2*thick,shor[1]+2*thick,fill=False,color = "red" ) ) if SHOW : show() else: fig.savefig(d+'insetcollage.png', dpi=200, bbox_inches='tight') fig = plt.figure() data = h5resp[str(chiave)]["data"][:]+1 data[0,0]=0 imshow(data+1.0, interpolation='nearest', norm=LogNorm(vmin=1.1 )) if SHOW : show() else: fig.savefig(d+'optical.png', dpi=200, bbox_inches='tight') x = arange(len(statsa))+sez xx = arange(len(statsbb)) fig = plt.figure() plot(x,statsa ) plot(xx,statsbb) # plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off') if SHOW : show() else: fig.savefig(d+'largeplot.png', dpi=200, bbox_inches='tight') fig = plt.figure() plot(x,statsa ) plot(x,statsb ) # plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off') if SHOW : show() else: fig.savefig(d+'smallplot.png', dpi=200, bbox_inches='tight') def mostra_massimo(bl, names): b=bl[0] k = argmax(b) j = k%b.shape[1] i = k//b.shape[1] for b, name in zip(bl, names): inset = zeros([7,7]) t = b[max(0,i-3):i+4, max(0,j-3):j+4] inset[ -min(0,i-3):-min(0,i-3) +t.shape[0] , -min(0,j-3):-min(0,j-3) +t.shape[1] ] = t print( "b.shape",inset.shape) imshow(inset, interpolation='nearest', cmap=temperature, norm=LogNorm(vmin=1 ) ) if SHOW : show() else: fig.savefig( name , dpi=200, bbox_inches='tight') mostra_massimo( [data_b_ori[0]], [d+"estremo_inizio.png"] ) mostra_massimo( [data_b[0] , data_a[0]] , [d+"segmento_inizio.png" , d+"segmentoExp_inizio.png" ] ) mostra_massimo( [data_b[-1] , data_a[-1] ], [d+"segmento_fine.png" , d+"segmentoExp_fine.png" ] ) mostra_massimo( [data_b_ori[-1] ] , [d+"estremo_fine.png"] ) print( " faccio chiave " , chiave) for chiave, oa, ob, sez in zip( roiskeys_list_a, origins_list_a, origins_list_b , sezione_list ) : # chiave = 3 data_a = get_data(filea, group_scan,chiave ) data_b = get_data(fileb, group_scanb,chiave ) data_b_ori = array(data_b) data_b = data_b[sez[0]:sez[1]] inset_b = data_b.sum(axis=0) c = oa[0]-ob[0], oa[1]-ob[1] sh = data_a.shape[1:] print( " CORNER A " , oa) print( " CORNER B " , ob) print( " SHAPE A ", data_a.shape) print( " SHAPE B ", data_b.shape) by0 = max( oa[0],ob[0] ) bx0 = max( oa[1],ob[1] ) by1 = min( oa[0]+ data_a.shape[1] ,ob[0] + data_b.shape[1] ) bx1 = min( oa[1] + data_a.shape[2] ,ob[1] + data_b.shape[2] ) print( " DATAB ", max(0,by0-ob[0]) , by1-ob[0] , max(0,bx0-ob[1] ),bx1-ob[1] ) print( " DATA1 ", max(0,by0-oa[0]) , by1-oa[0] , max(0,bx0-oa[1] ),bx1-oa[1] ) data_b = data_b[ : , max(0,by0-ob[0] ): by1-ob[0] , max(0,bx0-ob[1] ):bx1-ob[1] ] data_a = data_a[ : , max(0,by0-oa[0] ): by1-oa[0] , max(0,bx0-oa[1] ):bx1-oa[1] ] print( sez) print( oa) print( ob) mostra( chiave ,data_a, data_b , data_b_ori ,sez[0], ob , totf, data_b_ori.shape, inset_b) xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/compton_profiles_test.py000066400000000000000000000005731412732462000300560ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from XRStools import xrs_ComptonProfiles as CP import numpy as np from pylab import * ion() filename = '/home/christoph/sources/XRStools/data/ComptonProfiles.dat' cp = CP.AtomProfile('Si',filename) E0 = 9.7 twotheta = 35.0 cp.get_elossProfiles(E0, twotheta) xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/000077500000000000000000000000001412732462000264255ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/000077500000000000000000000000001412732462000273275ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/README.txt000066400000000000000000000016321412732462000310270ustar00rootroot00000000000000 This folder contains a series of non-regression test for XRS data extraction from ID20. All tests are based on the same data set taken during "run3_16/run4_es431" of a small stishovite SiO2 (reference sample for an official experiment). test1: auto_ROIs_pixel.yaml Automatic selection of ROIs, pixel-by-pixel compensation. test2: linear_ROIs_pixel.yaml Line ROIs selection, pixel-by-pixel compensation. test3: polygon_ROIs_pixel.yaml Chosing ROIs using polygons, pixel-by-pixel compensation. test4: zoom_ROIs_NNMF_pixel.yaml Zoom ROIs selection, refinement of the ROIs using non-negative matrix factorization based on a single oxygen K-edge scan, pixel-by-pixel compensation. test5: zoom_ROIs_pixel.yaml Zoom ROI selection, pixel-by-pixel compensation. test6: zoom_ROIs_sum.yaml Zoom ROI selection, simple sum of signals within each ROI, no compensation. test7: hydra_extraction.yaml Generic example. xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/README.txt~000066400000000000000000000000001412732462000312110ustar00rootroot00000000000000hydra_extraction.yaml000066400000000000000000000011511412732462000335010ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTShelp: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : zoom refinement_key : # (default), can be NNMF, cw, pw, or combination of those [NNMF, pw] refinement_scan : # (default), can be 32 or [32, 33, 34] scans : method : 0 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/home/christoph/Documents/scratch/SiO2_stishovite_pixel_by_pixel.h5' sio2_stishovite_example_pixel_vs_sum_hydra.py000066400000000000000000000033511412732462000404600ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTSfrom __future__ import absolute_import from __future__ import division from __future__ import print_function from .XRStools import xrs_read, roifinder_and_gui from pylab import * import numpy as np path = '/media/christoph/Seagate Expansion Drive/data_nonregressions/hydra_extraction/' ################################### # sio2 stishovite - no compensation ################################### sio2_sum = xrs_read.Hydra(path) image4roi = sio2_sum.SumDirect( [30] ) roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(image4roi) sio2_sum.set_roiObj(roifinder.roi_obj) sio2_sum.load_scan(31, direct=True, scan_type='long', method='sum') sio2_sum.load_scan(32, direct=True, scan_type='edge1', method='sum') sio2_sum.load_scan(33, direct=True, scan_type='edge2', method='sum') sio2_sum.load_scan(30, direct=True, scan_type='elastic', method='sum') sio2_sum.get_spectrum_new(method='sum',include_elastic=True) #ion() #plot(sio2_sum.eloss, sio2_sum.signals) ############################################### # sio2 stishovite - pixel-by-pixel compensation ############################################### sio2_pixel = xrs_read.Hydra(path) sio2_pixel.set_roiObj(roifinder.roi_obj) sio2_pixel.load_scan(31, direct=True, scan_type='long', method='pixel') sio2_pixel.load_scan(32, direct=True, scan_type='edge1', method='pixel') sio2_pixel.load_scan(33, direct=True, scan_type='edge2', method='pixel') sio2_pixel.load_scan(30, direct=True, scan_type='elastic', method='pixel') sio2_pixel.get_spectrum_new( method='pixel', include_elastic=True ) plot(sio2_sum.eloss, sio2_sum.signals[:,0]) plot(sio2_pixel.eloss, sio2_pixel.signals[:,0]) legend(['sum, ROI00', 'pixel, ROI00']) xlabel('energy loss [eV]') ylabel('intensity [arb. units]') show() xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test1/000077500000000000000000000000001412732462000303675ustar00rootroot00000000000000auto_ROIs_pixel.yaml000066400000000000000000000006711412732462000342450ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test1help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : auto scans : method : 1 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/tmp_14_days/XRStools_nonregression_test7.h55' auto_ROIs_pixel.yaml~000066400000000000000000000007171412732462000344440ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test1help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : auto scans : method : 1 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/home/christoph/Documents/scratch/SiO2_stishovite_pixel_by_pixel.h5' zoom_ROIs_NNMF_pixel.yaml~000066400000000000000000000007551412732462000353000ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test1help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431' rois : scan_number : 30 roi_type : zoom refinement_key : NNMF refinement_scan : [32] scans : method : 1 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/tmp_14_days/XRStools_nonregression_test1.h5' xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test2/000077500000000000000000000000001412732462000303705ustar00rootroot00000000000000linear_ROIs_pixel.yaml000066400000000000000000000006701412732462000345470ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test2help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : line scans : method : 1 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/tmp_14_days/XRStools_nonregression_test7.h5' linear_ROIs_pixel.yaml~000066400000000000000000000007171412732462000347470ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test2help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : line scans : method : 1 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/home/christoph/Documents/scratch/SiO2_stishovite_pixel_by_pixel.h5' xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test3/000077500000000000000000000000001412732462000303715ustar00rootroot00000000000000polygon_ROIs_pixel.yaml000066400000000000000000000006701412732462000347650ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test3help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : poly scans : method : 1 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/tmp_14_days/XRStools_nonregression_test7.h5' polygon_ROIs_pixel.yaml~000066400000000000000000000007171412732462000351650ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test3help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : poly scans : method : 1 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/home/christoph/Documents/scratch/SiO2_stishovite_pixel_by_pixel.h5' xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test4/000077500000000000000000000000001412732462000303725ustar00rootroot00000000000000zoom_ROIs_NNMF_pixel.yaml000066400000000000000000000007561412732462000351060ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test4help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : zoom refinement_key : NNMF refinement_scan : [32] scans : method : 1 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/tmp_14_days/XRStools_nonregression_test7.h5' zoom_ROIs_NNMF_pixel.yaml~000066400000000000000000000010051412732462000352700ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test4help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : zoom refinement_key : NNMF refinement_scan : [32] scans : method : 1 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/home/christoph/Documents/scratch/SiO2_stishovite_pixel_by_pixel.h5' xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test5/000077500000000000000000000000001412732462000303735ustar00rootroot00000000000000zoom_ROIs_pixel.yaml000066400000000000000000000006701412732462000342640ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test5help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : zoom scans : method : 1 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/tmp_14_days/XRStools_nonregression_test7.h5' zoom_ROIs_pixel.yaml~000066400000000000000000000007171412732462000344640ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test5help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : zoom scans : method : 1 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/home/christoph/Documents/scratch/SiO2_stishovite_pixel_by_pixel.h5' xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test6/000077500000000000000000000000001412732462000303745ustar00rootroot00000000000000zoom_ROIs_sum.yaml000066400000000000000000000006701412732462000337500ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test6help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : zoom scans : method : 0 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/tmp_14_days/XRStools_nonregression_test7.h5' zoom_ROIs_sum.yaml~000066400000000000000000000007171412732462000341500ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test6help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : zoom scans : method : 0 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/home/christoph/Documents/scratch/SiO2_stishovite_pixel_by_pixel.h5' xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test7/000077500000000000000000000000001412732462000303755ustar00rootroot00000000000000hydra_extraction.yaml000066400000000000000000000011221412732462000345450ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test7help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : zoom refinement_key : # (default), can be NNMF, cw, pw, or combination of those [NNMF, pw] refinement_scan : # (default), can be 32 or [32, 33, 34] scans : method : 0 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/tmp_14_days/XRStools_nonregression_test7.h5' hydra_extraction.yaml~000066400000000000000000000011511412732462000347450ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/hydra_extraction/TESTS/test7help: "Hydra_extraction" Hydra_extraction : active : 1 data : path : '/data/id20/inhouse/data/run3_16/run4_es431/' rois : scan_number : 30 roi_type : zoom refinement_key : # (default), can be NNMF, cw, pw, or combination of those [NNMF, pw] refinement_scan : # (default), can be 32 or [32, 33, 34] scans : method : 0 scan_numbers : [30, 31, 32, 33] scan_types : [elastic, long, edge1, edge2] include_elastic : True output : format : 'hdf5' file_name : '/home/christoph/Documents/scratch/SiO2_stishovite_pixel_by_pixel.h5' xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/nonregression1.py000066400000000000000000000036611412732462000264120ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from XRStools import xrs_read, theory, extraction, roiSelectionWidget, xrs_rois,roifinder_and_gui from pylab import * import pickle from six.moves import range from six.moves import input ion() import numpy as np from scipy import interpolate, signal, integrate, constants, optimize, ndimage from PyQt4 import Qt, QtCore lowq = list(range(12)) lowq.extend(list(range(36,60))) medq = list(range(12,24)) highq = list(range(24,36)) highq.extend(list(range(60,72))) ############################################################################## # H2o example ############################################################################## # repertorio = "/scisoft/users/mirone/WORKS/Christoph/for_alessandro" # repertorio = "/home/alex/WORKS/Christoph/for_alessandro" repertorio = open("conf.txt","r").readlines()[0].strip() h2o = xrs_read.read_id20(repertorio + '/hydra',monitorcolumn='kapraman') h2o.loadelastic([623]) roiob = roifinder_and_gui.get_auto_rois_eachdet(h2o.scans,256, [623],threshold =10) f = open(repertorio +'roi.pick','w') pickle.dump(roiob, f) f.close() # h2o.get_zoom_rois([623]) # h2o.save_rois('/home/christoph/data/ihr_feb15/rois/h2o_72_big.txt') h2o.set_roiObj(roiob) h2o.loadelasticdirect([623]) h2o.loadloopdirect([625,629,633,637,641],4) h2o.loadlongdirect(624) print( " OK " ) # h2o.getrawdata() h2o.getspectrum() h2o.geteloss() h2o.gettths(rvd=-41,rvu=85,rvb=121.8,rhl=41.0,rhr=41.0,rhb=121.8,order=[0,1,2,3,4,5]) # O K edge hf = theory.HFspectrum(h2o,['O'],[1.0],correctasym=[[0.0,0.0,0.0]]) extr1 = extraction.extraction(h2o,hf) extr1.analyzerAverage(lowq,errorweighing=False) #extr1.removePearsonAv2([480.0,533.0],[550.0,555.0],scale=3.6,hfcoreshift=0.0) extr1.removeLinearAv([520.0,532.0],scale=0.052) extr1.savetxtsqwav(repertorio+'/as_abs/low_q/h2o_OK.txt', emin=500.0, emax=700.0) x= input() xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/oneD_imaging_example.py000066400000000000000000000041571412732462000275320ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function #!/usr/bin/python # Filename: oneD_imaging_example.py from .XRStools import xrs_read,xrs_imaging, xrs_rois, roifinder_and_gui from pylab import * from six.moves import range from six.moves import zip ion() import numpy as np #data_home = '/home/christoph/data/ihr_jul15/' data_home = '/data/id20/inhouse/data/run3_15/run6_ihr/' ##### STY scan fey = xrs_imaging.oneD_imaging(data_home+'hydra',monitorcolumn='kapraman',energycolumn='sty') image4roi = fey.SumDirect( [270] ) roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(image4roi) fey.set_roiObj(roifinder.roi_obj) fey.loadscan_2Dimages(270,scantype='sty') # try making a 3D image from ROI 0 s = np.zeros((np.shape(fey.twoDimages['Scan270'][0].matrix)[0],np.shape(fey.twoDimages['Scan270'][0].matrix)[1],len(fey.twoDimages))) for ii,number in zip(list(range(s.shape[-1])),[270]): scanname = 'Scan%03d' % number s[:,:,ii] = fey.twoDimages[scanname][0].matrix from mayavi import mlab # s is a 3D np.ndarray src = mlab.pipeline.scalar_field(s) mlab.pipeline.volume(src,vmin=10.0, vmax=20.0) mlab.show() ##### STX scan fex = xrs_imaging.oneD_imaging(data_home+'hydra',monitorcolumn='kapraman',energycolumn='sty') image4roi = fex.SumDirect( [273] ) roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(image4roi) fex.set_roiObj(roifinder.roi_obj) fex.loadscan_2Dimages(273,scantype='stx') # try making a 3D image from ROI 0 s = np.zeros((np.shape(fex.twoDimages['Scan273'][0].matrix)[0],np.shape(fex.twoDimages['Scan273'][0].matrix)[1],len(fex.twoDimages))) for ii,number in zip(list(range(s.shape[-1])),[273]): scanname = 'Scan%03d' % number s[:,:,ii] = fex.twoDimages[scanname][0].matrix from mayavi import mlab # s is a 3D np.ndarray #src = mlab.pipeline.scalar_field(s) #mlab.pipeline.volume(src,vmin=1.0, vmax=20.0) #mlab.show() src = mlab.pipeline.scalar_field(s) mlab.pipeline.iso_surface(src, contours=[s.min()+0.05*s.ptp(), ], opacity=0.6) #mlab.pipeline.iso_surface(src, contours=[s.max()-0.1*s.ptp(), ],) mlab.show() xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/xrstools_XES_example.py000066400000000000000000000011301412732462000275520ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function reload(xes_read) from .XRStools import xes_read, xrs_rois, roifinder_and_gui import numpy as np from pylab import * ion() xest = xes_read.read_id20('/home/christoph/data/hc2270_xes/rixs') # ROI image4roi = xest.SumDirect( [71] ) roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(image4roi) # set ROI object, load scans xest.set_roiObj(roifinder.roi_obj) xest.loadscandirect(70,'part1') xest.loadscandirect(71,'part2') xest.getXESspectrum() xest.getrawdata() xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/xrstools_analysis_example.py000066400000000000000000000044721412732462000307520ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .XRStools import xrs_read, theory, extraction, roiSelectionWidget, xrs_rois from pylab import * import pickle from six.moves import range from six.moves import input ion() import numpy as np from scipy import interpolate, signal, integrate, constants, optimize, ndimage from PyQt4 import Qt, QtCore lowq = list(range(12)) lowq.extend(list(range(36,60))) medq = list(range(12,24)) highq = list(range(24,36)) highq.extend(list(range(60,72))) ############################################################################## # H2o example ############################################################################## # repertorio = "/scisoft/users/mirone/WORKS/Christoph/for_alessandro" # repertorio = "/home/alex/WORKS/Christoph/for_alessandro" repertorio = open("conf.txt","r").readlines()[0].strip() h2o = xrs_read.read_id20(repertorio + '/hydra',monitorcolumn='kapraman') image4roi = h2o.SumDirect( [623] ) app=Qt.QApplication([]) w4r = roiSelectionWidget.mainwindow() w4r.showImage( image4roi , xrs_rois.get_geo_informations(image4roi.shape) ) w4r.show() app.exec_() masks = w4r.getMasksDict() roiob = xrs_rois.roi_object() roiob.load_rois_fromMasksDict(masks , newshape = image4roi.shape, kind="zoom") print( masks) print( roiob.roi_matrix.max()) # f = open( repertorio +'/rois/as_2m_72_Dict.pick','wb' ) # pickle.dump(roi_obj.red_rois, f) # f.close() # h2o.getautorois_eachdet([623],thresfrac=10) # h2o.get_zoom_rois([623]) # h2o.save_rois('/home/christoph/data/ihr_feb15/rois/h2o_72_big.txt') h2o.set_roiObj(roiob) h2o.loadelasticdirect([623]) h2o.loadloopdirect([625,629,633,637,641],4) h2o.loadlongdirect(624) print( " OK " ) # h2o.getrawdata() h2o.getspectrum() print( h2o.signals) h2o.geteloss() h2o.gettths(rvd=-41,rvu=85,rvb=121.8,rhl=41.0,rhr=41.0,rhb=121.8,order=[0,1,2,3,4,5]) # O K edge hf = theory.HFspectrum(h2o,['O'],[1.0],correctasym=[[0.0,0.0,0.0]]) extr1 = extraction.extraction(h2o,hf) extr1.analyzerAverage(lowq,errorweighing=False) #extr1.removePearsonAv2([480.0,533.0],[550.0,555.0],scale=3.6,hfcoreshift=0.0) extr1.removeLinearAv([520.0,532.0],scale=0.052) extr1.savetxtsqwav(repertorio+'/as_abs/low_q/h2o_OK.txt', emin=500.0, emax=700.0) ioff() show() x= input() xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/xrstools_analysis_example_h2o.py000066400000000000000000000114221412732462000315130ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .XRStools import xrs_read, theory, extraction, xrs_rois, roifinder_and_gui from pylab import * import pickle from six.moves import range from six.moves import input ion() import numpy as np from scipy import interpolate, signal, integrate, constants, optimize, ndimage #from PyQt4 import Qt, QtCore lowq = list(range(12)) lowq.extend(list(range(36,60))) medq = list(range(12,24)) highq = list(range(24,36)) highq.extend(list(range(60,72))) ############################################################################## # H2o example ############################################################################## # repertorio = "/scisoft/users/mirone/WORKS/Christoph/for_alessandro" # repertorio = "/home/alex/WORKS/Christoph/for_alessandro" # repertorio = open("conf.txt","r").readlines()[0].strip() repertorio = "/home/christoph/data/hc990_2" h2o = xrs_read.read_id20(repertorio + '/hydra',monitorcolumn='kapraman') # manage ROIs image4roi = h2o.SumDirect( [210] ) roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(image4roi) # set the ROI object h2o.set_roiObj(roifinder.roi_obj) h2o.loadelastic([210]) h2o.loadscan([211],'edge1') # refine ROIs h2o.getrawdata_pixelwise() roifinder.refine_pw_rois(roifinder.roi_obj, h2o.scans['Scan211'].signals_pw,n_components=2,method='nnma') roifinder.show_rois() # reset the ROI object h2o.set_roiObj(roifinder.roi_obj) # reload scans h2o.loadelasticdirect([210]) h2o.loadscandirect([211],'edge1') # stitch and find eloss scale h2o.getspectrum() h2o.geteloss() # plot spectrum plot(h2o.eloss,h2o.signals) plot(h2o.scans['Scan210'].energy, h2o.scans['Scan210'].signals_pw[0]) from sklearn.decomposition import FastICA, PCA, ProjectedGradientNMF pca = PCA(n_components=2) H = pca.fit_transform(h2o.scans['Scan211'].signals_pw[0]) ica = FastICA(n_components=2) S = ica.fit_transform(h2o.scans['Scan211'].signals_pw[0]) nnm = ProjectedGradientNMF(n_components=2) N = nnm.fit_transform(h2o.scans['Scan211'].signals_pw[0]) dotproducts = np.array([]) for ii in range(len(h2o.scans['Scan211'].signals_pw[0][0,:])): dotproducts = np.append(dotproducts, (np.dot(H[:,0],h2o.scans['Scan211'].signals_pw[1][:,ii]))) dotproducts = dotproducts.reshape( (h2o.roi_obj.red_rois['ROI00'][1].shape[0]+1,h2o.roi_obj.red_rois['ROI00'][1].shape[1]+1) ) crosscorr = np.array([]) for ii in range(len(h2o.scans['Scan211'].signals_pw[0][0,:])): crosscorr = np.append(crosscorr, (np.correlate(N[:,1],h2o.scans['Scan211'].signals_pw[0][:,ii]))) covariance = np.array([]) for ii in range(len(h2o.scans['Scan211'].signals_pw[0][0,:])): covariance = np.append(covariance, np.cov(h2o.scans['Scan211'].signals_pw[0][:,ii],N[:,1])[0,0]) h2o.roi_obj.red_rois['ROI00'][1].shape covarianceI = covariance.reshape((h2o.roi_obj.red_rois['ROI00'][1].shape[0]+1,h2o.roi_obj.red_rois['ROI00'][1].shape[1]+1)) cla() imshow(covarianceI) # check how much elastic line moves across pixels: #for ii in range(len(h2o.scans['Scan210'].signals_pw[0])): # plot(h2o.scans['Scan210'].energy, h2o.scans['Scan210'].signals_pw[0][:,ii]/np.amax(h2o.scans['Scan210'].signals_pw[0][:,ii]) ) # -> looks like 0.1-0.2 eV, maybe it is worth shifting pixel by pixel doing... signals_med = signal.medfilt(h2o.scans['Scan211'].signals_pw[0]) plot(h2o.scans['Scan211'].energy, signals_med) y1 = sp.signal.medfilt(x2,21) roiob = roifinder_and_gui.get_zoom_rois(h2o.scans,210) h2o.set_roiObj() app=Qt.QApplication([]) w4r = roiSelectionWidget.mainwindow() w4r.showImage( image4roi , xrs_rois.get_geo_informations(image4roi.shape) ) w4r.show() app.exec_() masks = w4r.getMasksDict() roiob = xrs_rois.roi_object() roiob.load_rois_fromMasksDict(masks , newshape = image4roi.shape, kind="zoom") print( masks) print( roiob.roi_matrix.max()) # f = open( repertorio +'/rois/as_2m_72_Dict.pick','wb' ) # pickle.dump(roi_obj.red_rois, f) # f.close() # h2o.getautorois_eachdet([623],thresfrac=10) # h2o.get_zoom_rois([623]) # h2o.save_rois('/home/christoph/data/ihr_feb15/rois/h2o_72_big.txt') h2o.set_roiObj(roiob) h2o.loadelasticdirect([623]) h2o.loadloopdirect([625,629,633,637,641],4) h2o.loadlongdirect(624) print( " OK " ) # h2o.getrawdata() h2o.getspectrum() print( h2o.signals) h2o.geteloss() h2o.gettths(rvd=-41,rvu=85,rvb=121.8,rhl=41.0,rhr=41.0,rhb=121.8,order=[0,1,2,3,4,5]) # O K edge hf = theory.HFspectrum(h2o,['O'],[1.0],correctasym=[[0.0,0.0,0.0]]) extr1 = extraction.extraction(h2o,hf) extr1.analyzerAverage(lowq,errorweighing=False) #extr1.removePearsonAv2([480.0,533.0],[550.0,555.0],scale=3.6,hfcoreshift=0.0) extr1.removeLinearAv([520.0,532.0],scale=0.052) extr1.savetxtsqwav(repertorio+'/as_abs/low_q/h2o_OK.txt', emin=500.0, emax=700.0) ioff() show() x= input() xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/xrstools_imaging_example.py000066400000000000000000000035451412732462000305420ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range from six.moves import zip #!/usr/bin/python # Filename: oneD_imaging_example.py __doc__=""" ici la doc :: python xrstools_imaging_example.py """ from XRStools import xrs_read,xrs_imaging, xrs_rois, roifinder_and_gui import sys import pylab # from pylab import * def main(): pylab.ion() import numpy as np # data_home = '/home/christoph/data/ihr_jul15/' data_home = '/data/id20/inhouse/data/run3_15/run6_ihr/' gasket = xrs_imaging.oneD_imaging(data_home+'hydra',monitorcolumn='kapraman',energycolumn='sty') image4roi = gasket.SumDirect( [372] ) roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(image4roi) gasket.set_roiObj(roifinder.roi_obj) # gasket.loadscan_2Dimages(range(372,423),scantype='sty') gasket.loadscan_2Dimages(list(range(372,374)),scantype='sty') # try making a 3D image from ROI 0 s = np.zeros((np.shape(gasket.twoDimages['Scan372'][0].matrix)[0],np.shape(gasket.twoDimages['Scan372'][0].matrix)[1],len(gasket.twoDimages))) for ii,number in zip(list(range(s.shape[-1])),list(range(372,423))): scanname = 'Scan%03d' % number s[:,:,ii] = gasket.twoDimages[scanname][0].matrix from mayavi import mlab #x, y, z = np.ogrid[-10:10:20j, -10:10:20j, -10:10:20j] #s = np.sin(x*y*z)/(x*y*z) # s is a 3D np.ndarray src = mlab.pipeline.scalar_field(s) mlab.pipeline.iso_surface(src, contours=[s.min()+0.05*s.ptp(), ], opacity=0.6) #mlab.pipeline.iso_surface(src, contours=[s.max()-0.1*s.ptp(), ],) mlab.show() # s is a 3D np.ndarray src = mlab.pipeline.scalar_field(s) mlab.pipeline.volume(src,vmin=1000.0, vmax=2000.0) mlab.show() if(sys.argv[0][-12:]!="sphinx-build"): main() xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/xrstools_offdia_example.py000066400000000000000000000025471412732462000303600ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .XRStools import xrs_read, theory, xrs_extraction, xrs_rois, roifinder_and_gui, xrs_utilities, math_functions from pylab import * from six.moves import range ion() import numpy as np from scipy import interpolate, signal, integrate, constants, optimize, ndimage from .XRStools import ixs_offDiagonal,xrs_utilities ############################################################################## # off-focus resolution tests ############################################################################## repertorio = "/home/christoph/data/ihr_sep15/" ############ # elastic line ############ offdia = ixs_offDiagonal.offDiagonal(repertorio + 'rixs',scanMotor='srz',monitorName='kaprixs',edfName=None,armLength=1.0) image4roi = offdia.SumDirect( [442] ) roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(image4roi) offdia.set_roiObj(roifinder.roi_obj) offdia.loadRockingCurve(list(range(477,757,3)),direct=True) offdia.loadRockingCurve(list(range(478,758,3)),direct=True) offdia.stitchRockingCurves() offdia.offDiaDataSets[1].normalizeSignals() offdia.offDiaDataSets[1].alignRCmonitor() ion() imshow(offdia.offDiaDataSets[1].alignedRCmonitor) offdia.offDiaDataSets[1].removeElastic() offdia.offDiaDataSets[1].windowSignalMatrix(5.0,50) xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/xrstools_old-style_analysis.py000066400000000000000000000010031412732462000312160ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .XRStools import xrs_read, theory, extraction, xrs_rois, roifinder_and_gui import numpy as np from pylab import * ion() lif = xrs_read.read_id20('/home/christoph/data/ihr_may15/rixs',monitorcolumn='kaprixs') lif.loadelastic(59) lif.set_roiObj(roifinder_and_gui.get_zoom_rois(lif.scans,59)) lif.loadelasticdirect([59]) lif.loadloopdirect([60,63,66,69,72,75,78],2) lif.loadlongdirect(58) lif. xrstools-0.15.0+git20210910+c147919d/nonregressions/oldstuff/xrstools_rixs_example.py000066400000000000000000000011771412732462000301130ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function from .XRStools import xrs_read,xrs_imaging, xrs_rois, roifinder_and_gui, rixs_read from pylab import * ion() import numpy as np data_home = '/home/christoph/data/rixs_example/' #data_home = '/data/id20/inhouse/data/run3_15/run6_ihr/' rixs = rixs_read.read_id20(data_home+'rixs1',energyColumn='Anal Energy',monitorColumn='kap4dio') # ROI image4roi = rixs.SumDirect( [237] ) roifinder = roifinder_and_gui.roi_finder() roifinder.get_zoom_rois(image4roi) # rixs.set_roiObj(roifinder.roi_obj) rixs.loadscan(237,'elastic') xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/000077500000000000000000000000001412732462000227225ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/esrf_scans/000077500000000000000000000000001412732462000250505ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/esrf_scans/esynth/000077500000000000000000000000001412732462000263625ustar00rootroot00000000000000batch_extraction_esynth1.py000066400000000000000000000120161412732462000336510ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/esrf_scans/esynthimport numpy as np import h5py import glob import json import os import h5py import math from XRStools import tools_sequencer_esynth from XRStools import xrs_read, xrs_rois import os def main(): os.system("xz -dk ../mask.h5.xz --stdout > mask.h5") filter_path = "mask.h5:/FILTER_MASK/filter" roi_scan_num = [245,246,247] reference_scan_list = [245, 246, 247] monitor_column = "izero/0.000001" first_scan_num = 651 Ydim = None # not used. it is the scan lenght , it is given by the data, contained in the scan Zdim = 10 Edim = 7 elastic_scan_for_peaks_shifts = 42 energy_custom_grid = [ 2*13.253006- 13.25551 , 13.253006, 13.25551, 13.258008, 13.260506 , 13.263004, 13.268 ] ### custom_components_file = "abc.h5" custom_components_file = None datadir = "/data/id20/inhouse/data/run3_20/run3_es949" # reference_clip = None reference_clip = [ 90, 180 ] isolate_spot_by = 6 response_fit_options = dict( [ ["niter_optical" , 100], ["beta_optical" , 0.1], ["niter_global" , 3 ] ]) resynth_z_square = 0 selected_rois = list(range(0,72)) steps_to_do = { "do_step_make_roi": False, "do_step_sample_extraction": False, "do_step_extract_reference_scan": False, "do_step_fit_reference_response": False, "do_step_resynthetise_reference": False, "do_step_scalars" : False, "do_step_interpolation_coefficients": False, "do_step_finalise_for_fit": True } os.system("mkdir results") scalar_products_target_file = "results/scalar_products.h5" roi_target_path = "results/myrois.h5:/ROIS" signals_target_file = "results/signals.h5" extracted_reference_target_file = "results/reference.h5" response_target_file = "results/response.h5" resynthetised_reference_and_roi_target_file = "results/resynthetised_roi_and_scan.h5" interpolation_infos_file = "interpolation_infos.json" ########################################################################################### ###### LOADING PEAKS SHIFTS ###### peaks_shifts = h5py.File("../peaks_positions_for_analysers.h5","r")["peaks_positions"][()] ###### assert( len(peaks_shifts) == 72) if steps_to_do["do_step_interpolation_coefficients"]: roiob = xrs_rois.roi_object() roiob.loadH5( roi_target_path ) elastic = xrs_read.Hydra( datadir ) elastic.set_roiObj( roiob ) elastic.get_compensation_factor( elastic_scan_for_peaks_shifts , method='sum') el_dict = elastic.cenom_dict Enominal = np.median( list( el_dict.values() ) ) peaks_shifts = np.array([ el_dict["ROI%02d"%i] if ("ROI%02d"%i) in el_dict else nan for i in range( 72) ] ) peaks_shifts -= Enominal else: peaks_shifts = None ############################################################## ########################################################################## tools_sequencer_esynth.tools_sequencer( peaks_shifts = peaks_shifts , datadir = datadir , filter_path = filter_path , roi_scan_num = roi_scan_num , roi_target_path = roi_target_path , steps_to_do = steps_to_do, first_scan_num = first_scan_num, Ydim = Ydim , Zdim = Zdim , Edim = Edim , monitor_column = monitor_column, signals_target_file = signals_target_file, reference_scan_list = reference_scan_list, reference_clip = reference_clip, extracted_reference_target_file = extracted_reference_target_file , isolate_spot_by = isolate_spot_by, response_target_file = response_target_file, response_fit_options = response_fit_options, resynthetised_reference_and_roi_target_file = resynthetised_reference_and_roi_target_file, resynth_z_square = resynth_z_square, selected_rois = selected_rois, scalar_products_target_file = scalar_products_target_file , energy_custom_grid = energy_custom_grid, custom_components_file = custom_components_file, interpolation_infos_file = interpolation_infos_file ) main() xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/esrf_scans/fourmis/000077500000000000000000000000001412732462000265345ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/esrf_scans/fourmis/esynth/000077500000000000000000000000001412732462000300465ustar00rootroot00000000000000batch_extraction_esynth.py000066400000000000000000000133311412732462000352550ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/esrf_scans/fourmis/esynthimport numpy as np import h5py import glob import json import os import h5py import math from XRStools import tools_sequencer_esynth from XRStools import xrs_read, xrs_rois tools_sequencer_esynth.MAXNPROCS=2 import os def main(): filter_path = None # filter_path = "mask.h5:/FILTER_MASK/filter" roi_scan_num = list(range(592,600)) reference_scan_list = list(range(592,600)) monitor_column = "izero/0.000001" first_scan_num = 464 Ydim = None # not used, it is the scan lenght , it is given by the data, contained in the scang Zdim = 2 Edim = 62 elastic_scan_for_peaks_shifts = 38 energy_custom_grid = 9.96595 + np.arange(62)*0.0005 ### custom_components_file = "abc.h5" custom_components_file = None datadir = "/data/xrs/aless/data/run5_17/run7_ihr/" # datadir = "/data/id20/inhouse/data/run5_17/run7_ihr/" # If reference_clip is not None, then a smaller part of the reference scan is considered # This may be usefule to obtain smaller volumes containing the interesting part # The used reference scan will the correspond to the positions from reference_clip[0] to reference_clip[1]-1 included ########### reference_clip = None # reference_clip = [ 90, 180 ] ## in the reference scan for each position there is a spot with a maximum. We set zero the background which is further than ## such radius from the maximum isolate_spot_by = 7 #### For the fit of the response function based on reference scans response_fit_options = dict( [ ["niter_optical" , 40], ["beta_optical" , 0.1], ["niter_global" , 1 ] ]) resynth_z_square = 0 selected_rois = list(range(0,24)) + list( range(36,60) ) steps_to_do = { "do_step_make_roi": False, "do_step_sample_extraction": False, "do_step_extract_reference_scan": False, "do_step_fit_reference_response": False, "do_step_resynthetise_reference": False, "do_step_scalars" : False, "do_step_interpolation_coefficients": False, "do_step_finalise_for_fit": True } os.system("mkdir results") scalar_products_target_file = "results/scalar_products_target_file.h5" roi_target_path = "results/myrois.h5:/ROIS" signals_target_file = "results/signals.h5" extracted_reference_target_file = "results/reference.h5" response_target_file = "results/response.h5" resynthetised_reference_and_roi_target_file = "results/resynthetised_roi_and_scan.h5" interpolation_infos_file = "interpolation_infos.json" ########################################################################################### ###### LOADING PEAKS SHIFTS ###### peaks_shifts = h5py.File("../peaks_positions_for_analysers.h5","r")["peaks_positions"][()] ###### assert( len(peaks_shifts) == 72) if steps_to_do["do_step_interpolation_coefficients"]: ## in this case peaks shifts are needed. They are passed # to tools_sequencer which calculates the coefficients roiob = xrs_rois.roi_object() roiob.loadH5( roi_target_path ) elastic = xrs_read.Hydra( datadir ) elastic.set_roiObj( roiob ) elastic.get_compensation_factor( elastic_scan_for_peaks_shifts , method='sum') el_dict = elastic.cenom_dict Enominal = np.median( list( el_dict.values() ) ) peaks_shifts = np.array([ el_dict["ROI%02d"%i] if ("ROI%02d"%i) in el_dict else np.nan for i in range( 72) ] ) peaks_shifts -= Enominal else: peaks_shifts = None ############################################################## ########################################################################## tools_sequencer_esynth.tools_sequencer(peaks_shifts = peaks_shifts , datadir = datadir , filter_path = filter_path , roi_scan_num = roi_scan_num , roi_target_path = roi_target_path , steps_to_do = steps_to_do, first_scan_num = first_scan_num, Ydim = Ydim , # not used Zdim = Zdim , Edim = Edim , monitor_column = monitor_column, signals_target_file = signals_target_file, reference_scan_list = reference_scan_list, reference_clip = reference_clip, extracted_reference_target_file = extracted_reference_target_file , isolate_spot_by = isolate_spot_by, response_target_file = response_target_file, response_fit_options = response_fit_options, resynthetised_reference_and_roi_target_file = resynthetised_reference_and_roi_target_file, resynth_z_square = resynth_z_square, selected_rois = selected_rois, scalar_products_target_file = scalar_products_target_file , energy_custom_grid = energy_custom_grid, custom_components_file = custom_components_file, interpolation_infos_file = interpolation_infos_file ) main() xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/esrf_scans/fourmis/interp/000077500000000000000000000000001412732462000300355ustar00rootroot00000000000000batch_extraction_interp.py000066400000000000000000000136461412732462000352440ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/esrf_scans/fourmis/interpimport numpy as np import h5py import glob import json import os import h5py import math from XRStools import tools_sequencer_interp from XRStools import tools_sequencer_esynth from XRStools import xrs_read, xrs_rois tools_sequencer_interp.MAXNPROCS=2 import os def main(): filter_path = None # filter_path = "mask.h5:/FILTER_MASK/filter" roi_scan_num = list(range(592,600)) reference_scan_list = list(range(592,600)) monitor_column = "izero/0.000001" first_scan_num = 464 Ydim = None # not used, it is the scan lenght , it is given by the data, contained in the scan Zdim = 2 Edim = 62 elastic_scan_for_peaks_shifts = 38 datadir = "/data/xrs/aless/data/run5_17/run7_ihr/" # datadir = "/data/id20/inhouse/data/run5_17/run7_ihr/" reference_clip = None isolate_spot_by = 7 response_fit_options = dict( [ ["niter_optical" , 40], ["beta_optical" , 0.1], ["niter_global" , 1 ] ]) resynth_z_square = 0 selected_rois = list(range(0,24)) + list( range(36,60) ) scal_prod_use_optional_solution = False volume_retrieval_beta = 0.12 volume_retrieval_niter = 100 steps_to_do = { "do_step_make_roi": True, "do_step_sample_extraction": False, "do_step_interpolation": False, "do_step_extract_reference_scan": False, "do_step_fit_reference_response": False, "do_step_resynthetise_reference": False, "do_step_scalars" : False, "do_step_volume_retrieval" : False, "do_step_put_all_in_one_stack" : False } os.system("mkdir results") scalar_products_and_volume_target_file = "results/scalar_products_and_volume.h5" roi_target_path = "results/myrois.h5:/ROIS" signals_target_file = "results/signals.h5" interpolated_signals_target_file = "results/interpolated_signals.h5" extracted_reference_target_file = "results/reference.h5" response_target_file = "results/response.h5" resynthetised_reference_and_roi_target_file = "results/resynthetised_roi_and_scan.h5" ########################################################################################### ###### LOADING PEAKS SHIFTS ###### peaks_shifts = h5py.File("../peaks_positions_for_analysers.h5","r")["peaks_positions"][()] ###### assert( len(peaks_shifts) == 72) if steps_to_do["do_step_interpolation"]: roiob = xrs_rois.roi_object() roiob.loadH5( roi_target_path ) elastic = xrs_read.Hydra( datadir ) elastic.set_roiObj( roiob ) elastic.get_compensation_factor( elastic_scan_for_peaks_shifts , method='sum') el_dict = elastic.cenom_dict Enominal = np.median( list( el_dict.values() ) ) peaks_shifts = np.array([ el_dict["ROI%02d"%i] if ("ROI%02d"%i) in el_dict else np.nan for i in range( 72) ] ) peaks_shifts-= Enominal else: peaks_shifts = None ############################################################## ########################################################################## tools_sequencer_interp.tools_sequencer( peaks_shifts = peaks_shifts , datadir = datadir , filter_path = filter_path , roi_scan_num = roi_scan_num , roi_target_path = roi_target_path , steps_to_do = steps_to_do, first_scan_num = first_scan_num, Ydim = Ydim , # not used Zdim = Zdim , Edim = Edim , monitor_column = monitor_column, signals_target_file = signals_target_file, interpolated_signals_target_file = interpolated_signals_target_file, reference_scan_list = reference_scan_list, reference_clip = reference_clip, extracted_reference_target_file = extracted_reference_target_file , isolate_spot_by = isolate_spot_by, response_target_file = response_target_file, response_fit_options = response_fit_options, resynthetised_reference_and_roi_target_file = resynthetised_reference_and_roi_target_file, resynth_z_square = resynth_z_square, selected_rois = selected_rois, scal_prod_use_optional_solution = scal_prod_use_optional_solution , scalar_products_and_volume_target_file = scalar_products_and_volume_target_file , volume_retrieval_beta = volume_retrieval_beta , volume_retrieval_niter = volume_retrieval_niter ) if steps_to_do["do_step_put_all_in_one_stack"] : volumefile = scalar_products_and_volume_target_file h5file_root = h5py.File( volumefile ,"r+" ) scankeys = list( h5file_root.keys()) scankeys.sort() volumes = [] for k in scankeys: if k[:1]!="_": continue print( k) if "volume" in h5file_root[k]: volumes.append( h5file_root[k]["volume"] ) # volume = np.concatenate(volumes,axis=0) volume = np.array(volumes) h5py.File("concatenated_volume.h5","w")["volume"] = volume h5file_root.close() main() xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/esrf_scans/interpolation/000077500000000000000000000000001412732462000277375ustar00rootroot00000000000000batch_extraction_interp.py000066400000000000000000000134061412732462000351400ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/esrf_scans/interpolationimport numpy as np import h5py import glob import json import os import h5py import math from XRStools import tools_sequencer_interp, xrs_rois, xrs_read import os def main(): os.system("xz -dk ../mask.h5.xz --stdout > mask.h5 ") filter_path = "mask.h5:/FILTER_MASK/filter" roi_scan_num = [245,246,247] reference_scan_list = [245, 246, 247] monitor_column = "izero/0.000001" first_scan_num = 651 Ydim = 25 Zdim = 10 Edim = 7 elastic_scan_for_peaks_shifts = 42 datadir = "/data/id20/inhouse/data/run3_20/run3_es949" # reference_clip = None reference_clip = [ 90, 180 ] isolate_spot_by = 6 response_fit_options = dict( [ ["niter_optical" , 100], ["beta_optical" , 0.1], ["niter_global" , 3 ] ]) resynth_z_square = 0.0 # selected_rois = list(range(0,24)) + list( range(36,60) ) selected_rois = list(range(24,36)) + list( range(60,72) ) scal_prod_use_optional_solution = False volume_retrieval_beta = 6.0e-1 volume_retrieval_niter = 100 steps_to_do = { "do_step_make_roi": False, "do_step_sample_extraction": False, "do_step_interpolation": False, "do_step_extract_reference_scan": False, "do_step_fit_reference_response": False, "do_step_resynthetise_reference": False, "do_step_scalars" : False, "do_step_volume_retrieval" : True, "do_step_put_all_in_one_stack" : True } os.system("mkdir results") scalar_products_and_volume_target_file = "results/scalar_products_and_volume.h5" roi_target_path = "results/myrois.h5:/ROIS" signals_target_file = "results/signals.h5" interpolated_signals_target_file = "results/interpolated_signals.h5" extracted_reference_target_file = "results/reference.h5" response_target_file = "results/response.h5" resynthetised_reference_and_roi_target_file = "results/resynthetised_roi_and_scan.h5" ########################################################################################### ###### LOADING PEAKS SHIFTS ###### peaks_shifts = h5py.File("../peaks_positions_for_analysers.h5","r")["peaks_positions"][()] ###### assert( len(peaks_shifts) == 72) if steps_to_do["do_step_interpolation"]: roiob = xrs_rois.roi_object() roiob.loadH5( roi_target_path ) elastic = xrs_read.Hydra( datadir ) elastic.set_roiObj( roiob ) elastic.get_compensation_factor( elastic_scan_for_peaks_shifts , method='sum') el_dict = elastic.cenom_dict Enominal = np.median( list( el_dict.values() ) ) peaks_shifts = np.array([ el_dict["ROI%02d"%i] if ("ROI%02d"%i) in el_dict else nan for i in range( 72) ] ) Enominal = np.median(peaks_shifts) peaks_shifts-= Enominal else: peaks_shifts = None ############################################################## ########################################################################## tools_sequencer_interp.tools_sequencer( peaks_shifts = peaks_shifts , datadir = datadir , filter_path = filter_path , roi_scan_num = roi_scan_num , roi_target_path = roi_target_path , steps_to_do = steps_to_do, first_scan_num = first_scan_num, Ydim = Ydim , Zdim = Zdim , Edim = Edim , monitor_column = monitor_column, signals_target_file = signals_target_file, interpolated_signals_target_file = interpolated_signals_target_file, reference_scan_list = reference_scan_list, reference_clip = reference_clip, extracted_reference_target_file = extracted_reference_target_file , isolate_spot_by = isolate_spot_by, response_target_file = response_target_file, response_fit_options = response_fit_options, resynthetised_reference_and_roi_target_file = resynthetised_reference_and_roi_target_file, resynth_z_square = 0, selected_rois = selected_rois, scal_prod_use_optional_solution = scal_prod_use_optional_solution , scalar_products_and_volume_target_file = scalar_products_and_volume_target_file , volume_retrieval_beta = volume_retrieval_beta , volume_retrieval_niter = volume_retrieval_niter ) if steps_to_do["do_step_put_all_in_one_stack"] : volumefile = scalar_products_and_volume_target_file h5file_root = h5py.File( volumefile ,"r+" ) scankeys = list( h5file_root.keys()) scankeys.sort() volumes = [] for k in scankeys: if k[:1]!="_": continue print( k) if "volume" in h5file_root[k]: volumes.append( h5file_root[k]["volume"] ) # volume = np.concatenate(volumes,axis=0) volume = np.array(volumes) h5py.File("concatenated_volume.h5","w")["volume"] = volume h5file_root.close() main() xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/esrf_scans/mask.h5.xz000077500000000000000000006305741412732462000267230ustar00rootroot000000000000007zXZִF!t/D ]Doz{wJfOjrih92\"!nl4"Z1ew䊃hM{%FMp$ I'72 ʈ/S yXz9wIA sLѓ{wqÀyk;+\H7]D.< O9Ƴ}䎜|:qH}^3cݿZ= -FC(/ԓ-ݴ Y_9D ӬwVGA:5; qԨEgaw8l{[W7$yS dy*N }i1E <`|a_%؅=!rx܈rнhkw D@k d!D-  87Vw /t4ed/ڈd[z,@z/W:MSgl2<4/>,-7>]^%jOAVwi7“u`muf0-0- !*O9^#@-3`l(O 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A negative number stands for ISTA, positive for FISTA beta_pixel : 1000.0 # L1 factor for the pixel response regularisation ## The used trajectories are always written whith the calculated response ## They can be reloaded and used as initialization(and freezed with do_refine_trajectory : 0 ) ## Uncomment the following line if you want to reload a set of trajectories ## without this options trajectories are initialised from the spots drifts ## # reload_trajectories_file : "results/response.h5" ###### ## The method first find an estimation of the foil scan trajectory on each roi ## then, based on this, obtain a fit of the optical response function ## assuming a flat pixel response. Finally the pixel response is optimised ## ## There is a final phase where a global optimisation ## is done in niter_global steps. ## ## Each step is composed of optical response fit, followed by a pixel response fit. ## If do_refine_trajectory is different from zero, the trajectory is reoptimised at each step ## niter_global : 20 ## if do_refine_trajectory=1 the start and end point of the trajectory are free ## if =2 then the start and end point are forced to a trajectory which is obtained ## from a reference scan : the foil scan may be short, then one can use the scan of ## an object to get another one : key *trajectory_reference_scan_address* ## do_refine_trajectory : 1 ## optional: only if do_refine_trajectory = 2 trajectory_reference_scansequence_address : "results/demo_newrois.h5:/ROI_FOIL/images/scans/" trajectory_threshold : 0.1 ## if the pixel response function is forced to be symmetrical simmetrizza : 1 ## where the found responses are written target_file : "results/responses_bis.h5:/" filter_rois : 0xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test1/04_superR_recreate_rois.yaml000066400000000000000000000012611412732462000313370ustar00rootroot00000000000000superR_recreate_rois : ### we have calculated the responses in responsefilename ### and we want to enlarge the scan by a margin of 3 times ### the original scan on the right and on the left ### ( so for a total of a 7 expansion factor ) responsefilename : "results/responses_bis.h5:/" nex : 1 ## the old scan covered by the old rois old_scan_address : "results/response.h5:/Scan0" ## where new rois and bnew scan are written target_filename : "results/newreponse.h5:newrep/" filter_rois : 0 filter_path : "/data/scisofttmp/mirone/raffaela/mymask.h5:/mymask" # the target destination for r ois xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test1/05_loadscan_2Dimages_galaxies.yaml000066400000000000000000000010711412732462000323250ustar00rootroot00000000000000 loadscan_2Dimages_galaxies : # roiaddress : "/data/scisofttmp/mirone/raffaela/myrois.h5:/ROIS" # the same given in create_rois roiaddress : "results/newreponse.h5:/newrep" # the same given in create_rois expdata : "/data/scisofttmp/mirone/raffaela/GS9_dataGalaxies/GS9_%05d_01.nxs:/root_spyc_config1d_RIXS_00001/scan_data/data_07" monitor_address : None scan_interval : [1,257] # from 1 to 256 included Ydim : 16 Zdim : 16 Edim : 19 signalfile : "results/extracted.h5" # Target file for the signals 06_superR_scalarProducts.yaml_template000066400000000000000000000004051412732462000333170ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test1superR_scal_deltaXimages : sample_address : results/extracted.h5:/E{iE} delta_address : results/newreponse.h5:/newrep/Scan0 nbin : 1 # optional_solution : results/SEPT_vol.h5:/VOL1_423_548/Volume target_address : results/scalprods.h5:/VOL_E{iE}/scal_prods xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test1/07_superR_getVolume.yaml_template000066400000000000000000000003041412732462000323530ustar00rootroot00000000000000superR_getVolume : scalprods_address : results/scalprods.h5:/VOL_E{iE}/scal_prods target_address : results/volumes.h5:/VOL_E{iE}/Volume niter : 1000 beta : 0.05 eps : 2e-06 debin : [1, 1] xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test1/results/000077500000000000000000000000001412732462000254635ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test1/results/.dum000066400000000000000000000000001412732462000262370ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test1/test.py000066400000000000000000000023121412732462000253110ustar00rootroot00000000000000import glob import os import h5py import numpy as np if(1): inputs = glob.glob("0*yaml") inputs.sort() for fn in inputs: if fn != "01_create_rois_galaxies.yaml": res = os.system("mpirun -n 1 XRS_swissknife %s" % fn ) assert(res==0) templates_list = [ open("06_superR_scalarProducts.yaml_template").read(), open("07_superR_getVolume.yaml_template") .read() ] for iE in range(16) : for template in templates_list : s = template.format(iE=iE) open("input.par","w").write(s) res = os.system("mpirun -n 1 XRS_swissknife input.par" ) assert(res==0) f = h5py.File("results/volumes.h5", "r") stack = [] for i in range(16): stack.append( f["VOL_E%d/Volume"%i][:,:,45:65] ) h5py.File("results/volumes_all_energies.h5","w")["volume"] = np.array(stack) ref_stack = h5py.File("/data/scisofttmp/mirone/raffaela/volumes_all_energies_reference.h5","r")["volume"][()] assert (np.abs( ref_stack- stack ).max()<1.0 ) , "difference between stack and reference stack too big" save_path="results" if os.environ["CLEAN_RESULTS"] == "true" : os.system("rm %s/*" % save_path) xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test2/000077500000000000000000000000001412732462000237635ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test2/01_create_rois_galaxies.yaml000066400000000000000000000004601412732462000313230ustar00rootroot00000000000000create_rois_galaxies : expdata : "/data/scisofttmp/mirone/Loic1/data/HC-D_2Dmap_0130.nxs:/root.spyc.config1d_RIXS_0024/scan_data/data_03" filter_path : "/data/scisofttmp/mirone/Loic1/mask.h5:/filter" roiaddress : "results/myrois.h5:/ROIS" # the target destination for roisxrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test2/batch_extraction_esynth1.py000066400000000000000000000433031412732462000313340ustar00rootroot00000000000000# newfactors e' gia pronto , dopo verifica fuzionalita di roi_sel in ximages import numpy as np import h5py import glob import json BATCH_PARALLELISM = 1 import os def main(): peaks_shifts = np.array( [ 6471.983002, 6471.318152, 6470.612314, 6471.217828, ]) - 6470.6 datadir = "/data/scisofttmp/mirone/Loic1/data/" filter_path = datadir + "../mask.h5:/filter" filename_template = "HC-D_2Dmap_%04d" data_path_template = datadir + filename_template + ".nxs:/root.spyc.config1d_RIXS_0024/scan_data/data_03" monitor_path_template = datadir + filename_template +"_monitor.hd5:/monitor" # energy_custom_grid = np.array([6746.0,6754.0,6755.5,6756.2,6757.5,6759.3,6762.5,6770.0,6790.5]) # energy_custom_grid = np.array([6744.0,6754.0,6755.5,6756.2,6757.5,6759.3,6762.5,6770.0,6792]) energy_custom_grid = np.array([6744.0,6754.0, 6754.8, 6755.5,6756.2,6757.5, 6758.63, 6759.3,6762.5,6770.0,6792]) energy_exp_grid = h5py.File( datadir +(filename_template%1)+".nxs" ,"r")["/root.spyc.config1d_RIXS_0024/scan_data/actuator_1_1"][()] scan_interval = [1,476] # from 1 to 475 included Ydim = 25 Zdim = 19 Edim = 9 roi_target_path = "results/myrois.h5:/ROIS" reference_target_file = "results/response.h5" signals_target_file = "results/extracted.h5" scalarprods_target_file = "results/scalar_prods.h5" steps_to_do = { "do_step_make_roi": False, "do_step_make_reference": False, "do_step_sample_extraction": False, "do_step_scalar_products": True, "do_step_interpolation_coefficients": False, "do_step_finalise_for_fit": False } use_custom_components = None # use_custom_components = "components.h5" scans_to_use_for_roi = list(range(1,20)) tools_sequencer( peaks_shifts = peaks_shifts , datadir = datadir , filter_path = filter_path , filename_template = filename_template , data_path_template = data_path_template , energy_custom_grid = energy_custom_grid , energy_exp_grid = energy_exp_grid , monitor_path_template = monitor_path_template , scan_interval = scan_interval , scans_to_use_for_roi = scans_to_use_for_roi , Ydim = Ydim , Zdim = Zdim , Edim = Edim , roi_target_path = roi_target_path , reference_target_file = reference_target_file , signals_target_file = signals_target_file , scalarprods_target_file = scalarprods_target_file , steps_to_do = steps_to_do, use_custom_components = use_custom_components ) def process_input(s, go=0, exploit_slurm_mpi = 0, stop_omp = False): open("input_tmp_%d.par"%go, "w").write(s) background_activator = "" if (go % BATCH_PARALLELISM ): background_activator = "&" prefix="" if stop_omp: prefix = prefix +"export OMP_NUM_THREADS=1 ;" if ( exploit_slurm_mpi==0 ): comando = (prefix +"mpirun -n 1 XRS_swissknife input_tmp_%d.par %s"%(go, background_activator)) elif ( exploit_slurm_mpi>0 ): comando = (prefix + "mpirun XRS_swissknife input_tmp_%d.par %s"%(go, background_activator) ) else: comando = (prefix + "mpirun -n %d XRS_swissknife input_tmp_%d.par %s"%(abs( exploit_slurm_mpi ), go, background_activator) ) res = os.system( comando ) assert (res==0) , " something went wrong running command : " + comando def select_rois( data_path_template=None, filter_path=None, roi_target_path=None, scans_to_use=None ): inputstring = """ create_rois_galaxies : expdata : {data_path_template} filter_path : {filter_path} roiaddress : {roi_target_path} # the target destination for rois scans : {scans_to_use} """ .format(data_path_template = data_path_template, filter_path = filter_path, roi_target_path = roi_target_path, scans_to_use = scans_to_use ) process_input( inputstring , exploit_slurm_mpi = 0 ) def extract_sample_givenrois( roi_path = None, data_path_template = None, monitor_path_template = None, scan_interval = None, Ydim = None, Zdim = None, Edim = None, signals_target_file = None ): inputstring = """ loadscan_2Dimages_galaxies : roiaddress : {roi_path} expdata : {data_path_template} monitor_address : {monitor_path_template} scan_interval : {scan_interval} Ydim : {Ydim} Zdim : {Zdim} Edim : {Edim} signalfile : {signals_target_file} """.format( roi_path = roi_path, data_path_template = data_path_template, monitor_path_template = monitor_path_template, scan_interval = scan_interval, Ydim = Ydim, Zdim = Zdim, Edim = Edim, signals_target_file = signals_target_file) process_input( inputstring, exploit_slurm_mpi = 0) def InterpInfo_Esynt_components(peaks_shifts , energy_exp_grid = None, custom_ene_list = None, custom_components = None ): components = h5py.File( custom_components ,"r")["components"] [()] info_dict = {} for i_interval in range(len(components)): info_dict[str(i_interval)] = {} info_dict[str(i_interval)]["E"] = custom_ene_list[ i_interval ] info_dict[str(i_interval)]["coefficients"]={} for i_n in range(len(energy_exp_grid)): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ]={} for roi_num, de in enumerate( peaks_shifts ): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ][ str(roi_num) ] = 0 for ic in range(len(components)): for i_interval in range(len(custom_ene_list)-1): cE1 = custom_ene_list[ i_interval ] cE2 = custom_ene_list[ i_interval+1 ] for i_ene, t_ene in enumerate( energy_exp_grid) : for roi_num, de in enumerate( peaks_shifts ): if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2+cE1) info_dict[str(ic)]["coefficients" ][ str(i_ene) ][ str(roi_num) ] += alpha * components[ic][ i_interval ] info_dict[str(ic)]["coefficients" ][ str(i_ene) ][ str(roi_num) ] += (1-alpha)*components[ic][ i_interval+1 ] return info_dict def InterpInfo_Esynt( peaks_shifts , energy_exp_grid = None, custom_ene_list = None): info_dict = {"energy_exp_grid":list(energy_exp_grid), "de_list": list(peaks_shifts)} N_custom = len(custom_ene_list) N_data = len( energy_exp_grid ) for i_interval in range(len(custom_ene_list)): info_dict[str(i_interval)] = {} info_dict[str(i_interval)]["E"] = custom_ene_list[ i_interval ] info_dict[str(i_interval)]["coefficients"]={} for i_n in range(len(energy_exp_grid)): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ]={} for roi_num, de in enumerate( peaks_shifts ): info_dict[str(i_interval)]["coefficients" ][ str(i_n) ][ str(roi_num) ] = 0 for i_interval in range( N_custom -1): cE1 = custom_ene_list[ i_interval ] cE2 = custom_ene_list[ i_interval+1 ] for i_ene, t_ene in enumerate( energy_exp_grid) : for roi_num, de in enumerate( peaks_shifts ): if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2-cE1) info_dict[str(i_interval)]["coefficients" ][ str(i_ene) ][ str(roi_num) ] = alpha info_dict[str(i_interval+1)]["coefficients"][ str(i_ene) ][ str(roi_num) ] = 1-alpha return info_dict def __init__(self, peaks_shifts, interp_file, source, custom_ene_list = None): volum_list = list(interp_file[source].keys()) scan_num_list = np.array([ int( t.split("_") [1]) for t in volum_list]) ene_list = np.array([ interp_file[source][vn]["scans"]["Scan%03d"%sn ]["motorDict"]["energy"][()] for vn,sn in zip(volum_list, scan_num_list ) ]) print ( " ecco la scannumlist " , scan_num_list) print (" ecco ene_list", ene_list) self.volum_list = volum_list self.scan_num_list = scan_num_list self.ene_list = ene_list order = np.argsort( self.ene_list ) self.ene_list = self.ene_list [order] if custom_ene_list is None: self.custom_ene_list = self.ene_list else: self.custom_ene_list = custom_ene_list self.scan_num_list = self.scan_num_list [order] self.volum_list = [ self.volum_list [ii] for ii in order ] self.interp_file=interp_file self.source= source self.peaks_shifts=peaks_shifts # interpolation_infos_file = "interpolation_infos.json" # info_dict={} # for i in range(NC): # dizio = {} # info_dict[str(i)] = {"coefficients":dizio} # c = model.components_[i] # np = len(c) # for j in range(np): # dizio[str(j)] = float(c[j]) # json.dump(info_dict,open( interpolation_infos_file,"w"), indent=4) def interpola_Esynt(self, roi_sel=roi_sel ): print ( " ECCO I DATI ") print ( self.ene_list ) print ( self.peaks_shifts ) info_dict = {} for i_intervallo in range(len(self.custom_ene_list)): info_dict[str(i_intervallo)] = {} info_dict[str(i_intervallo)]["E"] = self.custom_ene_list[ i_intervallo ] info_dict[str(i_intervallo)]["coefficients"]={} for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list )): info_dict[str(i_intervallo)]["coefficients" ][ t_vn ]={} for i_intervallo in range(len(self.custom_ene_list)-1): cE1 = self.custom_ene_list[ i_intervallo ] cE2 = self.custom_ene_list[ i_intervallo+1 ] for t_vn, t_sn, t_ene in list(zip(self.volum_list, self.scan_num_list, self.ene_list ))[0:]: for roi_num, de in enumerate( self.peaks_shifts ): if roi_num not in roi_sel: continue if t_ene+de < cE1 or t_ene+de > cE2: continue alpha = (cE2-(t_ene+de) )/(cE2-cE1) info_dict[str(i_intervallo)]["coefficients" ][ str(t_vn) ][ str(roi_num) ] = alpha info_dict[str(i_intervallo+1)]["coefficients"][ str(t_vn) ][ str(roi_num) ] = 1-alpha return info_dict def get_reference( roi_path = None, reference_target_file = None ): inputstring = """ loadscan_2Dimages_galaxies_foilscan : roiaddress : {roi_path} expdata : None signalfile : {reference_target_file} # Target file for the signals """ .format( roi_path = roi_path, reference_target_file = reference_target_file ) process_input( inputstring , exploit_slurm_mpi = 0) def get_scalars( iE = None, signals_file = None, reference_file = None, target_file = None ): inputstring = """ superR_scal_deltaXimages_Esynt : sample_address : {signals_file}:/E{iE} delta_address : {reference_file}:/Scan0 load_factors_from : nbin : 1 target_address : {target_file}:/{iE}/scal_prods """ . format( iE = iE, signals_file = signals_file , reference_file = reference_file , target_file = target_file, ) process_input( inputstring, exploit_slurm_mpi = 0) def get_volume_Esynt( scalarprods_file = None, interpolation_file = None): os.system("mkdir DATASFORCC") inputstring = """ superR_getVolume_Esynt : scalprods_address : {scalarprods_file}:/ target_address : {scalarprods_file}:/data_for_volumes dict_interp : {interpolation_file} debin : [1, 1] output_prefix : DATASFORCC/test0_ """.format( scalarprods_file = scalarprods_file , interpolation_file = interpolation_file ) process_input( inputstring, exploit_slurm_mpi = 0) def myOrder(tok): if("volume" not in tok): tokens = tok.split("_") print( tokens) return int(tokens[1])*10000+ int(tokens[2]) else: return 0 def reshuffle( volumefile = "volumes.h5", nick = None ): h5file_root = h5py.File( volumefile ,"r+" ) h5file = h5file_root[nick] scankeys = list( h5file.keys()) scankeys.sort(key=myOrder) print( scankeys) volumes = [] for k in scankeys: if k[:1]!="_": continue print( k) if "volume" in h5file[k]: volumes.append( h5file[k]["volume"] ) # volume = np.concatenate(volumes,axis=0) volume = np.array(volumes) if "concatenated_volume" in h5file: del h5file["concatenated_volume"] h5file["concatenated_volume"]=volume h5file_root.close() ## THE FOLLOWING PART IS THE RELEVANT ONE def tools_sequencer( peaks_shifts = None, datadir = None, filter_path = None, filename_template = None, data_path_template = None, energy_custom_grid = None, energy_exp_grid = None, monitor_path_template = None, scan_interval = None, scans_to_use_for_roi = None, Ydim = None, Zdim = None, Edim = None, roi_target_path = None, reference_target_file = None, signals_target_file = None, scalarprods_target_file = None, steps_to_do = None, use_custom_components = None ) : if(steps_to_do["do_step_make_roi"]): # ROI selection and reference scan select_rois( data_path_template = data_path_template, filter_path = filter_path, roi_target_path = roi_target_path, scans_to_use=scans_to_use_for_roi ) roi_path = roi_target_path if(steps_to_do["do_step_make_reference"]): get_reference( roi_path = roi_path , reference_target_file = reference_target_file ) reference_file = reference_target_file if(steps_to_do["do_step_sample_extraction"]): # SAMPLE extraction extract_sample_givenrois( roi_path = roi_path , data_path_template = data_path_template , monitor_path_template = monitor_path_template , scan_interval = scan_interval , Ydim = Ydim , Zdim = Zdim , Edim = Edim , signals_target_file = signals_target_file ) signals_file = signals_target_file if(steps_to_do["do_step_scalar_products"]): os.system("rm %s"%scalarprods_target_file) for iE in range(Edim) : get_scalars( iE = iE, signals_file = signals_file, reference_file = reference_file, target_file = scalarprods_target_file ) scalarprods_file = scalarprods_target_file interpolation_infos_file = "interpolation_infos.json" if(steps_to_do["do_step_interpolation_coefficients"]): # INTERPOLATION ESYNTH if use_custom_components is None: info_dict = InterpInfo_Esynt( peaks_shifts , energy_exp_grid = energy_exp_grid, custom_ene_list = energy_custom_grid ) else: info_dict = InterpInfo_Esynt_components( peaks_shifts, energy_exp_grid = energy_exp_grid, custom_ene_list = energy_custom_grid, custom_components = use_custom_components ) json.dump(info_dict,open( interpolation_infos_file,"w"), indent=4) # ### ESYNTH if(steps_to_do["do_step_finalise_for_fit"]): get_volume_Esynt( scalarprods_file = scalarprods_file, interpolation_file = interpolation_infos_file) main() xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test2/colletta.py000066400000000000000000000012541412732462000261460ustar00rootroot00000000000000from numpy import * import h5py f=h5py.File("results/extracted.h5","r") res=[ [0 for i in range(9) ] for r in range(5)] for ie in range(9): he = f["E%d"%ie] for iscan in range(19): for ir in range(4): m = he["Scan%d/%02d/matrix"%(iscan, ir)][()] res[ir+1][ie] += m.sum() h5py.File("plot.h5","w")["plot"] = array( res) R = array(res) R[0,:] = array( [ 6745.981087876128, 6754.004616913128, 6755.549616942595, 6756.206827194552, 6757.550552579804, 6759.304498100658, 6762.542644309955, 6770.0295586460825, 6790.5063595030115 ]) savetxt( "plot.txt" , R.T ) xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test2/extract_components.py000066400000000000000000000013331412732462000302540ustar00rootroot00000000000000from h5py import * from sklearn.decomposition import NMF from pylab import * import json NC = 4 model = NMF(n_components=NC, init='random', random_state=0, max_iter = 200) d=File("solution_all_spectra.h5","r") ["data"][()] d.shape = d.shape[0],-1 W = model.fit_transform(d.T) for i in range(NC): plot(model.components_[i]) show() File("components.h5","w")["components"] = model.components_ interpolation_infos_file = "interpolation_infos.json" info_dict={} for i in range(NC): dizio = {} info_dict[str(i)] = {"coefficients":dizio} c = model.components_[i] np = len(c) for j in range(np): dizio[str(j)] = float(c[j]) json.dump(info_dict,open( interpolation_infos_file,"w"), indent=4) xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test3/000077500000000000000000000000001412732462000237645ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test3/esynth/000077500000000000000000000000001412732462000252765ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test3/esynth/batch_extraction_esynth1.py000066400000000000000000000120061412732462000326430ustar00rootroot00000000000000import numpy as np import h5py import glob import json import os import h5py import math from XRStools import tools_sequencer_esynth_galaxies from XRStools import xrs_read, xrs_rois import os def main(): os.system("mkdir results") peaks_shifts = np.array( [ 0.0, 0.0, 0.0, 0.0, ]) - 0.0 dataprefix = "/data/raffaela/" datadir = dataprefix + "GS9_dataGalaxies/" filter_path = dataprefix + "mymask.h5:/mymask" filename_template = "GS9_%05d_01" data_path_template = datadir + filename_template + ".nxs:/root_spyc_config1d_RIXS_00001/scan_data/data_07" reference_data_path_template = dataprefix + "kapton_%05d_01" + ".nxs:/root_spyc_config1d_RIXS_00001/scan_data/data_07" monitor_path_template = None # monitor_path_template = datadir + filename_template +"_monitor.hd5:/monitor" print( datadir +(filename_template%1)+".nxs" ) energy_exp_grid = h5py.File( datadir +(filename_template%1)+".nxs" ,"r")["/root_spyc_config1d_RIXS_00001/scan_data/actuator_1_1"][()] energy_custom_grid = np.array( energy_exp_grid ) energy_custom_grid [ 0 ] -= 0.1 energy_custom_grid [+1 ] -= 0.1 scan_interval = [1,257] # from 1 to 475 included Ydim = 16 Zdim = 16 Edim = 19 reference_scan_list = [1] custom_components_file = None # custom_components_file = "components.h5" roi_scan_num = list(range(1,3)) reference_clip = None reference_clip = [ 0, 200 ] ## in the reference scan for each position there is a spot with a maximum. We set zero the background which is further than ## such radius from the maximum isolate_spot_by = 7 #### For the fit of the response function based on reference scans response_fit_options = dict( [ ["niter_optical" , 40], ["beta_optical" , 0.1], ["niter_global" , 1 ] ]) resynth_z_square = 0 selected_rois = list(range(0,4) ) steps_to_do = { "do_step_make_roi": False, "do_step_sample_extraction": False, "do_step_extract_reference_scan": False, "do_step_fit_reference_response": False, "do_step_resynthetise_reference": False, "do_step_scalar": False, "do_step_interpolation_coefficients": False, "do_step_finalise_for_fit": True } scalar_products_target_file = "results/scalar_prods.h5" roi_target_path = "results/myrois.h5:/ROIS" reference_target_file = "results/response.h5" signals_target_file = "results/extracted.h5" extracted_reference_target_file = "results/reference.h5" response_target_file = "results/response.h5" interpolation_infos_file = "interpolation_infos.json" resynthetised_reference_and_roi_target_file = "results/resynthetised_roi_and_scan.h5" tools_sequencer_esynth_galaxies.tools_sequencer( peaks_shifts = peaks_shifts , filter_path = filter_path , roi_scan_num = roi_scan_num , roi_target_path = roi_target_path , data_path_template = data_path_template , reference_data_path_template = reference_data_path_template, steps_to_do = steps_to_do, scan_interval = scan_interval , Ydim = Ydim , Zdim = Zdim , Edim = Edim , monitor_path_template = monitor_path_template , signals_target_file = signals_target_file, reference_scan_list = reference_scan_list, reference_clip = reference_clip, extracted_reference_target_file = extracted_reference_target_file , isolate_spot_by = isolate_spot_by, response_target_file = response_target_file, response_fit_options = response_fit_options, resynthetised_reference_and_roi_target_file = resynthetised_reference_and_roi_target_file, resynth_z_square = resynth_z_square, selected_rois = selected_rois, scalar_products_target_file = scalar_products_target_file , energy_custom_grid = energy_custom_grid , custom_components_file = custom_components_file, interpolation_infos_file = interpolation_infos_file, energy_exp_grid = energy_exp_grid ) main() xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test3/interp/000077500000000000000000000000001412732462000252655ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/volumes/test3/interp/batch_extraction_interp.py000066400000000000000000000133371412732462000325500ustar00rootroot00000000000000import numpy as np import h5py import glob import json import os import h5py import math from XRStools import tools_sequencer_interp_galaxies from XRStools import xrs_read, xrs_rois import os def main(): os.system("mkdir results") peaks_shifts = np.array( [ 0.0, 0.0, 0.0, 0.0, ]) - 0.0 dataprefix = "/data/raffaela/" datadir = dataprefix + "GS9_dataGalaxies/" filter_path = dataprefix + "mymask.h5:/mymask" filename_template = "GS9_%05d_01" data_path_template = datadir + filename_template + ".nxs:/root_spyc_config1d_RIXS_00001/scan_data/data_07" reference_data_path_template = dataprefix + "kapton_%05d_01" + ".nxs:/root_spyc_config1d_RIXS_00001/scan_data/data_07" monitor_path_template = None # monitor_path_template = datadir + filename_template +"_monitor.hd5:/monitor" print( datadir +(filename_template%1)+".nxs" ) energy_exp_grid = h5py.File( datadir +(filename_template%1)+".nxs" ,"r")["/root_spyc_config1d_RIXS_00001/scan_data/actuator_1_1"][()] first_scan = 1 Ydim = 16 Zdim = 16 Edim = 19 reference_scan_list = [1] roi_scan_num = list(range(1,3)) reference_clip = None reference_clip = [ 0, 200 ] ## in the reference scan for each position there is a spot with a maximum. We set zero the background which is further than ## such radius from the maximum isolate_spot_by = 7 #### For the fit of the response function based on reference scans response_fit_options = dict( [ ["niter_optical" , 40], ["beta_optical" , 0.1], ["niter_global" , 1 ] ]) resynth_z_square = 0 selected_rois = list(range(0,4) ) scal_prod_use_optional_solution = True volume_retrieval_beta = 6.0e-1 volume_retrieval_niter = 100 steps_to_do = { "do_step_make_roi": False, "do_step_sample_extraction": False, "do_step_interpolation": False, "do_step_extract_reference_scan": False, "do_step_fit_reference_response": False, "do_step_resynthetise_reference": False, "do_step_scalar": False, "do_step_volume_retrieval" : False, "do_step_put_all_in_one_stack" : True } scalar_products_and_volume_target_file = "results/scalar_prods_and_volume.h5" roi_target_path = "results/myrois.h5:/ROIS" reference_target_file = "results/response.h5" signals_target_file = "results/extracted.h5" interpolated_signals_target_file = "results/interpolated_signals.h5" extracted_reference_target_file = "results/reference.h5" response_target_file = "results/response.h5" resynthetised_reference_and_roi_target_file = "results/resynthetised_roi_and_scan.h5" tools_sequencer_interp_galaxies.tools_sequencer( peaks_shifts = peaks_shifts , data_path_template = data_path_template , filter_path = filter_path , roi_scan_num = roi_scan_num , roi_target_path = roi_target_path , first_scan = first_scan , Ydim = Ydim , Zdim = Zdim , Edim = Edim , monitor_path_template = monitor_path_template , signals_target_file = signals_target_file, interpolated_signals_target_file = interpolated_signals_target_file, steps_to_do = steps_to_do, reference_clip = reference_clip, isolate_spot_by = isolate_spot_by, reference_scan_list = reference_scan_list, reference_data_path_template = reference_data_path_template, extracted_reference_target_file = extracted_reference_target_file , response_target_file = response_target_file, response_fit_options = response_fit_options, resynthetised_reference_and_roi_target_file = resynthetised_reference_and_roi_target_file, resynth_z_square = resynth_z_square, selected_rois = selected_rois, scal_prod_use_optional_solution = scal_prod_use_optional_solution , scalar_products_and_volume_target_file = scalar_products_and_volume_target_file , volume_retrieval_beta = volume_retrieval_beta , volume_retrieval_niter = volume_retrieval_niter , energy_exp_grid = energy_exp_grid ) if steps_to_do["do_step_put_all_in_one_stack"] : volumefile = scalar_products_and_volume_target_file h5file_root = h5py.File( volumefile ,"r+" ) scankeys = list( h5file_root.keys()) scankeys.sort() volumes = [] for k in scankeys: if k[:1]!="E": continue print( k) if "volume" in h5file_root[k]: volumes.append( h5file_root[k]["volume"] ) # volume = np.concatenate(volumes,axis=0) volume = np.array(volumes) h5py.File("concatenated_volume.h5","w")["volume"] = volume h5file_root.close() main() xrstools-0.15.0+git20210910+c147919d/nonregressions/xes/000077500000000000000000000000001412732462000220275ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/xes/preparation_run4_16/000077500000000000000000000000001412732462000256315ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/xes/preparation_run4_16/ROI.xz000066400000000000000000002056041412732462000266540ustar00rootroot000000000000007zXZִF!t/]Doz{wJfOjrif.\vTǖpmnƴAMGyF>N8ABבC X~JbvdN]G76ф%.̝fbYRk&T'g|+'”%-V2' -b $:wό)|@֒\[+:҇I]['du, =W 6qr[M64|Y3?tm AZ[. &dAm-\]pg.:k#oB  [;atJo_d ]ZKS[62ˆtI:BZ6|L<`Fkء>=jioa0:'I묢m;!ObAOEА4Ck/*Ψ@Wwǹ4㲝N< bc!3dy<`p)fcp+p׎xWTZGiLޤ ڇ:IRE_Sn0q %qҨW=9ZEJJg zOʩ<*mMd o񐀧L 9rc!٨KɅ,¿E7r@{nQ_>4KZƫBq^t-gow94ö?e=QX  ޹V&Pk[E/l}͌TȈ '7_1AW}FK^#͞N xe3rB>T_}\FlS68F]~qC0ᘹ=E#t[|g|q:F%眸"dž ѫԎX2e8ׁ!eQDVm(-6~#BeWg|5֬ (.e܂'gw0G)@\SFX`Pe櫓/k[p,T\;! 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xrstools-0.15.0+git20210910+c147919d/nonregressions/xes/xes_analysis.py000066400000000000000000000032361412732462000251070ustar00rootroot00000000000000import os import json import h5py import numpy as np import XRStools. XES_spectra_extraction as spectra_extraction if(1): os.system("xz -d --keep preparation_run4_16/ROI.xz ; mv preparation_run4_16/ROI preparation_run4_16/roi.h5" ) os.system("xz -d --keep preparation_run4_16/roi_sample.h5.xz" ) if 1: spectra_extraction.prepare( work_dir="preparation_run4_16", data = "/data/id20/inhouse/data/run4_16/run6_ihr/rixs" , scan_for_roi= 66 , reference_scan = 66 , # the scan used for the reference monitorcolumn = "izero", scan_for_sample_roi = 268 , # Not necessary if recentering is not needed. # If not given the rois file is assumed (when needed if needed) # to be already there do_roi = False, do_roi_sample = False, do_recentering= True, beta_response = 1.0 ) spectra_extraction.extract( work_dir="preparation_run4_16", scan_interval = [130, 132] , target_file = "SPECTRA.h5", temporary_file = "temporary.h5", # niter = 50, # niterLip = 20, # beta = 1000000 ) f=h5py.File("SPECTRA.h5","r") ex = f["/fromscans_130_132/2/energies_130"][()] ey = f["/fromscans_130_132/2/spectraByLine_130"][()] results = np.array( [ ex, ey ] ).T np.savetxt( "results/spectra.txt" , results ) ref = np.loadtxt( "results/spectra_ref.txt") assert( abs(ref-results).max( )< 1.0e-7) if "CLEAN_RESULTS" in os.environ and os.environ["CLEAN_RESULTS"] == "true" : os.system("rm results/*") xrstools-0.15.0+git20210910+c147919d/nonregressions/xrs_raman/000077500000000000000000000000001412732462000232225ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/xrs_raman/non_reg_testing_XRS.py000077500000000000000000000265721412732462000275330ustar00rootroot00000000000000from __future__ import print_function ##### # this is important with old version of matplotlib to avoid # that matplotlib import pyqt4 while silx imports pyqt5 # try: import PyQt4.QtCore except: pass import os import numpy as np # from pylab import * # ion() from XRStools import xrs_read, roifinder_and_gui, xrs_extraction, roiSelectionWidget ##################################################################### # for programmatic testing from silx.gui.utils.testutils import QTest, TestCaseQt from silx.gui import qt AUTOMATIC_TEST = 1 if AUTOMATIC_TEST : _qapp = qt.QApplication.instance() or qt.QApplication([]) def qWait( ms=None): if ms is None: ms = cls.DEFAULT_TIMEOUT_WAIT if qt.BINDING in ('PySide', 'PySide2'): # PySide has no qWait, provide a replacement timeout = int(ms) endTimeMS = int(time.time() * 1000) + timeout while timeout > 0: _qapp.processEvents(qt.QEventLoop.AllEvents, maxtime=timeout) timeout = endTimeMS - int(time.time() * 1000) else: QTest.qWait(ms ) ## ######################################################################## class Pippo(TestCaseQt): def runTest(self): pass lowq = list(range(24)) lowq.extend(list(range(36,60))) medq = list(range(24,36)) highq = list(range(60,72)) ######################################################################## data_path = '/data/id20/inhouse/data/run5_17/run7_ihr/' save_path = './results_non_reg_testing_XRS' rois_path = save_path ######################################################################## # liquid water ######################################################################## lw = xrs_read.Hydra(data_path) # ROI definition im4roi = lw.SumDirect([611]) # elastic on the sample ################################################################################################################## # manageQApp is set to false because we are programmatically generating events by using the QTest class from silx # which manages qapp by itself. # In case of a normal usage, dont pass the manageQApp argument if AUTOMATIC_TEST : delay=1000 roiSelectionWidget.SKIP_WARNING = True w4r = roiSelectionWidget.launch4MatPlotLib(im4roi=im4roi, layout = "2X3-12", manageQApp = False ) for act in w4r.menuActions .actions(): if str(act.objectName()) == "actionGlobalSpotDetection": act.trigger() QTest.qWait(delay) QTest.mouseClick( w4r.globalSpotDectionWidget.detectionButton ,qt.Qt.LeftButton, pos= qt.QPoint(3,3)) QTest.qWait(delay) # # Select the second Tab of the MainWindow # QTest.mouseClick( w4r.viewsTab.widget(3) , qt.Qt.LeftButton, qt.Qt.NoModifier ) # w4r.viewsTab.tabBar().tabBarClicked.emit(3) QTest.mouseClick( w4r.viewsTab.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( w4r.viewsTab.tabBar().tabRect(3).x()+w4r.viewsTab.tabBar().tabRect(3).width()/2 , w4r.viewsTab.tabBar().tabRect(3).y()+ w4r.viewsTab.tabBar().tabRect(3).height()/2 ) ) QTest.qWait(delay) # alternativa w4r.viewsTab.setCurrentIndex(3) QTest.qWait(delay) myw = w4r.mws[3] myw.show() myw.graph.setFocus() pos1 = myw.graph.dataToPixel( 115,250 ) pos2 = myw.graph.dataToPixel( 115+40,250-5 ) QTest.mouseMove( myw.graph ) QTest.qWait(delay) QTest.mouseMove( myw.graph , pos= qt.QPoint( pos1[0] ,pos1[1]), delay=-1) QTest.qWait(delay) print(myw.graph) # myw.graph.onMousePress( pos1[0], pos1[1], "left" ) QTest.mousePress( myw.graph , qt.Qt.LeftButton, pos= qt.QPoint( pos1[0] ,pos1[1]), delay=10) QTest.qWait(delay) QTest.mouseMove( myw.graph , pos= qt.QPoint( pos2[0] ,pos2[1]), delay=10) QTest.qWait(delay) QTest.mouseRelease( myw.graph , qt.Qt.LeftButton, pos= qt.QPoint( pos2[0] ,pos2[1]), delay=10) # myw.graph.onMouseRelease( pos2[0], pos2[1], "left" ) QTest.qWait(delay) QTest.mouseClick( w4r.globalSpotDectionWidget.relabeliseButton ,qt.Qt.LeftButton, pos= qt.QPoint(3,3)) QTest.qWait(delay) QTest.mouseClick( w4r.globalSpotDectionWidget.annotateButton ,qt.Qt.LeftButton, pos= qt.QPoint(3,3)) QTest.qWait(delay) for act in w4r.menuActions .actions(): if str(act.objectName()) == "actionRegistration": act.trigger() QTest.qWait(delay) for act in w4r.menuFIle .actions(): if str(act.objectName()) == "actionConfirm_And_Exit": act.trigger() QTest.qWait(delay) else: w4r = roiSelectionWidget.launch4MatPlotLib(im4roi=im4roi, layout = "2X3-12", manageQApp = True ) roi = w4r.getRoiObj() roi.writeH5(os.path.join(rois_path,'ROI_widget_roi.H5')) ###############################################################################################" # reduce roi groups to those elements which are found in roi and with the right indexing rois_there = list(roi.red_rois.keys()) rois_there.sort() rois_there_num = list( map(int , [ "".join( [ c for c in rkey if c.isdigit() ] ) for rkey in rois_there ] ) ) highq = [ rois_there_num.index(rnum) for rnum in highq if rnum in rois_there_num ] medq = [ rois_there_num.index(rnum) for rnum in medq if rnum in rois_there_num ] lowq = [ rois_there_num.index(rnum) for rnum in lowq if rnum in rois_there_num ] ########################################################################## # load data, use sum-algorithm ########################################################################## lw.set_roiObj(roi) lw.get_compensation_factor(611, method='sum') lw.load_scan([611], method='sum', direct=True, scan_type='elastic') lw.load_scan([612], method='sum', direct=True, scan_type='ok1') lw.load_scan([613], method='sum', direct=True, scan_type='ok2') lw.load_scan([614], method='sum', direct=True, scan_type='ok3') lw.get_spectrum_new(method='sum', include_elastic=True) lw.get_tths(rvd=28.0, rvu=28.0, rvb=65.0, rhr=30.0, rhl=30.0, rhb=143.0, order=[0, 1, 2, 3, 4, 5]) lw_ex = xrs_extraction.edge_extraction(lw,['H2O'],[1.0],{'O':['K']}) print("QUI 1 ") # O edge low-q lw_ex.analyzerAverage(lowq, errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[250.0,534.0],[570.0,600.0],weights=[2,1],HFcore_shift=-5.0, guess= [-1.07743447e+03, 8.42895443e+02, 4.99035465e+01, 3193e+01, -3.80090286e-07, 2.73774370e-03, 5.11920401e+03],scaling=1.2, show_plots = False) lw_ex.save_average_Sqw(os.path.join(save_path,'h2o_sum_lq.dat'), emin=00.0, emax=610.0, normrange=[520.,600.]) print("QUI OK") def check_results(fn_a, fn_b): if os.path.exists( fn_b ): a = np.loadtxt(fn_a) b = np.loadtxt(fn_b) amin = a[0, 0] amax = a[-1,0] bmin = b[0, 0] bmax = b[-1,0] c0 = max(amin,bmin) c1 = min(amax,bmax) try: assert( abs( ( c1-c0 ) /(amax-amin) ) >0.9 ) assert( abs( ( c1-c0 ) /(bmax-bmin) ) >0.9 ) except: print ( " amin,amx, bmib, bmax, c0, c1 " ,amin,amx, bmib, bmax, c0, c1 ) raise ref = np.interp( a[:,0], b[:,0], b[:,1] ) try: assert( (abs( a[:,1] - ref)[np.less( c0, a[:,0])* np.less( a[:,0], c1)* np.less( a[:,0], 100) ] ).sum() <2000.0 ) assert( (abs( a[:,1] - ref)[np.less( c0, a[:,0])* np.less( a[:,0], c1)* np.less( 100, a[:,0]) ] ).sum() <10.0 ) except: print ( a[:,1] - ref ) print( (abs( a[:,1] - ref)[np.less( c0, a[:,0])* np.less( a[:,0], c1) ] ).sum() ) raise check_results( os.path.join(save_path,'h2o_sum_lq.dat') , "h2o_sum_lq_ref.dat" ) # O edge med-q lw_ex.analyzerAverage(medq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[300.0,534.0],[570.0,600.0], weights=[2,1], HFcore_shift=-5.0, guess=[-1.39664220e+03 , 1.03655696e+03 , 7.67728511e+02, 7.30355600e+02, 7.93995221e-04, -4.76580011e-01, -1.37652621e+03], scaling=1.2, show_plots = False) lw_ex.save_average_Sqw(save_path+'/h2o_sum_mq.dat', emin=0.0, emax=610.0, normrange=[520.0,600.0]) check_results( os.path.join(save_path,'h2o_sum_mq.dat') , "h2o_sum_mq_ref.dat" ) # O edge high-q lw_ex.analyzerAverage(highq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[52.0,534.0],[570.0,600.0],weights=[2,1], guess=[ 3.40779687e+02, 2.57030454e+02, 1.27747244e+03, 4.55875194e-01, -8.59501907e-06, 1.39969288e-02, 2.60071705e+00], HFcore_shift=-5.0,scaling=3.55, show_plots = False) lw_ex.save_average_Sqw(save_path+'/h2o_sum_hq.dat', emin=0.0, emax=600.0, normrange=[520.0,600.0]) check_results( os.path.join(save_path,'h2o_sum_hq.dat') , "h2o_sum_hq_ref.dat" ) ######################################################################## # liquid water, use pixel-algorithm ######################################################################## lw = xrs_read.Hydra(data_path) # # ROI definition # im4roi = lw.SumDirect([611]) # elastic on the sample # w4r = roiSelectionWidget.launch4MatPlotLib(im4roi=im4roi, layout = "2X3-12") # roi = w4r.getRoiObj() # roi.writeH5('//data/id20/inhouse/data/run5_17/run7_ihr/rois_nr/ROI_widget_roi.H5') # load data lw.set_roiObj(roi) lw.get_compensation_factor(611, method='pixel') lw.load_scan([611], method='pixel', direct=True, scan_type='elastic') lw.load_scan([612], method='pixel', direct=True, scan_type='ok1') lw.load_scan([613], method='pixel', direct=True, scan_type='ok2') lw.load_scan([614], method='pixel', direct=True, scan_type='ok3') lw.get_spectrum_new(method='pixel', include_elastic=True) lw.get_tths(rvd=28.0, rvu=28.0, rvb=65.0, rhr=30.0, rhl=30.0, rhb=143.0, order=[0, 1, 2, 3, 4, 5]) lw_ex = xrs_extraction.edge_extraction(lw,['H2O'],[1.0],{'O':['K']}) # O edge low-q lw_ex.analyzerAverage(lowq, errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[250.0,534.0],[570.0,600.0],weights=[2,1],HFcore_shift=-5.0, guess= [-1.07743447e+03, 8.42895443e+02, 4.99035465e+01, 3193e+01, -3.80090286e-07, 2.73774370e-03, 5.11920401e+03],scaling=1.2, show_plots = False) lw_ex.save_average_Sqw(save_path+'/h2o_pixel_lq.dat', emin=00.0, emax=610.0, normrange=[520.,600.]) check_results( os.path.join(save_path,'h2o_pixel_lq.dat') , os.path.join(save_path,"h2o_pixel_lq_ref.dat") ) # O edge med-q lw_ex.analyzerAverage(medq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[300.0,534.0],[570.0,600.0], weights=[2,1], HFcore_shift=-5.0, guess=[-1.39664220e+03 , 1.03655696e+03 , 7.67728511e+02, 7.30355600e+02, 7.93995221e-04, -4.76580011e-01, -1.37652621e+03], scaling=1.2, show_plots = False) lw_ex.save_average_Sqw(save_path+'/h2o_pixel_mq.dat', emin=0.0, emax=610.0, normrange=[520.0,600.0]) check_results( os.path.join(save_path,'h2o_pixel_mq.dat') , os.path.join(save_path,"h2o_pixel_mq_ref.dat") ) # O edge high-q lw_ex.analyzerAverage(highq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[52.0,534.0],[570.0,600.0],weights=[2,1], guess=[ 3.40779687e+02, 2.57030454e+02, 1.27747244e+03, 4.55875194e-01, -8.59501907e-06, 1.39969288e-02, 2.60071705e+00], HFcore_shift=-5.0,scaling=3.55, show_plots = False) lw_ex.save_average_Sqw(save_path+'/h2o_pixel_hq.dat', emin=0.0, emax=600.0, normrange=[520.0,600.0]) check_results( os.path.join(save_path,'h2o_pixel_hq.dat') , os.path.join(save_path,"h2o_pixel_hq_ref.dat") ) if "CLEAN_RESULTS" in os.environ and os.environ["CLEAN_RESULTS"] == "true" : os.system("rm %s/*" % save_path) non_reg_testing_XRS_raman_extraction.py000066400000000000000000000266431412732462000330660ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/nonregressions/xrs_ramanfrom __future__ import print_function ##### # this is important with old version of matplotlib to avoid # that matplotlib import pyqt4 while silx imports pyqt5 # try: import PyQt4.QtCore print(" QT4 ") except: pass import os import numpy as np # from pylab import * # ion() import XRStools import XRStools.ramanWidget import XRStools.ramanWidget.MainWindow import XRStools.roiSelectionWidget XRStools.roiSelectionWidget.SKIP_WARNING = True save_path="results_non_reg_testing_XRS_raman_extraction" def tosavepath( filename) : return os.path.join("results_non_reg_testing_XRS_raman_extraction",filename ) def check_results(fn_a, fn_b): a = np.loadtxt(fn_a) b = np.loadtxt(fn_b) amin = a[0, 0] amax = a[-1,0] bmin = b[0, 0] bmax = b[-1,0] c0 = max(amin,bmin) c1 = min(amax,bmax) try: assert( abs( ( c1-c0 ) /(amax-amin) ) >0.9 ) assert( abs( ( c1-c0 ) /(bmax-bmin) ) >0.9 ) except: print ( " amin,amx, bmib, bmax, c0, c1 " ,amin,amx, bmib, bmax, c0, c1 ) raise ref = np.interp( a[:,0], b[:,0], b[:,1] ) try: assert( (abs( a[:,1] - ref)[np.less( c0, a[:,0])* np.less( a[:,0], c1)] ).sum() <1.0e-4 ) except: raise ##################################################################### # for programmatic testing from silx.gui.utils.testutils import QTest, TestCaseQt from silx.gui import qt config_file = None # config_file = "conf_raman.yaml" def qWait( ms=None): if ms is None: ms = cls.DEFAULT_TIMEOUT_WAIT if qt.BINDING in ('PySide', 'PySide2'): # PySide has no qWait, provide a replacement timeout = int(ms) endTimeMS = int(time.time() * 1000) + timeout while timeout > 0: _qapp.processEvents(qt.QEventLoop.AllEvents, maxtime=timeout) timeout = endTimeMS - int(time.time() * 1000) else: QTest.qWait(ms ) ## ######################################################################## class Pippo(TestCaseQt): def runTest(self): pass _qapp = qt.QApplication.instance() or qt.QApplication([]) wtest = XRStools.ramanWidget.MainWindow.main( manageQApp = False) qWait( ms=10) if config_file is None: ntab = 0 QTest.mouseClick( wtest.tabWidget.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( wtest.tabWidget.tabBar().tabRect(ntab).x()+wtest.tabWidget.tabBar().tabRect(ntab).width()/2 , wtest.tabWidget.tabBar().tabRect(ntab).y()+ wtest.tabWidget.tabBar().tabRect(ntab).height()/2 ) ) qWait( ms=40) wtest.rsw.LoadLocal( sf="/data/id20/inhouse/data/run5_17/run7_ihr/hydra", fn="/data/id20/inhouse/data/run5_17/run7_ihr/edf/hydra_12828.edf", ns=611) print( " PIPPO " ) qWait( ms=40) for act in wtest.rsw.menuActions .actions(): if str(act.objectName()) == "actionGlobalSpotDetection": act.trigger() QTest.mouseClick( wtest.rsw.globalSpotDectionWidget.detectionButton ,qt.Qt.LeftButton, pos= qt.QPoint(3,3)) wtest.rsw.write_maskDict_to_hdf5(tosavepath("spatialroi.h5")) qWait( ms=40) ntab = 1 QTest.mouseClick( wtest.tabWidget.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( wtest.tabWidget.tabBar().tabRect(ntab).x()+wtest.tabWidget.tabBar().tabRect(ntab).width()/2 , wtest.tabWidget.tabBar().tabRect(ntab).y()+ wtest.tabWidget.tabBar().tabRect(ntab).height()/2 ) ) qWait( ms=40) uinp = wtest.sprsw.load_user_input print (uinp) # uinp["sf"] = "/data/id20/inhouse/data/run5_17/run7_ihr/" c'e' gia # uinp["roi"] = "spatialroi.h5:/datas/ROI" c'e' gia uinp["ns_s"] = [613,617,621,625] wtest.sprsw.LoadLocalOption(uinp) qWait( ms=40) wtest.showMaximized() qWait( ms=40) for ntab in range( 1,7): QTest.mouseClick( wtest.sprsw.viewsTab.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( wtest.sprsw.viewsTab.tabBar().tabRect(ntab).x()+wtest.sprsw.viewsTab.tabBar().tabRect(ntab).width()/2 , wtest.sprsw.viewsTab.tabBar().tabRect(ntab).y()+ wtest.sprsw.viewsTab.tabBar().tabRect(ntab).height()/2 ) ) qWait( ms=40) mw = wtest.sprsw.mws[ntab] mw.gc.nofcomps.setText("3") mw.gc.choosedcomp.setText("0") mw.gc.threshold.setText("0.2") qWait( ms=40) mw.gc.pushButton_calccomps.clicked.emit(True) qWait( ms=40) mw.gc.pushButton_calccomps.clicked.emit(True) mw.gc.pushButton_threshold.clicked.emit(True) qWait( ms=40) wtest.sprsw.write_maskDict_to_hdf5_option(tosavepath("sp_roi.h5")) QTest.qWait(40) ntab = 2 QTest.mouseClick( wtest.tabWidget.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( wtest.tabWidget.tabBar().tabRect(ntab).x()+wtest.tabWidget.tabBar().tabRect(ntab).width()/2 , wtest.tabWidget.tabBar().tabRect(ntab).y()+ wtest.tabWidget.tabBar().tabRect(ntab).height()/2 ) ) qWait( ms=40) else: wtest.loadConfigurationOption(config_file) ntab = 3 QTest.mouseClick( wtest.tabWidget.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( wtest.tabWidget.tabBar().tabRect(ntab).x()+wtest.tabWidget.tabBar().tabRect(ntab).width()/2 , wtest.tabWidget.tabBar().tabRect(ntab).y()+ wtest.tabWidget.tabBar().tabRect(ntab).height()/2 ) ) qWait( ms=40) myw = wtest.tabWidget.currentWidget().widget() myw.plusLine.clicked.emit(True) qWait( ms=40) myw.abstract[0][1].setText("lowq") l = myw.abstract[0][3:3+24]+ myw.abstract[0][36+3:36+3+24] for t in l: t.cb.setChecked(True) myw.plusLine.clicked.emit(True) qWait( ms=40) myw.abstract[1][1].setText("mediumq") l = myw.abstract[1][3+24:3+24+11]+myw.abstract[1][3+24+12:3+24+24] for t in l: t.cb.setChecked(True) myw.plusLine.clicked.emit(True) qWait( ms=40) myw.abstract[2][1].setText("highq") l = myw.abstract[2][3+60:3+60+12] for t in l: t.cb.setChecked(True) qWait( ms=40) ntab = 4 QTest.mouseClick( wtest.tabWidget.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( wtest.tabWidget.tabBar().tabRect(ntab).x()+wtest.tabWidget.tabBar().tabRect(ntab).width()/2 , wtest.tabWidget.tabBar().tabRect(ntab).y()+ wtest.tabWidget.tabBar().tabRect(ntab).height()/2 ) ) qWait( ms=40) myw = wtest.tabWidget.currentWidget().widget() print(myw) myw.plusLine.clicked.emit(True) qWait( ms=40) myw.abstract[0][1].setText("elastic") qWait( ms=40) myw.plusLine.clicked.emit(True) qWait( ms=40) myw.abstract[1][1].setText("ok0") qWait( ms=40) myw.plusLine.clicked.emit(True) qWait( ms=40) myw.abstract[2][1].setText("ok1") qWait( ms=40) myw.plusLine.clicked.emit(True) qWait( ms=40) myw.abstract[3][1].setText("ok2") qWait( ms=40) myw.plusLine.clicked.emit(True) qWait( ms=40) myw.abstract[4][1].setText("ok3") qWait( ms=40) fois = [4,1,4,4,4] for row in range(5): for i in range(fois[row]): for k in myw.bottoni_info.keys(): if myw.bottoni_info[k] == (+1,row): k.clicked.emit(True) qWait( ms=40) break for f in range(fois[row]): if row ==1 : myw.abstract[row][3+f].setValue(628) else: myw.abstract[row][3+f].setValue(611 +f*4 +row-(row>1)) qWait( ms=40) ntab = 5 QTest.mouseClick( wtest.tabWidget.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( wtest.tabWidget.tabBar().tabRect(ntab).x()+wtest.tabWidget.tabBar().tabRect(ntab).width()/2 , wtest.tabWidget.tabBar().tabRect(ntab).y()+ wtest.tabWidget.tabBar().tabRect(ntab).height()/2 ) ) qWait( ms=40) myw = wtest.tabWidget.currentWidget() myw.plusLine.clicked.emit(True) qWait( ms=40) print(myw.abstract) myw.abstract[0][0].setText("H2O") myw.abstract[0][0].editingFinished.emit() qWait( ms=40) abstr_stoichio, edges = myw.get_abstractN() print(" abstr_stoichio " , abstr_stoichio) print(" edges " , edges) edges["O"]["K"]=1 myw.set_abstractN(abstr_stoichio, edges) abstr_stoichio, edges # myw.abstract[0][1].setText("lowq") # l = myw.abstract[0][3:3+24]+ myw.abstract[0][36+3:36+3+24] # for t in l: # t.cb.setChecked(True) # myw.plusLine.clicked.emit(True) # qWait( ms=40) # myw.abstract[1][1].setText("mediumq") # l = myw.abstract[1][3+24:3+24+24] # for t in l: # t.cb.setChecked(True) # myw.plusLine.clicked.emit(True) # qWait( ms=40) # myw.abstract[2][1].setText("highq") qWait( ms=40) ntab = 6 QTest.mouseClick( wtest.tabWidget.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( wtest.tabWidget.tabBar().tabRect(ntab).x()+wtest.tabWidget.tabBar().tabRect(ntab).width()/2 , wtest.tabWidget.tabBar().tabRect(ntab).y()+ wtest.tabWidget.tabBar().tabRect(ntab).height()/2 ) ) myw = wtest.tabWidget.currentWidget() myw.spinBox_scan.setValue(611) qWait( ms=40) myw.pushButton.clicked.emit(True) qWait( ms=40) for ntab in [7,8,9] : QTest.mouseClick( wtest.tabWidget.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( wtest.tabWidget.tabBar().tabRect(ntab).x()+wtest.tabWidget.tabBar().tabRect(ntab).width()/2 , wtest.tabWidget.tabBar().tabRect(ntab).y()+ wtest.tabWidget.tabBar().tabRect(ntab).height()/2 ) ) myw = wtest.tabWidget.currentWidget() qWait( ms=40) rois = myw.plot.getCurvesRoiDockWidget().getRois() rois["range1"].setFrom(100.0) rois["range1"].setTo (500.0) myw.lineEdit_hfshift .setText("-5.0") if ntab==9 : qWait( ms=40) myw.pushButton_guess.clicked.emit(True) myw.lineEdit_A0.setText("300.0") myw.lineEdit_A0.editingFinished.emit() qWait( ms=40) myw.pushButton_guess.clicked.emit(True) qWait( ms=40) rois["range2"].setFrom(550.0) rois["range2"].setTo (594.0) myw.pushButton_fit.clicked.emit(True) rois["Output"].setFrom(457.0) rois["Output"].setTo (577.0) rois["Norm"].setFrom(516.0) rois["Norm"].setTo (553.0) qWait( ms=40) ntab = 6 QTest.mouseClick( wtest.tabWidget.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( wtest.tabWidget.tabBar().tabRect(ntab).x()+wtest.tabWidget.tabBar().tabRect(ntab).width()/2 , wtest.tabWidget.tabBar().tabRect(ntab).y()+ wtest.tabWidget.tabBar().tabRect(ntab).height()/2 ) ) myw = wtest.tabWidget.currentWidget() myw.lineEdit_outputPrefix.setText(tosavepath("non_reg_output_gui_raman")) myw.pushButton_saveAnalysis.clicked.emit(True) qWait( ms=40) check_results(tosavepath("non_reg_output_gui_raman_lowq.txt"),tosavepath("non_reg_output_gui_raman_reference_lowq.txt")) check_results(tosavepath("non_reg_output_gui_raman_mediumq.txt"),tosavepath("non_reg_output_gui_raman_reference_mediumq.txt")) check_results(tosavepath("non_reg_output_gui_raman_highq.txt"),tosavepath("non_reg_output_gui_raman_reference_highq.txt")) print(" OK " ) if "CLEAN_RESULTS" in os.environ and os.environ["CLEAN_RESULTS"] == "true" : os.system("rm %s/*" % save_path) xrstools-0.15.0+git20210910+c147919d/nonregressions/xrs_raman/non_reg_testing_XRS_small.py000077500000000000000000000257561412732462000307260ustar00rootroot00000000000000from __future__ import print_function ##### # this is important with old version of matplotlib to avoid # that matplotlib import pyqt4 while silx imports pyqt5 # try: import PyQt4.QtCore except: pass import os import numpy as np # from pylab import * # ion() # ROI_widget_roi_small.H5.xz save_path = './results_non_reg_testing_XRS_small' os.system("unxz -k " + os.path.join(save_path,"ROI_widget_roi_small.H5.xz") ) from XRStools import xrs_read, roifinder_and_gui, xrs_extraction, roiSelectionWidget, xrs_rois ##################################################################### # for programmatic testing from silx.gui.utils.testutils import QTest, TestCaseQt from silx.gui import qt AUTOMATIC_TEST = 0 if AUTOMATIC_TEST : _qapp = qt.QApplication.instance() or qt.QApplication([]) def qWait( ms=None): if ms is None: ms = cls.DEFAULT_TIMEOUT_WAIT if qt.BINDING in ('PySide', 'PySide2'): # PySide has no qWait, provide a replacement timeout = int(ms) endTimeMS = int(time.time() * 1000) + timeout while timeout > 0: _qapp.processEvents(qt.QEventLoop.AllEvents, maxtime=timeout) timeout = endTimeMS - int(time.time() * 1000) else: QTest.qWait(ms ) ## ######################################################################## class Pippo(TestCaseQt): def runTest(self): pass lowq = [0,3,4] medq = [1] highq = [2,5] ######################################################################## data_path = '/data/id20/inhouse/data/run5_17/run7_ihr/' rois_path = './results_non_reg_testing_XRS_small' ######################################################################## # liquid water ######################################################################## lw = xrs_read.Hydra(data_path) # ROI definition im4roi = lw.SumDirect([611]) # elastic on the sample ################################################################################################################## # manageQApp is set to false because we are programmatically generating events by using the QTest class from silx # which manages qapp by itself. # In case of a normal usage, dont pass the manageQApp argument if AUTOMATIC_TEST : delay=1000 roiSelectionWidget.SKIP_WARNING = True w4r = roiSelectionWidget.launch4MatPlotLib(im4roi=im4roi, layout = "2X3-12", manageQApp = False ) for act in w4r.menuActions .actions(): if str(act.objectName()) == "actionGlobalSpotDetection": act.trigger() QTest.qWait(delay) QTest.mouseClick( w4r.globalSpotDectionWidget.detectionButton ,qt.Qt.LeftButton, pos= qt.QPoint(3,3)) QTest.qWait(delay) # # Select the second Tab of the MainWindow # QTest.mouseClick( w4r.viewsTab.widget(3) , qt.Qt.LeftButton, qt.Qt.NoModifier ) # w4r.viewsTab.tabBar().tabBarClicked.emit(3) QTest.mouseClick( w4r.viewsTab.tabBar(), qt.Qt.LeftButton , pos=qt.QPoint( w4r.viewsTab.tabBar().tabRect(3).x()+w4r.viewsTab.tabBar().tabRect(3).width()/2 , w4r.viewsTab.tabBar().tabRect(3).y()+ w4r.viewsTab.tabBar().tabRect(3).height()/2 ) ) QTest.qWait(delay) # alternativa w4r.viewsTab.setCurrentIndex(3) QTest.qWait(delay) myw = w4r.mws[3] myw.show() myw.graph.setFocus() pos1 = myw.graph.dataToPixel( 115,250 ) pos2 = myw.graph.dataToPixel( 115+40,250-5 ) QTest.mouseMove( myw.graph ) QTest.qWait(delay) QTest.mouseMove( myw.graph , pos= qt.QPoint( pos1[0] ,pos1[1]), delay=-1) QTest.qWait(delay) myw.graph.onMousePress( pos1[0], pos1[1], "left" ) # QTest.mousePress( myw.graph , qt.Qt.LeftButton, pos= qt.QPoint( pos1[0] ,pos1[1]), delay=10) QTest.qWait(delay) QTest.mouseMove( myw.graph , pos= qt.QPoint( pos2[0] ,pos2[1]), delay=10) QTest.qWait(delay) # QTest.mouseRelease( myw.graph , qt.Qt.LeftButton, pos= qt.QPoint( pos2[0] ,pos2[1]), delay=10) myw.graph.onMouseRelease( pos2[0], pos2[1], "left" ) QTest.qWait(delay) QTest.mouseClick( w4r.globalSpotDectionWidget.relabeliseButton ,qt.Qt.LeftButton, pos= qt.QPoint(3,3)) QTest.qWait(delay) QTest.mouseClick( w4r.globalSpotDectionWidget.annotateButton ,qt.Qt.LeftButton, pos= qt.QPoint(3,3)) QTest.qWait(delay) for act in w4r.menuActions .actions(): if str(act.objectName()) == "actionRegistration": act.trigger() QTest.qWait(delay) for act in w4r.menuFIle .actions(): if str(act.objectName()) == "actionConfirm_And_Exit": act.trigger() QTest.qWait(delay) else: # w4r = roiSelectionWidget.launch4MatPlotLib(im4roi=im4roi, layout = "2X3-12" ) pass # roi = w4r.getRoiObj() roi=xrs_rois.roi_object() roi.loadH5(os.path.join(rois_path,'ROI_widget_roi_small.H5')) ########################################################################## # load data, use sum-algorithm ########################################################################## lw.set_roiObj(roi) lw.get_compensation_factor(611, method='sum') lw.load_scan([611], method='sum', direct=True, scan_type='elastic') lw.load_scan([612], method='sum', direct=True, scan_type='ok1') lw.load_scan([613], method='sum', direct=True, scan_type='ok2') lw.load_scan([614], method='sum', direct=True, scan_type='ok3') lw.get_spectrum_new(method='sum', include_elastic=True, interpolation=True) print( " ELOSS ", lw.eloss ) lw.get_tths(rvd=28.0, rvu=28.0, rvb=65.0, rhr=30.0, rhl=30.0, rhb=143.0, order=[0, 1, 2, 3, 4, 5]) lw_ex = xrs_extraction.edge_extraction(lw,['H2O'],[1.0],{'O':['K']}) # O edge low-q lw_ex.analyzerAverage(lowq, errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[250.0,534.0],[570.0,600.0],weights=[2,1],HFcore_shift=-5.0, guess= [-1.07743447e+03, 8.42895443e+02, 4.99035465e+01, 3193e+01, -3.80090286e-07, 2.73774370e-03, 5.11920401e+03],scaling=1.2, show_plots = False) lw_ex.save_average_Sqw(os.path.join(save_path,'h2o_sum_lq_small.dat'), emin=00.0, emax=610.0, normrange=[520.,600.]) def check_results(fn_a, fn_b): if os.path.exists( fn_b ): a = np.loadtxt(fn_a) b = np.loadtxt(fn_b) amin = a[0, 0] amax = a[-1,0] bmin = b[0, 0] bmax = b[-1,0] c0 = max(amin,bmin) c1 = min(amax,bmax) try: assert( abs( ( c1-c0 ) /(amax-amin) ) >0.9 ) assert( abs( ( c1-c0 ) /(bmax-bmin) ) >0.9 ) except: print ( " amin,amx, bmib, bmax, c0, c1 " ,amin,amx, bmib, bmax, c0, c1 ) raise ref = np.interp( a[:,0], b[:,0], b[:,1] ) try: assert( (abs( a[:,1] - ref)[np.less( c0, a[:,0])* np.less( a[:,0], c1)* np.less( a[:,0], 100) ] ).sum() <2000.0 ) assert( (abs( a[:,1] - ref)[np.less( c0, a[:,0])* np.less( a[:,0], c1)* np.less( 100, a[:,0]) ] ).sum() <10.0 ) except: print ( a[:,1] - ref ) print( (abs( a[:,1] - ref)[np.less( c0, a[:,0])* np.less( a[:,0], c1) ] ).sum() ) raise check_results( os.path.join(save_path,'h2o_sum_lq_small.dat') , "h2o_sum_lq_small.dat.ref" ) # O edge med-q lw_ex.analyzerAverage(medq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[300.0,534.0],[570.0,600.0], weights=[2,1], HFcore_shift=-5.0, guess=[-1.39664220e+03 , 1.03655696e+03 , 7.67728511e+02, 7.30355600e+02, 7.93995221e-04, -4.76580011e-01, -1.37652621e+03], scaling=1.2, show_plots = False) lw_ex.save_average_Sqw(save_path+'/h2o_sum_mq_small.dat', emin=0.0, emax=610.0, normrange=[520.0,600.0]) check_results( os.path.join(save_path,'h2o_sum_mq_small.dat') , "h2o_sum_mq_small.dat.ref" ) # O edge high-q lw_ex.analyzerAverage(highq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[52.0,534.0],[570.0,600.0],weights=[2,1], guess=[ 3.40779687e+02, 2.57030454e+02, 1.27747244e+03, 4.55875194e-01, -8.59501907e-06, 1.39969288e-02, 2.60071705e+00], HFcore_shift=-5.0,scaling=1.55, show_plots = False) lw_ex.save_average_Sqw(save_path+'/h2o_sum_hq_small.dat', emin=0.0, emax=600.0, normrange=[520.0,600.0]) check_results( os.path.join(save_path,'h2o_sum_hq_small.dat') , "h2o_sum_hq_small.dat.ref" ) ######################################################################## # liquid water, use pixel-algorithm ######################################################################## lw = xrs_read.Hydra(data_path) # # ROI definition # im4roi = lw.SumDirect([611]) # elastic on the sample # w4r = roiSelectionWidget.launch4MatPlotLib(im4roi=im4roi, layout = "2X3-12") # roi = w4r.getRoiObj() # roi.writeH5('//data/id20/inhouse/data/run5_17/run7_ihr/rois_nr/ROI_widget_roi.H5') # load data lw.set_roiObj(roi) lw.get_compensation_factor(611, method='pixel') lw.load_scan([611], method='pixel', direct=True, scan_type='elastic') lw.load_scan([612], method='pixel', direct=True, scan_type='ok1') lw.load_scan([613], method='pixel', direct=True, scan_type='ok2') lw.load_scan([614], method='pixel', direct=True, scan_type='ok3') lw.get_spectrum_new(method='pixel', include_elastic=True) lw.get_tths(rvd=28.0, rvu=28.0, rvb=65.0, rhr=30.0, rhl=30.0, rhb=143.0, order=[0, 1, 2, 3, 4, 5]) lw_ex = xrs_extraction.edge_extraction(lw,['H2O'],[1.0],{'O':['K']}) # O edge low-q lw_ex.analyzerAverage(lowq, errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[250.0,534.0],[570.0,600.0],weights=[2,1],HFcore_shift=-5.0, guess= [-1.07743447e+03, 8.42895443e+02, 4.99035465e+01, 3193e+01, -3.80090286e-07, 2.73774370e-03, 5.11920401e+03],scaling=1.2, show_plots = False) lw_ex.save_average_Sqw(save_path+'/h2o_pixel_lq_small.dat', emin=00.0, emax=610.0, normrange=[520.,600.]) check_results( os.path.join(save_path,'h2o_pixel_lq_small.dat') , os.path.join(save_path,"h2o_pixel_lq_small.dat.ref") ) # O edge med-q lw_ex.analyzerAverage(medq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[300.0,534.0],[570.0,600.0], weights=[2,1], HFcore_shift=-5.0, guess=[-1.39664220e+03 , 1.03655696e+03 , 7.67728511e+02, 7.30355600e+02, 7.93995221e-04, -4.76580011e-01, -1.37652621e+03], scaling=1.2, show_plots = False) lw_ex.save_average_Sqw(save_path+'/h2o_pixel_mq_small.dat', emin=0.0, emax=610.0, normrange=[520.0,600.0]) check_results( os.path.join(save_path,'h2o_pixel_mq_small.dat') , os.path.join(save_path,"h2o_pixel_mq_small.dat.ref") ) # O edge high-q lw_ex.analyzerAverage(highq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[52.0,534.0],[570.0,600.0],weights=[2,1], guess=[ 3.40779687e+02, 2.57030454e+02, 1.27747244e+03, 4.55875194e-01, -8.59501907e-06, 1.39969288e-02, 2.60071705e+00], HFcore_shift=-5.0,scaling=1.55, show_plots = False) lw_ex.save_average_Sqw(save_path+'/h2o_pixel_hq_small.dat', emin=0.0, emax=600.0, normrange=[520.0,600.0]) check_results( os.path.join(save_path,'h2o_pixel_hq_small.dat') , os.path.join(save_path,"h2o_pixel_hq_small.dat.ref") ) if "CLEAN_RESULTS" in os.environ and 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ion() from XRStools import xrs_read, roifinder_and_gui, xrs_extraction, xrs_rois from PyQt4 import Qt, QtCore app=Qt.QApplication([]) import matplotlib matplotlib.use("Qt4Agg") from XRStools import roiSelectionWidget from XRStools.roiNmaSelectionGui import roiNmaSelectionWidget from XRStools import roiSelectionWidget lowq = list(range(24)) lowq.extend(range(36,60)) medq = list(range(24,36)) highq = list(range(60,72)) ######################################################################## # set the filename and path (this is the O K-edge of liquid water) path = '/data/id20/inhouse/data/run5_17/run7_ihr/' lw = xrs_read.Hydra(path) ######################################################################## ######################################################################## # define ROIs if 0 : # create the initial spatial mask roifinder = roifinder_and_gui.roi_finder() im4roi = lw.SumDirect([613,617,621,625]) w4r = roiSelectionWidget.mainwindow(layout="2X3-12") w4r.showImage(im4roi ) ####### TO RELOAD A PREVIOUS NNMA SELECTION inside the widget ######################## ## w4r.loadMaskDictFromH5( "spectral_myroi.h5:/datas/ROI" ) ################################################################### ####### TO RELOAD A PREVIOUS NNMA SELECTION inside the widget ######################## # w4r.loadMaskDictFromH5( "myroi.h5:/datas/ROI" ) ################################################################### w4r.show() app.exec_() assert(w4r.isOK) w4r.saveMaskDictOnH5( "Amyroi.h5:/datas/ROI" ) w4r = None if 0: # create the mask by spectral analisys spectral_w4r = roiNmaSelectionWidget.mainwindow() sf = '/data/id20/inhouse/data/run5_17/run7_ihr/hydra' fn = "Amyroi.h5:/datas/ROI" ns_s = [613,617,621,625] spectral_w4r.load_rois(sf, fn , ns_s ) spectral_w4r.show() ####### TO RELOAD A PREVIOUS NNMA SELECTION inside the widget ######################## # spectral_w4r.loadMaskDictFromH5( "spectral_myroi.h5:/datas/ROI" ) ################################################################### app .exec_() assert(spectral_w4r.isOK) spectral_w4r.saveMaskDictOnH5( "Aspectral_myroi.h5:/datas/ROI" ) myroi=spectral_w4r.getRoiObj() spectral_w4r = None else: # simply reuse an already written nnma mask myroi = xrs_rois.load_rois_fromh5_address("Aspectral_myroi.h5:/datas/ROI") lw.set_roiObj(myroi) print ( list(myroi.red_rois.keys() ) ) roinums = [ int(''.join(filter(str.isdigit, str(key) ))) for key in myroi.red_rois.keys() ] roinums.sort() lowq = [ i for i in range(len(roinums)) if roinums[i] in lowq ] medq = [ i for i in range(len(roinums)) if roinums[i] in medq ] highq = [ i for i in range(len(roinums)) if roinums[i] in highq ] ######################################################################## # load the spectra (something is going wrong during the normalization, # so I am adding this scaling parameter, I think it happens when loading the # scans that the monitor variable is multiplied by a wrong factor) scaling = np.zeros(72) scaling[lowq] = 4.3 ################ SCALING scaling[medq] = 4.3 scaling[highq]= 4.4 lw.get_compensation_factor(611, method='sum') print(" --------------------------------------------------------") print( lw.cenom_dict.keys()) lw.load_scan([611,615,619,623], method='sum', direct=True, scan_type='elastic', scaling=scaling) lw.load_scan([628], method='sum', direct=True, scan_type='ok0', scaling=scaling) lw.load_scan([612,616,620,624], method='sum', direct=True, scan_type='ok1', scaling=scaling) lw.load_scan([613,617,621,625], method='sum', direct=True, scan_type='ok2', scaling=scaling) lw.load_scan([614,618,622,626], method='sum', direct=True, scan_type='ok3', scaling=scaling) lw.get_spectrum_new(method='sum', include_elastic=True) #### include elastic print( lw.energy) print( lw.signals) print( lw.errors) ######################################################################## # setting the scattering angles lw.get_tths(rvd=28.0, rvu=28.0, rvb=65.0, rhr=30.0, rhl=30.0, rhb=143.0, order=[0, 1, 2, 3, 4, 5]) ######################################################################## # starting the subtraction of the background # 1. average over some crystals # 2. subtract a Pearson function # 3. write the spectra into a txt file lw_ex = xrs_extraction.edge_extraction( lw,['H2O'],[1.0],{'O':['K']}) # O edge low-q lw_ex.analyzerAverage(lowq, errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[250.0,532.0],[550.0,600.0],weights=[2,1],HFcore_shift=-5.0, guess= [-1.07743447e+03, 8.42895443e+02, 4.99035465e+01, 3193e+01, -3.80090286e-07, 2.73774370e-03, 5.11920401e+03],scaling=1.32) lw_ex.save_average_Sqw('water_OK_nnmf_lq.dat', emin=00.0, emax=610.0, normrange=[520.,600.]) # O edge med-q lw_ex.analyzerAverage(medq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[300.0,532.0],[550.0,600.0], weights=[2,1], HFcore_shift=-5.0, guess=[-1.39664220e+03 , 1.03655696e+03 , 7.67728511e+02, 7.30355600e+02, 7.93995221e-04, -4.76580011e-01, -1.37652621e+03], scaling=1.34) lw_ex.save_average_Sqw('water_OK_nnmf_mq.dat', emin=0.0, emax=610.0, normrange=[520.0,600.0]) # O edge high-q lw_ex.analyzerAverage(highq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[52.0,532.0],[550.0,600.0],weights=[2,1], guess=[ 3.40779687e+02, 2.57030454e+02, 1.27747244e+03, 4.55875194e-01, -8.59501907e-06, 1.39969288e-02, 2.60071705e+00], HFcore_shift=-5.0,scaling=3.55) lw_ex.save_average_Sqw('water_OK_nnmf_hq.dat', emin=0.0, emax=600.0, normrange=[520.0,600.0]) xrstools-0.15.0+git20210910+c147919d/sandbox/for_alessandro_pixel_extraction.py000066400000000000000000000066721412732462000266370ustar00rootroot00000000000000import matplotlib matplotlib.use("Qt4Agg") import numpy as np from pylab import * ion() from XRStools import xrs_read, roifinder_and_gui, xrs_extraction lowq = range(24) lowq.extend(range(36,60)) medq = range(24,36) highq = range(60,72) ######################################################################## # set the filename and path (this is the O K-edge of liquid water) path = '/data/id20/inhouse/data/run5_17/run7_ihr/' lw = xrs_read.Hydra(path) ######################################################################## ######################################################################## # define ROIs roifinder = roifinder_and_gui.roi_finder() #im4roi = lw.SumDirect([613,617,621,625]) #roifinder.get_zoom_rois(im4roi) #roifinder.roi_obj.writeH5(path+'rois/lw_OK_raw_bigroi.h5') roifinder.roi_obj.loadH5(path+'rois/lw_OK_raw_bigroi.h5') #roifinder.roi_obj.loadH5(path+'rois/lw_OK_MFrefined.h5') lw.set_roiObj(roifinder.roi_obj) ######################################################################## # load the spectra (something is going wrong during the normalization, # so I am adding this scaling parameter, I think it happens when loading the # scans that the monitor variable is multiplied by a wrong factor) scaling = np.zeros(72) scaling[lowq] = 4.3 scaling[medq] = 4.3 scaling[highq]= 4.4 method = 'pixel' lw.get_compensation_factor(611, method=method) #lw.load_scan([628], method=method, direct=True, scan_type='long', scaling=scaling) lw.load_scan([611,615,619,623], method=method, direct=True, scan_type='elastic') lw.load_scan([612,616,620,624], method=method, direct=True, scan_type='ok1') lw.load_scan([613,617,621,625], method=method, direct=True, scan_type='ok2') lw.load_scan([614,618,622,626], method=method, direct=True, scan_type='ok3') lw.get_spectrum_new(method=method, include_elastic=True) ######################################################################## # setting the scattering angles lw.get_tths(rvd=28.0, rvu=28.0, rvb=65.0, rhr=30.0, rhl=30.0, rhb=143.0, order=[0, 1, 2, 3, 4, 5]) ######################################################################## # starting the subtraction of the background # 1. average over some crystals # 2. subtract a Pearson function # 3. write the spectra into a txt file lw_ex = xrs_extraction.edge_extraction(lw,['H2O'],[1.0],{'O':['K']}) # O edge low-q lw_ex.analyzerAverage(lowq, errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[250.0,532.0],[550.0,600.0],weights=[2,1],HFcore_shift=-5.0, guess= [-1.07743447e+03, 8.42895443e+02, 4.99035465e+01, 3193e+01, -3.80090286e-07, 2.73774370e-03, 5.11920401e+03],scaling=1.32) lw_ex.save_average_Sqw('water_OK_pixel_lq.dat', emin=00.0, emax=610.0, normrange=[520.,600.]) # O edge med-q lw_ex.analyzerAverage(medq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[300.0,532.0],[550.0,600.0], weights=[2,1], HFcore_shift=-5.0, guess=[-1.39664220e+03 , 1.03655696e+03 , 7.67728511e+02, 7.30355600e+02, 7.93995221e-04, -4.76580011e-01, -1.37652621e+03], scaling=1.34) lw_ex.save_average_Sqw('water_OK_pixel_mq.dat', emin=0.0, emax=610.0, normrange=[520.0,600.0]) # O edge high-q lw_ex.analyzerAverage(highq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[52.0,532.0],[550.0,600.0],weights=[2,1], guess=[ 3.40779687e+02, 2.57030454e+02, 1.27747244e+03, 4.55875194e-01, -8.59501907e-06, 1.39969288e-02, 2.60071705e+00], HFcore_shift=-5.0,scaling=3.55) lw_ex.save_average_Sqw('water_OK_pixel_hq.dat', emin=0.0, emax=600.0, normrange=[520.0,600.0]) xrstools-0.15.0+git20210910+c147919d/sandbox/for_alessandro_sum_extraction.py000066400000000000000000000066331412732462000263170ustar00rootroot00000000000000import matplotlib matplotlib.use("Qt4Agg") import numpy as np from pylab import * ion() from XRStools import xrs_read, roifinder_and_gui, xrs_extraction lowq = range(24) lowq.extend(range(36,60)) medq = range(24,36) highq = range(60,72) ######################################################################## # set the filename and path (this is the O K-edge of liquid water) path = '/data/id20/inhouse/data/run5_17/run7_ihr/' lw = xrs_read.Hydra(path) ######################################################################## ######################################################################## # define ROIs roifinder = roifinder_and_gui.roi_finder() #im4roi = lw.SumDirect([613,617,621,625]) #roifinder.get_zoom_rois(im4roi) #roifinder.roi_obj.writeH5(path+'rois/lw_OK_raw_bigroi.h5') roifinder.roi_obj.loadH5(path+'rois/lw_OK_raw_bigroi.h5') #roifinder.roi_obj.loadH5(path+'rois/lw_OK_MFrefined.h5') lw.set_roiObj(roifinder.roi_obj) ######################################################################## # load the spectra (something is going wrong during the normalization, # so I am adding this scaling parameter, I think it happens when loading the # scans that the monitor variable is multiplied by a wrong factor) scaling = np.zeros(72) scaling[lowq] = 4.3 scaling[medq] = 4.3 scaling[highq]= 4.4 lw.get_compensation_factor(611, method='sum') lw.load_scan([611,615,619,623], method='sum', direct=True, scan_type='elastic') lw.load_scan([628], method='sum', direct=True, scan_type='ok0', scaling=scaling) lw.load_scan([612,616,620,624], method='sum', direct=True, scan_type='ok1') lw.load_scan([613,617,621,625], method='sum', direct=True, scan_type='ok2') lw.load_scan([614,618,622,626], method='sum', direct=True, scan_type='ok3') lw.get_spectrum_new(method='sum', include_elastic=True) ######################################################################## # setting the scattering angles lw.get_tths(rvd=28.0, rvu=28.0, rvb=65.0, rhr=30.0, rhl=30.0, rhb=143.0, order=[0, 1, 2, 3, 4, 5]) ######################################################################## # starting the subtraction of the background # 1. average over some crystals # 2. subtract a Pearson function # 3. write the spectra into a txt file lw_ex = xrs_extraction.edge_extraction(lw,['H2O'],[1.0],{'O':['K']}) # O edge low-q lw_ex.analyzerAverage(lowq, errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[250.0,532.0],[550.0,600.0],weights=[2,1],HFcore_shift=-5.0, guess= [-1.07743447e+03, 8.42895443e+02, 4.99035465e+01, 3193e+01, -3.80090286e-07, 2.73774370e-03, 5.11920401e+03],scaling=1.32) lw_ex.save_average_Sqw('water_OK_sum_lq.dat', emin=00.0, emax=610.0, normrange=[520.,600.]) # O edge med-q lw_ex.analyzerAverage(medq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[300.0,532.0],[550.0,600.0], weights=[2,1], HFcore_shift=-5.0, guess=[-1.39664220e+03 , 1.03655696e+03 , 7.67728511e+02, 7.30355600e+02, 7.93995221e-04, -4.76580011e-01, -1.37652621e+03], scaling=1.34) lw_ex.save_average_Sqw('water_OK_sum_mq.dat', emin=0.0, emax=610.0, normrange=[520.0,600.0]) # O edge high-q lw_ex.analyzerAverage(highq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[52.0,532.0],[550.0,600.0],weights=[2,1], guess=[ 3.40779687e+02, 2.57030454e+02, 1.27747244e+03, 4.55875194e-01, -8.59501907e-06, 1.39969288e-02, 2.60071705e+00], HFcore_shift=-5.0,scaling=3.55) lw_ex.save_average_Sqw('water_OK_sum_hq.dat', emin=0.0, emax=600.0, normrange=[520.0,600.0]) xrstools-0.15.0+git20210910+c147919d/sandbox/nnmf_highpressure.py000066400000000000000000000175331412732462000237210ustar00rootroot00000000000000 import numpy as np import subprocess from pylab import * ### ion() from XRStools import xrs_read, roifinder_and_gui, xrs_extraction, xrs_rois from PyQt4 import Qt, QtCore app=Qt.QApplication([]) import matplotlib matplotlib.use("Qt4Agg") from XRStools import roiSelectionWidget from XRStools.roiNmaSelectionGui import roiNmaSelectionWidget from XRStools import roiSelectionWidget lowq = list(range(24)) lowq.extend(range(36,60)) medq = list(range(24,36)) highq = list(range(60,72)) ######################################################################## # set the filename and path (this is the O K-edge of liquid water) # path = '/data/id20/inhouse/data/run5_17/run7_ihr/' # scans = [613,617,621,625] path = '/data/id20/inhouse/data/run1_18/run4_es689/' scans = list(range(63,79,2)) ######################################################################## ######################################################################## # define ROIs USE_SWISSKNIFE = 1 if 1 : # create the initial spatial mask if USE_SWISSKNIFE: s=""" create_rois : expdata : "{path}/hydra" # this points to a spec file scans : {scans} # a list containing one or more scans # for the elastic part. # They will be summed to an image # and rois will then be extracted from this image. roiaddress : "{target}" # the target destination for rois """ s=s.format(path=path, scans = scans, target = "myroi.h5:/datas/ROI" ) open("input.yaml","w").write(s) comando = 'XRS_swissknife input.yaml' p1 = subprocess.Popen(args=comando.split( " ") ,stdin=subprocess.PIPE,stdout=subprocess.PIPE,stderr=subprocess.PIPE) ms,errors= p1.communicate() if p1.returncode: print ( " ERRORS ") print ( errors.decode("utf-8") ) raise Exception("The create_rois instruction was stopped with error message ", errors.decode("utf-8")) else: lw = xrs_read.Hydra(path) im4roi = lw.SumDirect(scans) w4r = roiSelectionWidget.mainwindow(layout="2X3-12") w4r.showImage(im4roi ) ####### TO RELOAD A PREVIOUS NNMA SELECTION inside the widget ######################## ## w4r.loadMaskDictFromH5( "spectral_myroi.h5:/datas/ROI" ) ################################################################### ####### TO RELOAD A PREVIOUS NNMA SELECTION inside the widget ######################## # w4r.loadMaskDictFromH5( "myroi.h5:/datas/ROI" ) ################################################################### w4r.show() app.exec_() assert(w4r.isOK) w4r.saveMaskDictOnH5( "myroi.h5:/datas/ROI" ) w4r = None if 1: # create the mask by spectral analisys if USE_SWISSKNIFE: target = "spectral_myroi.h5:/datas/ROI" s=""" create_spectral_rois : expdata : "{path}/" # this points to a directory containing a spec file or to the specfile itself # You can simply pass the directory. By default the file hydra will be selected scans : {scans} # a list containing one or more scans # They will be summed up to form an image to display # and analysed in the depth direction for the spectral part to refine # the rois by NNMA decomposition spatial_roiaddress : {spatial_roi} # a previous ROIs spatial selection spectral_roiaddress : "{target}" # the target destination for rois """ s=s.format(path=path, scans = scans, spatial_roi = "myroi.h5:/datas/ROI" , target= "spectral_myroi.h5:/datas/ROI") open("input.yaml","w").write(s) comando = 'XRS_swissknife input.yaml' p1 = subprocess.Popen(args=comando.split( " ") ,stdin=subprocess.PIPE,stdout=subprocess.PIPE,stderr=subprocess.PIPE) ms,errors= p1.communicate() if p1.returncode: print ( " ERRORS ") print ( errors.decode("utf-8") ) raise Exception("The create_spectral_rois instruction was stopped with error message ", errors.decode("utf-8")) myroi = xrs_rois.load_rois_fromh5_address("spectral_myroi.h5:/datas/ROI") else: spectral_w4r = roiNmaSelectionWidget.mainwindow() sf = path fn = "myroi.h5:/datas/ROI" ns_s = scans spectral_w4r.load_rois(sf, fn , ns_s ) spectral_w4r.show() ####### TO RELOAD A PREVIOUS NNMA SELECTION inside the widget ######################## # spectral_w4r.loadMaskDictFromH5( "spectral_myroi.h5:/datas/ROI" ) ################################################################### app .exec_() assert(spectral_w4r.isOK) spectral_w4r.saveMaskDictOnH5( "spectral_myroi.h5:/datas/ROI" ) myroi=spectral_w4r.getRoiObj() spectral_w4r = None else: # simply reuse an already written nnma mask myroi = xrs_rois.load_rois_fromh5_address("spectral_myroi.h5:/datas/ROI") print ( list(myroi.red_rois.keys() ) ) roinums = [ int(''.join(filter(str.isdigit, str(key) ))) for key in myroi.red_rois.keys() ] roinums.sort() lowq = [ i for i in range(len(roinums)) if roinums[i] in lowq ] medq = [ i for i in range(len(roinums)) if roinums[i] in medq ] highq = [ i for i in range(len(roinums)) if roinums[i] in highq ] lw = xrs_read.Hydra(path) lw.set_roiObj(myroi) ######################################################################## # load the spectra (something is going wrong during the normalization, # so I am adding this scaling parameter, I think it happens when loading the # scans that the monitor variable is multiplied by a wrong factor) lw.get_compensation_factor(62, method='sum') lw.load_scan([62], method='sum', direct=True, scan_type='elastic') lw.load_scan(scans, method='sum', direct=True, scan_type='ok0') lw.get_spectrum_new(method='sum', include_elastic=True) ######################################################################## # setting the scattering angles lw.get_tths(rvd=28.0, rvu=28.0, rvb=65.0, rhr=30.0, rhl=30.0, rhb=143.0, order=[0, 1, 2, 3, 4, 5]) ######################################################################## # starting the subtraction of the background # 1. average over some crystals # 2. subtract a Pearson function # 3. write the spectra into a txt file lw_ex = xrs_extraction.edge_extraction(lw,['H2O'],[1.0],{'O':['K']}) # O edge low-q lw_ex.analyzerAverage(lowq, errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[250.0,532.0],[550.0,600.0],weights=[2,1],HFcore_shift=-5.0, guess= [-1.07743447e+03, 8.42895443e+02, 4.99035465e+01, 3193e+01, -3.80090286e-07, 2.73774370e-03, 5.11920401e+03],scaling=1.32) lw_ex.save_average_Sqw('water_OK_nnmf_lq.dat', emin=00.0, emax=610.0, normrange=[520.,600.]) # O edge med-q lw_ex.analyzerAverage(medq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[300.0,532.0],[550.0,600.0], weights=[2,1], HFcore_shift=-5.0, guess=[-1.39664220e+03 , 1.03655696e+03 , 7.67728511e+02, 7.30355600e+02, 7.93995221e-04, -4.76580011e-01, -1.37652621e+03], scaling=1.34) lw_ex.save_average_Sqw('water_OK_nnmf_mq.dat', emin=0.0, emax=610.0, normrange=[520.0,600.0]) # O edge high-q lw_ex.analyzerAverage(highq,errorweighing=False) lw_ex.removeCorePearsonAv('O','K',[52.0,532.0],[550.0,600.0],weights=[2,1], guess=[ 3.40779687e+02, 2.57030454e+02, 1.27747244e+03, 4.55875194e-01, -8.59501907e-06, 1.39969288e-02, 2.60071705e+00], HFcore_shift=-5.0,scaling=3.55) lw_ex.save_average_Sqw('water_OK_nnmf_hq.dat', emin=0.0, emax=600.0, normrange=[520.0,600.0]) xrstools-0.15.0+git20210910+c147919d/sandbox/pippo.py000066400000000000000000000011711412732462000213110ustar00rootroot00000000000000from XRStools.roiNmaSelectionGui import roiNmaSelectionWidget # import XRStools.roiSelectionWidget as roiNmaSelectionWidget from PyQt4 import Qt, QtCore app=Qt.QApplication([]) w4r = roiNmaSelectionWidget.mainwindow() w4r.show() sf = '/data/id20/inhouse/data/run5_17/run7_ihr/' fn = "myroi.h5:/datas/ROI" ns_s = [613,617,621,625] w4r.load_rois(sf, fn , ns_s ) w4r.loadMaskDictFromH5("pippo.h5:salva_qui" ) app .exec_() w4r.saveMaskDictOnH5( "pippo.h5:salva_qui" ) md, meta=w4r.getMasksDict(withmeta=True) print( md) print ( " META ") print( meta) roiob = w4r.getRoiObj() print ( " roiobj " ) print ( roiob) w4r = None 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xrstools-0.15.0+git20210910+c147919d/scripts/XRS_wizard.bat000066400000000000000000000000411412732462000223600ustar00rootroot00000000000000@echo off python "%~dpn0".py %* xrstools-0.15.0+git20210910+c147919d/scripts/scripto.py000066400000000000000000000004161412732462000216770ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import glob fs = glob.glob("../XRStools/scripts/*") print( fs) for f in fs: n = os.path.basename(f) os.system("copy tipo %s.bat"%n) xrstools-0.15.0+git20210910+c147919d/scripts/scripto.py~000066400000000000000000000002171412732462000220740ustar00rootroot00000000000000import os import glob fs = glob.glob("../XRStools/scripts/*") for f in fs: n = os.path.basename(f) os.system("copy tipo %s.bat"%n) xrstools-0.15.0+git20210910+c147919d/scripts/tipo000066400000000000000000000000351412732462000205350ustar00rootroot00000000000000@echo off python "%~dpn0" %* xrstools-0.15.0+git20210910+c147919d/setup.py000066400000000000000000001067141412732462000176750ustar00rootroot00000000000000 from __future__ import absolute_import from __future__ import division from __future__ import print_function import io import sys import os import platform import shutil import logging import glob logging.basicConfig(level=logging.INFO) logger = logging.getLogger("xrstools.setup") from distutils.command.clean import clean as Clean from distutils.command.build import build as _build try: from setuptools import Command from setuptools.command.build_py import build_py as _build_py from setuptools.command.build_ext import build_ext from setuptools.command.sdist import sdist logger.info("Use setuptools") except ImportError: try: from numpy.distutils.core import Command except ImportError: from distutils.core import Command from distutils.command.build_py import build_py as _build_py from distutils.command.build_ext import build_ext from distutils.command.sdist import sdist logger.info("Use distutils") try: import sphinx import sphinx.util.console sphinx.util.console.color_terminal = lambda: False from sphinx.setup_command import BuildDoc except ImportError: sphinx = None PROJECT = "XRStools" if "LANG" not in os.environ and sys.platform == "darwin" and sys.version_info[0] > 2: print("""WARNING: the LANG environment variable is not defined, an utf-8 LANG is mandatory to use setup.py, you may face unexpected UnicodeError. export LANG=en_US.utf-8 export LC_ALL=en_US.utf-8 """) def get_version(): """Returns current version number from version.py file""" import version return version.strictversion def get_readme(): """Returns content of README.rst file""" dirname = os.path.dirname(os.path.abspath(__file__)) filename = os.path.join(dirname, "README.rst") with io.open(filename, "r", encoding="utf-8") as fp: long_description = fp.read() return long_description # double check classifiers on https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers = ["Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Programming Language :: Python :: 2", "Programming Language :: Python :: 3", "Programming Language :: Cython", "Environment :: Console", "Environment :: X11 Applications :: Qt", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Topic :: Software Development :: Libraries :: Python Modules", "Operating System :: Microsoft :: Windows", "Operating System :: Unix", "Operating System :: MacOS :: MacOS X", "Operating System :: POSIX", "Topic :: Scientific/Engineering :: Physics" ] # ########## # # version.py # # ########## # class build_py(_build_py): """ Enhanced build_py which copies version.py to ._version.py """ def find_package_modules(self, package, package_dir): modules = _build_py.find_package_modules(self, package, package_dir) if package == PROJECT: modules.append((PROJECT, '_version', 'version.py')) return modules ######## # Test # ######## class PyTest(Command): """Command to start tests running the script: run_tests.py""" user_options = [] description = "Execute the unittests" def initialize_options(self): pass def finalize_options(self): pass def run(self): import subprocess errno = subprocess.call([sys.executable, 'run_tests.py']) if errno != 0: raise SystemExit(errno) # ################### # # build_doc command # # ################### # if sphinx is None: class SphinxExpectedCommand(Command): """Command to inform that sphinx is missing""" user_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): raise RuntimeError( 'Sphinx is required to build or test the documentation.\n' 'Please install Sphinx (http://www.sphinx-doc.org).') class BuildMan(Command): """Command to build man pages""" description = "Build man pages of the provided entry points" user_options = [] def initialize_options(self): pass def finalize_options(self): pass def entry_points_iterator(self): """Iterate other entry points available on the project.""" entry_points = self.distribution.entry_points console_scripts = entry_points.get('console_scripts', []) gui_scripts = entry_points.get('gui_scripts', []) scripts = [] scripts.extend(console_scripts) scripts.extend(gui_scripts) for script in scripts: # Remove ending extra dependencies script = script.split("[")[0] elements = script.split("=") target_name = elements[0].strip() elements = elements[1].split(":") module_name = elements[0].strip() function_name = elements[1].strip() yield target_name, module_name, function_name def run_targeted_script(self, target_name, script_name, env, log_output=False): """Execute targeted script using --help and --version to help checking errors. help2man is not very helpful to do it for us. :return: True is both return code are equal to 0 :rtype: bool """ import subprocess if log_output: extra_args = {} else: try: # Python 3 from subprocess import DEVNULL except ImportError: # Python 2 import os DEVNULL = open(os.devnull, 'wb') extra_args = {'stdout': DEVNULL, 'stderr': DEVNULL} succeeded = True command_line = [sys.executable, script_name, "--help"] if log_output: logger.info("See the following execution of: %s", " ".join(command_line)) p = subprocess.Popen(command_line, env=env, **extra_args) status = p.wait() if log_output: logger.info("Return code: %s", status) succeeded = succeeded and status == 0 command_line = [sys.executable, script_name, "--version"] if log_output: logger.info("See the following execution of: %s", " ".join(command_line)) p = subprocess.Popen(command_line, env=env, **extra_args) status = p.wait() if log_output: logger.info("Return code: %s", status) succeeded = succeeded and status == 0 return succeeded @staticmethod def _write_script(target_name, lst_lines=None): """Write a script to a temporary file and return its name :paran target_name: base of the script name :param lst_lines: list of lines to be written in the script :return: the actual filename of the script (for execution or removal) """ import tempfile import stat script_fid, script_name = tempfile.mkstemp(prefix="%s_" % target_name, text=True) with os.fdopen(script_fid, 'wt') as script: for line in lst_lines: if not line.endswith("\n"): line += "\n" script.write(line) # make it executable mode = os.stat(script_name).st_mode os.chmod(script_name, mode + stat.S_IEXEC) return script_name def get_synopsis(self, module_name, env, log_output=False): """Execute a script to retrieve the synopsis for help2man :return: synopsis :rtype: single line string """ import subprocess script_name = None synopsis = None script = ["#!%s\n" % sys.executable, "import logging", "logging.basicConfig(level=logging.ERROR)", "import %s as app" % module_name, "print(app.__doc__)"] try: script_name = self._write_script(module_name, script) command_line = [sys.executable, script_name] p = subprocess.Popen(command_line, env=env, stdout=subprocess.PIPE) status = p.wait() if status != 0: logger.warning("Error while getting synopsis for module '%s'.", module_name) synopsis = p.stdout.read().decode("utf-8").strip() if synopsis == 'None': synopsis = None finally: # clean up the script if script_name is not None: os.remove(script_name) return synopsis def run(self): build = self.get_finalized_command('build') path = sys.path path.insert(0, os.path.abspath(build.build_lib)) env = dict((str(k), str(v)) for k, v in os.environ.items()) env["PYTHONPATH"] = os.pathsep.join(path) if not os.path.isdir("build/man"): os.makedirs("build/man") import subprocess script_name = None entry_points = self.entry_points_iterator() for target_name, module_name, function_name in entry_points: logger.info("Build man for entry-point target '%s'" % target_name) # help2man expect a single executable file to extract the help # we create it, execute it, and delete it at the end py3 = sys.version_info >= (3, 0) try: script = ["#!%s" % sys.executable, "import logging", "logging.basicConfig(level=logging.ERROR)", "import %s as app" % module_name, "app.%s()" % function_name] script_name = self._write_script(target_name, script) # execute help2man man_file = "build/man/%s.1" % target_name command_line = ["help2man", "-N", script_name, "-o", man_file] synopsis = self.get_synopsis(module_name, env) if synopsis: command_line += ["-n", synopsis] if not py3: # Before Python 3.4, ArgParser --version was using # stderr to print the version command_line.append("--no-discard-stderr") # Then we dont know if the documentation will contains # durtty things succeeded = self.run_targeted_script(target_name, script_name, env, False) if not succeeded: logger.info("Error while generating man file for target '%s'.", target_name) self.run_targeted_script(target_name, script_name, env, True) raise RuntimeError("Fail to generate '%s' man documentation" % target_name) p = subprocess.Popen(command_line, env=env) status = p.wait() if status != 0: logger.info("Error while generating man file for target '%s'.", target_name) self.run_targeted_script(target_name, script_name, env, True) raise RuntimeError("Fail to generate '%s' man documentation" % target_name) finally: # clean up the script if script_name is not None: os.remove(script_name) if sphinx is not None: class BuildDocCommand(BuildDoc): """Command to build documentation using sphinx. Project should have already be built. """ def run(self): # make sure the python path is pointing to the newly built # code so that the documentation is built on this and not a # previously installed version build = self.get_finalized_command('build') sys.path.insert(0, os.path.abspath(build.build_lib)) # # Copy .ui files to the path: # dst = os.path.join( # os.path.abspath(build.build_lib), "silx", "gui") # if not os.path.isdir(dst): # os.makedirs(dst) # for i in os.listdir("gui"): # if i.endswith(".ui"): # src = os.path.join("gui", i) # idst = os.path.join(dst, i) # if not os.path.exists(idst): # shutil.copy(src, idst) # Build the Users Guide in HTML and TeX format for builder in ['html', 'latex']: self.builder = builder self.builder_target_dir = os.path.join(self.build_dir, builder) self.mkpath(self.builder_target_dir) BuildDoc.run(self) sys.path.pop(0) else: BuildDocCommand = SphinxExpectedCommand # ################### # # test_doc command # # ################### # if sphinx is not None: class TestDocCommand(BuildDoc): """Command to test the documentation using sphynx doctest. http://www.sphinx-doc.org/en/1.4.8/ext/doctest.html """ def run(self): # make sure the python path is pointing to the newly built # code so that the documentation is built on this and not a # previously installed version build = self.get_finalized_command('build') sys.path.insert(0, os.path.abspath(build.build_lib)) # Build the Users Guide in HTML and TeX format for builder in ['doctest']: self.builder = builder self.builder_target_dir = os.path.join(self.build_dir, builder) self.mkpath(self.builder_target_dir) BuildDoc.run(self) sys.path.pop(0) else: TestDocCommand = SphinxExpectedCommand # ############################# # # numpy.distutils Configuration # # ############################# # def configuration(parent_package='', top_path=None): """Recursive construction of package info to be used in setup(). See http://docs.scipy.org/doc/numpy/reference/distutils.html#numpy.distutils.misc_util.Configuration """ try: from numpy.distutils.misc_util import Configuration except ImportError: raise ImportError( "To install this package, you must install numpy first\n" "(See https://pypi.python.org/pypi/numpy)") config = Configuration(None, parent_package, top_path) config.set_options( ignore_setup_xxx_py=True, assume_default_configuration=True, delegate_options_to_subpackages=True, quiet=True) config.add_subpackage(PROJECT) return config # ############## # # Compiler flags # # ############## # class Build(_build): """Command to support more user options for the build.""" user_options = [ ('no-openmp', None, "do not use OpenMP for compiled extension modules"), ('openmp', None, "use OpenMP for the compiled extension modules"), ('no-cython', None, "do not compile Cython extension modules (use default compiled c-files)"), ('force-cython', None, "recompile all Cython extension modules"), ] user_options.extend(_build.user_options) boolean_options = ['no-openmp', 'openmp', 'no-cython', 'force-cython'] boolean_options.extend(_build.boolean_options) def initialize_options(self): _build.initialize_options(self) self.no_openmp = None self.openmp = None self.no_cython = None self.force_cython = None def finalize_options(self): _build.finalize_options(self) self.finalize_cython_options(min_version='0.21.1') self.finalize_openmp_options() def _parse_env_as_bool(self, key): content = os.environ.get(key, "") value = content.lower() if value in ["1", "true", "yes", "y"]: return True if value in ["0", "false", "no", "n"]: return False if value in ["none", ""]: return None msg = "Env variable '%s' contains '%s'. But a boolean or an empty \ string was expected. Variable ignored." logger.warning(msg, key, content) return None def finalize_openmp_options(self): """Check if extensions must be compiled with OpenMP. The result is stored into the object. """ if self.openmp: use_openmp = True elif self.no_openmp: use_openmp = False else: env_force_cython = self._parse_env_as_bool("WITH_OPENMP") if env_force_cython is not None: use_openmp = env_force_cython else: # Use it by default use_openmp = True if use_openmp: if platform.system() == "Darwin": # By default Xcode5 & XCode6 do not support OpenMP, Xcode4 is OK. osx = tuple([int(i) for i in platform.mac_ver()[0].split(".")]) if osx >= (10, 8): logger.warning("OpenMP support ignored. Your platform do not support it") use_openmp = False # Remove attributes used by distutils parsing # use 'use_openmp' instead del self.no_openmp del self.openmp self.use_openmp = use_openmp def finalize_cython_options(self, min_version=None): """ Check if cythonization must be used for the extensions. The result is stored into the object. """ if self.force_cython: use_cython = "force" elif self.no_cython: use_cython = "no" else: env_force_cython = self._parse_env_as_bool("FORCE_CYTHON") env_with_cython = self._parse_env_as_bool("WITH_CYTHON") if env_force_cython is True: use_cython = "force" elif env_with_cython is True: use_cython = "yes" elif env_with_cython is False: use_cython = "no" else: # Use it by default use_cython = "yes" if use_cython in ["force", "yes"]: try: import Cython.Compiler.Version if min_version and Cython.Compiler.Version.version < min_version: msg = "Cython version is too old. At least version is %s \ expected. Cythonization is skipped." logger.warning(msg, str(min_version)) use_cython = "no" except ImportError: msg = "Cython is not available. Cythonization is skipped." logger.warning(msg) use_cython = "no" # Remove attribute used by distutils parsing # use 'use_cython' and 'force_cython' instead del self.no_cython self.force_cython = use_cython == "force" self.use_cython = use_cython in ["force", "yes"] class BuildExt(build_ext): """Handle extension compilation. Command-line argument and environment can custom: - The use of cython to cythonize files, else a default version is used - Build extension with support of OpenMP (by default it is enabled) - If building with MSVC, compiler flags are converted from gcc flags. """ COMPILE_ARGS_CONVERTER = {'-fopenmp': '/openmp'} LINK_ARGS_CONVERTER = {'-fopenmp': ''} description = 'Build CI_test extensions' def finalize_options(self): build_ext.finalize_options(self) build_obj = self.distribution.get_command_obj("build") self.use_openmp = build_obj.use_openmp self.use_cython = build_obj.use_cython self.force_cython = build_obj.force_cython def patch_with_default_cythonized_files(self, ext): """Replace cython files by .c or .cpp files in extension's sources. It replaces the *.pyx and *.py source files of the extensions to either *.cpp or *.c source files. No compilation is performed. :param Extension ext: An extension to patch. """ new_sources = [] for source in ext.sources: base, file_ext = os.path.splitext(source) if file_ext in ('.pyx', '.py'): if ext.language == 'c++': cythonized = base + '.cpp' else: cythonized = base + '.c' if not os.path.isfile(cythonized): raise RuntimeError("Source file not found: %s. Cython is needed" % cythonized) print("Use default cythonized file for %s" % source) new_sources.append(cythonized) else: new_sources.append(source) ext.sources = new_sources def patch_extension(self, ext): """ Patch an extension according to requested Cython and OpenMP usage. :param Extension ext: An extension """ # Cytonize if not self.use_cython: self.patch_with_default_cythonized_files(ext) else: from Cython.Build import cythonize patched_exts = cythonize( [ext], compiler_directives={'embedsignature': True}, force=self.force_cython, compile_time_env={"HAVE_OPENMP": self.use_openmp} ) ext.sources = patched_exts[0].sources # Remove OpenMP flags if OpenMP is disabled if not self.use_openmp: ext.extra_compile_args = [ f for f in ext.extra_compile_args if f != '-fopenmp'] ext.extra_link_args = [ f for f in ext.extra_link_args if f != '-fopenmp'] # Convert flags from gcc to MSVC if required if self.compiler.compiler_type == 'msvc': ext.extra_compile_args = [self.COMPILE_ARGS_CONVERTER.get(f, f) for f in ext.extra_compile_args] ext.extra_link_args = [self.LINK_ARGS_CONVERTER.get(f, f) for f in ext.extra_link_args] elif self.compiler.compiler_type == 'unix': # Avoids runtime symbol collision for manylinux1 platform # See issue #1070 extern = 'extern "C" ' if ext.language == 'c++' else '' return_type = 'void' if sys.version_info[0] <= 2 else 'PyObject*' # ext.extra_compile_args.append( # '''-fvisibility=hidden -D'PyMODINIT_FUNC=%s__attribute__((visibility("default"))) %s ' ''' % (extern, return_type)) def is_debug_interpreter(self): """ Returns true if the script is executed with a debug interpreter. It looks to be a non-standard code. It is not working for Windows and Mac. But it have to work at least for Debian interpreters. :rtype: bool """ if sys.version_info >= (3, 0): # It is normalized on Python 3 # But it is not available on Windows CPython if hasattr(sys, "abiflags"): return "d" in sys.abiflags else: # It's a Python 2 interpreter # pydebug is not available on Windows/Mac OS interpreters if hasattr(sys, "pydebug"): return sys.pydebug # We can't know if we uses debug interpreter return False def patch_compiler(self): """ Patch the compiler to: - always compile extensions with debug symboles (-g) - only compile asserts in debug mode (-DNDEBUG) Plus numpy.distutils/setuptools/distutils inject a lot of duplicated flags. This function tries to clean up default debug options. """ build_obj = self.distribution.get_command_obj("build") if build_obj.debug: debug_mode = build_obj.debug else: # Force debug_mode also when it uses python-dbg # It is needed for Debian packaging debug_mode = self.is_debug_interpreter() if self.compiler.compiler_type == "unix": args = list(self.compiler.compiler_so) # clean up debug flags -g is included later in another way must_be_cleaned = ["-DNDEBUG", "-g"] args = filter(lambda x: x not in must_be_cleaned, args) args = list(args) # always insert symbols args.append("-g") # only strip asserts in release mode if not debug_mode: args.append('-DNDEBUG') # patch options self.compiler.compiler_so = list(args) def build_extensions(self): self.patch_compiler() for ext in self.extensions: self.patch_extension(ext) build_ext.build_extensions(self) ################################################################################ # Clean command ################################################################################ class CleanCommand(Clean): description = "Remove build artifacts from the source tree" def expand(self, path_list): """Expand a list of path using glob magic. :param list[str] path_list: A list of path which may contains magic :rtype: list[str] :returns: A list of path without magic """ path_list2 = [] for path in path_list: if glob.has_magic(path): iterator = glob.iglob(path) path_list2.extend(iterator) else: path_list2.append(path) return path_list2 def find(self, path_list): """Find a file pattern if directories. Could be done using "**/*.c" but it is only supported in Python 3.5. :param list[str] path_list: A list of path which may contains magic :rtype: list[str] :returns: A list of path without magic """ import fnmatch path_list2 = [] for pattern in path_list: for root, _, filenames in os.walk('.'): for filename in fnmatch.filter(filenames, pattern): path_list2.append(os.path.join(root, filename)) return path_list2 def run(self): Clean.run(self) cython_files = self.find(["*.pyx"]) cythonized_files = [path.replace(".pyx", ".c") for path in cython_files] cythonized_files += [path.replace(".pyx", ".cpp") for path in cython_files] # really remove the directories # and not only if they are empty to_remove = [self.build_base] to_remove = self.expand(to_remove) to_remove += cythonized_files if not self.dry_run: for path in to_remove: try: if os.path.isdir(path): shutil.rmtree(path) else: os.remove(path) logger.info("removing '%s'", path) except OSError: pass ################################################################################ # Source tree ################################################################################ class SourceDistWithCython(sdist): """ Force cythonization of the extensions before generating the source distribution. To provide the widest compatibility the cythonized files are provided without suppport of OpenMP. """ description = "Create a source distribution including cythonozed files (tarball, zip file, etc.)" def finalize_options(self): sdist.finalize_options(self) self.extensions = self.distribution.ext_modules def run(self): self.cythonize_extensions() sdist.run(self) def cythonize_extensions(self): from Cython.Build import cythonize cythonize( self.extensions, compiler_directives={'embedsignature': True}, force=True ) ################################################################################ # Debian source tree ################################################################################ class sdist_debian(sdist): """ Tailor made sdist for debian * remove auto-generated doc * remove cython generated .c files * remove cython generated .cpp files * remove .bat files * include .l man files """ description = "Create a source distribution for Debian (tarball, zip file, etc.)" @staticmethod def get_debian_name(): import version name = "%s_%s" % (PROJECT, version.debianversion) return name def prune_file_list(self): sdist.prune_file_list(self) to_remove = ["doc/build", "doc/pdf", "doc/html", "pylint", "epydoc"] print("Removing files for debian") for rm in to_remove: self.filelist.exclude_pattern(pattern="*", anchor=False, prefix=rm) # this is for Cython files specifically: remove C & html files search_root = os.path.dirname(os.path.abspath(__file__)) for root, _, files in os.walk(search_root): for afile in files: if os.path.splitext(afile)[1].lower() == ".pyx": base_file = os.path.join(root, afile)[len(search_root) + 1:-4] self.filelist.exclude_pattern(pattern=base_file + ".c") self.filelist.exclude_pattern(pattern=base_file + ".cpp") self.filelist.exclude_pattern(pattern=base_file + ".html") # do not include third_party/_local files self.filelist.exclude_pattern(pattern="*", prefix="CI_test/third_party/_local") def make_distribution(self): self.prune_file_list() sdist.make_distribution(self) dest = self.archive_files[0] dirname, basename = os.path.split(dest) base, ext = os.path.splitext(basename) while ext in [".zip", ".tar", ".bz2", ".gz", ".Z", ".lz", ".orig"]: base, ext = os.path.splitext(base) if ext: dest = "".join((base, ext)) else: dest = base # sp = dest.split("-") # base = sp[:-1] # nr = sp[-1] debian_arch = os.path.join(dirname, self.get_debian_name() + ".orig.tar.gz") os.rename(self.archive_files[0], debian_arch) self.archive_files = [debian_arch] print("Building debian .orig.tar.gz in %s" % self.archive_files[0]) ################# # PyFAI specific ################# class PyFaiTestData(Command): """ Tailor made tarball with test data """ user_options = [] def initialize_options(self): pass def finalize_options(self): pass def download_images(self): """ Download all test images and """ root_dir = os.path.dirname(os.path.abspath(__file__)) test_dir = os.path.join(root_dir, PROJECT, "test") sys.path.insert(0, test_dir) from utilstest import UtilsTest image_home = os.path.join(root_dir, "testimages") testimages = os.path.join(image_home, "all_testimages.json") UtilsTest.image_home = image_home UtilsTest.testimages = testimages if os.path.exists(testimages): import json with open(testimages) as f: all_files = set(json.load(f)) else: raise(RuntimeError("Please run 'python setup.py build test' to download all images")) return list(all_files) def run(self): datafiles = self.download_images() dist = "dist" arch = os.path.join(dist, PROJECT + "-testimages.tar.gz") print("Building testdata tarball in %s" % arch) if not os.path.isdir(dist): os.mkdir(dist) if os.path.exists(arch): os.unlink(arch) import tarfile with tarfile.open(name=arch, mode='w:gz') as tarball: for afile in datafiles: tarball.add(os.path.join("testimages", afile), afile) # ##### # # setup # # ##### # def get_project_configuration(dry_run): """Returns project arguments for setup""" install_requires = [ "numpy", # h5py was removed from dependencies cause it creates an issue with # Debian 8. Pip is not aware that h5py is installed and pkg_resources # check dependencies and in this case raise an exception # FIXME we still have to investigate "h5py", # "mpi4py", "PyYAML", "six", "fabio", "matplotlib", "scipy", "numexpr", "pymca", # for the use of pkg_resources on script launcher # "setuptools", "silx>=0.9" ] setup_requires = [ "setuptools", "numpy", "cython", "fabio" ] package_data = { 'XRStools.resources': [ 'data/*.dat', 'data/*.gif', 'data/*.yaml', '*.ui', 'WIZARD/*.ui', 'ramanWidget/*.ui', 'roiNmaSelectionGui/*.ui' ] } extras_require = { # 'calib2': ["fabio>=0.5"], } console_scripts = [ 'XRS_wizard = XRStools.WIZARD.XRS_wizard:main', 'XRS_swissknife = XRStools.XRS_swissknife:main', 'XRS_raman_extraction = XRStools.ramanWidget.MainWindow:main', 'XRS_roiNmaSelection = XRStools.roiNmaSelectionGui.roiNmaSelectionWidget:main' ] entry_points = { 'console_scripts': console_scripts, # 'gui_scripts': [], } cmdclass = dict( build=Build, build_py=build_py, test=PyTest, build_doc=BuildDocCommand, test_doc=TestDocCommand, build_ext=BuildExt, build_man=BuildMan, clean=CleanCommand, debian_src=sdist_debian, testimages=PyFaiTestData, ) if dry_run: # DRY_RUN implies actions which do not require NumPy # # And they are required to succeed without Numpy for example when # pip is used to install silx when Numpy is not yet present in # the system. setup_kwargs = {} else: config = configuration() setup_kwargs = config.todict() setup_kwargs.update(name=PROJECT, version=get_version(), url="", download_url="", author="", author_email="", classifiers=classifiers, description='', long_description=get_readme(), install_requires=install_requires, setup_requires=setup_requires, cmdclass=cmdclass, package_data=package_data, zip_safe=False, entry_points=entry_points, extras_require=extras_require, ) return setup_kwargs def setup_package(): """Run setup(**kwargs) Depending on the command, it either runs the complete setup which depends on numpy, or a *dry run* setup with no dependency on numpy. """ # Check if action requires build/install dry_run = len(sys.argv) == 1 or (len(sys.argv) >= 2 and ( '--help' in sys.argv[1:] or sys.argv[1] in ('--help-commands', 'egg_info', '--version', 'clean', '--name'))) if dry_run: # DRY_RUN implies actions which do not require dependancies, like NumPy try: from setuptools import setup logger.info("Use setuptools.setup") except ImportError: from distutils.core import setup logger.info("Use distutils.core.setup") else: try: from setuptools import setup except ImportError: from numpy.distutils.core import setup logger.info("Use numpydistutils.setup") setup_kwargs = get_project_configuration(dry_run) setup(**setup_kwargs) if __name__ == "__main__": setup_package() xrstools-0.15.0+git20210910+c147919d/stdeb.cfg000066400000000000000000000001271412732462000177340ustar00rootroot00000000000000[DEFAULT] Depends: python-pymca5, python-mpi4py, python-h5py, python-scipy, python-yamlxrstools-0.15.0+git20210910+c147919d/superr/000077500000000000000000000000001412732462000174725ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/superr/prog.py000066400000000000000000000105601412732462000210150ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import h5py import math from six.moves import filter from six.moves import range myrank=0 import skimage.restoration def main2(): file = "demo.hdf5" group_delta = "ROI_B_FIT8/scanXX/scans/Scan273/" group_sample = "ROI_B_FIT8/images/scans/" h5f = h5py.File(file,"r") h5 = h5f[group_delta] print( list(h5.keys())) sonde = {} width = 5 XDIM = None roi_keys = list(h5.keys()) roi_keys = [60, 64, 35, 69, 34, 24, 5, 6, 71, 70, 39, 58, 56, 33] roi_keys = [60,64,71,70,69] roi_keys = [str(t) for t in roi_keys] tot_dizio={} for t in roi_keys: m = np.array(h5[t+"/matrix"][:],"d") mm = m.sum(axis=0) tot_dizio[t]=[mm,0] m=m[:(m.shape[0]//width)*width].reshape(-1, width, m.shape[1],m.shape[2]).sum(axis=1)/width sonde [t] = m if XDIM is None: XDIM = m.shape[0] else: assert(XDIM==m.shape[0]) h5 = h5f[group_sample] zscan_keys = sorted( list(h5.keys()) , key = lambda x: int(list(filter(str.isdigit, str(x) ))) ) ZDIM = len(zscan_keys) m = h5[zscan_keys[0]][roi_keys[0]]["matrix"][:] YDIM = m.shape[0] scalDS = np.zeros( [ZDIM,YDIM,XDIM] ,"f" ) scalDD = 0.0 scalSS = np.zeros( [XDIM,XDIM] ,"f" ) Volume = np.zeros( [ZDIM,YDIM,XDIM] ,"f" ) for rk in roi_keys: print( rk) probes = sonde [rk] scalSS[:] += np.tensordot( probes, probes, axes = [ [1,2], [1,2] ] ) for iz in range(ZDIM): print( iz) zkey = zscan_keys[iz] for rk in roi_keys: m = np.array(h5[ zkey ][ rk ]["matrix"][:],"d") tot_dizio[rk][1]=tot_dizio[rk][1]+m probes = sonde [rk] assert( probes.shape[1:] == m.shape[1:]) assert( XDIM == probes.shape[0] ) assert( YDIM == m.shape[0] ) plane_contrib = np.tensordot( m, probes, axes = [ [1,2], [1,2] ] ) scalDS[iz] += plane_contrib scalDD += (m*m).sum() h5f.close() # import pickle # f=open("mats.pic","w") # pickle.dump(tot_dizio, f) # f.close() # for n in d.keys(): # print n # B=d[n][1] # A=d[n][0] # B=B.sum(axis=0) # pesiA = A.sum(axis=0) # pesiB = B.sum(axis=0) # medieA = (arange(A.shape[0])[:,None]*A).sum(axis=0)/pesiA # medieB = (arange(B.shape[0])[:,None]*B).sum(axis=0)/pesiB # plot(medieA) # plot(medieB) # show() # 60 64 35 69 34 24 5 6 71 70 39 58 56 33 # 67 68 47 12 # 20 21 44 45 40 1 3 4 59 19 53 # 28 2 15 31 52 55 Fista ( scalDD, scalDS, scalSS, Volume,niter=15, beta=1.0e-8) h5f = h5py.File("Volume.h5","r+") h5f["Volume_onedectonly_15iters_beta1.0em8"] = Volume h5f.close() def calculate_grad( scalDD, scalDS , scalSS, solution, grad) : grad [:] = np.tensordot( solution, scalSS, axes=[[-1],[-1]]) err = (grad*solution).sum() if scalDS is not None: err -= (scalDS*solution).sum()*2 err += scalDD grad [:] -= scalDS return err/2 def Fista( scalDD, scalDS , scalSS, solution , niter=500, beta=0.1 ): grad = np.zeros_like(solution) grad2 = np.zeros_like(solution) x_old = np.zeros_like(solution) y = np.zeros_like(solution) err = 0.0 err=calculate_grad( scalDD, scalDS , scalSS, solution, grad) for i in range(20): calculate_grad(None, None , scalSS, grad, grad2) Lip = math.sqrt( np.linalg.norm(grad2/100000.0) )*100000 grad[:] = grad2/ Lip if myrank==0: print( "LIP ", Lip) Lip = Lip*1.2 t=1.0 y[:] = solution x_old[:] = solution for iter in range(abs(niter)): err = calculate_grad(scalDD, scalDS , scalSS, y, grad) solution[:] = y - grad/Lip solution[:]=skimage.restoration.denoise_tv_chambolle(solution, weight=beta, eps=0.000002) ## solution[:] = np.maximum(solution, 0) tnew = ( 1+math.sqrt(1.0+4*t*t) )/2 y[:] = solution +(t-1)/tnew *( solution - x_old ) t = tnew if niter<0: t=1 x_old[:] = solution if myrank==0: print( " errore est %e mod_grad est %e\n" % ( err, grad.std()) ) main() xrstools-0.15.0+git20210910+c147919d/useful_scripts/000077500000000000000000000000001412732462000212245ustar00rootroot00000000000000xrstools-0.15.0+git20210910+c147919d/useful_scripts/map.py000066400000000000000000000016701412732462000223570ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import h5py import numpy as np from six.moves import filter "SPECTRA.h5:/myexp_84_106" f=h5py.File("SPECTRA.h5","r") datagroup = f["/myexp_84_106/2/"] def reorder(sl): sl= sorted( sl , key = lambda x: int(list(filter(str.isdigit, str(x) ))) ) return sl e0keys = reorder([k for k in datagroup.keys() if k[:3]=="E0_"] ) ekeys = reorder([k for k in datagroup.keys() if k[:3]=="ene"] ) skeys = reorder([k for k in datagroup.keys() if k[:4]=="spec"]) print( skeys) print( ekeys) print( e0keys) first_spectra = datagroup[skeys[0] ][:] elen = len(first_spectra) map = np.zeros([ len(e0keys) , elen ]) for i,k in enumerate(skeys): spectra = datagroup[k ][:] map[i,:] = spectra print( datagroup[ekeys[0] ][:]- datagroup[ e0keys[0] ].value*1000) import pylab pylab.imshow(map) pylab.show() xrstools-0.15.0+git20210910+c147919d/version.py000066400000000000000000000103601412732462000202110ustar00rootroot00000000000000#!/usr/bin/env python # coding: utf-8 # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # . # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # . # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. """Unique place where the version number is defined. provides: * version = "1.2.3" or "1.2.3-beta4" * version_info = named tuple (1,2,3,"beta",4) * hexversion: 0x010203B4 * strictversion = "1.2.3b4 * debianversion = "1.2.3~beta4" * calc_hexversion: the function to transform a version_tuple into an integer This is called hexversion since it only really looks meaningful when viewed as the result of passing it to the built-in hex() function. The version_info value may be used for a more human-friendly encoding of the same information. The hexversion is a 32-bit number with the following layout: Bits (big endian order) Meaning 1-8 PY_MAJOR_VERSION (the 2 in 2.1.0a3) 9-16 PY_MINOR_VERSION (the 1 in 2.1.0a3) 17-24 PY_MICRO_VERSION (the 0 in 2.1.0a3) 25-28 PY_RELEASE_LEVEL (0xA for alpha, 0xB for beta, 0xC for release candidate and 0xF for final) 29-32 PY_RELEASE_SERIAL (the 3 in 2.1.0a3, zero for final releases) Thus 2.1.0a3 is hexversion 0x020100a3. """ from __future__ import absolute_import, print_function, division __authors__ = ["Jérôme Kieffer"] __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" __date__ = "14/09/2017" __status__ = "production" __docformat__ = 'restructuredtext' __all__ = ["date", "version_info", "strictversion", "hexversion", "debianversion", "calc_hexversion", "citation"] RELEASE_LEVEL_VALUE = {"dev": 0, "alpha": 10, "beta": 11, "gamma": 12, "rc": 13, "final": 15} MAJOR = 0 MINOR = 15 MICRO = 0 RELEV = "dev" # <16 SERIAL = 1 # <16 date = __date__ from collections import namedtuple _version_info = namedtuple("version_info", ["major", "minor", "micro", "releaselevel", "serial"]) version_info = _version_info(MAJOR, MINOR, MICRO, RELEV, SERIAL) strictversion = version = debianversion = "%d.%d.%d" % version_info[:3] if version_info.releaselevel != "final": version += "-%s%s" % version_info[-2:] debianversion += "~adev%i" % version_info[-1] if RELEV == "dev" else "~%s%i" % version_info[-2:] prerel = "a" if RELEASE_LEVEL_VALUE.get(version_info[3], 0) < 10 else "b" if prerel not in "ab": prerel = "a" strictversion += prerel + str(version_info[-1]) def calc_hexversion(major=0, minor=0, micro=0, releaselevel="dev", serial=0): """Calculate the hexadecimal version number from the tuple version_info: :param major: integer :param minor: integer :param micro: integer :param relev: integer or string :param serial: integer :return: integer always increasing with revision numbers """ global RELEASE_LEVEL_VALUE try: releaselevel = int(releaselevel) except ValueError: releaselevel = RELEASE_LEVEL_VALUE.get(releaselevel, 0) hex_version = int(serial) hex_version |= releaselevel * 1 << 4 hex_version |= int(micro) * 1 << 8 hex_version |= int(minor) * 1 << 16 hex_version |= int(major) * 1 << 24 return hex_version hexversion = calc_hexversion(*version_info) citation = "doi:10.1107/S1600576715004306" if __name__ == "__main__": print(version)